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Model + - Checkpoints + - Best Checkpoint + - Classes + - Input Res + - Output Res + - Model + * - setup04 + - 975000, 625000, 1820500 + - 1820500 + - ecs, pm, mito, mito_mem, ves, ves_mem, endo, endo_mem, er, er_mem, eres, nuc, mt, mt_out + - 8 nm + - 4 nm + - Upsample U-Net + * - setup26.1 + - 650000, 2580000 + - 2580000 + - mito, mito_mem, mito_ribo + - 8 nm + - 4 nm + - Upsample U-Net + * - setup28 + - 775000 + - 775000 + - er, er_mem + - 8 nm + - 4 nm + - Upsample U-Net + * - setup36 + - 500000, 1100000 + - 1100000 + - nuc, nucleo + - 8 nm + - 4 nm + - Upsample U-Net + * - setup45 + - 625000, 1634500 + - 1634500 + - ecs, pm + - 4 nm + - 4 nm + - U-Net + Notes ----- diff --git a/_sources/notebooks/minimal_tutorial.ipynb.txt b/_sources/notebooks/minimal_tutorial.ipynb.txt index 0c662dc02..b349d315d 100644 --- a/_sources/notebooks/minimal_tutorial.ipynb.txt +++ b/_sources/notebooks/minimal_tutorial.ipynb.txt @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "60a686e2", + "id": "51dfe9a0", "metadata": { "lines_to_next_cell": 2 }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "cacdf23f", + "id": "4bedda6b", "metadata": {}, "source": [ "## Needed Libraries for this Tutorial\n", @@ -28,7 +28,7 @@ }, { "cell_type": "markdown", - "id": "c6170c8a", + "id": "94dc08a6", "metadata": {}, "source": [ "## Introduction and overview\n", @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "2124c691", + "id": "30e87c66", "metadata": {}, "source": [ "## Environment setup\n", @@ -79,7 +79,7 @@ }, { "cell_type": "markdown", - "id": "566a2562", + "id": "aa1a03a3", "metadata": {}, "source": [ "## Config Store\n", @@ -108,13 +108,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "b21279b5", + "id": "35fbc1d8", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:12.643700Z", - "iopub.status.busy": "2024-11-07T15:36:12.643122Z", - "iopub.status.idle": "2024-11-07T15:36:20.907151Z", - "shell.execute_reply": "2024-11-07T15:36:20.906522Z" + "iopub.execute_input": "2024-11-07T15:59:36.334096Z", + "iopub.status.busy": "2024-11-07T15:59:36.333887Z", + "iopub.status.idle": "2024-11-07T15:59:44.196468Z", + "shell.execute_reply": "2024-11-07T15:59:44.195863Z" } }, "outputs": [ @@ -148,7 +148,7 @@ }, { "cell_type": "markdown", - "id": "39f4d67e", + "id": "96503150", "metadata": { "lines_to_next_cell": 0 }, @@ -160,13 +160,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "f702a26d", + "id": "1e2d0892", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:20.909866Z", - "iopub.status.busy": "2024-11-07T15:36:20.908962Z", - "iopub.status.idle": "2024-11-07T15:36:21.636175Z", - "shell.execute_reply": "2024-11-07T15:36:21.635535Z" + "iopub.execute_input": "2024-11-07T15:59:44.198937Z", + "iopub.status.busy": "2024-11-07T15:59:44.198350Z", + "iopub.status.idle": "2024-11-07T15:59:44.980272Z", + "shell.execute_reply": "2024-11-07T15:59:44.979565Z" }, "lines_to_next_cell": 0, "title": "Create some data" @@ -259,7 +259,7 @@ }, { "cell_type": "markdown", - "id": "a280675c", + "id": "f366355b", "metadata": { "lines_to_next_cell": 0 }, @@ -270,13 +270,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "995d8c1e", + "id": "1dc94120", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.638729Z", - "iopub.status.busy": "2024-11-07T15:36:21.637955Z", - "iopub.status.idle": "2024-11-07T15:36:21.929130Z", - "shell.execute_reply": "2024-11-07T15:36:21.928427Z" + "iopub.execute_input": "2024-11-07T15:59:44.983400Z", + "iopub.status.busy": "2024-11-07T15:59:44.982544Z", + "iopub.status.idle": "2024-11-07T15:59:45.287186Z", + "shell.execute_reply": "2024-11-07T15:59:45.286437Z" }, "lines_to_next_cell": 2 }, @@ -311,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "4f17bfb0", + "id": "7dfd1825", "metadata": {}, "source": [ "## Datasplit\n", @@ -327,13 +327,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "51c4f703", + "id": "2727e96d", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.931640Z", - "iopub.status.busy": "2024-11-07T15:36:21.931387Z", - "iopub.status.idle": "2024-11-07T15:36:21.949615Z", - "shell.execute_reply": "2024-11-07T15:36:21.949111Z" + "iopub.execute_input": "2024-11-07T15:59:45.289506Z", + "iopub.status.busy": "2024-11-07T15:59:45.289265Z", + "iopub.status.idle": "2024-11-07T15:59:45.308606Z", + "shell.execute_reply": "2024-11-07T15:59:45.307907Z" }, "lines_to_next_cell": 2 }, @@ -397,13 +397,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "758bcb9d", + "id": "4e37e880", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.951468Z", - "iopub.status.busy": "2024-11-07T15:36:21.951207Z", - "iopub.status.idle": "2024-11-07T15:36:21.959506Z", - "shell.execute_reply": "2024-11-07T15:36:21.958958Z" + "iopub.execute_input": "2024-11-07T15:59:45.311075Z", + "iopub.status.busy": "2024-11-07T15:59:45.310692Z", + "iopub.status.idle": "2024-11-07T15:59:45.322053Z", + "shell.execute_reply": "2024-11-07T15:59:45.321305Z" } }, "outputs": [], @@ -415,13 +415,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "0fa89105", + "id": "f55aca11", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.961418Z", - "iopub.status.busy": "2024-11-07T15:36:21.961051Z", - "iopub.status.idle": "2024-11-07T15:36:21.970992Z", - "shell.execute_reply": "2024-11-07T15:36:21.970470Z" + "iopub.execute_input": "2024-11-07T15:59:45.324225Z", + "iopub.status.busy": "2024-11-07T15:59:45.324008Z", + "iopub.status.idle": "2024-11-07T15:59:45.335111Z", + "shell.execute_reply": "2024-11-07T15:59:45.334409Z" } }, "outputs": [], @@ -431,7 +431,7 @@ }, { "cell_type": "markdown", - "id": "d2d0be0d", + "id": "95a1b684", "metadata": {}, "source": [ "## Task\n", @@ -449,13 +449,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "0f6ba119", + "id": "0b312f0c", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.973046Z", - "iopub.status.busy": "2024-11-07T15:36:21.972668Z", - "iopub.status.idle": "2024-11-07T15:36:21.980045Z", - "shell.execute_reply": "2024-11-07T15:36:21.979554Z" + "iopub.execute_input": "2024-11-07T15:59:45.337485Z", + "iopub.status.busy": "2024-11-07T15:59:45.337001Z", + "iopub.status.idle": "2024-11-07T15:59:45.345154Z", + "shell.execute_reply": "2024-11-07T15:59:45.344457Z" } }, "outputs": [], @@ -487,7 +487,7 @@ }, { "cell_type": "markdown", - "id": "b7f3046a", + "id": "38b90709", "metadata": {}, "source": [ "## Architecture\n", @@ -501,13 +501,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "dbce17aa", + "id": "160ac1ba", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.981992Z", - "iopub.status.busy": "2024-11-07T15:36:21.981620Z", - "iopub.status.idle": "2024-11-07T15:36:21.987863Z", - "shell.execute_reply": "2024-11-07T15:36:21.987334Z" + "iopub.execute_input": "2024-11-07T15:59:45.347487Z", + "iopub.status.busy": "2024-11-07T15:59:45.347278Z", + "iopub.status.idle": "2024-11-07T15:59:45.354057Z", + "shell.execute_reply": "2024-11-07T15:59:45.353532Z" } }, "outputs": [], @@ -535,7 +535,7 @@ }, { "cell_type": "markdown", - "id": "930031b1", + "id": "99d1dcba", "metadata": {}, "source": [ "## Trainer\n", @@ -549,13 +549,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "343b027c", + "id": "9990a15f", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.989795Z", - "iopub.status.busy": "2024-11-07T15:36:21.989411Z", - "iopub.status.idle": "2024-11-07T15:36:21.994315Z", - "shell.execute_reply": "2024-11-07T15:36:21.993818Z" + "iopub.execute_input": "2024-11-07T15:59:45.356095Z", + "iopub.status.busy": "2024-11-07T15:59:45.355733Z", + "iopub.status.idle": "2024-11-07T15:59:45.360595Z", + "shell.execute_reply": "2024-11-07T15:59:45.360099Z" } }, "outputs": [], @@ -576,7 +576,7 @@ }, { "cell_type": "markdown", - "id": "fa2d0029", + "id": "24c377eb", "metadata": {}, "source": [ "## Run\n", @@ -588,13 +588,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "46d2c7a9", + "id": "5a12c0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:21.996352Z", - "iopub.status.busy": "2024-11-07T15:36:21.995916Z", - "iopub.status.idle": "2024-11-07T15:36:22.009243Z", - "shell.execute_reply": "2024-11-07T15:36:22.008734Z" + "iopub.execute_input": "2024-11-07T15:59:45.363012Z", + "iopub.status.busy": "2024-11-07T15:59:45.362462Z", + "iopub.status.idle": "2024-11-07T15:59:45.376001Z", + "shell.execute_reply": "2024-11-07T15:59:45.375378Z" } }, "outputs": [], @@ -619,7 +619,7 @@ }, { "cell_type": "markdown", - "id": "0e3ce199", + "id": "5e9a9e36", "metadata": {}, "source": [ "## Retrieve Configurations\n", @@ -653,7 +653,7 @@ }, { "cell_type": "markdown", - "id": "3330d8e9", + "id": "4b93b9d6", "metadata": {}, "source": [ "## Train\n", @@ -667,13 +667,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "6e0fceb0", + "id": "67e9b015", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:36:22.011183Z", - "iopub.status.busy": "2024-11-07T15:36:22.010822Z", - "iopub.status.idle": "2024-11-07T15:49:36.282450Z", - "shell.execute_reply": "2024-11-07T15:49:36.281800Z" + "iopub.execute_input": "2024-11-07T15:59:45.378170Z", + "iopub.status.busy": "2024-11-07T15:59:45.377816Z", + "iopub.status.idle": "2024-11-07T16:12:39.974464Z", + "shell.execute_reply": "2024-11-07T16:12:39.973858Z" } }, "outputs": [ @@ -708,7 +708,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING:dacapo.experiments.trainers.gunpowder_trainer:Saving Snapshot. Iteration: 0, Loss: 0.6597984433174133!\n" + "WARNING:dacapo.experiments.trainers.gunpowder_trainer:Saving Snapshot. Iteration: 0, Loss: 0.7832373380661011!\n" ] }, { @@ -716,7 +716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 1/2000 [00:00<13:01, 2.56it/s]" + "training until 2000: 0%| | 1/2000 [00:00<12:24, 2.69it/s]" ] }, { @@ -724,7 +724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 1/2000 [00:00<13:01, 2.56it/s, loss=0.66]" + "training until 2000: 0%| | 1/2000 [00:00<12:24, 2.69it/s, loss=0.783]" ] }, { @@ -732,7 +732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 2/2000 [00:00<12:07, 2.75it/s, loss=0.66]" + "training until 2000: 0%| | 2/2000 [00:00<11:23, 2.92it/s, loss=0.783]" ] }, { @@ -740,7 +740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 2/2000 [00:00<12:07, 2.75it/s, loss=0.611]" + "training until 2000: 0%| | 2/2000 [00:00<11:23, 2.92it/s, loss=0.734]" ] }, { @@ -748,7 +748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 3/2000 [00:01<11:29, 2.90it/s, loss=0.611]" + "training until 2000: 0%| | 3/2000 [00:01<10:52, 3.06it/s, loss=0.734]" ] }, { @@ -756,7 +756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 3/2000 [00:01<11:29, 2.90it/s, loss=0.589]" + "training until 2000: 0%| | 3/2000 [00:01<10:52, 3.06it/s, loss=0.759]" ] }, { @@ -764,7 +764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 4/2000 [00:01<11:14, 2.96it/s, loss=0.589]" + "training until 2000: 0%| | 4/2000 [00:01<10:42, 3.11it/s, loss=0.759]" ] }, { @@ -772,7 +772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 4/2000 [00:01<11:14, 2.96it/s, loss=0.609]" + "training until 2000: 0%| | 4/2000 [00:01<10:42, 3.11it/s, loss=0.749]" ] }, { @@ -780,7 +780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 5/2000 [00:01<11:12, 2.97it/s, loss=0.609]" + "training until 2000: 0%| | 5/2000 [00:01<10:42, 3.10it/s, loss=0.749]" ] }, { @@ -788,7 +788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 5/2000 [00:01<11:12, 2.97it/s, loss=0.7] " + "training until 2000: 0%| | 5/2000 [00:01<10:42, 3.10it/s, loss=0.74] " ] }, { @@ -796,7 +796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 6/2000 [00:02<11:01, 3.01it/s, loss=0.7]" + "training until 2000: 0%| | 6/2000 [00:01<10:33, 3.15it/s, loss=0.74]" ] }, { @@ -804,7 +804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 6/2000 [00:02<11:01, 3.01it/s, loss=0.576]" + "training until 2000: 0%| | 6/2000 [00:01<10:33, 3.15it/s, loss=0.745]" ] }, { @@ -812,7 +812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 7/2000 [00:02<10:57, 3.03it/s, loss=0.576]" + "training until 2000: 0%| | 7/2000 [00:02<10:37, 3.12it/s, loss=0.745]" ] }, { @@ -820,7 +820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 7/2000 [00:02<10:57, 3.03it/s, loss=0.57] " + "training until 2000: 0%| | 7/2000 [00:02<10:37, 3.12it/s, loss=0.739]" ] }, { @@ -828,7 +828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 8/2000 [00:02<11:04, 3.00it/s, loss=0.57]" + "training until 2000: 0%| | 8/2000 [00:02<10:33, 3.14it/s, loss=0.739]" ] }, { @@ -836,7 +836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 8/2000 [00:02<11:04, 3.00it/s, loss=0.68]" + "training until 2000: 0%| | 8/2000 [00:02<10:33, 3.14it/s, loss=0.708]" ] }, { @@ -844,7 +844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 9/2000 [00:03<10:56, 3.03it/s, loss=0.68]" + "training until 2000: 0%| | 9/2000 [00:02<10:41, 3.11it/s, loss=0.708]" ] }, { @@ -852,7 +852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 9/2000 [00:03<10:56, 3.03it/s, loss=0.592]" + "training until 2000: 0%| | 9/2000 [00:02<10:41, 3.11it/s, loss=0.761]" ] }, { @@ -860,7 +860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 10/2000 [00:03<11:00, 3.01it/s, loss=0.592]" + "training until 2000: 0%| | 10/2000 [00:03<10:47, 3.07it/s, loss=0.761]" ] }, { @@ -868,7 +868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 0%| | 10/2000 [00:03<11:00, 3.01it/s, loss=0.565]" + "training until 2000: 0%| | 10/2000 [00:03<10:47, 3.07it/s, loss=0.735]" ] }, { @@ -876,7 +876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 11/2000 [00:03<10:58, 3.02it/s, loss=0.565]" + "training until 2000: 1%| | 11/2000 [00:03<10:37, 3.12it/s, loss=0.735]" ] }, { @@ -884,7 +884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 11/2000 [00:03<10:58, 3.02it/s, loss=0.62] " + "training until 2000: 1%| | 11/2000 [00:03<10:37, 3.12it/s, loss=0.759]" ] }, { @@ -892,7 +892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 12/2000 [00:04<11:03, 2.99it/s, loss=0.62]" + "training until 2000: 1%| | 12/2000 [00:03<10:54, 3.04it/s, loss=0.759]" ] }, { @@ -900,7 +900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 12/2000 [00:04<11:03, 2.99it/s, loss=0.554]" + "training until 2000: 1%| | 12/2000 [00:03<10:54, 3.04it/s, loss=0.7] " ] }, { @@ -908,7 +908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 13/2000 [00:04<10:49, 3.06it/s, loss=0.554]" + "training until 2000: 1%| | 13/2000 [00:04<10:48, 3.06it/s, loss=0.7]" ] }, { @@ -916,7 +916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 13/2000 [00:04<10:49, 3.06it/s, loss=0.639]" + "training until 2000: 1%| | 13/2000 [00:04<10:48, 3.06it/s, loss=0.717]" ] }, { @@ -924,7 +924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 14/2000 [00:04<10:48, 3.06it/s, loss=0.639]" + "training until 2000: 1%| | 14/2000 [00:04<10:48, 3.06it/s, loss=0.717]" ] }, { @@ -932,7 +932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 14/2000 [00:04<10:48, 3.06it/s, loss=0.742]" + "training until 2000: 1%| | 14/2000 [00:04<10:48, 3.06it/s, loss=0.72] " ] }, { @@ -940,7 +940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 15/2000 [00:05<10:51, 3.05it/s, loss=0.742]" + "training until 2000: 1%| | 15/2000 [00:04<10:46, 3.07it/s, loss=0.72]" ] }, { @@ -948,7 +948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 15/2000 [00:05<10:51, 3.05it/s, loss=0.588]" + "training until 2000: 1%| | 15/2000 [00:04<10:46, 3.07it/s, loss=0.748]" ] }, { @@ -956,7 +956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 16/2000 [00:05<10:56, 3.02it/s, loss=0.588]" + "training until 2000: 1%| | 16/2000 [00:05<10:44, 3.08it/s, loss=0.748]" ] }, { @@ -964,7 +964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 16/2000 [00:05<10:56, 3.02it/s, loss=0.592]" + "training until 2000: 1%| | 16/2000 [00:05<10:44, 3.08it/s, loss=0.722]" ] }, { @@ -972,7 +972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 17/2000 [00:05<10:53, 3.03it/s, loss=0.592]" + "training until 2000: 1%| | 17/2000 [00:05<10:37, 3.11it/s, loss=0.722]" ] }, { @@ -980,7 +980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 17/2000 [00:05<10:53, 3.03it/s, loss=0.579]" + "training until 2000: 1%| | 17/2000 [00:05<10:37, 3.11it/s, loss=0.728]" ] }, { @@ -988,7 +988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 18/2000 [00:05<10:53, 3.03it/s, loss=0.579]" + "training until 2000: 1%| | 18/2000 [00:05<10:28, 3.15it/s, loss=0.728]" ] }, { @@ -996,7 +996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 18/2000 [00:05<10:53, 3.03it/s, loss=0.71] " + "training until 2000: 1%| | 18/2000 [00:05<10:28, 3.15it/s, loss=0.75] " ] }, { @@ -1004,7 +1004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 19/2000 [00:06<10:58, 3.01it/s, loss=0.71]" + "training until 2000: 1%| | 19/2000 [00:06<10:25, 3.17it/s, loss=0.75]" ] }, { @@ -1012,7 +1012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 19/2000 [00:06<10:58, 3.01it/s, loss=0.561]" + "training until 2000: 1%| | 19/2000 [00:06<10:25, 3.17it/s, loss=0.741]" ] }, { @@ -1020,7 +1020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 20/2000 [00:06<10:54, 3.03it/s, loss=0.561]" + "training until 2000: 1%| | 20/2000 [00:06<12:37, 2.61it/s, loss=0.741]" ] }, { @@ -1028,7 +1028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 20/2000 [00:06<10:54, 3.03it/s, loss=0.6] " + "training until 2000: 1%| | 20/2000 [00:06<12:37, 2.61it/s, loss=0.731]" ] }, { @@ -1036,7 +1036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 21/2000 [00:07<13:21, 2.47it/s, loss=0.6]" + "training until 2000: 1%| | 21/2000 [00:06<11:57, 2.76it/s, loss=0.731]" ] }, { @@ -1044,7 +1044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 21/2000 [00:07<13:21, 2.47it/s, loss=0.584]" + "training until 2000: 1%| | 21/2000 [00:06<11:57, 2.76it/s, loss=0.748]" ] }, { @@ -1052,7 +1052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 22/2000 [00:07<12:37, 2.61it/s, loss=0.584]" + "training until 2000: 1%| | 22/2000 [00:07<11:29, 2.87it/s, loss=0.748]" ] }, { @@ -1060,7 +1060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 22/2000 [00:07<12:37, 2.61it/s, loss=0.6] " + "training until 2000: 1%| | 22/2000 [00:07<11:29, 2.87it/s, loss=0.72] " ] }, { @@ -1068,7 +1068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 23/2000 [00:07<12:08, 2.72it/s, loss=0.6]" + "training until 2000: 1%| | 23/2000 [00:07<11:10, 2.95it/s, loss=0.72]" ] }, { @@ -1076,7 +1076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 23/2000 [00:07<12:08, 2.72it/s, loss=0.527]" + "training until 2000: 1%| | 23/2000 [00:07<11:10, 2.95it/s, loss=0.743]" ] }, { @@ -1084,7 +1084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 24/2000 [00:08<11:47, 2.79it/s, loss=0.527]" + "training until 2000: 1%| | 24/2000 [00:07<10:51, 3.03it/s, loss=0.743]" ] }, { @@ -1092,7 +1092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%| | 24/2000 [00:08<11:47, 2.79it/s, loss=0.676]" + "training until 2000: 1%| | 24/2000 [00:07<10:51, 3.03it/s, loss=0.711]" ] }, { @@ -1100,7 +1100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 25/2000 [00:08<11:32, 2.85it/s, loss=0.676]" + "training until 2000: 1%|▏ | 25/2000 [00:08<10:54, 3.02it/s, loss=0.711]" ] }, { @@ -1108,7 +1108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 25/2000 [00:08<11:32, 2.85it/s, loss=0.585]" + "training until 2000: 1%|▏ | 25/2000 [00:08<10:54, 3.02it/s, loss=0.735]" ] }, { @@ -1116,7 +1116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 26/2000 [00:08<11:45, 2.80it/s, loss=0.585]" + "training until 2000: 1%|▏ | 26/2000 [00:08<10:45, 3.06it/s, loss=0.735]" ] }, { @@ -1124,7 +1124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 26/2000 [00:08<11:45, 2.80it/s, loss=0.621]" + "training until 2000: 1%|▏ | 26/2000 [00:08<10:45, 3.06it/s, loss=0.746]" ] }, { @@ -1132,7 +1132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 27/2000 [00:09<11:21, 2.90it/s, loss=0.621]" + "training until 2000: 1%|▏ | 27/2000 [00:08<10:43, 3.07it/s, loss=0.746]" ] }, { @@ -1140,7 +1140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 27/2000 [00:09<11:21, 2.90it/s, loss=0.642]" + "training until 2000: 1%|▏ | 27/2000 [00:08<10:43, 3.07it/s, loss=0.722]" ] }, { @@ -1148,7 +1148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 28/2000 [00:09<11:03, 2.97it/s, loss=0.642]" + "training until 2000: 1%|▏ | 28/2000 [00:09<10:40, 3.08it/s, loss=0.722]" ] }, { @@ -1156,7 +1156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 28/2000 [00:09<11:03, 2.97it/s, loss=0.66] " + "training until 2000: 1%|▏ | 28/2000 [00:09<10:40, 3.08it/s, loss=0.752]" ] }, { @@ -1164,7 +1164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 29/2000 [00:09<11:04, 2.97it/s, loss=0.66]" + "training until 2000: 1%|▏ | 29/2000 [00:09<10:41, 3.07it/s, loss=0.752]" ] }, { @@ -1172,7 +1172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 1%|▏ | 29/2000 [00:09<11:04, 2.97it/s, loss=0.682]" + "training until 2000: 1%|▏ | 29/2000 [00:09<10:41, 3.07it/s, loss=0.715]" ] }, { @@ -1180,7 +1180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 30/2000 [00:10<10:53, 3.02it/s, loss=0.682]" + "training until 2000: 2%|▏ | 30/2000 [00:09<10:46, 3.05it/s, loss=0.715]" ] }, { @@ -1188,7 +1188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 30/2000 [00:10<10:53, 3.02it/s, loss=0.586]" + "training until 2000: 2%|▏ | 30/2000 [00:09<10:46, 3.05it/s, loss=0.681]" ] }, { @@ -1196,7 +1196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 31/2000 [00:10<10:50, 3.03it/s, loss=0.586]" + "training until 2000: 2%|▏ | 31/2000 [00:10<10:41, 3.07it/s, loss=0.681]" ] }, { @@ -1204,7 +1204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 31/2000 [00:10<10:50, 3.03it/s, loss=0.552]" + "training until 2000: 2%|▏ | 31/2000 [00:10<10:41, 3.07it/s, loss=0.779]" ] }, { @@ -1212,7 +1212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 32/2000 [00:10<10:53, 3.01it/s, loss=0.552]" + "training until 2000: 2%|▏ | 32/2000 [00:10<10:51, 3.02it/s, loss=0.779]" ] }, { @@ -1220,7 +1220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 32/2000 [00:10<10:53, 3.01it/s, loss=0.589]" + "training until 2000: 2%|▏ | 32/2000 [00:10<10:51, 3.02it/s, loss=0.731]" ] }, { @@ -1228,7 +1228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 33/2000 [00:11<10:57, 2.99it/s, loss=0.589]" + "training until 2000: 2%|▏ | 33/2000 [00:10<10:46, 3.04it/s, loss=0.731]" ] }, { @@ -1236,7 +1236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 33/2000 [00:11<10:57, 2.99it/s, loss=0.561]" + "training until 2000: 2%|▏ | 33/2000 [00:10<10:46, 3.04it/s, loss=0.732]" ] }, { @@ -1244,7 +1244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 34/2000 [00:11<10:52, 3.01it/s, loss=0.561]" + "training until 2000: 2%|▏ | 34/2000 [00:11<10:47, 3.04it/s, loss=0.732]" ] }, { @@ -1252,7 +1252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 34/2000 [00:11<10:52, 3.01it/s, loss=0.569]" + "training until 2000: 2%|▏ | 34/2000 [00:11<10:47, 3.04it/s, loss=0.762]" ] }, { @@ -1260,7 +1260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 35/2000 [00:11<10:57, 2.99it/s, loss=0.569]" + "training until 2000: 2%|▏ | 35/2000 [00:11<10:53, 3.01it/s, loss=0.762]" ] }, { @@ -1268,7 +1268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 35/2000 [00:11<10:57, 2.99it/s, loss=0.637]" + "training until 2000: 2%|▏ | 35/2000 [00:11<10:53, 3.01it/s, loss=0.72] " ] }, { @@ -1276,7 +1276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 36/2000 [00:12<10:55, 2.99it/s, loss=0.637]" + "training until 2000: 2%|▏ | 36/2000 [00:11<10:49, 3.02it/s, loss=0.72]" ] }, { @@ -1284,7 +1284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 36/2000 [00:12<10:55, 2.99it/s, loss=0.569]" + "training until 2000: 2%|▏ | 36/2000 [00:11<10:49, 3.02it/s, loss=0.757]" ] }, { @@ -1292,7 +1292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 37/2000 [00:12<10:44, 3.05it/s, loss=0.569]" + "training until 2000: 2%|▏ | 37/2000 [00:12<10:41, 3.06it/s, loss=0.757]" ] }, { @@ -1300,7 +1300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 37/2000 [00:12<10:44, 3.05it/s, loss=0.606]" + "training until 2000: 2%|▏ | 37/2000 [00:12<10:41, 3.06it/s, loss=0.757]" ] }, { @@ -1308,7 +1308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 38/2000 [00:12<10:37, 3.08it/s, loss=0.606]" + "training until 2000: 2%|▏ | 38/2000 [00:12<10:32, 3.10it/s, loss=0.757]" ] }, { @@ -1316,7 +1316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 38/2000 [00:12<10:37, 3.08it/s, loss=0.547]" + "training until 2000: 2%|▏ | 38/2000 [00:12<10:32, 3.10it/s, loss=0.739]" ] }, { @@ -1324,7 +1324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 39/2000 [00:13<10:37, 3.08it/s, loss=0.547]" + "training until 2000: 2%|▏ | 39/2000 [00:12<10:29, 3.12it/s, loss=0.739]" ] }, { @@ -1332,7 +1332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 39/2000 [00:13<10:37, 3.08it/s, loss=0.613]" + "training until 2000: 2%|▏ | 39/2000 [00:12<10:29, 3.12it/s, loss=0.666]" ] }, { @@ -1340,7 +1340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 40/2000 [00:13<10:37, 3.08it/s, loss=0.613]" + "training until 2000: 2%|▏ | 40/2000 [00:13<10:30, 3.11it/s, loss=0.666]" ] }, { @@ -1348,7 +1348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 40/2000 [00:13<10:37, 3.08it/s, loss=0.56] " + "training until 2000: 2%|▏ | 40/2000 [00:13<10:30, 3.11it/s, loss=0.748]" ] }, { @@ -1356,7 +1356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 41/2000 [00:13<10:36, 3.08it/s, loss=0.56]" + "training until 2000: 2%|▏ | 41/2000 [00:13<10:29, 3.11it/s, loss=0.748]" ] }, { @@ -1364,7 +1364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 41/2000 [00:13<10:36, 3.08it/s, loss=0.595]" + "training until 2000: 2%|▏ | 41/2000 [00:13<10:29, 3.11it/s, loss=0.712]" ] }, { @@ -1372,7 +1372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 42/2000 [00:14<10:51, 3.01it/s, loss=0.595]" + "training until 2000: 2%|▏ | 42/2000 [00:13<10:35, 3.08it/s, loss=0.712]" ] }, { @@ -1380,7 +1380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 42/2000 [00:14<10:51, 3.01it/s, loss=0.654]" + "training until 2000: 2%|▏ | 42/2000 [00:13<10:35, 3.08it/s, loss=0.759]" ] }, { @@ -1388,7 +1388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 43/2000 [00:14<10:50, 3.01it/s, loss=0.654]" + "training until 2000: 2%|▏ | 43/2000 [00:14<10:36, 3.07it/s, loss=0.759]" ] }, { @@ -1396,7 +1396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 43/2000 [00:14<10:50, 3.01it/s, loss=0.571]" + "training until 2000: 2%|▏ | 43/2000 [00:14<10:36, 3.07it/s, loss=0.782]" ] }, { @@ -1404,7 +1404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 44/2000 [00:14<10:46, 3.03it/s, loss=0.571]" + "training until 2000: 2%|▏ | 44/2000 [00:14<10:39, 3.06it/s, loss=0.782]" ] }, { @@ -1412,7 +1412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 44/2000 [00:14<10:46, 3.03it/s, loss=0.557]" + "training until 2000: 2%|▏ | 44/2000 [00:14<10:39, 3.06it/s, loss=0.732]" ] }, { @@ -1420,7 +1420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 45/2000 [00:15<10:48, 3.02it/s, loss=0.557]" + "training until 2000: 2%|▏ | 45/2000 [00:14<10:32, 3.09it/s, loss=0.732]" ] }, { @@ -1428,7 +1428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 45/2000 [00:15<10:48, 3.02it/s, loss=0.653]" + "training until 2000: 2%|▏ | 45/2000 [00:14<10:32, 3.09it/s, loss=0.767]" ] }, { @@ -1436,7 +1436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 46/2000 [00:15<10:58, 2.97it/s, loss=0.653]" + "training until 2000: 2%|▏ | 46/2000 [00:15<10:32, 3.09it/s, loss=0.767]" ] }, { @@ -1444,7 +1444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 46/2000 [00:15<10:58, 2.97it/s, loss=0.637]" + "training until 2000: 2%|▏ | 46/2000 [00:15<10:32, 3.09it/s, loss=0.742]" ] }, { @@ -1452,7 +1452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 47/2000 [00:15<11:04, 2.94it/s, loss=0.637]" + "training until 2000: 2%|▏ | 47/2000 [00:15<10:25, 3.12it/s, loss=0.742]" ] }, { @@ -1460,7 +1460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 47/2000 [00:15<11:04, 2.94it/s, loss=0.639]" + "training until 2000: 2%|▏ | 47/2000 [00:15<10:25, 3.12it/s, loss=0.769]" ] }, { @@ -1468,7 +1468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 48/2000 [00:16<11:04, 2.94it/s, loss=0.639]" + "training until 2000: 2%|▏ | 48/2000 [00:15<10:23, 3.13it/s, loss=0.769]" ] }, { @@ -1476,7 +1476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 48/2000 [00:16<11:04, 2.94it/s, loss=0.754]" + "training until 2000: 2%|▏ | 48/2000 [00:15<10:23, 3.13it/s, loss=0.684]" ] }, { @@ -1484,7 +1484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 49/2000 [00:16<10:52, 2.99it/s, loss=0.754]" + "training until 2000: 2%|▏ | 49/2000 [00:16<10:17, 3.16it/s, loss=0.684]" ] }, { @@ -1492,7 +1492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▏ | 49/2000 [00:16<10:52, 2.99it/s, loss=0.643]" + "training until 2000: 2%|▏ | 49/2000 [00:16<10:17, 3.16it/s, loss=0.755]" ] }, { @@ -1500,7 +1500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▎ | 50/2000 [00:16<10:51, 2.99it/s, loss=0.643]" + "training until 2000: 2%|▎ | 50/2000 [00:16<10:18, 3.15it/s, loss=0.755]" ] }, { @@ -1508,7 +1508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 2%|▎ | 50/2000 [00:16<10:51, 2.99it/s, loss=0.574]" + "training until 2000: 2%|▎ | 50/2000 [00:16<10:18, 3.15it/s, loss=0.753]" ] }, { @@ -1516,7 +1516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 51/2000 [00:17<11:02, 2.94it/s, loss=0.574]" + "training until 2000: 3%|▎ | 51/2000 [00:16<10:21, 3.14it/s, loss=0.753]" ] }, { @@ -1524,7 +1524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 51/2000 [00:17<11:02, 2.94it/s, loss=0.693]" + "training until 2000: 3%|▎ | 51/2000 [00:16<10:21, 3.14it/s, loss=0.777]" ] }, { @@ -1532,7 +1532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 52/2000 [00:17<11:01, 2.94it/s, loss=0.693]" + "training until 2000: 3%|▎ | 52/2000 [00:17<10:29, 3.09it/s, loss=0.777]" ] }, { @@ -1540,7 +1540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 52/2000 [00:17<11:01, 2.94it/s, loss=0.67] " + "training until 2000: 3%|▎ | 52/2000 [00:17<10:29, 3.09it/s, loss=0.718]" ] }, { @@ -1548,7 +1548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 53/2000 [00:17<10:59, 2.95it/s, loss=0.67]" + "training until 2000: 3%|▎ | 53/2000 [00:17<10:34, 3.07it/s, loss=0.718]" ] }, { @@ -1556,7 +1556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 53/2000 [00:17<10:59, 2.95it/s, loss=0.609]" + "training until 2000: 3%|▎ | 53/2000 [00:17<10:34, 3.07it/s, loss=0.713]" ] }, { @@ -1564,7 +1564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 54/2000 [00:18<11:01, 2.94it/s, loss=0.609]" + "training until 2000: 3%|▎ | 54/2000 [00:17<10:31, 3.08it/s, loss=0.713]" ] }, { @@ -1572,7 +1572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 54/2000 [00:18<11:01, 2.94it/s, loss=0.608]" + "training until 2000: 3%|▎ | 54/2000 [00:17<10:31, 3.08it/s, loss=0.754]" ] }, { @@ -1580,7 +1580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 55/2000 [00:18<11:00, 2.94it/s, loss=0.608]" + "training until 2000: 3%|▎ | 55/2000 [00:17<10:23, 3.12it/s, loss=0.754]" ] }, { @@ -1588,7 +1588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 55/2000 [00:18<11:00, 2.94it/s, loss=0.629]" + "training until 2000: 3%|▎ | 55/2000 [00:17<10:23, 3.12it/s, loss=0.689]" ] }, { @@ -1596,7 +1596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 56/2000 [00:18<10:46, 3.01it/s, loss=0.629]" + "training until 2000: 3%|▎ | 56/2000 [00:18<10:14, 3.17it/s, loss=0.689]" ] }, { @@ -1604,7 +1604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 56/2000 [00:18<10:46, 3.01it/s, loss=0.56] " + "training until 2000: 3%|▎ | 56/2000 [00:18<10:14, 3.17it/s, loss=0.686]" ] }, { @@ -1612,7 +1612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 57/2000 [00:19<10:55, 2.96it/s, loss=0.56]" + "training until 2000: 3%|▎ | 57/2000 [00:18<10:22, 3.12it/s, loss=0.686]" ] }, { @@ -1620,7 +1620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 57/2000 [00:19<10:55, 2.96it/s, loss=0.633]" + "training until 2000: 3%|▎ | 57/2000 [00:18<10:22, 3.12it/s, loss=0.75] " ] }, { @@ -1628,7 +1628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 58/2000 [00:19<10:48, 2.99it/s, loss=0.633]" + "training until 2000: 3%|▎ | 58/2000 [00:18<10:17, 3.15it/s, loss=0.75]" ] }, { @@ -1636,7 +1636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 58/2000 [00:19<10:48, 2.99it/s, loss=0.714]" + "training until 2000: 3%|▎ | 58/2000 [00:18<10:17, 3.15it/s, loss=0.672]" ] }, { @@ -1644,7 +1644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 59/2000 [00:19<10:59, 2.94it/s, loss=0.714]" + "training until 2000: 3%|▎ | 59/2000 [00:19<10:19, 3.13it/s, loss=0.672]" ] }, { @@ -1652,7 +1652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 59/2000 [00:19<10:59, 2.94it/s, loss=0.559]" + "training until 2000: 3%|▎ | 59/2000 [00:19<10:19, 3.13it/s, loss=0.72] " ] }, { @@ -1660,7 +1660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 60/2000 [00:20<10:58, 2.95it/s, loss=0.559]" + "training until 2000: 3%|▎ | 60/2000 [00:19<10:16, 3.15it/s, loss=0.72]" ] }, { @@ -1668,7 +1668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 60/2000 [00:20<10:58, 2.95it/s, loss=0.556]" + "training until 2000: 3%|▎ | 60/2000 [00:19<10:16, 3.15it/s, loss=0.735]" ] }, { @@ -1676,7 +1676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 61/2000 [00:20<10:55, 2.96it/s, loss=0.556]" + "training until 2000: 3%|▎ | 61/2000 [00:19<10:11, 3.17it/s, loss=0.735]" ] }, { @@ -1684,7 +1684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 61/2000 [00:20<10:55, 2.96it/s, loss=0.601]" + "training until 2000: 3%|▎ | 61/2000 [00:19<10:11, 3.17it/s, loss=0.761]" ] }, { @@ -1692,7 +1692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 62/2000 [00:20<10:57, 2.95it/s, loss=0.601]" + "training until 2000: 3%|▎ | 62/2000 [00:20<10:02, 3.22it/s, loss=0.761]" ] }, { @@ -1700,7 +1700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 62/2000 [00:20<10:57, 2.95it/s, loss=0.575]" + "training until 2000: 3%|▎ | 62/2000 [00:20<10:02, 3.22it/s, loss=0.75] " ] }, { @@ -1708,7 +1708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 63/2000 [00:21<10:53, 2.96it/s, loss=0.575]" + "training until 2000: 3%|▎ | 63/2000 [00:20<10:06, 3.19it/s, loss=0.75]" ] }, { @@ -1716,7 +1716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 63/2000 [00:21<10:53, 2.96it/s, loss=0.619]" + "training until 2000: 3%|▎ | 63/2000 [00:20<10:06, 3.19it/s, loss=0.737]" ] }, { @@ -1724,7 +1724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 64/2000 [00:21<10:45, 3.00it/s, loss=0.619]" + "training until 2000: 3%|▎ | 64/2000 [00:20<10:13, 3.16it/s, loss=0.737]" ] }, { @@ -1732,7 +1732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 64/2000 [00:21<10:45, 3.00it/s, loss=0.58] " + "training until 2000: 3%|▎ | 64/2000 [00:20<10:13, 3.16it/s, loss=0.746]" ] }, { @@ -1740,7 +1740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 65/2000 [00:21<10:51, 2.97it/s, loss=0.58]" + "training until 2000: 3%|▎ | 65/2000 [00:21<10:12, 3.16it/s, loss=0.746]" ] }, { @@ -1748,7 +1748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 65/2000 [00:21<10:51, 2.97it/s, loss=0.596]" + "training until 2000: 3%|▎ | 65/2000 [00:21<10:12, 3.16it/s, loss=0.753]" ] }, { @@ -1756,7 +1756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 66/2000 [00:22<10:50, 2.97it/s, loss=0.596]" + "training until 2000: 3%|▎ | 66/2000 [00:21<10:10, 3.17it/s, loss=0.753]" ] }, { @@ -1764,7 +1764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 66/2000 [00:22<10:50, 2.97it/s, loss=0.57] " + "training until 2000: 3%|▎ | 66/2000 [00:21<10:10, 3.17it/s, loss=0.686]" ] }, { @@ -1772,7 +1772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 67/2000 [00:22<10:47, 2.98it/s, loss=0.57]" + "training until 2000: 3%|▎ | 67/2000 [00:21<10:24, 3.09it/s, loss=0.686]" ] }, { @@ -1780,7 +1780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 67/2000 [00:22<10:47, 2.98it/s, loss=0.654]" + "training until 2000: 3%|▎ | 67/2000 [00:21<10:24, 3.09it/s, loss=0.765]" ] }, { @@ -1788,7 +1788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 68/2000 [00:22<10:54, 2.95it/s, loss=0.654]" + "training until 2000: 3%|▎ | 68/2000 [00:22<10:24, 3.09it/s, loss=0.765]" ] }, { @@ -1796,7 +1796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 68/2000 [00:22<10:54, 2.95it/s, loss=0.617]" + "training until 2000: 3%|▎ | 68/2000 [00:22<10:24, 3.09it/s, loss=0.71] " ] }, { @@ -1804,7 +1804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 69/2000 [00:23<10:49, 2.98it/s, loss=0.617]" + "training until 2000: 3%|▎ | 69/2000 [00:22<10:19, 3.12it/s, loss=0.71]" ] }, { @@ -1812,7 +1812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 3%|▎ | 69/2000 [00:23<10:49, 2.98it/s, loss=0.539]" + "training until 2000: 3%|▎ | 69/2000 [00:22<10:19, 3.12it/s, loss=0.75]" ] }, { @@ -1820,7 +1820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 70/2000 [00:23<10:42, 3.00it/s, loss=0.539]" + "training until 2000: 4%|▎ | 70/2000 [00:22<10:20, 3.11it/s, loss=0.75]" ] }, { @@ -1828,7 +1828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 70/2000 [00:23<10:42, 3.00it/s, loss=0.577]" + "training until 2000: 4%|▎ | 70/2000 [00:22<10:20, 3.11it/s, loss=0.747]" ] }, { @@ -1836,7 +1836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 71/2000 [00:23<10:55, 2.94it/s, loss=0.577]" + "training until 2000: 4%|▎ | 71/2000 [00:23<10:22, 3.10it/s, loss=0.747]" ] }, { @@ -1844,7 +1844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 71/2000 [00:23<10:55, 2.94it/s, loss=0.639]" + "training until 2000: 4%|▎ | 71/2000 [00:23<10:22, 3.10it/s, loss=0.742]" ] }, { @@ -1852,7 +1852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 72/2000 [00:24<10:50, 2.96it/s, loss=0.639]" + "training until 2000: 4%|▎ | 72/2000 [00:23<10:18, 3.12it/s, loss=0.742]" ] }, { @@ -1860,7 +1860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 72/2000 [00:24<10:50, 2.96it/s, loss=0.62] " + "training until 2000: 4%|▎ | 72/2000 [00:23<10:18, 3.12it/s, loss=0.683]" ] }, { @@ -1868,7 +1868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 73/2000 [00:24<10:45, 2.98it/s, loss=0.62]" + "training until 2000: 4%|▎ | 73/2000 [00:23<10:22, 3.10it/s, loss=0.683]" ] }, { @@ -1876,7 +1876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 73/2000 [00:24<10:45, 2.98it/s, loss=0.592]" + "training until 2000: 4%|▎ | 73/2000 [00:23<10:22, 3.10it/s, loss=0.738]" ] }, { @@ -1884,7 +1884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 74/2000 [00:24<10:37, 3.02it/s, loss=0.592]" + "training until 2000: 4%|▎ | 74/2000 [00:24<10:19, 3.11it/s, loss=0.738]" ] }, { @@ -1892,7 +1892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▎ | 74/2000 [00:24<10:37, 3.02it/s, loss=0.532]" + "training until 2000: 4%|▎ | 74/2000 [00:24<10:19, 3.11it/s, loss=0.76] " ] }, { @@ -1900,7 +1900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 75/2000 [00:25<10:27, 3.07it/s, loss=0.532]" + "training until 2000: 4%|▍ | 75/2000 [00:24<10:18, 3.11it/s, loss=0.76]" ] }, { @@ -1908,7 +1908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 75/2000 [00:25<10:27, 3.07it/s, loss=0.626]" + "training until 2000: 4%|▍ | 75/2000 [00:24<10:18, 3.11it/s, loss=0.746]" ] }, { @@ -1916,7 +1916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 76/2000 [00:25<10:26, 3.07it/s, loss=0.626]" + "training until 2000: 4%|▍ | 76/2000 [00:24<10:18, 3.11it/s, loss=0.746]" ] }, { @@ -1924,7 +1924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 76/2000 [00:25<10:26, 3.07it/s, loss=0.52] " + "training until 2000: 4%|▍ | 76/2000 [00:24<10:18, 3.11it/s, loss=0.773]" ] }, { @@ -1932,7 +1932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 77/2000 [00:25<10:21, 3.09it/s, loss=0.52]" + "training until 2000: 4%|▍ | 77/2000 [00:24<10:15, 3.12it/s, loss=0.773]" ] }, { @@ -1940,7 +1940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 77/2000 [00:25<10:21, 3.09it/s, loss=0.549]" + "training until 2000: 4%|▍ | 77/2000 [00:24<10:15, 3.12it/s, loss=0.711]" ] }, { @@ -1948,7 +1948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 78/2000 [00:26<10:26, 3.07it/s, loss=0.549]" + "training until 2000: 4%|▍ | 78/2000 [00:25<10:08, 3.16it/s, loss=0.711]" ] }, { @@ -1956,7 +1956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 78/2000 [00:26<10:26, 3.07it/s, loss=0.565]" + "training until 2000: 4%|▍ | 78/2000 [00:25<10:08, 3.16it/s, loss=0.695]" ] }, { @@ -1964,7 +1964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 79/2000 [00:26<10:24, 3.08it/s, loss=0.565]" + "training until 2000: 4%|▍ | 79/2000 [00:25<10:18, 3.11it/s, loss=0.695]" ] }, { @@ -1972,7 +1972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 79/2000 [00:26<10:24, 3.08it/s, loss=0.642]" + "training until 2000: 4%|▍ | 79/2000 [00:25<10:18, 3.11it/s, loss=0.735]" ] }, { @@ -1980,7 +1980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 80/2000 [00:26<10:17, 3.11it/s, loss=0.642]" + "training until 2000: 4%|▍ | 80/2000 [00:25<10:15, 3.12it/s, loss=0.735]" ] }, { @@ -1988,7 +1988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 80/2000 [00:26<10:17, 3.11it/s, loss=0.546]" + "training until 2000: 4%|▍ | 80/2000 [00:25<10:15, 3.12it/s, loss=0.741]" ] }, { @@ -1996,7 +1996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 81/2000 [00:27<10:21, 3.09it/s, loss=0.546]" + "training until 2000: 4%|▍ | 81/2000 [00:26<12:23, 2.58it/s, loss=0.741]" ] }, { @@ -2004,7 +2004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 81/2000 [00:27<10:21, 3.09it/s, loss=0.571]" + "training until 2000: 4%|▍ | 81/2000 [00:26<12:23, 2.58it/s, loss=0.667]" ] }, { @@ -2012,7 +2012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 82/2000 [00:27<10:37, 3.01it/s, loss=0.571]" + "training until 2000: 4%|▍ | 82/2000 [00:26<11:51, 2.70it/s, loss=0.667]" ] }, { @@ -2020,7 +2020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 82/2000 [00:27<10:37, 3.01it/s, loss=0.581]" + "training until 2000: 4%|▍ | 82/2000 [00:26<11:51, 2.70it/s, loss=0.728]" ] }, { @@ -2028,7 +2028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 83/2000 [00:27<10:26, 3.06it/s, loss=0.581]" + "training until 2000: 4%|▍ | 83/2000 [00:27<11:16, 2.83it/s, loss=0.728]" ] }, { @@ -2036,7 +2036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 83/2000 [00:27<10:26, 3.06it/s, loss=0.558]" + "training until 2000: 4%|▍ | 83/2000 [00:27<11:16, 2.83it/s, loss=0.723]" ] }, { @@ -2044,7 +2044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 84/2000 [00:28<12:40, 2.52it/s, loss=0.558]" + "training until 2000: 4%|▍ | 84/2000 [00:27<10:55, 2.92it/s, loss=0.723]" ] }, { @@ -2052,7 +2052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 84/2000 [00:28<12:40, 2.52it/s, loss=0.57] " + "training until 2000: 4%|▍ | 84/2000 [00:27<10:55, 2.92it/s, loss=0.689]" ] }, { @@ -2060,7 +2060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 85/2000 [00:28<11:49, 2.70it/s, loss=0.57]" + "training until 2000: 4%|▍ | 85/2000 [00:27<10:40, 2.99it/s, loss=0.689]" ] }, { @@ -2068,7 +2068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 85/2000 [00:28<11:49, 2.70it/s, loss=0.552]" + "training until 2000: 4%|▍ | 85/2000 [00:27<10:40, 2.99it/s, loss=0.758]" ] }, { @@ -2076,7 +2076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 86/2000 [00:29<11:22, 2.80it/s, loss=0.552]" + "training until 2000: 4%|▍ | 86/2000 [00:28<10:24, 3.06it/s, loss=0.758]" ] }, { @@ -2084,7 +2084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 86/2000 [00:29<11:22, 2.80it/s, loss=0.608]" + "training until 2000: 4%|▍ | 86/2000 [00:28<10:24, 3.06it/s, loss=0.755]" ] }, { @@ -2092,7 +2092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 87/2000 [00:29<11:02, 2.89it/s, loss=0.608]" + "training until 2000: 4%|▍ | 87/2000 [00:28<10:22, 3.07it/s, loss=0.755]" ] }, { @@ -2100,7 +2100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 87/2000 [00:29<11:02, 2.89it/s, loss=0.579]" + "training until 2000: 4%|▍ | 87/2000 [00:28<10:22, 3.07it/s, loss=0.721]" ] }, { @@ -2108,7 +2108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 88/2000 [00:29<10:45, 2.96it/s, loss=0.579]" + "training until 2000: 4%|▍ | 88/2000 [00:28<10:16, 3.10it/s, loss=0.721]" ] }, { @@ -2116,7 +2116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 88/2000 [00:29<10:45, 2.96it/s, loss=0.683]" + "training until 2000: 4%|▍ | 88/2000 [00:28<10:16, 3.10it/s, loss=0.74] " ] }, { @@ -2124,7 +2124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 89/2000 [00:30<10:35, 3.01it/s, loss=0.683]" + "training until 2000: 4%|▍ | 89/2000 [00:29<10:10, 3.13it/s, loss=0.74]" ] }, { @@ -2132,7 +2132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 89/2000 [00:30<10:35, 3.01it/s, loss=0.588]" + "training until 2000: 4%|▍ | 89/2000 [00:29<10:10, 3.13it/s, loss=0.703]" ] }, { @@ -2140,7 +2140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 90/2000 [00:30<10:30, 3.03it/s, loss=0.588]" + "training until 2000: 4%|▍ | 90/2000 [00:29<10:08, 3.14it/s, loss=0.703]" ] }, { @@ -2148,7 +2148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 4%|▍ | 90/2000 [00:30<10:30, 3.03it/s, loss=0.587]" + "training until 2000: 4%|▍ | 90/2000 [00:29<10:08, 3.14it/s, loss=0.702]" ] }, { @@ -2156,7 +2156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 91/2000 [00:30<10:19, 3.08it/s, loss=0.587]" + "training until 2000: 5%|▍ | 91/2000 [00:29<10:08, 3.14it/s, loss=0.702]" ] }, { @@ -2164,7 +2164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 91/2000 [00:30<10:19, 3.08it/s, loss=0.668]" + "training until 2000: 5%|▍ | 91/2000 [00:29<10:08, 3.14it/s, loss=0.735]" ] }, { @@ -2172,7 +2172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 92/2000 [00:30<10:18, 3.08it/s, loss=0.668]" + "training until 2000: 5%|▍ | 92/2000 [00:29<10:08, 3.14it/s, loss=0.735]" ] }, { @@ -2180,7 +2180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 92/2000 [00:30<10:18, 3.08it/s, loss=0.734]" + "training until 2000: 5%|▍ | 92/2000 [00:29<10:08, 3.14it/s, loss=0.733]" ] }, { @@ -2188,7 +2188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 93/2000 [00:31<10:14, 3.10it/s, loss=0.734]" + "training until 2000: 5%|▍ | 93/2000 [00:30<10:04, 3.16it/s, loss=0.733]" ] }, { @@ -2196,7 +2196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 93/2000 [00:31<10:14, 3.10it/s, loss=0.62] " + "training until 2000: 5%|▍ | 93/2000 [00:30<10:04, 3.16it/s, loss=0.757]" ] }, { @@ -2204,7 +2204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 94/2000 [00:31<10:12, 3.11it/s, loss=0.62]" + "training until 2000: 5%|▍ | 94/2000 [00:30<10:03, 3.16it/s, loss=0.757]" ] }, { @@ -2212,7 +2212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 94/2000 [00:31<10:12, 3.11it/s, loss=0.641]" + "training until 2000: 5%|▍ | 94/2000 [00:30<10:03, 3.16it/s, loss=0.721]" ] }, { @@ -2220,7 +2220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 95/2000 [00:31<10:12, 3.11it/s, loss=0.641]" + "training until 2000: 5%|▍ | 95/2000 [00:30<10:02, 3.16it/s, loss=0.721]" ] }, { @@ -2228,7 +2228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 95/2000 [00:31<10:12, 3.11it/s, loss=0.598]" + "training until 2000: 5%|▍ | 95/2000 [00:30<10:02, 3.16it/s, loss=0.741]" ] }, { @@ -2236,7 +2236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 96/2000 [00:32<10:20, 3.07it/s, loss=0.598]" + "training until 2000: 5%|▍ | 96/2000 [00:31<10:02, 3.16it/s, loss=0.741]" ] }, { @@ -2244,7 +2244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 96/2000 [00:32<10:20, 3.07it/s, loss=0.56] " + "training until 2000: 5%|▍ | 96/2000 [00:31<10:02, 3.16it/s, loss=0.772]" ] }, { @@ -2252,7 +2252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 97/2000 [00:32<10:14, 3.10it/s, loss=0.56]" + "training until 2000: 5%|▍ | 97/2000 [00:31<10:06, 3.14it/s, loss=0.772]" ] }, { @@ -2260,7 +2260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 97/2000 [00:32<10:14, 3.10it/s, loss=0.648]" + "training until 2000: 5%|▍ | 97/2000 [00:31<10:06, 3.14it/s, loss=0.689]" ] }, { @@ -2268,7 +2268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 98/2000 [00:32<10:13, 3.10it/s, loss=0.648]" + "training until 2000: 5%|▍ | 98/2000 [00:31<10:14, 3.09it/s, loss=0.689]" ] }, { @@ -2276,7 +2276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 98/2000 [00:32<10:13, 3.10it/s, loss=0.638]" + "training until 2000: 5%|▍ | 98/2000 [00:31<10:14, 3.09it/s, loss=0.69] " ] }, { @@ -2284,7 +2284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 99/2000 [00:33<10:15, 3.09it/s, loss=0.638]" + "training until 2000: 5%|▍ | 99/2000 [00:32<10:13, 3.10it/s, loss=0.69]" ] }, { @@ -2292,7 +2292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▍ | 99/2000 [00:33<10:15, 3.09it/s, loss=0.535]" + "training until 2000: 5%|▍ | 99/2000 [00:32<10:13, 3.10it/s, loss=0.737]" ] }, { @@ -2300,7 +2300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 100/2000 [00:33<10:12, 3.10it/s, loss=0.535]" + "training until 2000: 5%|▌ | 100/2000 [00:32<10:19, 3.07it/s, loss=0.737]" ] }, { @@ -2308,7 +2308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 100/2000 [00:33<10:12, 3.10it/s, loss=0.547]" + "training until 2000: 5%|▌ | 100/2000 [00:32<10:19, 3.07it/s, loss=0.716]" ] }, { @@ -2316,7 +2316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 101/2000 [00:33<10:05, 3.13it/s, loss=0.547]" + "training until 2000: 5%|▌ | 101/2000 [00:32<10:13, 3.09it/s, loss=0.716]" ] }, { @@ -2324,7 +2324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 101/2000 [00:33<10:05, 3.13it/s, loss=0.626]" + "training until 2000: 5%|▌ | 101/2000 [00:32<10:13, 3.09it/s, loss=0.726]" ] }, { @@ -2332,7 +2332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 102/2000 [00:34<10:04, 3.14it/s, loss=0.626]" + "training until 2000: 5%|▌ | 102/2000 [00:33<10:05, 3.14it/s, loss=0.726]" ] }, { @@ -2340,7 +2340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 102/2000 [00:34<10:04, 3.14it/s, loss=0.599]" + "training until 2000: 5%|▌ | 102/2000 [00:33<10:05, 3.14it/s, loss=0.775]" ] }, { @@ -2348,7 +2348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 103/2000 [00:34<10:03, 3.14it/s, loss=0.599]" + "training until 2000: 5%|▌ | 103/2000 [00:33<10:03, 3.14it/s, loss=0.775]" ] }, { @@ -2356,7 +2356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 103/2000 [00:34<10:03, 3.14it/s, loss=0.586]" + "training until 2000: 5%|▌ | 103/2000 [00:33<10:03, 3.14it/s, loss=0.749]" ] }, { @@ -2364,7 +2364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 104/2000 [00:34<10:06, 3.13it/s, loss=0.586]" + "training until 2000: 5%|▌ | 104/2000 [00:33<10:01, 3.15it/s, loss=0.749]" ] }, { @@ -2372,7 +2372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 104/2000 [00:34<10:06, 3.13it/s, loss=0.616]" + "training until 2000: 5%|▌ | 104/2000 [00:33<10:01, 3.15it/s, loss=0.736]" ] }, { @@ -2380,7 +2380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 105/2000 [00:35<10:15, 3.08it/s, loss=0.616]" + "training until 2000: 5%|▌ | 105/2000 [00:34<10:01, 3.15it/s, loss=0.736]" ] }, { @@ -2388,7 +2388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 105/2000 [00:35<10:15, 3.08it/s, loss=0.555]" + "training until 2000: 5%|▌ | 105/2000 [00:34<10:01, 3.15it/s, loss=0.759]" ] }, { @@ -2396,7 +2396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 106/2000 [00:35<10:13, 3.09it/s, loss=0.555]" + "training until 2000: 5%|▌ | 106/2000 [00:34<09:56, 3.17it/s, loss=0.759]" ] }, { @@ -2404,7 +2404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 106/2000 [00:35<10:13, 3.09it/s, loss=0.592]" + "training until 2000: 5%|▌ | 106/2000 [00:34<09:56, 3.17it/s, loss=0.741]" ] }, { @@ -2412,7 +2412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 107/2000 [00:35<10:09, 3.10it/s, loss=0.592]" + "training until 2000: 5%|▌ | 107/2000 [00:34<09:56, 3.17it/s, loss=0.741]" ] }, { @@ -2420,7 +2420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 107/2000 [00:35<10:09, 3.10it/s, loss=0.533]" + "training until 2000: 5%|▌ | 107/2000 [00:34<09:56, 3.17it/s, loss=0.728]" ] }, { @@ -2428,7 +2428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 108/2000 [00:36<10:15, 3.07it/s, loss=0.533]" + "training until 2000: 5%|▌ | 108/2000 [00:35<09:50, 3.20it/s, loss=0.728]" ] }, { @@ -2436,7 +2436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 108/2000 [00:36<10:15, 3.07it/s, loss=0.571]" + "training until 2000: 5%|▌ | 108/2000 [00:35<09:50, 3.20it/s, loss=0.724]" ] }, { @@ -2444,7 +2444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 109/2000 [00:36<10:07, 3.11it/s, loss=0.571]" + "training until 2000: 5%|▌ | 109/2000 [00:35<09:44, 3.23it/s, loss=0.724]" ] }, { @@ -2452,7 +2452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 5%|▌ | 109/2000 [00:36<10:07, 3.11it/s, loss=0.623]" + "training until 2000: 5%|▌ | 109/2000 [00:35<09:44, 3.23it/s, loss=0.769]" ] }, { @@ -2460,7 +2460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 110/2000 [00:36<10:03, 3.13it/s, loss=0.623]" + "training until 2000: 6%|▌ | 110/2000 [00:35<09:45, 3.23it/s, loss=0.769]" ] }, { @@ -2468,7 +2468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 110/2000 [00:36<10:03, 3.13it/s, loss=0.688]" + "training until 2000: 6%|▌ | 110/2000 [00:35<09:45, 3.23it/s, loss=0.734]" ] }, { @@ -2476,7 +2476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 111/2000 [00:37<10:07, 3.11it/s, loss=0.688]" + "training until 2000: 6%|▌ | 111/2000 [00:35<09:50, 3.20it/s, loss=0.734]" ] }, { @@ -2484,7 +2484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 111/2000 [00:37<10:07, 3.11it/s, loss=0.574]" + "training until 2000: 6%|▌ | 111/2000 [00:35<09:50, 3.20it/s, loss=0.706]" ] }, { @@ -2492,7 +2492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 112/2000 [00:37<10:12, 3.08it/s, loss=0.574]" + "training until 2000: 6%|▌ | 112/2000 [00:36<09:52, 3.19it/s, loss=0.706]" ] }, { @@ -2500,7 +2500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 112/2000 [00:37<10:12, 3.08it/s, loss=0.675]" + "training until 2000: 6%|▌ | 112/2000 [00:36<09:52, 3.19it/s, loss=0.719]" ] }, { @@ -2508,7 +2508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 113/2000 [00:37<10:15, 3.07it/s, loss=0.675]" + "training until 2000: 6%|▌ | 113/2000 [00:36<09:55, 3.17it/s, loss=0.719]" ] }, { @@ -2516,7 +2516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 113/2000 [00:37<10:15, 3.07it/s, loss=0.552]" + "training until 2000: 6%|▌ | 113/2000 [00:36<09:55, 3.17it/s, loss=0.76] " ] }, { @@ -2524,7 +2524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 114/2000 [00:38<10:08, 3.10it/s, loss=0.552]" + "training until 2000: 6%|▌ | 114/2000 [00:36<09:55, 3.17it/s, loss=0.76]" ] }, { @@ -2532,7 +2532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 114/2000 [00:38<10:08, 3.10it/s, loss=0.57] " + "training until 2000: 6%|▌ | 114/2000 [00:36<09:55, 3.17it/s, loss=0.691]" ] }, { @@ -2540,7 +2540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 115/2000 [00:38<10:02, 3.13it/s, loss=0.57]" + "training until 2000: 6%|▌ | 115/2000 [00:37<09:55, 3.17it/s, loss=0.691]" ] }, { @@ -2548,7 +2548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 115/2000 [00:38<10:02, 3.13it/s, loss=0.563]" + "training until 2000: 6%|▌ | 115/2000 [00:37<09:55, 3.17it/s, loss=0.7] " ] }, { @@ -2556,7 +2556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 116/2000 [00:38<10:02, 3.13it/s, loss=0.563]" + "training until 2000: 6%|▌ | 116/2000 [00:37<09:57, 3.16it/s, loss=0.7]" ] }, { @@ -2564,7 +2564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 116/2000 [00:38<10:02, 3.13it/s, loss=0.52] " + "training until 2000: 6%|▌ | 116/2000 [00:37<09:57, 3.16it/s, loss=0.709]" ] }, { @@ -2572,7 +2572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 117/2000 [00:39<10:01, 3.13it/s, loss=0.52]" + "training until 2000: 6%|▌ | 117/2000 [00:37<10:09, 3.09it/s, loss=0.709]" ] }, { @@ -2580,7 +2580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 117/2000 [00:39<10:01, 3.13it/s, loss=0.59]" + "training until 2000: 6%|▌ | 117/2000 [00:37<10:09, 3.09it/s, loss=0.755]" ] }, { @@ -2588,7 +2588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 118/2000 [00:39<10:08, 3.09it/s, loss=0.59]" + "training until 2000: 6%|▌ | 118/2000 [00:38<10:00, 3.13it/s, loss=0.755]" ] }, { @@ -2596,7 +2596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 118/2000 [00:39<10:08, 3.09it/s, loss=0.59]" + "training until 2000: 6%|▌ | 118/2000 [00:38<10:00, 3.13it/s, loss=0.764]" ] }, { @@ -2604,7 +2604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 119/2000 [00:39<10:08, 3.09it/s, loss=0.59]" + "training until 2000: 6%|▌ | 119/2000 [00:38<09:58, 3.14it/s, loss=0.764]" ] }, { @@ -2612,7 +2612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 119/2000 [00:39<10:08, 3.09it/s, loss=0.669]" + "training until 2000: 6%|▌ | 119/2000 [00:38<09:58, 3.14it/s, loss=0.744]" ] }, { @@ -2620,7 +2620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 120/2000 [00:40<10:09, 3.08it/s, loss=0.669]" + "training until 2000: 6%|▌ | 120/2000 [00:38<09:56, 3.15it/s, loss=0.744]" ] }, { @@ -2628,7 +2628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 120/2000 [00:40<10:09, 3.08it/s, loss=0.558]" + "training until 2000: 6%|▌ | 120/2000 [00:38<09:56, 3.15it/s, loss=0.754]" ] }, { @@ -2636,7 +2636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 121/2000 [00:40<10:11, 3.07it/s, loss=0.558]" + "training until 2000: 6%|▌ | 121/2000 [00:39<09:52, 3.17it/s, loss=0.754]" ] }, { @@ -2644,7 +2644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 121/2000 [00:40<10:11, 3.07it/s, loss=0.555]" + "training until 2000: 6%|▌ | 121/2000 [00:39<09:52, 3.17it/s, loss=0.73] " ] }, { @@ -2652,7 +2652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 122/2000 [00:40<10:10, 3.08it/s, loss=0.555]" + "training until 2000: 6%|▌ | 122/2000 [00:39<10:05, 3.10it/s, loss=0.73]" ] }, { @@ -2660,7 +2660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 122/2000 [00:40<10:10, 3.08it/s, loss=0.666]" + "training until 2000: 6%|▌ | 122/2000 [00:39<10:05, 3.10it/s, loss=0.741]" ] }, { @@ -2668,7 +2668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 123/2000 [00:40<10:06, 3.10it/s, loss=0.666]" + "training until 2000: 6%|▌ | 123/2000 [00:39<10:08, 3.08it/s, loss=0.741]" ] }, { @@ -2676,7 +2676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 123/2000 [00:40<10:06, 3.10it/s, loss=0.696]" + "training until 2000: 6%|▌ | 123/2000 [00:39<10:08, 3.08it/s, loss=0.753]" ] }, { @@ -2684,7 +2684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 124/2000 [00:41<10:06, 3.09it/s, loss=0.696]" + "training until 2000: 6%|▌ | 124/2000 [00:40<10:08, 3.08it/s, loss=0.753]" ] }, { @@ -2692,7 +2692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▌ | 124/2000 [00:41<10:06, 3.09it/s, loss=0.563]" + "training until 2000: 6%|▌ | 124/2000 [00:40<10:08, 3.08it/s, loss=0.773]" ] }, { @@ -2700,7 +2700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 125/2000 [00:41<10:07, 3.09it/s, loss=0.563]" + "training until 2000: 6%|▋ | 125/2000 [00:40<10:07, 3.09it/s, loss=0.773]" ] }, { @@ -2708,7 +2708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 125/2000 [00:41<10:07, 3.09it/s, loss=0.633]" + "training until 2000: 6%|▋ | 125/2000 [00:40<10:07, 3.09it/s, loss=0.75] " ] }, { @@ -2716,7 +2716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 126/2000 [00:41<10:02, 3.11it/s, loss=0.633]" + "training until 2000: 6%|▋ | 126/2000 [00:40<10:10, 3.07it/s, loss=0.75]" ] }, { @@ -2724,7 +2724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 126/2000 [00:41<10:02, 3.11it/s, loss=0.615]" + "training until 2000: 6%|▋ | 126/2000 [00:40<10:10, 3.07it/s, loss=0.746]" ] }, { @@ -2732,7 +2732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 127/2000 [00:42<10:00, 3.12it/s, loss=0.615]" + "training until 2000: 6%|▋ | 127/2000 [00:41<10:02, 3.11it/s, loss=0.746]" ] }, { @@ -2740,7 +2740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 127/2000 [00:42<10:00, 3.12it/s, loss=0.657]" + "training until 2000: 6%|▋ | 127/2000 [00:41<10:02, 3.11it/s, loss=0.738]" ] }, { @@ -2748,7 +2748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 128/2000 [00:42<09:55, 3.14it/s, loss=0.657]" + "training until 2000: 6%|▋ | 128/2000 [00:41<10:00, 3.12it/s, loss=0.738]" ] }, { @@ -2756,7 +2756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 128/2000 [00:42<09:55, 3.14it/s, loss=0.618]" + "training until 2000: 6%|▋ | 128/2000 [00:41<10:00, 3.12it/s, loss=0.693]" ] }, { @@ -2764,7 +2764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 129/2000 [00:42<09:55, 3.14it/s, loss=0.618]" + "training until 2000: 6%|▋ | 129/2000 [00:41<09:56, 3.14it/s, loss=0.693]" ] }, { @@ -2772,7 +2772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 129/2000 [00:42<09:55, 3.14it/s, loss=0.65] " + "training until 2000: 6%|▋ | 129/2000 [00:41<09:56, 3.14it/s, loss=0.755]" ] }, { @@ -2780,7 +2780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 130/2000 [00:43<09:56, 3.14it/s, loss=0.65]" + "training until 2000: 6%|▋ | 130/2000 [00:42<10:02, 3.11it/s, loss=0.755]" ] }, { @@ -2788,7 +2788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 6%|▋ | 130/2000 [00:43<09:56, 3.14it/s, loss=0.524]" + "training until 2000: 6%|▋ | 130/2000 [00:42<10:02, 3.11it/s, loss=0.76] " ] }, { @@ -2796,7 +2796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 131/2000 [00:43<09:55, 3.14it/s, loss=0.524]" + "training until 2000: 7%|▋ | 131/2000 [00:42<10:01, 3.11it/s, loss=0.76]" ] }, { @@ -2804,7 +2804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 131/2000 [00:43<09:55, 3.14it/s, loss=0.644]" + "training until 2000: 7%|▋ | 131/2000 [00:42<10:01, 3.11it/s, loss=0.747]" ] }, { @@ -2812,7 +2812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 132/2000 [00:43<09:55, 3.14it/s, loss=0.644]" + "training until 2000: 7%|▋ | 132/2000 [00:42<09:58, 3.12it/s, loss=0.747]" ] }, { @@ -2820,7 +2820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 132/2000 [00:43<09:55, 3.14it/s, loss=0.626]" + "training until 2000: 7%|▋ | 132/2000 [00:42<09:58, 3.12it/s, loss=0.706]" ] }, { @@ -2828,7 +2828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 133/2000 [00:44<09:57, 3.13it/s, loss=0.626]" + "training until 2000: 7%|▋ | 133/2000 [00:43<09:53, 3.14it/s, loss=0.706]" ] }, { @@ -2836,7 +2836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 133/2000 [00:44<09:57, 3.13it/s, loss=0.576]" + "training until 2000: 7%|▋ | 133/2000 [00:43<09:53, 3.14it/s, loss=0.734]" ] }, { @@ -2844,7 +2844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 134/2000 [00:44<09:56, 3.13it/s, loss=0.576]" + "training until 2000: 7%|▋ | 134/2000 [00:43<09:50, 3.16it/s, loss=0.734]" ] }, { @@ -2852,7 +2852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 134/2000 [00:44<09:56, 3.13it/s, loss=0.704]" + "training until 2000: 7%|▋ | 134/2000 [00:43<09:50, 3.16it/s, loss=0.683]" ] }, { @@ -2860,7 +2860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 135/2000 [00:44<09:59, 3.11it/s, loss=0.704]" + "training until 2000: 7%|▋ | 135/2000 [00:43<09:50, 3.16it/s, loss=0.683]" ] }, { @@ -2868,7 +2868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 135/2000 [00:44<09:59, 3.11it/s, loss=0.651]" + "training until 2000: 7%|▋ | 135/2000 [00:43<09:50, 3.16it/s, loss=0.737]" ] }, { @@ -2876,7 +2876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 136/2000 [00:45<09:58, 3.11it/s, loss=0.651]" + "training until 2000: 7%|▋ | 136/2000 [00:43<09:52, 3.14it/s, loss=0.737]" ] }, { @@ -2884,7 +2884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 136/2000 [00:45<09:58, 3.11it/s, loss=0.542]" + "training until 2000: 7%|▋ | 136/2000 [00:43<09:52, 3.14it/s, loss=0.752]" ] }, { @@ -2892,7 +2892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 137/2000 [00:45<10:03, 3.09it/s, loss=0.542]" + "training until 2000: 7%|▋ | 137/2000 [00:44<09:45, 3.18it/s, loss=0.752]" ] }, { @@ -2900,7 +2900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 137/2000 [00:45<10:03, 3.09it/s, loss=0.576]" + "training until 2000: 7%|▋ | 137/2000 [00:44<09:45, 3.18it/s, loss=0.732]" ] }, { @@ -2908,7 +2908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 138/2000 [00:45<09:58, 3.11it/s, loss=0.576]" + "training until 2000: 7%|▋ | 138/2000 [00:44<10:04, 3.08it/s, loss=0.732]" ] }, { @@ -2916,7 +2916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 138/2000 [00:45<09:58, 3.11it/s, loss=0.621]" + "training until 2000: 7%|▋ | 138/2000 [00:44<10:04, 3.08it/s, loss=0.693]" ] }, { @@ -2924,7 +2924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 139/2000 [00:46<09:59, 3.11it/s, loss=0.621]" + "training until 2000: 7%|▋ | 139/2000 [00:44<10:07, 3.07it/s, loss=0.693]" ] }, { @@ -2932,7 +2932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 139/2000 [00:46<09:59, 3.11it/s, loss=0.595]" + "training until 2000: 7%|▋ | 139/2000 [00:44<10:07, 3.07it/s, loss=0.723]" ] }, { @@ -2940,7 +2940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 140/2000 [00:46<09:59, 3.10it/s, loss=0.595]" + "training until 2000: 7%|▋ | 140/2000 [00:45<09:59, 3.10it/s, loss=0.723]" ] }, { @@ -2948,7 +2948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 140/2000 [00:46<09:59, 3.10it/s, loss=0.564]" + "training until 2000: 7%|▋ | 140/2000 [00:45<09:59, 3.10it/s, loss=0.754]" ] }, { @@ -2956,7 +2956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 141/2000 [00:46<09:57, 3.11it/s, loss=0.564]" + "training until 2000: 7%|▋ | 141/2000 [00:45<09:58, 3.10it/s, loss=0.754]" ] }, { @@ -2964,7 +2964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 141/2000 [00:46<09:57, 3.11it/s, loss=0.675]" + "training until 2000: 7%|▋ | 141/2000 [00:45<09:58, 3.10it/s, loss=0.729]" ] }, { @@ -2972,7 +2972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 142/2000 [00:47<09:57, 3.11it/s, loss=0.675]" + "training until 2000: 7%|▋ | 142/2000 [00:45<09:55, 3.12it/s, loss=0.729]" ] }, { @@ -2980,7 +2980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 142/2000 [00:47<09:57, 3.11it/s, loss=0.696]" + "training until 2000: 7%|▋ | 142/2000 [00:45<09:55, 3.12it/s, loss=0.7] " ] }, { @@ -2988,7 +2988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 143/2000 [00:47<10:02, 3.08it/s, loss=0.696]" + "training until 2000: 7%|▋ | 143/2000 [00:46<09:53, 3.13it/s, loss=0.7]" ] }, { @@ -2996,7 +2996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 143/2000 [00:47<10:02, 3.08it/s, loss=0.669]" + "training until 2000: 7%|▋ | 143/2000 [00:46<09:53, 3.13it/s, loss=0.69]" ] }, { @@ -3004,7 +3004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 144/2000 [00:47<10:07, 3.06it/s, loss=0.669]" + "training until 2000: 7%|▋ | 144/2000 [00:46<11:50, 2.61it/s, loss=0.69]" ] }, { @@ -3012,7 +3012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 144/2000 [00:47<10:07, 3.06it/s, loss=0.63] " + "training until 2000: 7%|▋ | 144/2000 [00:46<11:50, 2.61it/s, loss=0.707]" ] }, { @@ -3020,7 +3020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 145/2000 [00:48<10:05, 3.07it/s, loss=0.63]" + "training until 2000: 7%|▋ | 145/2000 [00:47<11:14, 2.75it/s, loss=0.707]" ] }, { @@ -3028,7 +3028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 145/2000 [00:48<10:05, 3.07it/s, loss=0.652]" + "training until 2000: 7%|▋ | 145/2000 [00:47<11:14, 2.75it/s, loss=0.725]" ] }, { @@ -3036,7 +3036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 146/2000 [00:48<09:59, 3.09it/s, loss=0.652]" + "training until 2000: 7%|▋ | 146/2000 [00:47<10:44, 2.88it/s, loss=0.725]" ] }, { @@ -3044,7 +3044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 146/2000 [00:48<09:59, 3.09it/s, loss=0.676]" + "training until 2000: 7%|▋ | 146/2000 [00:47<10:44, 2.88it/s, loss=0.706]" ] }, { @@ -3052,7 +3052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 147/2000 [00:48<10:02, 3.08it/s, loss=0.676]" + "training until 2000: 7%|▋ | 147/2000 [00:47<10:30, 2.94it/s, loss=0.706]" ] }, { @@ -3060,7 +3060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 147/2000 [00:48<10:02, 3.08it/s, loss=0.581]" + "training until 2000: 7%|▋ | 147/2000 [00:47<10:30, 2.94it/s, loss=0.752]" ] }, { @@ -3068,7 +3068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 148/2000 [00:49<10:00, 3.08it/s, loss=0.581]" + "training until 2000: 7%|▋ | 148/2000 [00:48<10:17, 3.00it/s, loss=0.752]" ] }, { @@ -3076,7 +3076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 148/2000 [00:49<10:00, 3.08it/s, loss=0.737]" + "training until 2000: 7%|▋ | 148/2000 [00:48<10:17, 3.00it/s, loss=0.679]" ] }, { @@ -3084,7 +3084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 149/2000 [00:49<09:54, 3.11it/s, loss=0.737]" + "training until 2000: 7%|▋ | 149/2000 [00:48<10:04, 3.06it/s, loss=0.679]" ] }, { @@ -3092,7 +3092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 7%|▋ | 149/2000 [00:49<09:54, 3.11it/s, loss=0.595]" + "training until 2000: 7%|▋ | 149/2000 [00:48<10:04, 3.06it/s, loss=0.704]" ] }, { @@ -3100,7 +3100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 150/2000 [00:49<11:55, 2.59it/s, loss=0.595]" + "training until 2000: 8%|▊ | 150/2000 [00:48<09:57, 3.10it/s, loss=0.704]" ] }, { @@ -3108,7 +3108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 150/2000 [00:49<11:55, 2.59it/s, loss=0.609]" + "training until 2000: 8%|▊ | 150/2000 [00:48<09:57, 3.10it/s, loss=0.752]" ] }, { @@ -3116,7 +3116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 151/2000 [00:50<11:18, 2.73it/s, loss=0.609]" + "training until 2000: 8%|▊ | 151/2000 [00:48<09:51, 3.12it/s, loss=0.752]" ] }, { @@ -3124,7 +3124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 151/2000 [00:50<11:18, 2.73it/s, loss=0.608]" + "training until 2000: 8%|▊ | 151/2000 [00:48<09:51, 3.12it/s, loss=0.75] " ] }, { @@ -3132,7 +3132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 152/2000 [00:50<10:58, 2.81it/s, loss=0.608]" + "training until 2000: 8%|▊ | 152/2000 [00:49<09:50, 3.13it/s, loss=0.75]" ] }, { @@ -3140,7 +3140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 152/2000 [00:50<10:58, 2.81it/s, loss=0.645]" + "training until 2000: 8%|▊ | 152/2000 [00:49<09:50, 3.13it/s, loss=0.755]" ] }, { @@ -3148,7 +3148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 153/2000 [00:50<10:30, 2.93it/s, loss=0.645]" + "training until 2000: 8%|▊ | 153/2000 [00:49<09:57, 3.09it/s, loss=0.755]" ] }, { @@ -3156,7 +3156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 153/2000 [00:50<10:30, 2.93it/s, loss=0.678]" + "training until 2000: 8%|▊ | 153/2000 [00:49<09:57, 3.09it/s, loss=0.706]" ] }, { @@ -3164,7 +3164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 154/2000 [00:51<10:18, 2.99it/s, loss=0.678]" + "training until 2000: 8%|▊ | 154/2000 [00:49<09:52, 3.11it/s, loss=0.706]" ] }, { @@ -3172,7 +3172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 154/2000 [00:51<10:18, 2.99it/s, loss=0.528]" + "training until 2000: 8%|▊ | 154/2000 [00:49<09:52, 3.11it/s, loss=0.691]" ] }, { @@ -3180,7 +3180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 155/2000 [00:51<10:02, 3.06it/s, loss=0.528]" + "training until 2000: 8%|▊ | 155/2000 [00:50<09:52, 3.11it/s, loss=0.691]" ] }, { @@ -3188,7 +3188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 155/2000 [00:51<10:02, 3.06it/s, loss=0.599]" + "training until 2000: 8%|▊ | 155/2000 [00:50<09:52, 3.11it/s, loss=0.749]" ] }, { @@ -3196,7 +3196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 156/2000 [00:51<09:51, 3.12it/s, loss=0.599]" + "training until 2000: 8%|▊ | 156/2000 [00:50<09:51, 3.12it/s, loss=0.749]" ] }, { @@ -3204,7 +3204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 156/2000 [00:51<09:51, 3.12it/s, loss=0.526]" + "training until 2000: 8%|▊ | 156/2000 [00:50<09:51, 3.12it/s, loss=0.722]" ] }, { @@ -3212,7 +3212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 157/2000 [00:52<09:49, 3.13it/s, loss=0.526]" + "training until 2000: 8%|▊ | 157/2000 [00:50<09:51, 3.11it/s, loss=0.722]" ] }, { @@ -3220,7 +3220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 157/2000 [00:52<09:49, 3.13it/s, loss=0.622]" + "training until 2000: 8%|▊ | 157/2000 [00:50<09:51, 3.11it/s, loss=0.749]" ] }, { @@ -3228,7 +3228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 158/2000 [00:52<09:48, 3.13it/s, loss=0.622]" + "training until 2000: 8%|▊ | 158/2000 [00:51<09:47, 3.14it/s, loss=0.749]" ] }, { @@ -3236,7 +3236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 158/2000 [00:52<09:48, 3.13it/s, loss=0.525]" + "training until 2000: 8%|▊ | 158/2000 [00:51<09:47, 3.14it/s, loss=0.715]" ] }, { @@ -3244,7 +3244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 159/2000 [00:52<09:49, 3.12it/s, loss=0.525]" + "training until 2000: 8%|▊ | 159/2000 [00:51<09:46, 3.14it/s, loss=0.715]" ] }, { @@ -3252,7 +3252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 159/2000 [00:52<09:49, 3.12it/s, loss=0.562]" + "training until 2000: 8%|▊ | 159/2000 [00:51<09:46, 3.14it/s, loss=0.756]" ] }, { @@ -3260,7 +3260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 160/2000 [00:53<09:51, 3.11it/s, loss=0.562]" + "training until 2000: 8%|▊ | 160/2000 [00:51<09:47, 3.13it/s, loss=0.756]" ] }, { @@ -3268,7 +3268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 160/2000 [00:53<09:51, 3.11it/s, loss=0.576]" + "training until 2000: 8%|▊ | 160/2000 [00:51<09:47, 3.13it/s, loss=0.721]" ] }, { @@ -3276,7 +3276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 161/2000 [00:53<09:50, 3.11it/s, loss=0.576]" + "training until 2000: 8%|▊ | 161/2000 [00:52<09:51, 3.11it/s, loss=0.721]" ] }, { @@ -3284,7 +3284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 161/2000 [00:53<09:50, 3.11it/s, loss=0.527]" + "training until 2000: 8%|▊ | 161/2000 [00:52<09:51, 3.11it/s, loss=0.714]" ] }, { @@ -3292,7 +3292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 162/2000 [00:53<09:47, 3.13it/s, loss=0.527]" + "training until 2000: 8%|▊ | 162/2000 [00:52<09:52, 3.10it/s, loss=0.714]" ] }, { @@ -3300,7 +3300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 162/2000 [00:53<09:47, 3.13it/s, loss=0.594]" + "training until 2000: 8%|▊ | 162/2000 [00:52<09:52, 3.10it/s, loss=0.683]" ] }, { @@ -3308,7 +3308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 163/2000 [00:54<09:46, 3.13it/s, loss=0.594]" + "training until 2000: 8%|▊ | 163/2000 [00:52<09:43, 3.15it/s, loss=0.683]" ] }, { @@ -3316,7 +3316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 163/2000 [00:54<09:46, 3.13it/s, loss=0.615]" + "training until 2000: 8%|▊ | 163/2000 [00:52<09:43, 3.15it/s, loss=0.709]" ] }, { @@ -3324,7 +3324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 164/2000 [00:54<09:47, 3.12it/s, loss=0.615]" + "training until 2000: 8%|▊ | 164/2000 [00:53<09:44, 3.14it/s, loss=0.709]" ] }, { @@ -3332,7 +3332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 164/2000 [00:54<09:47, 3.12it/s, loss=0.578]" + "training until 2000: 8%|▊ | 164/2000 [00:53<09:44, 3.14it/s, loss=0.738]" ] }, { @@ -3340,7 +3340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 165/2000 [00:54<09:54, 3.09it/s, loss=0.578]" + "training until 2000: 8%|▊ | 165/2000 [00:53<09:38, 3.17it/s, loss=0.738]" ] }, { @@ -3348,7 +3348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 165/2000 [00:54<09:54, 3.09it/s, loss=0.535]" + "training until 2000: 8%|▊ | 165/2000 [00:53<09:38, 3.17it/s, loss=0.692]" ] }, { @@ -3356,7 +3356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 166/2000 [00:54<09:49, 3.11it/s, loss=0.535]" + "training until 2000: 8%|▊ | 166/2000 [00:53<09:34, 3.20it/s, loss=0.692]" ] }, { @@ -3364,7 +3364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 166/2000 [00:54<09:49, 3.11it/s, loss=0.573]" + "training until 2000: 8%|▊ | 166/2000 [00:53<09:34, 3.20it/s, loss=0.679]" ] }, { @@ -3372,7 +3372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 167/2000 [00:55<09:45, 3.13it/s, loss=0.573]" + "training until 2000: 8%|▊ | 167/2000 [00:54<09:31, 3.21it/s, loss=0.679]" ] }, { @@ -3380,7 +3380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 167/2000 [00:55<09:45, 3.13it/s, loss=0.579]" + "training until 2000: 8%|▊ | 167/2000 [00:54<09:31, 3.21it/s, loss=0.729]" ] }, { @@ -3388,7 +3388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 168/2000 [00:55<09:48, 3.11it/s, loss=0.579]" + "training until 2000: 8%|▊ | 168/2000 [00:54<09:34, 3.19it/s, loss=0.729]" ] }, { @@ -3396,7 +3396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 168/2000 [00:55<09:48, 3.11it/s, loss=0.567]" + "training until 2000: 8%|▊ | 168/2000 [00:54<09:34, 3.19it/s, loss=0.752]" ] }, { @@ -3404,7 +3404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 169/2000 [00:55<09:47, 3.12it/s, loss=0.567]" + "training until 2000: 8%|▊ | 169/2000 [00:54<09:33, 3.19it/s, loss=0.752]" ] }, { @@ -3412,7 +3412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 169/2000 [00:55<09:47, 3.12it/s, loss=0.594]" + "training until 2000: 8%|▊ | 169/2000 [00:54<09:33, 3.19it/s, loss=0.69] " ] }, { @@ -3420,7 +3420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 170/2000 [00:56<09:48, 3.11it/s, loss=0.594]" + "training until 2000: 8%|▊ | 170/2000 [00:55<09:32, 3.19it/s, loss=0.69]" ] }, { @@ -3428,7 +3428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 8%|▊ | 170/2000 [00:56<09:48, 3.11it/s, loss=0.684]" + "training until 2000: 8%|▊ | 170/2000 [00:55<09:32, 3.19it/s, loss=0.748]" ] }, { @@ -3436,7 +3436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 171/2000 [00:56<09:40, 3.15it/s, loss=0.684]" + "training until 2000: 9%|▊ | 171/2000 [00:55<09:30, 3.21it/s, loss=0.748]" ] }, { @@ -3444,7 +3444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 171/2000 [00:56<09:40, 3.15it/s, loss=0.542]" + "training until 2000: 9%|▊ | 171/2000 [00:55<09:30, 3.21it/s, loss=0.776]" ] }, { @@ -3452,7 +3452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 172/2000 [00:56<09:45, 3.12it/s, loss=0.542]" + "training until 2000: 9%|▊ | 172/2000 [00:55<09:37, 3.17it/s, loss=0.776]" ] }, { @@ -3460,7 +3460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 172/2000 [00:56<09:45, 3.12it/s, loss=0.651]" + "training until 2000: 9%|▊ | 172/2000 [00:55<09:37, 3.17it/s, loss=0.682]" ] }, { @@ -3468,7 +3468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 173/2000 [00:57<09:48, 3.10it/s, loss=0.651]" + "training until 2000: 9%|▊ | 173/2000 [00:55<09:34, 3.18it/s, loss=0.682]" ] }, { @@ -3476,7 +3476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 173/2000 [00:57<09:48, 3.10it/s, loss=0.638]" + "training until 2000: 9%|▊ | 173/2000 [00:55<09:34, 3.18it/s, loss=0.733]" ] }, { @@ -3484,7 +3484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 174/2000 [00:57<09:45, 3.12it/s, loss=0.638]" + "training until 2000: 9%|▊ | 174/2000 [00:56<09:42, 3.14it/s, loss=0.733]" ] }, { @@ -3492,7 +3492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▊ | 174/2000 [00:57<09:45, 3.12it/s, loss=0.54] " + "training until 2000: 9%|▊ | 174/2000 [00:56<09:42, 3.14it/s, loss=0.719]" ] }, { @@ -3500,7 +3500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 175/2000 [00:57<09:47, 3.11it/s, loss=0.54]" + "training until 2000: 9%|▉ | 175/2000 [00:56<09:47, 3.11it/s, loss=0.719]" ] }, { @@ -3508,7 +3508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 175/2000 [00:57<09:47, 3.11it/s, loss=0.58]" + "training until 2000: 9%|▉ | 175/2000 [00:56<09:47, 3.11it/s, loss=0.757]" ] }, { @@ -3516,7 +3516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 176/2000 [00:58<09:39, 3.15it/s, loss=0.58]" + "training until 2000: 9%|▉ | 176/2000 [00:56<09:44, 3.12it/s, loss=0.757]" ] }, { @@ -3524,7 +3524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 176/2000 [00:58<09:39, 3.15it/s, loss=0.555]" + "training until 2000: 9%|▉ | 176/2000 [00:56<09:44, 3.12it/s, loss=0.714]" ] }, { @@ -3532,7 +3532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 177/2000 [00:58<09:37, 3.16it/s, loss=0.555]" + "training until 2000: 9%|▉ | 177/2000 [00:57<09:37, 3.16it/s, loss=0.714]" ] }, { @@ -3540,7 +3540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 177/2000 [00:58<09:37, 3.16it/s, loss=0.622]" + "training until 2000: 9%|▉ | 177/2000 [00:57<09:37, 3.16it/s, loss=0.744]" ] }, { @@ -3548,7 +3548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 178/2000 [00:58<09:43, 3.12it/s, loss=0.622]" + "training until 2000: 9%|▉ | 178/2000 [00:57<09:35, 3.17it/s, loss=0.744]" ] }, { @@ -3556,7 +3556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 178/2000 [00:58<09:43, 3.12it/s, loss=0.58] " + "training until 2000: 9%|▉ | 178/2000 [00:57<09:35, 3.17it/s, loss=0.726]" ] }, { @@ -3564,7 +3564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 179/2000 [00:59<09:45, 3.11it/s, loss=0.58]" + "training until 2000: 9%|▉ | 179/2000 [00:57<09:33, 3.18it/s, loss=0.726]" ] }, { @@ -3572,7 +3572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 179/2000 [00:59<09:45, 3.11it/s, loss=0.547]" + "training until 2000: 9%|▉ | 179/2000 [00:57<09:33, 3.18it/s, loss=0.67] " ] }, { @@ -3580,7 +3580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 180/2000 [00:59<09:42, 3.12it/s, loss=0.547]" + "training until 2000: 9%|▉ | 180/2000 [00:58<09:36, 3.16it/s, loss=0.67]" ] }, { @@ -3588,7 +3588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 180/2000 [00:59<09:42, 3.12it/s, loss=0.683]" + "training until 2000: 9%|▉ | 180/2000 [00:58<09:36, 3.16it/s, loss=0.726]" ] }, { @@ -3596,7 +3596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 181/2000 [00:59<09:43, 3.12it/s, loss=0.683]" + "training until 2000: 9%|▉ | 181/2000 [00:58<09:31, 3.18it/s, loss=0.726]" ] }, { @@ -3604,7 +3604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 181/2000 [00:59<09:43, 3.12it/s, loss=0.679]" + "training until 2000: 9%|▉ | 181/2000 [00:58<09:31, 3.18it/s, loss=0.698]" ] }, { @@ -3612,7 +3612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 182/2000 [01:00<09:34, 3.16it/s, loss=0.679]" + "training until 2000: 9%|▉ | 182/2000 [00:58<09:34, 3.16it/s, loss=0.698]" ] }, { @@ -3620,7 +3620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 182/2000 [01:00<09:34, 3.16it/s, loss=0.6] " + "training until 2000: 9%|▉ | 182/2000 [00:58<09:34, 3.16it/s, loss=0.73] " ] }, { @@ -3628,7 +3628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 183/2000 [01:00<09:27, 3.20it/s, loss=0.6]" + "training until 2000: 9%|▉ | 183/2000 [00:59<09:35, 3.16it/s, loss=0.73]" ] }, { @@ -3636,7 +3636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 183/2000 [01:00<09:27, 3.20it/s, loss=0.569]" + "training until 2000: 9%|▉ | 183/2000 [00:59<09:35, 3.16it/s, loss=0.775]" ] }, { @@ -3644,7 +3644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 184/2000 [01:00<09:29, 3.19it/s, loss=0.569]" + "training until 2000: 9%|▉ | 184/2000 [00:59<09:32, 3.17it/s, loss=0.775]" ] }, { @@ -3652,7 +3652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 184/2000 [01:00<09:29, 3.19it/s, loss=0.607]" + "training until 2000: 9%|▉ | 184/2000 [00:59<09:32, 3.17it/s, loss=0.716]" ] }, { @@ -3660,7 +3660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 185/2000 [01:01<09:33, 3.16it/s, loss=0.607]" + "training until 2000: 9%|▉ | 185/2000 [00:59<09:32, 3.17it/s, loss=0.716]" ] }, { @@ -3668,7 +3668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 185/2000 [01:01<09:33, 3.16it/s, loss=0.634]" + "training until 2000: 9%|▉ | 185/2000 [00:59<09:32, 3.17it/s, loss=0.741]" ] }, { @@ -3676,7 +3676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 186/2000 [01:01<09:36, 3.15it/s, loss=0.634]" + "training until 2000: 9%|▉ | 186/2000 [01:00<09:32, 3.17it/s, loss=0.741]" ] }, { @@ -3684,7 +3684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 186/2000 [01:01<09:36, 3.15it/s, loss=0.61] " + "training until 2000: 9%|▉ | 186/2000 [01:00<09:32, 3.17it/s, loss=0.718]" ] }, { @@ -3692,7 +3692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 187/2000 [01:01<09:47, 3.08it/s, loss=0.61]" + "training until 2000: 9%|▉ | 187/2000 [01:00<09:38, 3.13it/s, loss=0.718]" ] }, { @@ -3700,7 +3700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 187/2000 [01:01<09:47, 3.08it/s, loss=0.61]" + "training until 2000: 9%|▉ | 187/2000 [01:00<09:38, 3.13it/s, loss=0.765]" ] }, { @@ -3708,7 +3708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 188/2000 [01:02<09:45, 3.10it/s, loss=0.61]" + "training until 2000: 9%|▉ | 188/2000 [01:00<09:37, 3.14it/s, loss=0.765]" ] }, { @@ -3716,7 +3716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 188/2000 [01:02<09:45, 3.10it/s, loss=0.599]" + "training until 2000: 9%|▉ | 188/2000 [01:00<09:37, 3.14it/s, loss=0.754]" ] }, { @@ -3724,7 +3724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 189/2000 [01:02<09:46, 3.09it/s, loss=0.599]" + "training until 2000: 9%|▉ | 189/2000 [01:01<09:30, 3.17it/s, loss=0.754]" ] }, { @@ -3732,7 +3732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 9%|▉ | 189/2000 [01:02<09:46, 3.09it/s, loss=0.589]" + "training until 2000: 9%|▉ | 189/2000 [01:01<09:30, 3.17it/s, loss=0.674]" ] }, { @@ -3740,7 +3740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 190/2000 [01:02<09:35, 3.14it/s, loss=0.589]" + "training until 2000: 10%|▉ | 190/2000 [01:01<09:35, 3.14it/s, loss=0.674]" ] }, { @@ -3748,7 +3748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 190/2000 [01:02<09:35, 3.14it/s, loss=0.559]" + "training until 2000: 10%|▉ | 190/2000 [01:01<09:35, 3.14it/s, loss=0.701]" ] }, { @@ -3756,7 +3756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 191/2000 [01:02<09:41, 3.11it/s, loss=0.559]" + "training until 2000: 10%|▉ | 191/2000 [01:01<09:30, 3.17it/s, loss=0.701]" ] }, { @@ -3764,7 +3764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 191/2000 [01:02<09:41, 3.11it/s, loss=0.545]" + "training until 2000: 10%|▉ | 191/2000 [01:01<09:30, 3.17it/s, loss=0.713]" ] }, { @@ -3772,7 +3772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 192/2000 [01:03<09:38, 3.12it/s, loss=0.545]" + "training until 2000: 10%|▉ | 192/2000 [01:01<09:28, 3.18it/s, loss=0.713]" ] }, { @@ -3780,7 +3780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 192/2000 [01:03<09:38, 3.12it/s, loss=0.607]" + "training until 2000: 10%|▉ | 192/2000 [01:01<09:28, 3.18it/s, loss=0.775]" ] }, { @@ -3788,7 +3788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 193/2000 [01:03<09:37, 3.13it/s, loss=0.607]" + "training until 2000: 10%|▉ | 193/2000 [01:02<09:34, 3.15it/s, loss=0.775]" ] }, { @@ -3796,7 +3796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 193/2000 [01:03<09:37, 3.13it/s, loss=0.694]" + "training until 2000: 10%|▉ | 193/2000 [01:02<09:34, 3.15it/s, loss=0.764]" ] }, { @@ -3804,7 +3804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 194/2000 [01:03<09:42, 3.10it/s, loss=0.694]" + "training until 2000: 10%|▉ | 194/2000 [01:02<09:29, 3.17it/s, loss=0.764]" ] }, { @@ -3812,7 +3812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 194/2000 [01:03<09:42, 3.10it/s, loss=0.532]" + "training until 2000: 10%|▉ | 194/2000 [01:02<09:29, 3.17it/s, loss=0.74] " ] }, { @@ -3820,7 +3820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 195/2000 [01:04<09:39, 3.12it/s, loss=0.532]" + "training until 2000: 10%|▉ | 195/2000 [01:02<09:27, 3.18it/s, loss=0.74]" ] }, { @@ -3828,7 +3828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 195/2000 [01:04<09:39, 3.12it/s, loss=0.642]" + "training until 2000: 10%|▉ | 195/2000 [01:02<09:27, 3.18it/s, loss=0.73]" ] }, { @@ -3836,7 +3836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 196/2000 [01:04<09:42, 3.10it/s, loss=0.642]" + "training until 2000: 10%|▉ | 196/2000 [01:03<09:29, 3.17it/s, loss=0.73]" ] }, { @@ -3844,7 +3844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 196/2000 [01:04<09:42, 3.10it/s, loss=0.562]" + "training until 2000: 10%|▉ | 196/2000 [01:03<09:29, 3.17it/s, loss=0.776]" ] }, { @@ -3852,7 +3852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 197/2000 [01:04<09:43, 3.09it/s, loss=0.562]" + "training until 2000: 10%|▉ | 197/2000 [01:03<09:29, 3.16it/s, loss=0.776]" ] }, { @@ -3860,7 +3860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 197/2000 [01:04<09:43, 3.09it/s, loss=0.606]" + "training until 2000: 10%|▉ | 197/2000 [01:03<09:29, 3.16it/s, loss=0.693]" ] }, { @@ -3868,7 +3868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 198/2000 [01:05<09:42, 3.09it/s, loss=0.606]" + "training until 2000: 10%|▉ | 198/2000 [01:03<09:26, 3.18it/s, loss=0.693]" ] }, { @@ -3876,7 +3876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 198/2000 [01:05<09:42, 3.09it/s, loss=0.665]" + "training until 2000: 10%|▉ | 198/2000 [01:03<09:26, 3.18it/s, loss=0.679]" ] }, { @@ -3884,7 +3884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 199/2000 [01:05<09:39, 3.11it/s, loss=0.665]" + "training until 2000: 10%|▉ | 199/2000 [01:04<09:24, 3.19it/s, loss=0.679]" ] }, { @@ -3892,7 +3892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|▉ | 199/2000 [01:05<09:39, 3.11it/s, loss=0.59] " + "training until 2000: 10%|▉ | 199/2000 [01:04<09:24, 3.19it/s, loss=0.744]" ] }, { @@ -3900,7 +3900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 200/2000 [01:05<09:42, 3.09it/s, loss=0.59]" + "training until 2000: 10%|█ | 200/2000 [01:04<09:32, 3.14it/s, loss=0.744]" ] }, { @@ -3908,7 +3908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 200/2000 [01:05<09:42, 3.09it/s, loss=0.64]" + "training until 2000: 10%|█ | 200/2000 [01:04<09:32, 3.14it/s, loss=0.753]" ] }, { @@ -3916,7 +3916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 201/2000 [01:06<09:38, 3.11it/s, loss=0.64]" + "training until 2000: 10%|█ | 201/2000 [01:04<09:34, 3.13it/s, loss=0.753]" ] }, { @@ -3924,7 +3924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 201/2000 [01:06<09:38, 3.11it/s, loss=0.67]" + "training until 2000: 10%|█ | 201/2000 [01:04<09:34, 3.13it/s, loss=0.776]" ] }, { @@ -3932,7 +3932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 202/2000 [01:06<09:34, 3.13it/s, loss=0.67]" + "training until 2000: 10%|█ | 202/2000 [01:05<09:36, 3.12it/s, loss=0.776]" ] }, { @@ -3940,7 +3940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 202/2000 [01:06<09:34, 3.13it/s, loss=0.597]" + "training until 2000: 10%|█ | 202/2000 [01:05<09:36, 3.12it/s, loss=0.717]" ] }, { @@ -3948,7 +3948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 203/2000 [01:06<09:37, 3.11it/s, loss=0.597]" + "training until 2000: 10%|█ | 203/2000 [01:05<09:36, 3.11it/s, loss=0.717]" ] }, { @@ -3956,7 +3956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 203/2000 [01:06<09:37, 3.11it/s, loss=0.565]" + "training until 2000: 10%|█ | 203/2000 [01:05<09:36, 3.11it/s, loss=0.732]" ] }, { @@ -3964,7 +3964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 204/2000 [01:07<09:40, 3.10it/s, loss=0.565]" + "training until 2000: 10%|█ | 204/2000 [01:05<09:38, 3.10it/s, loss=0.732]" ] }, { @@ -3972,7 +3972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 204/2000 [01:07<09:40, 3.10it/s, loss=0.559]" + "training until 2000: 10%|█ | 204/2000 [01:05<09:38, 3.10it/s, loss=0.718]" ] }, { @@ -3980,7 +3980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 205/2000 [01:07<09:36, 3.11it/s, loss=0.559]" + "training until 2000: 10%|█ | 205/2000 [01:06<09:34, 3.13it/s, loss=0.718]" ] }, { @@ -3988,7 +3988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 205/2000 [01:07<09:36, 3.11it/s, loss=0.594]" + "training until 2000: 10%|█ | 205/2000 [01:06<09:34, 3.13it/s, loss=0.702]" ] }, { @@ -3996,7 +3996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 206/2000 [01:07<09:35, 3.12it/s, loss=0.594]" + "training until 2000: 10%|█ | 206/2000 [01:06<09:33, 3.13it/s, loss=0.702]" ] }, { @@ -4004,7 +4004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 206/2000 [01:07<09:35, 3.12it/s, loss=0.59] " + "training until 2000: 10%|█ | 206/2000 [01:06<09:33, 3.13it/s, loss=0.725]" ] }, { @@ -4012,7 +4012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 207/2000 [01:08<09:30, 3.15it/s, loss=0.59]" + "training until 2000: 10%|█ | 207/2000 [01:06<11:27, 2.61it/s, loss=0.725]" ] }, { @@ -4020,7 +4020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 207/2000 [01:08<09:30, 3.15it/s, loss=0.654]" + "training until 2000: 10%|█ | 207/2000 [01:06<11:27, 2.61it/s, loss=0.788]" ] }, { @@ -4028,7 +4028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 208/2000 [01:08<09:29, 3.15it/s, loss=0.654]" + "training until 2000: 10%|█ | 208/2000 [01:07<10:58, 2.72it/s, loss=0.788]" ] }, { @@ -4036,7 +4036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 208/2000 [01:08<09:29, 3.15it/s, loss=0.585]" + "training until 2000: 10%|█ | 208/2000 [01:07<10:58, 2.72it/s, loss=0.752]" ] }, { @@ -4044,7 +4044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 209/2000 [01:08<09:28, 3.15it/s, loss=0.585]" + "training until 2000: 10%|█ | 209/2000 [01:07<10:33, 2.83it/s, loss=0.752]" ] }, { @@ -4052,7 +4052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 209/2000 [01:08<09:28, 3.15it/s, loss=0.561]" + "training until 2000: 10%|█ | 209/2000 [01:07<10:33, 2.83it/s, loss=0.723]" ] }, { @@ -4060,7 +4060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 210/2000 [01:09<09:33, 3.12it/s, loss=0.561]" + "training until 2000: 10%|█ | 210/2000 [01:07<10:18, 2.90it/s, loss=0.723]" ] }, { @@ -4068,7 +4068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 10%|█ | 210/2000 [01:09<09:33, 3.12it/s, loss=0.546]" + "training until 2000: 10%|█ | 210/2000 [01:07<10:18, 2.90it/s, loss=0.709]" ] }, { @@ -4076,7 +4076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 211/2000 [01:09<09:30, 3.13it/s, loss=0.546]" + "training until 2000: 11%|█ | 211/2000 [01:08<10:07, 2.94it/s, loss=0.709]" ] }, { @@ -4084,7 +4084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 211/2000 [01:09<09:30, 3.13it/s, loss=0.635]" + "training until 2000: 11%|█ | 211/2000 [01:08<10:07, 2.94it/s, loss=0.712]" ] }, { @@ -4092,7 +4092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 212/2000 [01:09<09:28, 3.14it/s, loss=0.635]" + "training until 2000: 11%|█ | 212/2000 [01:08<09:51, 3.02it/s, loss=0.712]" ] }, { @@ -4100,7 +4100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 212/2000 [01:09<09:28, 3.14it/s, loss=0.7] " + "training until 2000: 11%|█ | 212/2000 [01:08<09:51, 3.02it/s, loss=0.732]" ] }, { @@ -4108,7 +4108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 213/2000 [01:10<09:30, 3.13it/s, loss=0.7]" + "training until 2000: 11%|█ | 213/2000 [01:08<09:46, 3.05it/s, loss=0.732]" ] }, { @@ -4116,7 +4116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 213/2000 [01:10<09:30, 3.13it/s, loss=0.574]" + "training until 2000: 11%|█ | 213/2000 [01:08<09:46, 3.05it/s, loss=0.743]" ] }, { @@ -4124,7 +4124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 214/2000 [01:10<11:28, 2.59it/s, loss=0.574]" + "training until 2000: 11%|█ | 214/2000 [01:09<09:40, 3.07it/s, loss=0.743]" ] }, { @@ -4132,7 +4132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 214/2000 [01:10<11:28, 2.59it/s, loss=0.555]" + "training until 2000: 11%|█ | 214/2000 [01:09<09:40, 3.07it/s, loss=0.722]" ] }, { @@ -4140,7 +4140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 215/2000 [01:10<10:54, 2.73it/s, loss=0.555]" + "training until 2000: 11%|█ | 215/2000 [01:09<09:36, 3.10it/s, loss=0.722]" ] }, { @@ -4148,7 +4148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 215/2000 [01:10<10:54, 2.73it/s, loss=0.651]" + "training until 2000: 11%|█ | 215/2000 [01:09<09:36, 3.10it/s, loss=0.697]" ] }, { @@ -4156,7 +4156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 216/2000 [01:11<10:28, 2.84it/s, loss=0.651]" + "training until 2000: 11%|█ | 216/2000 [01:09<09:35, 3.10it/s, loss=0.697]" ] }, { @@ -4164,7 +4164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 216/2000 [01:11<10:28, 2.84it/s, loss=0.682]" + "training until 2000: 11%|█ | 216/2000 [01:09<09:35, 3.10it/s, loss=0.757]" ] }, { @@ -4172,7 +4172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 217/2000 [01:11<10:03, 2.95it/s, loss=0.682]" + "training until 2000: 11%|█ | 217/2000 [01:10<09:38, 3.08it/s, loss=0.757]" ] }, { @@ -4180,7 +4180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 217/2000 [01:11<10:03, 2.95it/s, loss=0.579]" + "training until 2000: 11%|█ | 217/2000 [01:10<09:38, 3.08it/s, loss=0.671]" ] }, { @@ -4188,7 +4188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 218/2000 [01:11<09:54, 3.00it/s, loss=0.579]" + "training until 2000: 11%|█ | 218/2000 [01:10<09:42, 3.06it/s, loss=0.671]" ] }, { @@ -4196,7 +4196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 218/2000 [01:11<09:54, 3.00it/s, loss=0.706]" + "training until 2000: 11%|█ | 218/2000 [01:10<09:42, 3.06it/s, loss=0.74] " ] }, { @@ -4204,7 +4204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 219/2000 [01:12<10:01, 2.96it/s, loss=0.706]" + "training until 2000: 11%|█ | 219/2000 [01:10<09:37, 3.09it/s, loss=0.74]" ] }, { @@ -4212,7 +4212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 219/2000 [01:12<10:01, 2.96it/s, loss=0.645]" + "training until 2000: 11%|█ | 219/2000 [01:10<09:37, 3.09it/s, loss=0.7] " ] }, { @@ -4220,7 +4220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 220/2000 [01:12<09:52, 3.01it/s, loss=0.645]" + "training until 2000: 11%|█ | 220/2000 [01:11<09:30, 3.12it/s, loss=0.7]" ] }, { @@ -4228,7 +4228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 220/2000 [01:12<09:52, 3.01it/s, loss=0.569]" + "training until 2000: 11%|█ | 220/2000 [01:11<09:30, 3.12it/s, loss=0.733]" ] }, { @@ -4236,7 +4236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 221/2000 [01:12<09:40, 3.06it/s, loss=0.569]" + "training until 2000: 11%|█ | 221/2000 [01:11<09:27, 3.13it/s, loss=0.733]" ] }, { @@ -4244,7 +4244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 221/2000 [01:12<09:40, 3.06it/s, loss=0.522]" + "training until 2000: 11%|█ | 221/2000 [01:11<09:27, 3.13it/s, loss=0.73] " ] }, { @@ -4252,7 +4252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 222/2000 [01:13<09:35, 3.09it/s, loss=0.522]" + "training until 2000: 11%|█ | 222/2000 [01:11<09:34, 3.10it/s, loss=0.73]" ] }, { @@ -4260,7 +4260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 222/2000 [01:13<09:35, 3.09it/s, loss=0.611]" + "training until 2000: 11%|█ | 222/2000 [01:11<09:34, 3.10it/s, loss=0.726]" ] }, { @@ -4268,7 +4268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 223/2000 [01:13<09:38, 3.07it/s, loss=0.611]" + "training until 2000: 11%|█ | 223/2000 [01:12<09:33, 3.10it/s, loss=0.726]" ] }, { @@ -4276,7 +4276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 223/2000 [01:13<09:38, 3.07it/s, loss=0.591]" + "training until 2000: 11%|█ | 223/2000 [01:12<09:33, 3.10it/s, loss=0.722]" ] }, { @@ -4284,7 +4284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 224/2000 [01:13<09:33, 3.10it/s, loss=0.591]" + "training until 2000: 11%|█ | 224/2000 [01:12<09:29, 3.12it/s, loss=0.722]" ] }, { @@ -4292,7 +4292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█ | 224/2000 [01:13<09:33, 3.10it/s, loss=0.631]" + "training until 2000: 11%|█ | 224/2000 [01:12<09:29, 3.12it/s, loss=0.72] " ] }, { @@ -4300,7 +4300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 225/2000 [01:14<09:29, 3.12it/s, loss=0.631]" + "training until 2000: 11%|█▏ | 225/2000 [01:12<09:30, 3.11it/s, loss=0.72]" ] }, { @@ -4308,7 +4308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 225/2000 [01:14<09:29, 3.12it/s, loss=0.723]" + "training until 2000: 11%|█▏ | 225/2000 [01:12<09:30, 3.11it/s, loss=0.733]" ] }, { @@ -4316,7 +4316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 226/2000 [01:14<09:32, 3.10it/s, loss=0.723]" + "training until 2000: 11%|█▏ | 226/2000 [01:13<09:28, 3.12it/s, loss=0.733]" ] }, { @@ -4324,7 +4324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 226/2000 [01:14<09:32, 3.10it/s, loss=0.556]" + "training until 2000: 11%|█▏ | 226/2000 [01:13<09:28, 3.12it/s, loss=0.677]" ] }, { @@ -4332,7 +4332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 227/2000 [01:14<09:26, 3.13it/s, loss=0.556]" + "training until 2000: 11%|█▏ | 227/2000 [01:13<09:27, 3.13it/s, loss=0.677]" ] }, { @@ -4340,7 +4340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 227/2000 [01:14<09:26, 3.13it/s, loss=0.541]" + "training until 2000: 11%|█▏ | 227/2000 [01:13<09:27, 3.13it/s, loss=0.751]" ] }, { @@ -4348,7 +4348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 228/2000 [01:15<09:31, 3.10it/s, loss=0.541]" + "training until 2000: 11%|█▏ | 228/2000 [01:13<09:21, 3.16it/s, loss=0.751]" ] }, { @@ -4356,7 +4356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 228/2000 [01:15<09:31, 3.10it/s, loss=0.569]" + "training until 2000: 11%|█▏ | 228/2000 [01:13<09:21, 3.16it/s, loss=0.737]" ] }, { @@ -4364,7 +4364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 229/2000 [01:15<09:33, 3.09it/s, loss=0.569]" + "training until 2000: 11%|█▏ | 229/2000 [01:14<09:20, 3.16it/s, loss=0.737]" ] }, { @@ -4372,7 +4372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 11%|█▏ | 229/2000 [01:15<09:33, 3.09it/s, loss=0.554]" + "training until 2000: 11%|█▏ | 229/2000 [01:14<09:20, 3.16it/s, loss=0.748]" ] }, { @@ -4380,7 +4380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 230/2000 [01:15<09:26, 3.13it/s, loss=0.554]" + "training until 2000: 12%|█▏ | 230/2000 [01:14<09:23, 3.14it/s, loss=0.748]" ] }, { @@ -4388,7 +4388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 230/2000 [01:15<09:26, 3.13it/s, loss=0.61] " + "training until 2000: 12%|█▏ | 230/2000 [01:14<09:23, 3.14it/s, loss=0.723]" ] }, { @@ -4396,7 +4396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 231/2000 [01:16<09:26, 3.12it/s, loss=0.61]" + "training until 2000: 12%|█▏ | 231/2000 [01:14<09:33, 3.08it/s, loss=0.723]" ] }, { @@ -4404,7 +4404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 231/2000 [01:16<09:26, 3.12it/s, loss=0.624]" + "training until 2000: 12%|█▏ | 231/2000 [01:14<09:33, 3.08it/s, loss=0.703]" ] }, { @@ -4412,7 +4412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 232/2000 [01:16<09:23, 3.14it/s, loss=0.624]" + "training until 2000: 12%|█▏ | 232/2000 [01:15<09:30, 3.10it/s, loss=0.703]" ] }, { @@ -4420,7 +4420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 232/2000 [01:16<09:23, 3.14it/s, loss=0.6] " + "training until 2000: 12%|█▏ | 232/2000 [01:15<09:30, 3.10it/s, loss=0.755]" ] }, { @@ -4428,7 +4428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 233/2000 [01:16<09:26, 3.12it/s, loss=0.6]" + "training until 2000: 12%|█▏ | 233/2000 [01:15<09:34, 3.08it/s, loss=0.755]" ] }, { @@ -4436,7 +4436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 233/2000 [01:16<09:26, 3.12it/s, loss=0.623]" + "training until 2000: 12%|█▏ | 233/2000 [01:15<09:34, 3.08it/s, loss=0.707]" ] }, { @@ -4444,7 +4444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 234/2000 [01:16<09:27, 3.11it/s, loss=0.623]" + "training until 2000: 12%|█▏ | 234/2000 [01:15<09:29, 3.10it/s, loss=0.707]" ] }, { @@ -4452,7 +4452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 234/2000 [01:16<09:27, 3.11it/s, loss=0.526]" + "training until 2000: 12%|█▏ | 234/2000 [01:15<09:29, 3.10it/s, loss=0.757]" ] }, { @@ -4460,7 +4460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 235/2000 [01:17<09:24, 3.13it/s, loss=0.526]" + "training until 2000: 12%|█▏ | 235/2000 [01:15<09:25, 3.12it/s, loss=0.757]" ] }, { @@ -4468,7 +4468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 235/2000 [01:17<09:24, 3.13it/s, loss=0.608]" + "training until 2000: 12%|█▏ | 235/2000 [01:15<09:25, 3.12it/s, loss=0.715]" ] }, { @@ -4476,7 +4476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 236/2000 [01:17<09:23, 3.13it/s, loss=0.608]" + "training until 2000: 12%|█▏ | 236/2000 [01:16<09:24, 3.13it/s, loss=0.715]" ] }, { @@ -4484,7 +4484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 236/2000 [01:17<09:23, 3.13it/s, loss=0.575]" + "training until 2000: 12%|█▏ | 236/2000 [01:16<09:24, 3.13it/s, loss=0.719]" ] }, { @@ -4492,7 +4492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 237/2000 [01:17<09:20, 3.15it/s, loss=0.575]" + "training until 2000: 12%|█▏ | 237/2000 [01:16<09:25, 3.12it/s, loss=0.719]" ] }, { @@ -4500,7 +4500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 237/2000 [01:17<09:20, 3.15it/s, loss=0.578]" + "training until 2000: 12%|█▏ | 237/2000 [01:16<09:25, 3.12it/s, loss=0.726]" ] }, { @@ -4508,7 +4508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 238/2000 [01:18<09:21, 3.14it/s, loss=0.578]" + "training until 2000: 12%|█▏ | 238/2000 [01:16<09:27, 3.10it/s, loss=0.726]" ] }, { @@ -4516,7 +4516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 238/2000 [01:18<09:21, 3.14it/s, loss=0.641]" + "training until 2000: 12%|█▏ | 238/2000 [01:16<09:27, 3.10it/s, loss=0.712]" ] }, { @@ -4524,7 +4524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 239/2000 [01:18<09:22, 3.13it/s, loss=0.641]" + "training until 2000: 12%|█▏ | 239/2000 [01:17<09:29, 3.09it/s, loss=0.712]" ] }, { @@ -4532,7 +4532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 239/2000 [01:18<09:22, 3.13it/s, loss=0.576]" + "training until 2000: 12%|█▏ | 239/2000 [01:17<09:29, 3.09it/s, loss=0.738]" ] }, { @@ -4540,7 +4540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 240/2000 [01:18<09:22, 3.13it/s, loss=0.576]" + "training until 2000: 12%|█▏ | 240/2000 [01:17<09:26, 3.11it/s, loss=0.738]" ] }, { @@ -4548,7 +4548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 240/2000 [01:18<09:22, 3.13it/s, loss=0.624]" + "training until 2000: 12%|█▏ | 240/2000 [01:17<09:26, 3.11it/s, loss=0.727]" ] }, { @@ -4556,7 +4556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 241/2000 [01:19<09:25, 3.11it/s, loss=0.624]" + "training until 2000: 12%|█▏ | 241/2000 [01:17<09:26, 3.10it/s, loss=0.727]" ] }, { @@ -4564,7 +4564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 241/2000 [01:19<09:25, 3.11it/s, loss=0.623]" + "training until 2000: 12%|█▏ | 241/2000 [01:17<09:26, 3.10it/s, loss=0.706]" ] }, { @@ -4572,7 +4572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 242/2000 [01:19<09:26, 3.11it/s, loss=0.623]" + "training until 2000: 12%|█▏ | 242/2000 [01:18<09:28, 3.09it/s, loss=0.706]" ] }, { @@ -4580,7 +4580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 242/2000 [01:19<09:26, 3.11it/s, loss=0.525]" + "training until 2000: 12%|█▏ | 242/2000 [01:18<09:28, 3.09it/s, loss=0.734]" ] }, { @@ -4588,7 +4588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 243/2000 [01:19<09:25, 3.11it/s, loss=0.525]" + "training until 2000: 12%|█▏ | 243/2000 [01:18<09:16, 3.15it/s, loss=0.734]" ] }, { @@ -4596,7 +4596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 243/2000 [01:19<09:25, 3.11it/s, loss=0.635]" + "training until 2000: 12%|█▏ | 243/2000 [01:18<09:16, 3.15it/s, loss=0.68] " ] }, { @@ -4604,7 +4604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 244/2000 [01:20<09:30, 3.08it/s, loss=0.635]" + "training until 2000: 12%|█▏ | 244/2000 [01:18<09:16, 3.16it/s, loss=0.68]" ] }, { @@ -4612,7 +4612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 244/2000 [01:20<09:30, 3.08it/s, loss=0.525]" + "training until 2000: 12%|█▏ | 244/2000 [01:18<09:16, 3.16it/s, loss=0.685]" ] }, { @@ -4620,7 +4620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 245/2000 [01:20<09:29, 3.08it/s, loss=0.525]" + "training until 2000: 12%|█▏ | 245/2000 [01:19<09:15, 3.16it/s, loss=0.685]" ] }, { @@ -4628,7 +4628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 245/2000 [01:20<09:29, 3.08it/s, loss=0.618]" + "training until 2000: 12%|█▏ | 245/2000 [01:19<09:15, 3.16it/s, loss=0.703]" ] }, { @@ -4636,7 +4636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 246/2000 [01:20<09:31, 3.07it/s, loss=0.618]" + "training until 2000: 12%|█▏ | 246/2000 [01:19<09:13, 3.17it/s, loss=0.703]" ] }, { @@ -4644,7 +4644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 246/2000 [01:20<09:31, 3.07it/s, loss=0.539]" + "training until 2000: 12%|█▏ | 246/2000 [01:19<09:13, 3.17it/s, loss=0.777]" ] }, { @@ -4652,7 +4652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 247/2000 [01:21<09:29, 3.08it/s, loss=0.539]" + "training until 2000: 12%|█▏ | 247/2000 [01:19<09:06, 3.21it/s, loss=0.777]" ] }, { @@ -4660,7 +4660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 247/2000 [01:21<09:29, 3.08it/s, loss=0.565]" + "training until 2000: 12%|█▏ | 247/2000 [01:19<09:06, 3.21it/s, loss=0.646]" ] }, { @@ -4668,7 +4668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 248/2000 [01:21<09:24, 3.10it/s, loss=0.565]" + "training until 2000: 12%|█▏ | 248/2000 [01:20<09:06, 3.21it/s, loss=0.646]" ] }, { @@ -4676,7 +4676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 248/2000 [01:21<09:24, 3.10it/s, loss=0.617]" + "training until 2000: 12%|█▏ | 248/2000 [01:20<09:06, 3.21it/s, loss=0.694]" ] }, { @@ -4684,7 +4684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 249/2000 [01:21<09:22, 3.11it/s, loss=0.617]" + "training until 2000: 12%|█▏ | 249/2000 [01:20<09:07, 3.20it/s, loss=0.694]" ] }, { @@ -4692,7 +4692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▏ | 249/2000 [01:21<09:22, 3.11it/s, loss=0.533]" + "training until 2000: 12%|█▏ | 249/2000 [01:20<09:07, 3.20it/s, loss=0.729]" ] }, { @@ -4700,7 +4700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▎ | 250/2000 [01:22<09:21, 3.12it/s, loss=0.533]" + "training until 2000: 12%|█▎ | 250/2000 [01:20<09:14, 3.16it/s, loss=0.729]" ] }, { @@ -4708,7 +4708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 12%|█▎ | 250/2000 [01:22<09:21, 3.12it/s, loss=0.566]" + "training until 2000: 12%|█▎ | 250/2000 [01:20<09:14, 3.16it/s, loss=0.657]" ] }, { @@ -4716,7 +4716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 251/2000 [01:22<09:14, 3.15it/s, loss=0.566]" + "training until 2000: 13%|█▎ | 251/2000 [01:21<09:23, 3.10it/s, loss=0.657]" ] }, { @@ -4724,7 +4724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 251/2000 [01:22<09:14, 3.15it/s, loss=0.572]" + "training until 2000: 13%|█▎ | 251/2000 [01:21<09:23, 3.10it/s, loss=0.738]" ] }, { @@ -4732,7 +4732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 252/2000 [01:22<09:19, 3.13it/s, loss=0.572]" + "training until 2000: 13%|█▎ | 252/2000 [01:21<09:25, 3.09it/s, loss=0.738]" ] }, { @@ -4740,7 +4740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 252/2000 [01:22<09:19, 3.13it/s, loss=0.593]" + "training until 2000: 13%|█▎ | 252/2000 [01:21<09:25, 3.09it/s, loss=0.74] " ] }, { @@ -4748,7 +4748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 253/2000 [01:23<09:16, 3.14it/s, loss=0.593]" + "training until 2000: 13%|█▎ | 253/2000 [01:21<09:19, 3.12it/s, loss=0.74]" ] }, { @@ -4756,7 +4756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 253/2000 [01:23<09:16, 3.14it/s, loss=0.602]" + "training until 2000: 13%|█▎ | 253/2000 [01:21<09:19, 3.12it/s, loss=0.692]" ] }, { @@ -4764,7 +4764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 254/2000 [01:23<09:15, 3.15it/s, loss=0.602]" + "training until 2000: 13%|█▎ | 254/2000 [01:22<09:11, 3.17it/s, loss=0.692]" ] }, { @@ -4772,7 +4772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 254/2000 [01:23<09:15, 3.15it/s, loss=0.571]" + "training until 2000: 13%|█▎ | 254/2000 [01:22<09:11, 3.17it/s, loss=0.692]" ] }, { @@ -4780,7 +4780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 255/2000 [01:23<09:12, 3.16it/s, loss=0.571]" + "training until 2000: 13%|█▎ | 255/2000 [01:22<09:13, 3.15it/s, loss=0.692]" ] }, { @@ -4788,7 +4788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 255/2000 [01:23<09:12, 3.16it/s, loss=0.591]" + "training until 2000: 13%|█▎ | 255/2000 [01:22<09:13, 3.15it/s, loss=0.723]" ] }, { @@ -4796,7 +4796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 256/2000 [01:24<09:10, 3.17it/s, loss=0.591]" + "training until 2000: 13%|█▎ | 256/2000 [01:22<09:17, 3.13it/s, loss=0.723]" ] }, { @@ -4804,7 +4804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 256/2000 [01:24<09:10, 3.17it/s, loss=0.534]" + "training until 2000: 13%|█▎ | 256/2000 [01:22<09:17, 3.13it/s, loss=0.736]" ] }, { @@ -4812,7 +4812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 257/2000 [01:24<09:10, 3.17it/s, loss=0.534]" + "training until 2000: 13%|█▎ | 257/2000 [01:22<09:17, 3.13it/s, loss=0.736]" ] }, { @@ -4820,7 +4820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 257/2000 [01:24<09:10, 3.17it/s, loss=0.614]" + "training until 2000: 13%|█▎ | 257/2000 [01:22<09:17, 3.13it/s, loss=0.71] " ] }, { @@ -4828,7 +4828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 258/2000 [01:24<09:15, 3.14it/s, loss=0.614]" + "training until 2000: 13%|█▎ | 258/2000 [01:23<09:12, 3.15it/s, loss=0.71]" ] }, { @@ -4836,7 +4836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 258/2000 [01:24<09:15, 3.14it/s, loss=0.58] " + "training until 2000: 13%|█▎ | 258/2000 [01:23<09:12, 3.15it/s, loss=0.725]" ] }, { @@ -4844,7 +4844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 259/2000 [01:24<09:18, 3.12it/s, loss=0.58]" + "training until 2000: 13%|█▎ | 259/2000 [01:23<09:16, 3.13it/s, loss=0.725]" ] }, { @@ -4852,7 +4852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 259/2000 [01:24<09:18, 3.12it/s, loss=0.533]" + "training until 2000: 13%|█▎ | 259/2000 [01:23<09:16, 3.13it/s, loss=0.723]" ] }, { @@ -4860,7 +4860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 260/2000 [01:25<09:23, 3.09it/s, loss=0.533]" + "training until 2000: 13%|█▎ | 260/2000 [01:23<09:13, 3.14it/s, loss=0.723]" ] }, { @@ -4868,7 +4868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 260/2000 [01:25<09:23, 3.09it/s, loss=0.582]" + "training until 2000: 13%|█▎ | 260/2000 [01:23<09:13, 3.14it/s, loss=0.747]" ] }, { @@ -4876,7 +4876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 261/2000 [01:25<09:15, 3.13it/s, loss=0.582]" + "training until 2000: 13%|█▎ | 261/2000 [01:24<09:13, 3.14it/s, loss=0.747]" ] }, { @@ -4884,7 +4884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 261/2000 [01:25<09:15, 3.13it/s, loss=0.632]" + "training until 2000: 13%|█▎ | 261/2000 [01:24<09:13, 3.14it/s, loss=0.696]" ] }, { @@ -4892,7 +4892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 262/2000 [01:25<09:16, 3.12it/s, loss=0.632]" + "training until 2000: 13%|█▎ | 262/2000 [01:24<09:15, 3.13it/s, loss=0.696]" ] }, { @@ -4900,7 +4900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 262/2000 [01:25<09:16, 3.12it/s, loss=0.56] " + "training until 2000: 13%|█▎ | 262/2000 [01:24<09:15, 3.13it/s, loss=0.744]" ] }, { @@ -4908,7 +4908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 263/2000 [01:26<09:19, 3.10it/s, loss=0.56]" + "training until 2000: 13%|█▎ | 263/2000 [01:24<09:14, 3.13it/s, loss=0.744]" ] }, { @@ -4916,7 +4916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 263/2000 [01:26<09:19, 3.10it/s, loss=0.699]" + "training until 2000: 13%|█▎ | 263/2000 [01:24<09:14, 3.13it/s, loss=0.7] " ] }, { @@ -4924,7 +4924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 264/2000 [01:26<09:23, 3.08it/s, loss=0.699]" + "training until 2000: 13%|█▎ | 264/2000 [01:25<09:16, 3.12it/s, loss=0.7]" ] }, { @@ -4932,7 +4932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 264/2000 [01:26<09:23, 3.08it/s, loss=0.58] " + "training until 2000: 13%|█▎ | 264/2000 [01:25<09:16, 3.12it/s, loss=0.696]" ] }, { @@ -4940,7 +4940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 265/2000 [01:26<09:17, 3.11it/s, loss=0.58]" + "training until 2000: 13%|█▎ | 265/2000 [01:25<09:17, 3.11it/s, loss=0.696]" ] }, { @@ -4948,7 +4948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 265/2000 [01:26<09:17, 3.11it/s, loss=0.624]" + "training until 2000: 13%|█▎ | 265/2000 [01:25<09:17, 3.11it/s, loss=0.734]" ] }, { @@ -4956,7 +4956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 266/2000 [01:27<09:18, 3.11it/s, loss=0.624]" + "training until 2000: 13%|█▎ | 266/2000 [01:25<09:24, 3.07it/s, loss=0.734]" ] }, { @@ -4964,7 +4964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 266/2000 [01:27<09:18, 3.11it/s, loss=0.581]" + "training until 2000: 13%|█▎ | 266/2000 [01:25<09:24, 3.07it/s, loss=0.745]" ] }, { @@ -4972,7 +4972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 267/2000 [01:27<09:17, 3.11it/s, loss=0.581]" + "training until 2000: 13%|█▎ | 267/2000 [01:26<09:15, 3.12it/s, loss=0.745]" ] }, { @@ -4980,7 +4980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 267/2000 [01:27<09:17, 3.11it/s, loss=0.605]" + "training until 2000: 13%|█▎ | 267/2000 [01:26<09:15, 3.12it/s, loss=0.749]" ] }, { @@ -4988,7 +4988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 268/2000 [01:27<09:12, 3.13it/s, loss=0.605]" + "training until 2000: 13%|█▎ | 268/2000 [01:26<09:19, 3.09it/s, loss=0.749]" ] }, { @@ -4996,7 +4996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 268/2000 [01:27<09:12, 3.13it/s, loss=0.624]" + "training until 2000: 13%|█▎ | 268/2000 [01:26<09:19, 3.09it/s, loss=0.692]" ] }, { @@ -5004,7 +5004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 269/2000 [01:28<09:12, 3.13it/s, loss=0.624]" + "training until 2000: 13%|█▎ | 269/2000 [01:26<09:08, 3.16it/s, loss=0.692]" ] }, { @@ -5012,7 +5012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 13%|█▎ | 269/2000 [01:28<09:12, 3.13it/s, loss=0.622]" + "training until 2000: 13%|█▎ | 269/2000 [01:26<09:08, 3.16it/s, loss=0.703]" ] }, { @@ -5020,7 +5020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 270/2000 [01:28<09:11, 3.14it/s, loss=0.622]" + "training until 2000: 14%|█▎ | 270/2000 [01:27<09:06, 3.17it/s, loss=0.703]" ] }, { @@ -5028,7 +5028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 270/2000 [01:28<09:11, 3.14it/s, loss=0.533]" + "training until 2000: 14%|█▎ | 270/2000 [01:27<09:06, 3.17it/s, loss=0.748]" ] }, { @@ -5036,7 +5036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 271/2000 [01:28<09:07, 3.16it/s, loss=0.533]" + "training until 2000: 14%|█▎ | 271/2000 [01:27<11:02, 2.61it/s, loss=0.748]" ] }, { @@ -5044,7 +5044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 271/2000 [01:28<09:07, 3.16it/s, loss=0.578]" + "training until 2000: 14%|█▎ | 271/2000 [01:27<11:02, 2.61it/s, loss=0.651]" ] }, { @@ -5052,7 +5052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 272/2000 [01:29<09:07, 3.15it/s, loss=0.578]" + "training until 2000: 14%|█▎ | 272/2000 [01:27<10:30, 2.74it/s, loss=0.651]" ] }, { @@ -5060,7 +5060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 272/2000 [01:29<09:07, 3.15it/s, loss=0.569]" + "training until 2000: 14%|█▎ | 272/2000 [01:27<10:30, 2.74it/s, loss=0.748]" ] }, { @@ -5068,7 +5068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 273/2000 [01:29<09:03, 3.18it/s, loss=0.569]" + "training until 2000: 14%|█▎ | 273/2000 [01:28<10:02, 2.86it/s, loss=0.748]" ] }, { @@ -5076,7 +5076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 273/2000 [01:29<09:03, 3.18it/s, loss=0.551]" + "training until 2000: 14%|█▎ | 273/2000 [01:28<10:02, 2.86it/s, loss=0.713]" ] }, { @@ -5084,7 +5084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 274/2000 [01:29<08:59, 3.20it/s, loss=0.551]" + "training until 2000: 14%|█▎ | 274/2000 [01:28<09:48, 2.93it/s, loss=0.713]" ] }, { @@ -5092,7 +5092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▎ | 274/2000 [01:29<08:59, 3.20it/s, loss=0.531]" + "training until 2000: 14%|█▎ | 274/2000 [01:28<09:48, 2.93it/s, loss=0.692]" ] }, { @@ -5100,7 +5100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 275/2000 [01:30<09:08, 3.15it/s, loss=0.531]" + "training until 2000: 14%|█▍ | 275/2000 [01:28<09:35, 3.00it/s, loss=0.692]" ] }, { @@ -5108,7 +5108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 275/2000 [01:30<09:08, 3.15it/s, loss=0.561]" + "training until 2000: 14%|█▍ | 275/2000 [01:28<09:35, 3.00it/s, loss=0.687]" ] }, { @@ -5116,7 +5116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 276/2000 [01:30<09:04, 3.17it/s, loss=0.561]" + "training until 2000: 14%|█▍ | 276/2000 [01:29<09:17, 3.09it/s, loss=0.687]" ] }, { @@ -5124,7 +5124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 276/2000 [01:30<09:04, 3.17it/s, loss=0.632]" + "training until 2000: 14%|█▍ | 276/2000 [01:29<09:17, 3.09it/s, loss=0.673]" ] }, { @@ -5132,7 +5132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 277/2000 [01:30<08:59, 3.19it/s, loss=0.632]" + "training until 2000: 14%|█▍ | 277/2000 [01:29<09:09, 3.14it/s, loss=0.673]" ] }, { @@ -5140,7 +5140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 277/2000 [01:30<08:59, 3.19it/s, loss=0.522]" + "training until 2000: 14%|█▍ | 277/2000 [01:29<09:09, 3.14it/s, loss=0.74] " ] }, { @@ -5148,7 +5148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 278/2000 [01:31<11:00, 2.61it/s, loss=0.522]" + "training until 2000: 14%|█▍ | 278/2000 [01:29<09:04, 3.16it/s, loss=0.74]" ] }, { @@ -5156,7 +5156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 278/2000 [01:31<11:00, 2.61it/s, loss=0.557]" + "training until 2000: 14%|█▍ | 278/2000 [01:29<09:04, 3.16it/s, loss=0.706]" ] }, { @@ -5164,7 +5164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 279/2000 [01:31<10:30, 2.73it/s, loss=0.557]" + "training until 2000: 14%|█▍ | 279/2000 [01:30<09:11, 3.12it/s, loss=0.706]" ] }, { @@ -5172,7 +5172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 279/2000 [01:31<10:30, 2.73it/s, loss=0.588]" + "training until 2000: 14%|█▍ | 279/2000 [01:30<09:11, 3.12it/s, loss=0.745]" ] }, { @@ -5180,7 +5180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 280/2000 [01:31<10:02, 2.86it/s, loss=0.588]" + "training until 2000: 14%|█▍ | 280/2000 [01:30<09:10, 3.12it/s, loss=0.745]" ] }, { @@ -5188,7 +5188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 280/2000 [01:31<10:02, 2.86it/s, loss=0.549]" + "training until 2000: 14%|█▍ | 280/2000 [01:30<09:10, 3.12it/s, loss=0.68] " ] }, { @@ -5196,7 +5196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 281/2000 [01:32<09:46, 2.93it/s, loss=0.549]" + "training until 2000: 14%|█▍ | 281/2000 [01:30<09:17, 3.09it/s, loss=0.68]" ] }, { @@ -5204,7 +5204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 281/2000 [01:32<09:46, 2.93it/s, loss=0.525]" + "training until 2000: 14%|█▍ | 281/2000 [01:30<09:17, 3.09it/s, loss=0.713]" ] }, { @@ -5212,7 +5212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 282/2000 [01:32<09:43, 2.95it/s, loss=0.525]" + "training until 2000: 14%|█▍ | 282/2000 [01:31<09:20, 3.06it/s, loss=0.713]" ] }, { @@ -5220,7 +5220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 282/2000 [01:32<09:43, 2.95it/s, loss=0.647]" + "training until 2000: 14%|█▍ | 282/2000 [01:31<09:20, 3.06it/s, loss=0.711]" ] }, { @@ -5228,7 +5228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 283/2000 [01:32<09:33, 2.99it/s, loss=0.647]" + "training until 2000: 14%|█▍ | 283/2000 [01:31<09:21, 3.06it/s, loss=0.711]" ] }, { @@ -5236,7 +5236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 283/2000 [01:32<09:33, 2.99it/s, loss=0.693]" + "training until 2000: 14%|█▍ | 283/2000 [01:31<09:21, 3.06it/s, loss=0.726]" ] }, { @@ -5244,7 +5244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 284/2000 [01:33<09:28, 3.02it/s, loss=0.693]" + "training until 2000: 14%|█▍ | 284/2000 [01:31<09:17, 3.08it/s, loss=0.726]" ] }, { @@ -5252,7 +5252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 284/2000 [01:33<09:28, 3.02it/s, loss=0.545]" + "training until 2000: 14%|█▍ | 284/2000 [01:31<09:17, 3.08it/s, loss=0.708]" ] }, { @@ -5260,7 +5260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 285/2000 [01:33<09:23, 3.04it/s, loss=0.545]" + "training until 2000: 14%|█▍ | 285/2000 [01:32<09:13, 3.10it/s, loss=0.708]" ] }, { @@ -5268,7 +5268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 285/2000 [01:33<09:23, 3.04it/s, loss=0.549]" + "training until 2000: 14%|█▍ | 285/2000 [01:32<09:13, 3.10it/s, loss=0.691]" ] }, { @@ -5276,7 +5276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 286/2000 [01:33<09:17, 3.08it/s, loss=0.549]" + "training until 2000: 14%|█▍ | 286/2000 [01:32<09:07, 3.13it/s, loss=0.691]" ] }, { @@ -5284,7 +5284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 286/2000 [01:33<09:17, 3.08it/s, loss=0.565]" + "training until 2000: 14%|█▍ | 286/2000 [01:32<09:07, 3.13it/s, loss=0.663]" ] }, { @@ -5292,7 +5292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 287/2000 [01:34<09:12, 3.10it/s, loss=0.565]" + "training until 2000: 14%|█▍ | 287/2000 [01:32<09:01, 3.16it/s, loss=0.663]" ] }, { @@ -5300,7 +5300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 287/2000 [01:34<09:12, 3.10it/s, loss=0.566]" + "training until 2000: 14%|█▍ | 287/2000 [01:32<09:01, 3.16it/s, loss=0.737]" ] }, { @@ -5308,7 +5308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 288/2000 [01:34<09:10, 3.11it/s, loss=0.566]" + "training until 2000: 14%|█▍ | 288/2000 [01:33<08:57, 3.19it/s, loss=0.737]" ] }, { @@ -5316,7 +5316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 288/2000 [01:34<09:10, 3.11it/s, loss=0.613]" + "training until 2000: 14%|█▍ | 288/2000 [01:33<08:57, 3.19it/s, loss=0.707]" ] }, { @@ -5324,7 +5324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 289/2000 [01:34<09:07, 3.12it/s, loss=0.613]" + "training until 2000: 14%|█▍ | 289/2000 [01:33<08:54, 3.20it/s, loss=0.707]" ] }, { @@ -5332,7 +5332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 289/2000 [01:34<09:07, 3.12it/s, loss=0.584]" + "training until 2000: 14%|█▍ | 289/2000 [01:33<08:54, 3.20it/s, loss=0.737]" ] }, { @@ -5340,7 +5340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 290/2000 [01:35<09:10, 3.11it/s, loss=0.584]" + "training until 2000: 14%|█▍ | 290/2000 [01:33<08:56, 3.19it/s, loss=0.737]" ] }, { @@ -5348,7 +5348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 14%|█▍ | 290/2000 [01:35<09:10, 3.11it/s, loss=0.577]" + "training until 2000: 14%|█▍ | 290/2000 [01:33<08:56, 3.19it/s, loss=0.741]" ] }, { @@ -5356,7 +5356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 291/2000 [01:35<09:09, 3.11it/s, loss=0.577]" + "training until 2000: 15%|█▍ | 291/2000 [01:34<09:04, 3.14it/s, loss=0.741]" ] }, { @@ -5364,7 +5364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 291/2000 [01:35<09:09, 3.11it/s, loss=0.6] " + "training until 2000: 15%|█▍ | 291/2000 [01:34<09:04, 3.14it/s, loss=0.7] " ] }, { @@ -5372,7 +5372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 292/2000 [01:35<09:13, 3.09it/s, loss=0.6]" + "training until 2000: 15%|█▍ | 292/2000 [01:34<09:05, 3.13it/s, loss=0.7]" ] }, { @@ -5380,7 +5380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 292/2000 [01:35<09:13, 3.09it/s, loss=0.637]" + "training until 2000: 15%|█▍ | 292/2000 [01:34<09:05, 3.13it/s, loss=0.692]" ] }, { @@ -5388,7 +5388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 293/2000 [01:36<09:05, 3.13it/s, loss=0.637]" + "training until 2000: 15%|█▍ | 293/2000 [01:34<09:03, 3.14it/s, loss=0.692]" ] }, { @@ -5396,7 +5396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 293/2000 [01:36<09:05, 3.13it/s, loss=0.602]" + "training until 2000: 15%|█▍ | 293/2000 [01:34<09:03, 3.14it/s, loss=0.698]" ] }, { @@ -5404,7 +5404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 294/2000 [01:36<09:09, 3.10it/s, loss=0.602]" + "training until 2000: 15%|█▍ | 294/2000 [01:34<08:57, 3.17it/s, loss=0.698]" ] }, { @@ -5412,7 +5412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 294/2000 [01:36<09:09, 3.10it/s, loss=0.59] " + "training until 2000: 15%|█▍ | 294/2000 [01:34<08:57, 3.17it/s, loss=0.755]" ] }, { @@ -5420,7 +5420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 295/2000 [01:36<09:08, 3.11it/s, loss=0.59]" + "training until 2000: 15%|█▍ | 295/2000 [01:35<09:00, 3.15it/s, loss=0.755]" ] }, { @@ -5428,7 +5428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 295/2000 [01:36<09:08, 3.11it/s, loss=0.578]" + "training until 2000: 15%|█▍ | 295/2000 [01:35<09:00, 3.15it/s, loss=0.708]" ] }, { @@ -5436,7 +5436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 296/2000 [01:37<09:05, 3.12it/s, loss=0.578]" + "training until 2000: 15%|█▍ | 296/2000 [01:35<09:00, 3.15it/s, loss=0.708]" ] }, { @@ -5444,7 +5444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 296/2000 [01:37<09:05, 3.12it/s, loss=0.564]" + "training until 2000: 15%|█▍ | 296/2000 [01:35<09:00, 3.15it/s, loss=0.708]" ] }, { @@ -5452,7 +5452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 297/2000 [01:37<09:00, 3.15it/s, loss=0.564]" + "training until 2000: 15%|█▍ | 297/2000 [01:35<09:02, 3.14it/s, loss=0.708]" ] }, { @@ -5460,7 +5460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 297/2000 [01:37<09:00, 3.15it/s, loss=0.556]" + "training until 2000: 15%|█▍ | 297/2000 [01:35<09:02, 3.14it/s, loss=0.666]" ] }, { @@ -5468,7 +5468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 298/2000 [01:37<08:59, 3.15it/s, loss=0.556]" + "training until 2000: 15%|█▍ | 298/2000 [01:36<09:05, 3.12it/s, loss=0.666]" ] }, { @@ -5476,7 +5476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 298/2000 [01:37<08:59, 3.15it/s, loss=0.649]" + "training until 2000: 15%|█▍ | 298/2000 [01:36<09:05, 3.12it/s, loss=0.707]" ] }, { @@ -5484,7 +5484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 299/2000 [01:37<08:59, 3.15it/s, loss=0.649]" + "training until 2000: 15%|█▍ | 299/2000 [01:36<09:02, 3.14it/s, loss=0.707]" ] }, { @@ -5492,7 +5492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▍ | 299/2000 [01:37<08:59, 3.15it/s, loss=0.533]" + "training until 2000: 15%|█▍ | 299/2000 [01:36<09:02, 3.14it/s, loss=0.685]" ] }, { @@ -5500,7 +5500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 300/2000 [01:38<09:08, 3.10it/s, loss=0.533]" + "training until 2000: 15%|█▌ | 300/2000 [01:36<09:04, 3.12it/s, loss=0.685]" ] }, { @@ -5508,7 +5508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 300/2000 [01:38<09:08, 3.10it/s, loss=0.649]" + "training until 2000: 15%|█▌ | 300/2000 [01:36<09:04, 3.12it/s, loss=0.705]" ] }, { @@ -5516,7 +5516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 301/2000 [01:38<09:10, 3.09it/s, loss=0.649]" + "training until 2000: 15%|█▌ | 301/2000 [01:37<09:03, 3.13it/s, loss=0.705]" ] }, { @@ -5524,7 +5524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 301/2000 [01:38<09:10, 3.09it/s, loss=0.634]" + "training until 2000: 15%|█▌ | 301/2000 [01:37<09:03, 3.13it/s, loss=0.757]" ] }, { @@ -5532,7 +5532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 302/2000 [01:38<09:02, 3.13it/s, loss=0.634]" + "training until 2000: 15%|█▌ | 302/2000 [01:37<08:59, 3.15it/s, loss=0.757]" ] }, { @@ -5540,7 +5540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 302/2000 [01:38<09:02, 3.13it/s, loss=0.565]" + "training until 2000: 15%|█▌ | 302/2000 [01:37<08:59, 3.15it/s, loss=0.684]" ] }, { @@ -5548,7 +5548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 303/2000 [01:39<09:09, 3.09it/s, loss=0.565]" + "training until 2000: 15%|█▌ | 303/2000 [01:37<09:01, 3.13it/s, loss=0.684]" ] }, { @@ -5556,7 +5556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 303/2000 [01:39<09:09, 3.09it/s, loss=0.61] " + "training until 2000: 15%|█▌ | 303/2000 [01:37<09:01, 3.13it/s, loss=0.638]" ] }, { @@ -5564,7 +5564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 304/2000 [01:39<09:05, 3.11it/s, loss=0.61]" + "training until 2000: 15%|█▌ | 304/2000 [01:38<08:59, 3.14it/s, loss=0.638]" ] }, { @@ -5572,7 +5572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 304/2000 [01:39<09:05, 3.11it/s, loss=0.565]" + "training until 2000: 15%|█▌ | 304/2000 [01:38<08:59, 3.14it/s, loss=0.677]" ] }, { @@ -5580,7 +5580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 305/2000 [01:39<09:03, 3.12it/s, loss=0.565]" + "training until 2000: 15%|█▌ | 305/2000 [01:38<08:59, 3.14it/s, loss=0.677]" ] }, { @@ -5588,7 +5588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 305/2000 [01:39<09:03, 3.12it/s, loss=0.521]" + "training until 2000: 15%|█▌ | 305/2000 [01:38<08:59, 3.14it/s, loss=0.703]" ] }, { @@ -5596,7 +5596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 306/2000 [01:40<09:03, 3.12it/s, loss=0.521]" + "training until 2000: 15%|█▌ | 306/2000 [01:38<08:56, 3.16it/s, loss=0.703]" ] }, { @@ -5604,7 +5604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 306/2000 [01:40<09:03, 3.12it/s, loss=0.565]" + "training until 2000: 15%|█▌ | 306/2000 [01:38<08:56, 3.16it/s, loss=0.743]" ] }, { @@ -5612,7 +5612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 307/2000 [01:40<09:06, 3.10it/s, loss=0.565]" + "training until 2000: 15%|█▌ | 307/2000 [01:39<09:02, 3.12it/s, loss=0.743]" ] }, { @@ -5620,7 +5620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 307/2000 [01:40<09:06, 3.10it/s, loss=0.702]" + "training until 2000: 15%|█▌ | 307/2000 [01:39<09:02, 3.12it/s, loss=0.71] " ] }, { @@ -5628,7 +5628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 308/2000 [01:40<09:08, 3.08it/s, loss=0.702]" + "training until 2000: 15%|█▌ | 308/2000 [01:39<09:04, 3.11it/s, loss=0.71]" ] }, { @@ -5636,7 +5636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 308/2000 [01:40<09:08, 3.08it/s, loss=0.606]" + "training until 2000: 15%|█▌ | 308/2000 [01:39<09:04, 3.11it/s, loss=0.715]" ] }, { @@ -5644,7 +5644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 309/2000 [01:41<09:11, 3.06it/s, loss=0.606]" + "training until 2000: 15%|█▌ | 309/2000 [01:39<09:01, 3.12it/s, loss=0.715]" ] }, { @@ -5652,7 +5652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 15%|█▌ | 309/2000 [01:41<09:11, 3.06it/s, loss=0.591]" + "training until 2000: 15%|█▌ | 309/2000 [01:39<09:01, 3.12it/s, loss=0.681]" ] }, { @@ -5660,7 +5660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 310/2000 [01:41<09:12, 3.06it/s, loss=0.591]" + "training until 2000: 16%|█▌ | 310/2000 [01:40<08:58, 3.14it/s, loss=0.681]" ] }, { @@ -5668,7 +5668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 310/2000 [01:41<09:12, 3.06it/s, loss=0.621]" + "training until 2000: 16%|█▌ | 310/2000 [01:40<08:58, 3.14it/s, loss=0.699]" ] }, { @@ -5676,7 +5676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 311/2000 [01:41<09:18, 3.03it/s, loss=0.621]" + "training until 2000: 16%|█▌ | 311/2000 [01:40<09:02, 3.11it/s, loss=0.699]" ] }, { @@ -5684,7 +5684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 311/2000 [01:41<09:18, 3.03it/s, loss=0.65] " + "training until 2000: 16%|█▌ | 311/2000 [01:40<09:02, 3.11it/s, loss=0.724]" ] }, { @@ -5692,7 +5692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 312/2000 [01:42<09:08, 3.08it/s, loss=0.65]" + "training until 2000: 16%|█▌ | 312/2000 [01:40<09:00, 3.12it/s, loss=0.724]" ] }, { @@ -5700,7 +5700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 312/2000 [01:42<09:08, 3.08it/s, loss=0.563]" + "training until 2000: 16%|█▌ | 312/2000 [01:40<09:00, 3.12it/s, loss=0.685]" ] }, { @@ -5708,7 +5708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 313/2000 [01:42<09:07, 3.08it/s, loss=0.563]" + "training until 2000: 16%|█▌ | 313/2000 [01:41<09:00, 3.12it/s, loss=0.685]" ] }, { @@ -5716,7 +5716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 313/2000 [01:42<09:07, 3.08it/s, loss=0.538]" + "training until 2000: 16%|█▌ | 313/2000 [01:41<09:00, 3.12it/s, loss=0.742]" ] }, { @@ -5724,7 +5724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 314/2000 [01:42<09:03, 3.10it/s, loss=0.538]" + "training until 2000: 16%|█▌ | 314/2000 [01:41<09:03, 3.10it/s, loss=0.742]" ] }, { @@ -5732,7 +5732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 314/2000 [01:42<09:03, 3.10it/s, loss=0.634]" + "training until 2000: 16%|█▌ | 314/2000 [01:41<09:03, 3.10it/s, loss=0.738]" ] }, { @@ -5740,7 +5740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 315/2000 [01:43<09:00, 3.12it/s, loss=0.634]" + "training until 2000: 16%|█▌ | 315/2000 [01:41<09:06, 3.09it/s, loss=0.738]" ] }, { @@ -5748,7 +5748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 315/2000 [01:43<09:00, 3.12it/s, loss=0.636]" + "training until 2000: 16%|█▌ | 315/2000 [01:41<09:06, 3.09it/s, loss=0.725]" ] }, { @@ -5756,7 +5756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 316/2000 [01:43<09:03, 3.10it/s, loss=0.636]" + "training until 2000: 16%|█▌ | 316/2000 [01:42<09:06, 3.08it/s, loss=0.725]" ] }, { @@ -5764,7 +5764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 316/2000 [01:43<09:03, 3.10it/s, loss=0.55] " + "training until 2000: 16%|█▌ | 316/2000 [01:42<09:06, 3.08it/s, loss=0.702]" ] }, { @@ -5772,7 +5772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 317/2000 [01:43<08:59, 3.12it/s, loss=0.55]" + "training until 2000: 16%|█▌ | 317/2000 [01:42<09:06, 3.08it/s, loss=0.702]" ] }, { @@ -5780,7 +5780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 317/2000 [01:43<08:59, 3.12it/s, loss=0.617]" + "training until 2000: 16%|█▌ | 317/2000 [01:42<09:06, 3.08it/s, loss=0.734]" ] }, { @@ -5788,7 +5788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 318/2000 [01:44<08:53, 3.15it/s, loss=0.617]" + "training until 2000: 16%|█▌ | 318/2000 [01:42<09:02, 3.10it/s, loss=0.734]" ] }, { @@ -5796,7 +5796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 318/2000 [01:44<08:53, 3.15it/s, loss=0.556]" + "training until 2000: 16%|█▌ | 318/2000 [01:42<09:02, 3.10it/s, loss=0.677]" ] }, { @@ -5804,7 +5804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 319/2000 [01:44<08:51, 3.16it/s, loss=0.556]" + "training until 2000: 16%|█▌ | 319/2000 [01:42<09:00, 3.11it/s, loss=0.677]" ] }, { @@ -5812,7 +5812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 319/2000 [01:44<08:51, 3.16it/s, loss=0.578]" + "training until 2000: 16%|█▌ | 319/2000 [01:42<09:00, 3.11it/s, loss=0.756]" ] }, { @@ -5820,7 +5820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 320/2000 [01:44<08:59, 3.11it/s, loss=0.578]" + "training until 2000: 16%|█▌ | 320/2000 [01:43<08:58, 3.12it/s, loss=0.756]" ] }, { @@ -5828,7 +5828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 320/2000 [01:44<08:59, 3.11it/s, loss=0.651]" + "training until 2000: 16%|█▌ | 320/2000 [01:43<08:58, 3.12it/s, loss=0.657]" ] }, { @@ -5836,7 +5836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 321/2000 [01:45<08:56, 3.13it/s, loss=0.651]" + "training until 2000: 16%|█▌ | 321/2000 [01:43<08:53, 3.14it/s, loss=0.657]" ] }, { @@ -5844,7 +5844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 321/2000 [01:45<08:56, 3.13it/s, loss=0.615]" + "training until 2000: 16%|█▌ | 321/2000 [01:43<08:53, 3.14it/s, loss=0.648]" ] }, { @@ -5852,7 +5852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 322/2000 [01:45<08:51, 3.15it/s, loss=0.615]" + "training until 2000: 16%|█▌ | 322/2000 [01:43<08:52, 3.15it/s, loss=0.648]" ] }, { @@ -5860,7 +5860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 322/2000 [01:45<08:51, 3.15it/s, loss=0.555]" + "training until 2000: 16%|█▌ | 322/2000 [01:43<08:52, 3.15it/s, loss=0.699]" ] }, { @@ -5868,7 +5868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 323/2000 [01:45<09:00, 3.10it/s, loss=0.555]" + "training until 2000: 16%|█▌ | 323/2000 [01:44<08:50, 3.16it/s, loss=0.699]" ] }, { @@ -5876,7 +5876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 323/2000 [01:45<09:00, 3.10it/s, loss=0.585]" + "training until 2000: 16%|█▌ | 323/2000 [01:44<08:50, 3.16it/s, loss=0.724]" ] }, { @@ -5884,7 +5884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 324/2000 [01:46<08:59, 3.11it/s, loss=0.585]" + "training until 2000: 16%|█▌ | 324/2000 [01:44<08:50, 3.16it/s, loss=0.724]" ] }, { @@ -5892,7 +5892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▌ | 324/2000 [01:46<08:59, 3.11it/s, loss=0.597]" + "training until 2000: 16%|█▌ | 324/2000 [01:44<08:50, 3.16it/s, loss=0.696]" ] }, { @@ -5900,7 +5900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 325/2000 [01:46<09:00, 3.10it/s, loss=0.597]" + "training until 2000: 16%|█▋ | 325/2000 [01:44<08:51, 3.15it/s, loss=0.696]" ] }, { @@ -5908,7 +5908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 325/2000 [01:46<09:00, 3.10it/s, loss=0.661]" + "training until 2000: 16%|█▋ | 325/2000 [01:44<08:51, 3.15it/s, loss=0.721]" ] }, { @@ -5916,7 +5916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 326/2000 [01:46<08:59, 3.10it/s, loss=0.661]" + "training until 2000: 16%|█▋ | 326/2000 [01:45<08:59, 3.10it/s, loss=0.721]" ] }, { @@ -5924,7 +5924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 326/2000 [01:46<08:59, 3.10it/s, loss=0.537]" + "training until 2000: 16%|█▋ | 326/2000 [01:45<08:59, 3.10it/s, loss=0.668]" ] }, { @@ -5932,7 +5932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 327/2000 [01:47<08:54, 3.13it/s, loss=0.537]" + "training until 2000: 16%|█▋ | 327/2000 [01:45<08:55, 3.13it/s, loss=0.668]" ] }, { @@ -5940,7 +5940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 327/2000 [01:47<08:54, 3.13it/s, loss=0.539]" + "training until 2000: 16%|█▋ | 327/2000 [01:45<08:55, 3.13it/s, loss=0.697]" ] }, { @@ -5948,7 +5948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 328/2000 [01:47<08:54, 3.13it/s, loss=0.539]" + "training until 2000: 16%|█▋ | 328/2000 [01:45<08:55, 3.12it/s, loss=0.697]" ] }, { @@ -5956,7 +5956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 328/2000 [01:47<08:54, 3.13it/s, loss=0.6] " + "training until 2000: 16%|█▋ | 328/2000 [01:45<08:55, 3.12it/s, loss=0.684]" ] }, { @@ -5964,7 +5964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 329/2000 [01:47<08:55, 3.12it/s, loss=0.6]" + "training until 2000: 16%|█▋ | 329/2000 [01:46<08:50, 3.15it/s, loss=0.684]" ] }, { @@ -5972,7 +5972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 329/2000 [01:47<08:55, 3.12it/s, loss=0.617]" + "training until 2000: 16%|█▋ | 329/2000 [01:46<08:50, 3.15it/s, loss=0.673]" ] }, { @@ -5980,7 +5980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 330/2000 [01:47<08:51, 3.14it/s, loss=0.617]" + "training until 2000: 16%|█▋ | 330/2000 [01:46<08:52, 3.14it/s, loss=0.673]" ] }, { @@ -5988,7 +5988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 16%|█▋ | 330/2000 [01:47<08:51, 3.14it/s, loss=0.64] " + "training until 2000: 16%|█▋ | 330/2000 [01:46<08:52, 3.14it/s, loss=0.719]" ] }, { @@ -5996,7 +5996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 331/2000 [01:48<08:54, 3.12it/s, loss=0.64]" + "training until 2000: 17%|█▋ | 331/2000 [01:46<08:51, 3.14it/s, loss=0.719]" ] }, { @@ -6004,7 +6004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 331/2000 [01:48<08:54, 3.12it/s, loss=0.586]" + "training until 2000: 17%|█▋ | 331/2000 [01:46<08:51, 3.14it/s, loss=0.711]" ] }, { @@ -6012,7 +6012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 332/2000 [01:48<08:51, 3.14it/s, loss=0.586]" + "training until 2000: 17%|█▋ | 332/2000 [01:47<08:49, 3.15it/s, loss=0.711]" ] }, { @@ -6020,7 +6020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 332/2000 [01:48<08:51, 3.14it/s, loss=0.579]" + "training until 2000: 17%|█▋ | 332/2000 [01:47<08:49, 3.15it/s, loss=0.718]" ] }, { @@ -6028,7 +6028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 333/2000 [01:48<08:52, 3.13it/s, loss=0.579]" + "training until 2000: 17%|█▋ | 333/2000 [01:47<08:46, 3.16it/s, loss=0.718]" ] }, { @@ -6036,7 +6036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 333/2000 [01:48<08:52, 3.13it/s, loss=0.589]" + "training until 2000: 17%|█▋ | 333/2000 [01:47<08:46, 3.16it/s, loss=0.711]" ] }, { @@ -6044,7 +6044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 334/2000 [01:49<08:48, 3.15it/s, loss=0.589]" + "training until 2000: 17%|█▋ | 334/2000 [01:47<08:44, 3.18it/s, loss=0.711]" ] }, { @@ -6052,7 +6052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 334/2000 [01:49<08:48, 3.15it/s, loss=0.706]" + "training until 2000: 17%|█▋ | 334/2000 [01:47<08:44, 3.18it/s, loss=0.706]" ] }, { @@ -6060,7 +6060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 335/2000 [01:49<08:47, 3.16it/s, loss=0.706]" + "training until 2000: 17%|█▋ | 335/2000 [01:48<08:38, 3.21it/s, loss=0.706]" ] }, { @@ -6068,7 +6068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 335/2000 [01:49<08:47, 3.16it/s, loss=0.558]" + "training until 2000: 17%|█▋ | 335/2000 [01:48<08:38, 3.21it/s, loss=0.691]" ] }, { @@ -6076,7 +6076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 336/2000 [01:49<08:50, 3.14it/s, loss=0.558]" + "training until 2000: 17%|█▋ | 336/2000 [01:48<10:37, 2.61it/s, loss=0.691]" ] }, { @@ -6084,7 +6084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 336/2000 [01:49<08:50, 3.14it/s, loss=0.592]" + "training until 2000: 17%|█▋ | 336/2000 [01:48<10:37, 2.61it/s, loss=0.672]" ] }, { @@ -6092,7 +6092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 337/2000 [01:50<08:57, 3.09it/s, loss=0.592]" + "training until 2000: 17%|█▋ | 337/2000 [01:48<10:04, 2.75it/s, loss=0.672]" ] }, { @@ -6100,7 +6100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 337/2000 [01:50<08:57, 3.09it/s, loss=0.57] " + "training until 2000: 17%|█▋ | 337/2000 [01:48<10:04, 2.75it/s, loss=0.665]" ] }, { @@ -6108,7 +6108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 338/2000 [01:50<08:56, 3.10it/s, loss=0.57]" + "training until 2000: 17%|█▋ | 338/2000 [01:49<09:39, 2.87it/s, loss=0.665]" ] }, { @@ -6116,7 +6116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 338/2000 [01:50<08:56, 3.10it/s, loss=0.562]" + "training until 2000: 17%|█▋ | 338/2000 [01:49<09:39, 2.87it/s, loss=0.727]" ] }, { @@ -6124,7 +6124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 339/2000 [01:50<08:58, 3.08it/s, loss=0.562]" + "training until 2000: 17%|█▋ | 339/2000 [01:49<09:19, 2.97it/s, loss=0.727]" ] }, { @@ -6132,7 +6132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 339/2000 [01:50<08:58, 3.08it/s, loss=0.657]" + "training until 2000: 17%|█▋ | 339/2000 [01:49<09:19, 2.97it/s, loss=0.678]" ] }, { @@ -6140,7 +6140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 340/2000 [01:51<08:57, 3.09it/s, loss=0.657]" + "training until 2000: 17%|█▋ | 340/2000 [01:49<09:10, 3.02it/s, loss=0.678]" ] }, { @@ -6148,7 +6148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 340/2000 [01:51<08:57, 3.09it/s, loss=0.529]" + "training until 2000: 17%|█▋ | 340/2000 [01:49<09:10, 3.02it/s, loss=0.662]" ] }, { @@ -6156,7 +6156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 341/2000 [01:51<08:55, 3.10it/s, loss=0.529]" + "training until 2000: 17%|█▋ | 341/2000 [01:50<09:07, 3.03it/s, loss=0.662]" ] }, { @@ -6164,7 +6164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 341/2000 [01:51<08:55, 3.10it/s, loss=0.537]" + "training until 2000: 17%|█▋ | 341/2000 [01:50<09:07, 3.03it/s, loss=0.681]" ] }, { @@ -6172,7 +6172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 342/2000 [01:51<09:04, 3.05it/s, loss=0.537]" + "training until 2000: 17%|█▋ | 342/2000 [01:50<08:57, 3.08it/s, loss=0.681]" ] }, { @@ -6180,7 +6180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 342/2000 [01:51<09:04, 3.05it/s, loss=0.562]" + "training until 2000: 17%|█▋ | 342/2000 [01:50<08:57, 3.08it/s, loss=0.692]" ] }, { @@ -6188,7 +6188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 343/2000 [01:52<10:51, 2.54it/s, loss=0.562]" + "training until 2000: 17%|█▋ | 343/2000 [01:50<08:53, 3.11it/s, loss=0.692]" ] }, { @@ -6196,7 +6196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 343/2000 [01:52<10:51, 2.54it/s, loss=0.614]" + "training until 2000: 17%|█▋ | 343/2000 [01:50<08:53, 3.11it/s, loss=0.708]" ] }, { @@ -6204,7 +6204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 344/2000 [01:52<10:14, 2.70it/s, loss=0.614]" + "training until 2000: 17%|█▋ | 344/2000 [01:51<08:51, 3.12it/s, loss=0.708]" ] }, { @@ -6212,7 +6212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 344/2000 [01:52<10:14, 2.70it/s, loss=0.556]" + "training until 2000: 17%|█▋ | 344/2000 [01:51<08:51, 3.12it/s, loss=0.697]" ] }, { @@ -6220,7 +6220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 345/2000 [01:53<09:42, 2.84it/s, loss=0.556]" + "training until 2000: 17%|█▋ | 345/2000 [01:51<08:46, 3.14it/s, loss=0.697]" ] }, { @@ -6228,7 +6228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 345/2000 [01:53<09:42, 2.84it/s, loss=0.624]" + "training until 2000: 17%|█▋ | 345/2000 [01:51<08:46, 3.14it/s, loss=0.676]" ] }, { @@ -6236,7 +6236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 346/2000 [01:53<09:22, 2.94it/s, loss=0.624]" + "training until 2000: 17%|█▋ | 346/2000 [01:51<08:50, 3.12it/s, loss=0.676]" ] }, { @@ -6244,7 +6244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 346/2000 [01:53<09:22, 2.94it/s, loss=0.523]" + "training until 2000: 17%|█▋ | 346/2000 [01:51<08:50, 3.12it/s, loss=0.697]" ] }, { @@ -6252,7 +6252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 347/2000 [01:53<09:10, 3.00it/s, loss=0.523]" + "training until 2000: 17%|█▋ | 347/2000 [01:52<08:45, 3.15it/s, loss=0.697]" ] }, { @@ -6260,7 +6260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 347/2000 [01:53<09:10, 3.00it/s, loss=0.54] " + "training until 2000: 17%|█▋ | 347/2000 [01:52<08:45, 3.15it/s, loss=0.724]" ] }, { @@ -6268,7 +6268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 348/2000 [01:53<09:09, 3.00it/s, loss=0.54]" + "training until 2000: 17%|█▋ | 348/2000 [01:52<08:49, 3.12it/s, loss=0.724]" ] }, { @@ -6276,7 +6276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 348/2000 [01:53<09:09, 3.00it/s, loss=0.616]" + "training until 2000: 17%|█▋ | 348/2000 [01:52<08:49, 3.12it/s, loss=0.662]" ] }, { @@ -6284,7 +6284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 349/2000 [01:54<09:05, 3.03it/s, loss=0.616]" + "training until 2000: 17%|█▋ | 349/2000 [01:52<08:48, 3.12it/s, loss=0.662]" ] }, { @@ -6292,7 +6292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 17%|█▋ | 349/2000 [01:54<09:05, 3.03it/s, loss=0.569]" + "training until 2000: 17%|█▋ | 349/2000 [01:52<08:48, 3.12it/s, loss=0.693]" ] }, { @@ -6300,7 +6300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 350/2000 [01:54<08:59, 3.06it/s, loss=0.569]" + "training until 2000: 18%|█▊ | 350/2000 [01:53<08:44, 3.15it/s, loss=0.693]" ] }, { @@ -6308,7 +6308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 350/2000 [01:54<08:59, 3.06it/s, loss=0.568]" + "training until 2000: 18%|█▊ | 350/2000 [01:53<08:44, 3.15it/s, loss=0.724]" ] }, { @@ -6316,7 +6316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 351/2000 [01:54<08:55, 3.08it/s, loss=0.568]" + "training until 2000: 18%|█▊ | 351/2000 [01:53<08:42, 3.16it/s, loss=0.724]" ] }, { @@ -6324,7 +6324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 351/2000 [01:54<08:55, 3.08it/s, loss=0.536]" + "training until 2000: 18%|█▊ | 351/2000 [01:53<08:42, 3.16it/s, loss=0.741]" ] }, { @@ -6332,7 +6332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 352/2000 [01:55<08:54, 3.08it/s, loss=0.536]" + "training until 2000: 18%|█▊ | 352/2000 [01:53<08:38, 3.18it/s, loss=0.741]" ] }, { @@ -6340,7 +6340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 352/2000 [01:55<08:54, 3.08it/s, loss=0.583]" + "training until 2000: 18%|█▊ | 352/2000 [01:53<08:38, 3.18it/s, loss=0.713]" ] }, { @@ -6348,7 +6348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 353/2000 [01:55<08:46, 3.13it/s, loss=0.583]" + "training until 2000: 18%|█▊ | 353/2000 [01:53<08:38, 3.17it/s, loss=0.713]" ] }, { @@ -6356,7 +6356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 353/2000 [01:55<08:46, 3.13it/s, loss=0.575]" + "training until 2000: 18%|█▊ | 353/2000 [01:53<08:38, 3.17it/s, loss=0.709]" ] }, { @@ -6364,7 +6364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 354/2000 [01:55<08:43, 3.14it/s, loss=0.575]" + "training until 2000: 18%|█▊ | 354/2000 [01:54<08:38, 3.18it/s, loss=0.709]" ] }, { @@ -6372,7 +6372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 354/2000 [01:55<08:43, 3.14it/s, loss=0.576]" + "training until 2000: 18%|█▊ | 354/2000 [01:54<08:38, 3.18it/s, loss=0.735]" ] }, { @@ -6380,7 +6380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 355/2000 [01:56<08:47, 3.12it/s, loss=0.576]" + "training until 2000: 18%|█▊ | 355/2000 [01:54<08:35, 3.19it/s, loss=0.735]" ] }, { @@ -6388,7 +6388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 355/2000 [01:56<08:47, 3.12it/s, loss=0.625]" + "training until 2000: 18%|█▊ | 355/2000 [01:54<08:35, 3.19it/s, loss=0.688]" ] }, { @@ -6396,7 +6396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 356/2000 [01:56<08:43, 3.14it/s, loss=0.625]" + "training until 2000: 18%|█▊ | 356/2000 [01:54<08:31, 3.21it/s, loss=0.688]" ] }, { @@ -6404,7 +6404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 356/2000 [01:56<08:43, 3.14it/s, loss=0.673]" + "training until 2000: 18%|█▊ | 356/2000 [01:54<08:31, 3.21it/s, loss=0.711]" ] }, { @@ -6412,7 +6412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 357/2000 [01:56<08:45, 3.13it/s, loss=0.673]" + "training until 2000: 18%|█▊ | 357/2000 [01:55<08:29, 3.22it/s, loss=0.711]" ] }, { @@ -6420,7 +6420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 357/2000 [01:56<08:45, 3.13it/s, loss=0.636]" + "training until 2000: 18%|█▊ | 357/2000 [01:55<08:29, 3.22it/s, loss=0.682]" ] }, { @@ -6428,7 +6428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 358/2000 [01:57<08:42, 3.14it/s, loss=0.636]" + "training until 2000: 18%|█▊ | 358/2000 [01:55<08:33, 3.20it/s, loss=0.682]" ] }, { @@ -6436,7 +6436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 358/2000 [01:57<08:42, 3.14it/s, loss=0.624]" + "training until 2000: 18%|█▊ | 358/2000 [01:55<08:33, 3.20it/s, loss=0.693]" ] }, { @@ -6444,7 +6444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 359/2000 [01:57<08:39, 3.16it/s, loss=0.624]" + "training until 2000: 18%|█▊ | 359/2000 [01:55<08:34, 3.19it/s, loss=0.693]" ] }, { @@ -6452,7 +6452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 359/2000 [01:57<08:39, 3.16it/s, loss=0.542]" + "training until 2000: 18%|█▊ | 359/2000 [01:55<08:34, 3.19it/s, loss=0.662]" ] }, { @@ -6460,7 +6460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 360/2000 [01:57<08:42, 3.14it/s, loss=0.542]" + "training until 2000: 18%|█▊ | 360/2000 [01:56<08:35, 3.18it/s, loss=0.662]" ] }, { @@ -6468,7 +6468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 360/2000 [01:57<08:42, 3.14it/s, loss=0.607]" + "training until 2000: 18%|█▊ | 360/2000 [01:56<08:35, 3.18it/s, loss=0.691]" ] }, { @@ -6476,7 +6476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 361/2000 [01:58<08:39, 3.15it/s, loss=0.607]" + "training until 2000: 18%|█▊ | 361/2000 [01:56<08:33, 3.19it/s, loss=0.691]" ] }, { @@ -6484,7 +6484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 361/2000 [01:58<08:39, 3.15it/s, loss=0.636]" + "training until 2000: 18%|█▊ | 361/2000 [01:56<08:33, 3.19it/s, loss=0.704]" ] }, { @@ -6492,7 +6492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 362/2000 [01:58<08:44, 3.12it/s, loss=0.636]" + "training until 2000: 18%|█▊ | 362/2000 [01:56<08:31, 3.20it/s, loss=0.704]" ] }, { @@ -6500,7 +6500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 362/2000 [01:58<08:44, 3.12it/s, loss=0.555]" + "training until 2000: 18%|█▊ | 362/2000 [01:56<08:31, 3.20it/s, loss=0.655]" ] }, { @@ -6508,7 +6508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 363/2000 [01:58<08:41, 3.14it/s, loss=0.555]" + "training until 2000: 18%|█▊ | 363/2000 [01:57<08:39, 3.15it/s, loss=0.655]" ] }, { @@ -6516,7 +6516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 363/2000 [01:58<08:41, 3.14it/s, loss=0.552]" + "training until 2000: 18%|█▊ | 363/2000 [01:57<08:39, 3.15it/s, loss=0.684]" ] }, { @@ -6524,7 +6524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 364/2000 [01:59<08:39, 3.15it/s, loss=0.552]" + "training until 2000: 18%|█▊ | 364/2000 [01:57<08:39, 3.15it/s, loss=0.684]" ] }, { @@ -6532,7 +6532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 364/2000 [01:59<08:39, 3.15it/s, loss=0.656]" + "training until 2000: 18%|█▊ | 364/2000 [01:57<08:39, 3.15it/s, loss=0.692]" ] }, { @@ -6540,7 +6540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 365/2000 [01:59<08:37, 3.16it/s, loss=0.656]" + "training until 2000: 18%|█▊ | 365/2000 [01:57<08:37, 3.16it/s, loss=0.692]" ] }, { @@ -6548,7 +6548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 365/2000 [01:59<08:37, 3.16it/s, loss=0.549]" + "training until 2000: 18%|█▊ | 365/2000 [01:57<08:37, 3.16it/s, loss=0.661]" ] }, { @@ -6556,7 +6556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 366/2000 [01:59<08:41, 3.14it/s, loss=0.549]" + "training until 2000: 18%|█▊ | 366/2000 [01:58<08:35, 3.17it/s, loss=0.661]" ] }, { @@ -6564,7 +6564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 366/2000 [01:59<08:41, 3.14it/s, loss=0.546]" + "training until 2000: 18%|█▊ | 366/2000 [01:58<08:35, 3.17it/s, loss=0.731]" ] }, { @@ -6572,7 +6572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 367/2000 [02:00<08:40, 3.14it/s, loss=0.546]" + "training until 2000: 18%|█▊ | 367/2000 [01:58<08:38, 3.15it/s, loss=0.731]" ] }, { @@ -6580,7 +6580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 367/2000 [02:00<08:40, 3.14it/s, loss=0.702]" + "training until 2000: 18%|█▊ | 367/2000 [01:58<08:38, 3.15it/s, loss=0.7] " ] }, { @@ -6588,7 +6588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 368/2000 [02:00<08:35, 3.16it/s, loss=0.702]" + "training until 2000: 18%|█▊ | 368/2000 [01:58<08:35, 3.17it/s, loss=0.7]" ] }, { @@ -6596,7 +6596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 368/2000 [02:00<08:35, 3.16it/s, loss=0.638]" + "training until 2000: 18%|█▊ | 368/2000 [01:58<08:35, 3.17it/s, loss=0.651]" ] }, { @@ -6604,7 +6604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 369/2000 [02:00<08:39, 3.14it/s, loss=0.638]" + "training until 2000: 18%|█▊ | 369/2000 [01:59<08:29, 3.20it/s, loss=0.651]" ] }, { @@ -6612,7 +6612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 369/2000 [02:00<08:39, 3.14it/s, loss=0.611]" + "training until 2000: 18%|█▊ | 369/2000 [01:59<08:29, 3.20it/s, loss=0.642]" ] }, { @@ -6620,7 +6620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 370/2000 [02:01<08:45, 3.10it/s, loss=0.611]" + "training until 2000: 18%|█▊ | 370/2000 [01:59<08:31, 3.19it/s, loss=0.642]" ] }, { @@ -6628,7 +6628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 18%|█▊ | 370/2000 [02:01<08:45, 3.10it/s, loss=0.548]" + "training until 2000: 18%|█▊ | 370/2000 [01:59<08:31, 3.19it/s, loss=0.688]" ] }, { @@ -6636,7 +6636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 371/2000 [02:01<08:40, 3.13it/s, loss=0.548]" + "training until 2000: 19%|█▊ | 371/2000 [01:59<08:31, 3.19it/s, loss=0.688]" ] }, { @@ -6644,7 +6644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 371/2000 [02:01<08:40, 3.13it/s, loss=0.641]" + "training until 2000: 19%|█▊ | 371/2000 [01:59<08:31, 3.19it/s, loss=0.651]" ] }, { @@ -6652,7 +6652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 372/2000 [02:01<08:38, 3.14it/s, loss=0.641]" + "training until 2000: 19%|█▊ | 372/2000 [01:59<08:29, 3.19it/s, loss=0.651]" ] }, { @@ -6660,7 +6660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 372/2000 [02:01<08:38, 3.14it/s, loss=0.633]" + "training until 2000: 19%|█▊ | 372/2000 [01:59<08:29, 3.19it/s, loss=0.737]" ] }, { @@ -6668,7 +6668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 373/2000 [02:01<08:41, 3.12it/s, loss=0.633]" + "training until 2000: 19%|█▊ | 373/2000 [02:00<08:29, 3.19it/s, loss=0.737]" ] }, { @@ -6676,7 +6676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 373/2000 [02:01<08:41, 3.12it/s, loss=0.563]" + "training until 2000: 19%|█▊ | 373/2000 [02:00<08:29, 3.19it/s, loss=0.734]" ] }, { @@ -6684,7 +6684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 374/2000 [02:02<08:38, 3.14it/s, loss=0.563]" + "training until 2000: 19%|█▊ | 374/2000 [02:00<08:28, 3.20it/s, loss=0.734]" ] }, { @@ -6692,7 +6692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▊ | 374/2000 [02:02<08:38, 3.14it/s, loss=0.712]" + "training until 2000: 19%|█▊ | 374/2000 [02:00<08:28, 3.20it/s, loss=0.684]" ] }, { @@ -6700,7 +6700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 375/2000 [02:02<08:34, 3.16it/s, loss=0.712]" + "training until 2000: 19%|█▉ | 375/2000 [02:00<08:26, 3.21it/s, loss=0.684]" ] }, { @@ -6708,7 +6708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 375/2000 [02:02<08:34, 3.16it/s, loss=0.609]" + "training until 2000: 19%|█▉ | 375/2000 [02:00<08:26, 3.21it/s, loss=0.653]" ] }, { @@ -6716,7 +6716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 376/2000 [02:02<08:35, 3.15it/s, loss=0.609]" + "training until 2000: 19%|█▉ | 376/2000 [02:01<08:23, 3.22it/s, loss=0.653]" ] }, { @@ -6724,7 +6724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 376/2000 [02:02<08:35, 3.15it/s, loss=0.554]" + "training until 2000: 19%|█▉ | 376/2000 [02:01<08:23, 3.22it/s, loss=0.725]" ] }, { @@ -6732,7 +6732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 377/2000 [02:03<08:32, 3.17it/s, loss=0.554]" + "training until 2000: 19%|█▉ | 377/2000 [02:01<08:26, 3.21it/s, loss=0.725]" ] }, { @@ -6740,7 +6740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 377/2000 [02:03<08:32, 3.17it/s, loss=0.588]" + "training until 2000: 19%|█▉ | 377/2000 [02:01<08:26, 3.21it/s, loss=0.697]" ] }, { @@ -6748,7 +6748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 378/2000 [02:03<08:32, 3.16it/s, loss=0.588]" + "training until 2000: 19%|█▉ | 378/2000 [02:01<08:40, 3.12it/s, loss=0.697]" ] }, { @@ -6756,7 +6756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 378/2000 [02:03<08:32, 3.16it/s, loss=0.62] " + "training until 2000: 19%|█▉ | 378/2000 [02:01<08:40, 3.12it/s, loss=0.678]" ] }, { @@ -6764,7 +6764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 379/2000 [02:03<08:36, 3.14it/s, loss=0.62]" + "training until 2000: 19%|█▉ | 379/2000 [02:02<08:37, 3.13it/s, loss=0.678]" ] }, { @@ -6772,7 +6772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 379/2000 [02:03<08:36, 3.14it/s, loss=0.617]" + "training until 2000: 19%|█▉ | 379/2000 [02:02<08:37, 3.13it/s, loss=0.651]" ] }, { @@ -6780,7 +6780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 380/2000 [02:04<08:40, 3.11it/s, loss=0.617]" + "training until 2000: 19%|█▉ | 380/2000 [02:02<08:33, 3.15it/s, loss=0.651]" ] }, { @@ -6788,7 +6788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 380/2000 [02:04<08:40, 3.11it/s, loss=0.554]" + "training until 2000: 19%|█▉ | 380/2000 [02:02<08:33, 3.15it/s, loss=0.717]" ] }, { @@ -6796,7 +6796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 381/2000 [02:04<08:35, 3.14it/s, loss=0.554]" + "training until 2000: 19%|█▉ | 381/2000 [02:02<08:35, 3.14it/s, loss=0.717]" ] }, { @@ -6804,7 +6804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 381/2000 [02:04<08:35, 3.14it/s, loss=0.606]" + "training until 2000: 19%|█▉ | 381/2000 [02:02<08:35, 3.14it/s, loss=0.753]" ] }, { @@ -6812,7 +6812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 382/2000 [02:04<08:33, 3.15it/s, loss=0.606]" + "training until 2000: 19%|█▉ | 382/2000 [02:03<08:32, 3.16it/s, loss=0.753]" ] }, { @@ -6820,7 +6820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 382/2000 [02:04<08:33, 3.15it/s, loss=0.526]" + "training until 2000: 19%|█▉ | 382/2000 [02:03<08:32, 3.16it/s, loss=0.718]" ] }, { @@ -6828,7 +6828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 383/2000 [02:05<08:34, 3.14it/s, loss=0.526]" + "training until 2000: 19%|█▉ | 383/2000 [02:03<08:31, 3.16it/s, loss=0.718]" ] }, { @@ -6836,7 +6836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 383/2000 [02:05<08:34, 3.14it/s, loss=0.583]" + "training until 2000: 19%|█▉ | 383/2000 [02:03<08:31, 3.16it/s, loss=0.68] " ] }, { @@ -6844,7 +6844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 384/2000 [02:05<08:30, 3.16it/s, loss=0.583]" + "training until 2000: 19%|█▉ | 384/2000 [02:03<08:29, 3.17it/s, loss=0.68]" ] }, { @@ -6852,7 +6852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 384/2000 [02:05<08:30, 3.16it/s, loss=0.584]" + "training until 2000: 19%|█▉ | 384/2000 [02:03<08:29, 3.17it/s, loss=0.685]" ] }, { @@ -6860,7 +6860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 385/2000 [02:05<08:33, 3.15it/s, loss=0.584]" + "training until 2000: 19%|█▉ | 385/2000 [02:04<08:31, 3.16it/s, loss=0.685]" ] }, { @@ -6868,7 +6868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 385/2000 [02:05<08:33, 3.15it/s, loss=0.546]" + "training until 2000: 19%|█▉ | 385/2000 [02:04<08:31, 3.16it/s, loss=0.72] " ] }, { @@ -6876,7 +6876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 386/2000 [02:06<08:41, 3.09it/s, loss=0.546]" + "training until 2000: 19%|█▉ | 386/2000 [02:04<08:28, 3.17it/s, loss=0.72]" ] }, { @@ -6884,7 +6884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 386/2000 [02:06<08:41, 3.09it/s, loss=0.65] " + "training until 2000: 19%|█▉ | 386/2000 [02:04<08:28, 3.17it/s, loss=0.722]" ] }, { @@ -6892,7 +6892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 387/2000 [02:06<08:46, 3.06it/s, loss=0.65]" + "training until 2000: 19%|█▉ | 387/2000 [02:04<08:30, 3.16it/s, loss=0.722]" ] }, { @@ -6900,7 +6900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 387/2000 [02:06<08:46, 3.06it/s, loss=0.541]" + "training until 2000: 19%|█▉ | 387/2000 [02:04<08:30, 3.16it/s, loss=0.697]" ] }, { @@ -6908,7 +6908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 388/2000 [02:06<08:53, 3.02it/s, loss=0.541]" + "training until 2000: 19%|█▉ | 388/2000 [02:04<08:29, 3.16it/s, loss=0.697]" ] }, { @@ -6916,7 +6916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 388/2000 [02:06<08:53, 3.02it/s, loss=0.576]" + "training until 2000: 19%|█▉ | 388/2000 [02:05<08:29, 3.16it/s, loss=0.694]" ] }, { @@ -6924,7 +6924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 389/2000 [02:07<08:48, 3.05it/s, loss=0.576]" + "training until 2000: 19%|█▉ | 389/2000 [02:05<08:22, 3.21it/s, loss=0.694]" ] }, { @@ -6932,7 +6932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 19%|█▉ | 389/2000 [02:07<08:48, 3.05it/s, loss=0.589]" + "training until 2000: 19%|█▉ | 389/2000 [02:05<08:22, 3.21it/s, loss=0.723]" ] }, { @@ -6940,7 +6940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 390/2000 [02:07<08:38, 3.10it/s, loss=0.589]" + "training until 2000: 20%|█▉ | 390/2000 [02:05<08:23, 3.19it/s, loss=0.723]" ] }, { @@ -6948,7 +6948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 390/2000 [02:07<08:38, 3.10it/s, loss=0.536]" + "training until 2000: 20%|█▉ | 390/2000 [02:05<08:23, 3.19it/s, loss=0.683]" ] }, { @@ -6956,7 +6956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 391/2000 [02:07<08:34, 3.13it/s, loss=0.536]" + "training until 2000: 20%|█▉ | 391/2000 [02:05<08:20, 3.22it/s, loss=0.683]" ] }, { @@ -6964,7 +6964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 391/2000 [02:07<08:34, 3.13it/s, loss=0.551]" + "training until 2000: 20%|█▉ | 391/2000 [02:05<08:20, 3.22it/s, loss=0.625]" ] }, { @@ -6972,7 +6972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 392/2000 [02:08<08:34, 3.13it/s, loss=0.551]" + "training until 2000: 20%|█▉ | 392/2000 [02:06<08:21, 3.21it/s, loss=0.625]" ] }, { @@ -6980,7 +6980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 392/2000 [02:08<08:34, 3.13it/s, loss=0.582]" + "training until 2000: 20%|█▉ | 392/2000 [02:06<08:21, 3.21it/s, loss=0.685]" ] }, { @@ -6988,7 +6988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 393/2000 [02:08<08:31, 3.14it/s, loss=0.582]" + "training until 2000: 20%|█▉ | 393/2000 [02:06<08:24, 3.19it/s, loss=0.685]" ] }, { @@ -6996,7 +6996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 393/2000 [02:08<08:31, 3.14it/s, loss=0.565]" + "training until 2000: 20%|█▉ | 393/2000 [02:06<08:24, 3.19it/s, loss=0.684]" ] }, { @@ -7004,7 +7004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 394/2000 [02:08<08:32, 3.14it/s, loss=0.565]" + "training until 2000: 20%|█▉ | 394/2000 [02:06<08:25, 3.18it/s, loss=0.684]" ] }, { @@ -7012,7 +7012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 394/2000 [02:08<08:32, 3.14it/s, loss=0.627]" + "training until 2000: 20%|█▉ | 394/2000 [02:06<08:25, 3.18it/s, loss=0.708]" ] }, { @@ -7020,7 +7020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 395/2000 [02:08<08:30, 3.14it/s, loss=0.627]" + "training until 2000: 20%|█▉ | 395/2000 [02:07<08:22, 3.20it/s, loss=0.708]" ] }, { @@ -7028,7 +7028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 395/2000 [02:08<08:30, 3.14it/s, loss=0.645]" + "training until 2000: 20%|█▉ | 395/2000 [02:07<08:22, 3.20it/s, loss=0.673]" ] }, { @@ -7036,7 +7036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 396/2000 [02:09<08:34, 3.12it/s, loss=0.645]" + "training until 2000: 20%|█▉ | 396/2000 [02:07<08:20, 3.21it/s, loss=0.673]" ] }, { @@ -7044,7 +7044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 396/2000 [02:09<08:34, 3.12it/s, loss=0.624]" + "training until 2000: 20%|█▉ | 396/2000 [02:07<08:20, 3.21it/s, loss=0.765]" ] }, { @@ -7052,7 +7052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 397/2000 [02:09<08:37, 3.10it/s, loss=0.624]" + "training until 2000: 20%|█▉ | 397/2000 [02:07<08:24, 3.18it/s, loss=0.765]" ] }, { @@ -7060,7 +7060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 397/2000 [02:09<08:37, 3.10it/s, loss=0.549]" + "training until 2000: 20%|█▉ | 397/2000 [02:07<08:24, 3.18it/s, loss=0.702]" ] }, { @@ -7068,7 +7068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 398/2000 [02:09<08:38, 3.09it/s, loss=0.549]" + "training until 2000: 20%|█▉ | 398/2000 [02:08<08:23, 3.18it/s, loss=0.702]" ] }, { @@ -7076,7 +7076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 398/2000 [02:09<08:38, 3.09it/s, loss=0.603]" + "training until 2000: 20%|█▉ | 398/2000 [02:08<08:23, 3.18it/s, loss=0.664]" ] }, { @@ -7084,7 +7084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 399/2000 [02:10<08:41, 3.07it/s, loss=0.603]" + "training until 2000: 20%|█▉ | 399/2000 [02:08<08:16, 3.23it/s, loss=0.664]" ] }, { @@ -7092,7 +7092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|█▉ | 399/2000 [02:10<08:41, 3.07it/s, loss=0.64] " + "training until 2000: 20%|█▉ | 399/2000 [02:08<08:16, 3.23it/s, loss=0.6] " ] }, { @@ -7100,7 +7100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 400/2000 [02:10<08:41, 3.07it/s, loss=0.64]" + "training until 2000: 20%|██ | 400/2000 [02:08<08:24, 3.17it/s, loss=0.6]" ] }, { @@ -7108,7 +7108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 400/2000 [02:10<08:41, 3.07it/s, loss=0.6] " + "training until 2000: 20%|██ | 400/2000 [02:08<08:24, 3.17it/s, loss=0.632]" ] }, { @@ -7116,7 +7116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 401/2000 [02:10<08:38, 3.08it/s, loss=0.6]" + "training until 2000: 20%|██ | 401/2000 [02:09<08:20, 3.19it/s, loss=0.632]" ] }, { @@ -7124,7 +7124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 401/2000 [02:10<08:38, 3.08it/s, loss=0.633]" + "training until 2000: 20%|██ | 401/2000 [02:09<08:20, 3.19it/s, loss=0.697]" ] }, { @@ -7132,7 +7132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 402/2000 [02:11<08:36, 3.09it/s, loss=0.633]" + "training until 2000: 20%|██ | 402/2000 [02:09<08:26, 3.15it/s, loss=0.697]" ] }, { @@ -7140,7 +7140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 402/2000 [02:11<08:36, 3.09it/s, loss=0.592]" + "training until 2000: 20%|██ | 402/2000 [02:09<08:26, 3.15it/s, loss=0.682]" ] }, { @@ -7148,7 +7148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 403/2000 [02:11<08:31, 3.12it/s, loss=0.592]" + "training until 2000: 20%|██ | 403/2000 [02:09<10:12, 2.61it/s, loss=0.682]" ] }, { @@ -7156,7 +7156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 403/2000 [02:11<08:31, 3.12it/s, loss=0.597]" + "training until 2000: 20%|██ | 403/2000 [02:09<10:12, 2.61it/s, loss=0.675]" ] }, { @@ -7164,7 +7164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 404/2000 [02:11<08:30, 3.13it/s, loss=0.597]" + "training until 2000: 20%|██ | 404/2000 [02:10<09:42, 2.74it/s, loss=0.675]" ] }, { @@ -7172,7 +7172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 404/2000 [02:11<08:30, 3.13it/s, loss=0.529]" + "training until 2000: 20%|██ | 404/2000 [02:10<09:42, 2.74it/s, loss=0.73] " ] }, { @@ -7180,7 +7180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 405/2000 [02:12<10:14, 2.59it/s, loss=0.529]" + "training until 2000: 20%|██ | 405/2000 [02:10<09:18, 2.85it/s, loss=0.73]" ] }, { @@ -7188,7 +7188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 405/2000 [02:12<10:14, 2.59it/s, loss=0.609]" + "training until 2000: 20%|██ | 405/2000 [02:10<09:18, 2.85it/s, loss=0.664]" ] }, { @@ -7196,7 +7196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 406/2000 [02:12<09:44, 2.73it/s, loss=0.609]" + "training until 2000: 20%|██ | 406/2000 [02:10<09:01, 2.94it/s, loss=0.664]" ] }, { @@ -7204,7 +7204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 406/2000 [02:12<09:44, 2.73it/s, loss=0.632]" + "training until 2000: 20%|██ | 406/2000 [02:10<09:01, 2.94it/s, loss=0.662]" ] }, { @@ -7212,7 +7212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 407/2000 [02:13<09:22, 2.83it/s, loss=0.632]" + "training until 2000: 20%|██ | 407/2000 [02:11<08:51, 2.99it/s, loss=0.662]" ] }, { @@ -7220,7 +7220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 407/2000 [02:13<09:22, 2.83it/s, loss=0.571]" + "training until 2000: 20%|██ | 407/2000 [02:11<08:51, 2.99it/s, loss=0.635]" ] }, { @@ -7228,7 +7228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 408/2000 [02:13<09:05, 2.92it/s, loss=0.571]" + "training until 2000: 20%|██ | 408/2000 [02:11<08:51, 3.00it/s, loss=0.635]" ] }, { @@ -7236,7 +7236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 408/2000 [02:13<09:05, 2.92it/s, loss=0.712]" + "training until 2000: 20%|██ | 408/2000 [02:11<08:51, 3.00it/s, loss=0.692]" ] }, { @@ -7244,7 +7244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 409/2000 [02:13<08:50, 3.00it/s, loss=0.712]" + "training until 2000: 20%|██ | 409/2000 [02:11<08:49, 3.00it/s, loss=0.692]" ] }, { @@ -7252,7 +7252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 409/2000 [02:13<08:50, 3.00it/s, loss=0.564]" + "training until 2000: 20%|██ | 409/2000 [02:11<08:49, 3.00it/s, loss=0.726]" ] }, { @@ -7260,7 +7260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 410/2000 [02:14<08:41, 3.05it/s, loss=0.564]" + "training until 2000: 20%|██ | 410/2000 [02:12<08:45, 3.03it/s, loss=0.726]" ] }, { @@ -7268,7 +7268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 20%|██ | 410/2000 [02:14<08:41, 3.05it/s, loss=0.599]" + "training until 2000: 20%|██ | 410/2000 [02:12<08:45, 3.03it/s, loss=0.667]" ] }, { @@ -7276,7 +7276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 411/2000 [02:14<08:46, 3.02it/s, loss=0.599]" + "training until 2000: 21%|██ | 411/2000 [02:12<08:34, 3.09it/s, loss=0.667]" ] }, { @@ -7284,7 +7284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 411/2000 [02:14<08:46, 3.02it/s, loss=0.609]" + "training until 2000: 21%|██ | 411/2000 [02:12<08:34, 3.09it/s, loss=0.689]" ] }, { @@ -7292,7 +7292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 412/2000 [02:14<08:41, 3.04it/s, loss=0.609]" + "training until 2000: 21%|██ | 412/2000 [02:12<08:32, 3.10it/s, loss=0.689]" ] }, { @@ -7300,7 +7300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 412/2000 [02:14<08:41, 3.04it/s, loss=0.631]" + "training until 2000: 21%|██ | 412/2000 [02:12<08:32, 3.10it/s, loss=0.715]" ] }, { @@ -7308,7 +7308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 413/2000 [02:15<08:39, 3.06it/s, loss=0.631]" + "training until 2000: 21%|██ | 413/2000 [02:13<08:33, 3.09it/s, loss=0.715]" ] }, { @@ -7316,7 +7316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 413/2000 [02:15<08:39, 3.06it/s, loss=0.551]" + "training until 2000: 21%|██ | 413/2000 [02:13<08:33, 3.09it/s, loss=0.677]" ] }, { @@ -7324,7 +7324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 414/2000 [02:15<08:41, 3.04it/s, loss=0.551]" + "training until 2000: 21%|██ | 414/2000 [02:13<08:32, 3.10it/s, loss=0.677]" ] }, { @@ -7332,7 +7332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 414/2000 [02:15<08:41, 3.04it/s, loss=0.546]" + "training until 2000: 21%|██ | 414/2000 [02:13<08:32, 3.10it/s, loss=0.677]" ] }, { @@ -7340,7 +7340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 415/2000 [02:15<08:38, 3.06it/s, loss=0.546]" + "training until 2000: 21%|██ | 415/2000 [02:13<08:32, 3.09it/s, loss=0.677]" ] }, { @@ -7348,7 +7348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 415/2000 [02:15<08:38, 3.06it/s, loss=0.615]" + "training until 2000: 21%|██ | 415/2000 [02:13<08:32, 3.09it/s, loss=0.675]" ] }, { @@ -7356,7 +7356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 416/2000 [02:15<08:31, 3.10it/s, loss=0.615]" + "training until 2000: 21%|██ | 416/2000 [02:14<08:27, 3.12it/s, loss=0.675]" ] }, { @@ -7364,7 +7364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 416/2000 [02:15<08:31, 3.10it/s, loss=0.786]" + "training until 2000: 21%|██ | 416/2000 [02:14<08:27, 3.12it/s, loss=0.665]" ] }, { @@ -7372,7 +7372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 417/2000 [02:16<08:26, 3.12it/s, loss=0.786]" + "training until 2000: 21%|██ | 417/2000 [02:14<08:28, 3.11it/s, loss=0.665]" ] }, { @@ -7380,7 +7380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 417/2000 [02:16<08:26, 3.12it/s, loss=0.54] " + "training until 2000: 21%|██ | 417/2000 [02:14<08:28, 3.11it/s, loss=0.699]" ] }, { @@ -7388,7 +7388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 418/2000 [02:16<08:34, 3.07it/s, loss=0.54]" + "training until 2000: 21%|██ | 418/2000 [02:14<08:29, 3.11it/s, loss=0.699]" ] }, { @@ -7396,7 +7396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 418/2000 [02:16<08:34, 3.07it/s, loss=0.63]" + "training until 2000: 21%|██ | 418/2000 [02:14<08:29, 3.11it/s, loss=0.695]" ] }, { @@ -7404,7 +7404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 419/2000 [02:16<08:33, 3.08it/s, loss=0.63]" + "training until 2000: 21%|██ | 419/2000 [02:15<08:25, 3.13it/s, loss=0.695]" ] }, { @@ -7412,7 +7412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 419/2000 [02:16<08:33, 3.08it/s, loss=0.622]" + "training until 2000: 21%|██ | 419/2000 [02:15<08:25, 3.13it/s, loss=0.684]" ] }, { @@ -7420,7 +7420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 420/2000 [02:17<08:34, 3.07it/s, loss=0.622]" + "training until 2000: 21%|██ | 420/2000 [02:15<08:24, 3.13it/s, loss=0.684]" ] }, { @@ -7428,7 +7428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 420/2000 [02:17<08:34, 3.07it/s, loss=0.553]" + "training until 2000: 21%|██ | 420/2000 [02:15<08:24, 3.13it/s, loss=0.657]" ] }, { @@ -7436,7 +7436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 421/2000 [02:17<08:29, 3.10it/s, loss=0.553]" + "training until 2000: 21%|██ | 421/2000 [02:15<08:19, 3.16it/s, loss=0.657]" ] }, { @@ -7444,7 +7444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 421/2000 [02:17<08:29, 3.10it/s, loss=0.558]" + "training until 2000: 21%|██ | 421/2000 [02:15<08:19, 3.16it/s, loss=0.651]" ] }, { @@ -7452,7 +7452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 422/2000 [02:17<08:27, 3.11it/s, loss=0.558]" + "training until 2000: 21%|██ | 422/2000 [02:16<08:21, 3.14it/s, loss=0.651]" ] }, { @@ -7460,7 +7460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 422/2000 [02:17<08:27, 3.11it/s, loss=0.571]" + "training until 2000: 21%|██ | 422/2000 [02:16<08:21, 3.14it/s, loss=0.602]" ] }, { @@ -7468,7 +7468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 423/2000 [02:18<08:25, 3.12it/s, loss=0.571]" + "training until 2000: 21%|██ | 423/2000 [02:16<08:21, 3.15it/s, loss=0.602]" ] }, { @@ -7476,7 +7476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 423/2000 [02:18<08:25, 3.12it/s, loss=0.568]" + "training until 2000: 21%|██ | 423/2000 [02:16<08:21, 3.15it/s, loss=0.637]" ] }, { @@ -7484,7 +7484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 424/2000 [02:18<08:24, 3.13it/s, loss=0.568]" + "training until 2000: 21%|██ | 424/2000 [02:16<08:18, 3.16it/s, loss=0.637]" ] }, { @@ -7492,7 +7492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██ | 424/2000 [02:18<08:24, 3.13it/s, loss=0.643]" + "training until 2000: 21%|██ | 424/2000 [02:16<08:18, 3.16it/s, loss=0.72] " ] }, { @@ -7500,7 +7500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 425/2000 [02:18<08:25, 3.11it/s, loss=0.643]" + "training until 2000: 21%|██▏ | 425/2000 [02:16<08:32, 3.07it/s, loss=0.72]" ] }, { @@ -7508,7 +7508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 425/2000 [02:18<08:25, 3.11it/s, loss=0.628]" + "training until 2000: 21%|██▏ | 425/2000 [02:16<08:32, 3.07it/s, loss=0.729]" ] }, { @@ -7516,7 +7516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 426/2000 [02:19<08:23, 3.12it/s, loss=0.628]" + "training until 2000: 21%|██▏ | 426/2000 [02:17<08:27, 3.10it/s, loss=0.729]" ] }, { @@ -7524,7 +7524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 426/2000 [02:19<08:23, 3.12it/s, loss=0.598]" + "training until 2000: 21%|██▏ | 426/2000 [02:17<08:27, 3.10it/s, loss=0.738]" ] }, { @@ -7532,7 +7532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 427/2000 [02:19<08:21, 3.14it/s, loss=0.598]" + "training until 2000: 21%|██▏ | 427/2000 [02:17<08:23, 3.13it/s, loss=0.738]" ] }, { @@ -7540,7 +7540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 427/2000 [02:19<08:21, 3.14it/s, loss=0.543]" + "training until 2000: 21%|██▏ | 427/2000 [02:17<08:23, 3.13it/s, loss=0.669]" ] }, { @@ -7548,7 +7548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 428/2000 [02:19<08:21, 3.14it/s, loss=0.543]" + "training until 2000: 21%|██▏ | 428/2000 [02:17<08:23, 3.12it/s, loss=0.669]" ] }, { @@ -7556,7 +7556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 428/2000 [02:19<08:21, 3.14it/s, loss=0.537]" + "training until 2000: 21%|██▏ | 428/2000 [02:17<08:23, 3.12it/s, loss=0.699]" ] }, { @@ -7564,7 +7564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 429/2000 [02:20<08:22, 3.13it/s, loss=0.537]" + "training until 2000: 21%|██▏ | 429/2000 [02:18<08:19, 3.14it/s, loss=0.699]" ] }, { @@ -7572,7 +7572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 21%|██▏ | 429/2000 [02:20<08:22, 3.13it/s, loss=0.605]" + "training until 2000: 21%|██▏ | 429/2000 [02:18<08:19, 3.14it/s, loss=0.716]" ] }, { @@ -7580,7 +7580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 430/2000 [02:20<08:19, 3.14it/s, loss=0.605]" + "training until 2000: 22%|██▏ | 430/2000 [02:18<08:21, 3.13it/s, loss=0.716]" ] }, { @@ -7588,7 +7588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 430/2000 [02:20<08:19, 3.14it/s, loss=0.582]" + "training until 2000: 22%|██▏ | 430/2000 [02:18<08:21, 3.13it/s, loss=0.606]" ] }, { @@ -7596,7 +7596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 431/2000 [02:20<08:21, 3.13it/s, loss=0.582]" + "training until 2000: 22%|██▏ | 431/2000 [02:18<08:27, 3.09it/s, loss=0.606]" ] }, { @@ -7604,7 +7604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 431/2000 [02:20<08:21, 3.13it/s, loss=0.55] " + "training until 2000: 22%|██▏ | 431/2000 [02:18<08:27, 3.09it/s, loss=0.679]" ] }, { @@ -7612,7 +7612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 432/2000 [02:21<08:17, 3.15it/s, loss=0.55]" + "training until 2000: 22%|██▏ | 432/2000 [02:19<08:22, 3.12it/s, loss=0.679]" ] }, { @@ -7620,7 +7620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 432/2000 [02:21<08:17, 3.15it/s, loss=0.628]" + "training until 2000: 22%|██▏ | 432/2000 [02:19<08:22, 3.12it/s, loss=0.694]" ] }, { @@ -7628,7 +7628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 433/2000 [02:21<08:22, 3.12it/s, loss=0.628]" + "training until 2000: 22%|██▏ | 433/2000 [02:19<08:15, 3.16it/s, loss=0.694]" ] }, { @@ -7636,7 +7636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 433/2000 [02:21<08:22, 3.12it/s, loss=0.618]" + "training until 2000: 22%|██▏ | 433/2000 [02:19<08:15, 3.16it/s, loss=0.649]" ] }, { @@ -7644,7 +7644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 434/2000 [02:21<08:18, 3.14it/s, loss=0.618]" + "training until 2000: 22%|██▏ | 434/2000 [02:19<08:19, 3.14it/s, loss=0.649]" ] }, { @@ -7652,7 +7652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 434/2000 [02:21<08:18, 3.14it/s, loss=0.602]" + "training until 2000: 22%|██▏ | 434/2000 [02:19<08:19, 3.14it/s, loss=0.667]" ] }, { @@ -7660,7 +7660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 435/2000 [02:22<08:13, 3.17it/s, loss=0.602]" + "training until 2000: 22%|██▏ | 435/2000 [02:20<08:13, 3.17it/s, loss=0.667]" ] }, { @@ -7668,7 +7668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 435/2000 [02:22<08:13, 3.17it/s, loss=0.561]" + "training until 2000: 22%|██▏ | 435/2000 [02:20<08:13, 3.17it/s, loss=0.631]" ] }, { @@ -7676,7 +7676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 436/2000 [02:22<08:18, 3.14it/s, loss=0.561]" + "training until 2000: 22%|██▏ | 436/2000 [02:20<08:11, 3.18it/s, loss=0.631]" ] }, { @@ -7684,7 +7684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 436/2000 [02:22<08:18, 3.14it/s, loss=0.612]" + "training until 2000: 22%|██▏ | 436/2000 [02:20<08:11, 3.18it/s, loss=0.678]" ] }, { @@ -7692,7 +7692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 437/2000 [02:22<08:22, 3.11it/s, loss=0.612]" + "training until 2000: 22%|██▏ | 437/2000 [02:20<08:13, 3.17it/s, loss=0.678]" ] }, { @@ -7700,7 +7700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 437/2000 [02:22<08:22, 3.11it/s, loss=0.602]" + "training until 2000: 22%|██▏ | 437/2000 [02:20<08:13, 3.17it/s, loss=0.654]" ] }, { @@ -7708,7 +7708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 438/2000 [02:23<08:19, 3.13it/s, loss=0.602]" + "training until 2000: 22%|██▏ | 438/2000 [02:21<08:14, 3.16it/s, loss=0.654]" ] }, { @@ -7716,7 +7716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 438/2000 [02:23<08:19, 3.13it/s, loss=0.554]" + "training until 2000: 22%|██▏ | 438/2000 [02:21<08:14, 3.16it/s, loss=0.673]" ] }, { @@ -7724,7 +7724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 439/2000 [02:23<08:24, 3.10it/s, loss=0.554]" + "training until 2000: 22%|██▏ | 439/2000 [02:21<08:09, 3.19it/s, loss=0.673]" ] }, { @@ -7732,7 +7732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 439/2000 [02:23<08:24, 3.10it/s, loss=0.585]" + "training until 2000: 22%|██▏ | 439/2000 [02:21<08:09, 3.19it/s, loss=0.69] " ] }, { @@ -7740,7 +7740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 440/2000 [02:23<08:19, 3.12it/s, loss=0.585]" + "training until 2000: 22%|██▏ | 440/2000 [02:21<08:15, 3.15it/s, loss=0.69]" ] }, { @@ -7748,7 +7748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 440/2000 [02:23<08:19, 3.12it/s, loss=0.544]" + "training until 2000: 22%|██▏ | 440/2000 [02:21<08:15, 3.15it/s, loss=0.659]" ] }, { @@ -7756,7 +7756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 441/2000 [02:23<08:15, 3.15it/s, loss=0.544]" + "training until 2000: 22%|██▏ | 441/2000 [02:22<08:14, 3.15it/s, loss=0.659]" ] }, { @@ -7764,7 +7764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 441/2000 [02:23<08:15, 3.15it/s, loss=0.552]" + "training until 2000: 22%|██▏ | 441/2000 [02:22<08:14, 3.15it/s, loss=0.669]" ] }, { @@ -7772,7 +7772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 442/2000 [02:24<08:14, 3.15it/s, loss=0.552]" + "training until 2000: 22%|██▏ | 442/2000 [02:22<08:13, 3.16it/s, loss=0.669]" ] }, { @@ -7780,7 +7780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 442/2000 [02:24<08:14, 3.15it/s, loss=0.601]" + "training until 2000: 22%|██▏ | 442/2000 [02:22<08:13, 3.16it/s, loss=0.672]" ] }, { @@ -7788,7 +7788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 443/2000 [02:24<08:10, 3.17it/s, loss=0.601]" + "training until 2000: 22%|██▏ | 443/2000 [02:22<08:11, 3.17it/s, loss=0.672]" ] }, { @@ -7796,7 +7796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 443/2000 [02:24<08:10, 3.17it/s, loss=0.68] " + "training until 2000: 22%|██▏ | 443/2000 [02:22<08:11, 3.17it/s, loss=0.649]" ] }, { @@ -7804,7 +7804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 444/2000 [02:24<08:13, 3.15it/s, loss=0.68]" + "training until 2000: 22%|██▏ | 444/2000 [02:23<08:26, 3.07it/s, loss=0.649]" ] }, { @@ -7812,7 +7812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 444/2000 [02:24<08:13, 3.15it/s, loss=0.582]" + "training until 2000: 22%|██▏ | 444/2000 [02:23<08:26, 3.07it/s, loss=0.664]" ] }, { @@ -7820,7 +7820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 445/2000 [02:25<08:12, 3.16it/s, loss=0.582]" + "training until 2000: 22%|██▏ | 445/2000 [02:23<08:26, 3.07it/s, loss=0.664]" ] }, { @@ -7828,7 +7828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 445/2000 [02:25<08:12, 3.16it/s, loss=0.638]" + "training until 2000: 22%|██▏ | 445/2000 [02:23<08:26, 3.07it/s, loss=0.668]" ] }, { @@ -7836,7 +7836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 446/2000 [02:25<08:11, 3.16it/s, loss=0.638]" + "training until 2000: 22%|██▏ | 446/2000 [02:23<08:23, 3.09it/s, loss=0.668]" ] }, { @@ -7844,7 +7844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 446/2000 [02:25<08:11, 3.16it/s, loss=0.559]" + "training until 2000: 22%|██▏ | 446/2000 [02:23<08:23, 3.09it/s, loss=0.723]" ] }, { @@ -7852,7 +7852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 447/2000 [02:25<08:14, 3.14it/s, loss=0.559]" + "training until 2000: 22%|██▏ | 447/2000 [02:23<08:17, 3.12it/s, loss=0.723]" ] }, { @@ -7860,7 +7860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 447/2000 [02:25<08:14, 3.14it/s, loss=0.54] " + "training until 2000: 22%|██▏ | 447/2000 [02:23<08:17, 3.12it/s, loss=0.724]" ] }, { @@ -7868,7 +7868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 448/2000 [02:26<08:13, 3.14it/s, loss=0.54]" + "training until 2000: 22%|██▏ | 448/2000 [02:24<08:15, 3.13it/s, loss=0.724]" ] }, { @@ -7876,7 +7876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 448/2000 [02:26<08:13, 3.14it/s, loss=0.626]" + "training until 2000: 22%|██▏ | 448/2000 [02:24<08:15, 3.13it/s, loss=0.6] " ] }, { @@ -7884,7 +7884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 449/2000 [02:26<08:12, 3.15it/s, loss=0.626]" + "training until 2000: 22%|██▏ | 449/2000 [02:24<08:16, 3.13it/s, loss=0.6]" ] }, { @@ -7892,7 +7892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▏ | 449/2000 [02:26<08:12, 3.15it/s, loss=0.571]" + "training until 2000: 22%|██▏ | 449/2000 [02:24<08:16, 3.13it/s, loss=0.656]" ] }, { @@ -7900,7 +7900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▎ | 450/2000 [02:26<08:19, 3.10it/s, loss=0.571]" + "training until 2000: 22%|██▎ | 450/2000 [02:24<08:16, 3.12it/s, loss=0.656]" ] }, { @@ -7908,7 +7908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 22%|██▎ | 450/2000 [02:26<08:19, 3.10it/s, loss=0.585]" + "training until 2000: 22%|██▎ | 450/2000 [02:24<08:16, 3.12it/s, loss=0.637]" ] }, { @@ -7916,7 +7916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 451/2000 [02:27<08:17, 3.11it/s, loss=0.585]" + "training until 2000: 23%|██▎ | 451/2000 [02:25<08:12, 3.14it/s, loss=0.637]" ] }, { @@ -7924,7 +7924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 451/2000 [02:27<08:17, 3.11it/s, loss=0.572]" + "training until 2000: 23%|██▎ | 451/2000 [02:25<08:12, 3.14it/s, loss=0.644]" ] }, { @@ -7932,7 +7932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 452/2000 [02:27<08:20, 3.09it/s, loss=0.572]" + "training until 2000: 23%|██▎ | 452/2000 [02:25<08:17, 3.11it/s, loss=0.644]" ] }, { @@ -7940,7 +7940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 452/2000 [02:27<08:20, 3.09it/s, loss=0.566]" + "training until 2000: 23%|██▎ | 452/2000 [02:25<08:17, 3.11it/s, loss=0.632]" ] }, { @@ -7948,7 +7948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 453/2000 [02:27<08:18, 3.10it/s, loss=0.566]" + "training until 2000: 23%|██▎ | 453/2000 [02:25<08:29, 3.04it/s, loss=0.632]" ] }, { @@ -7956,7 +7956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 453/2000 [02:27<08:18, 3.10it/s, loss=0.644]" + "training until 2000: 23%|██▎ | 453/2000 [02:25<08:29, 3.04it/s, loss=0.678]" ] }, { @@ -7964,7 +7964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 454/2000 [02:28<08:14, 3.12it/s, loss=0.644]" + "training until 2000: 23%|██▎ | 454/2000 [02:26<08:29, 3.04it/s, loss=0.678]" ] }, { @@ -7972,7 +7972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 454/2000 [02:28<08:14, 3.12it/s, loss=0.583]" + "training until 2000: 23%|██▎ | 454/2000 [02:26<08:29, 3.04it/s, loss=0.671]" ] }, { @@ -7980,7 +7980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 455/2000 [02:28<08:12, 3.14it/s, loss=0.583]" + "training until 2000: 23%|██▎ | 455/2000 [02:26<08:23, 3.07it/s, loss=0.671]" ] }, { @@ -7988,7 +7988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 455/2000 [02:28<08:12, 3.14it/s, loss=0.644]" + "training until 2000: 23%|██▎ | 455/2000 [02:26<08:23, 3.07it/s, loss=0.66] " ] }, { @@ -7996,7 +7996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 456/2000 [02:28<08:10, 3.15it/s, loss=0.644]" + "training until 2000: 23%|██▎ | 456/2000 [02:26<08:19, 3.09it/s, loss=0.66]" ] }, { @@ -8004,7 +8004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 456/2000 [02:28<08:10, 3.15it/s, loss=0.544]" + "training until 2000: 23%|██▎ | 456/2000 [02:26<08:19, 3.09it/s, loss=0.665]" ] }, { @@ -8012,7 +8012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 457/2000 [02:29<08:10, 3.14it/s, loss=0.544]" + "training until 2000: 23%|██▎ | 457/2000 [02:27<08:22, 3.07it/s, loss=0.665]" ] }, { @@ -8020,7 +8020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 457/2000 [02:29<08:10, 3.14it/s, loss=0.585]" + "training until 2000: 23%|██▎ | 457/2000 [02:27<08:22, 3.07it/s, loss=0.644]" ] }, { @@ -8028,7 +8028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 458/2000 [02:29<08:09, 3.15it/s, loss=0.585]" + "training until 2000: 23%|██▎ | 458/2000 [02:27<08:14, 3.12it/s, loss=0.644]" ] }, { @@ -8036,7 +8036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 458/2000 [02:29<08:09, 3.15it/s, loss=0.568]" + "training until 2000: 23%|██▎ | 458/2000 [02:27<08:14, 3.12it/s, loss=0.644]" ] }, { @@ -8044,7 +8044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 459/2000 [02:29<08:08, 3.15it/s, loss=0.568]" + "training until 2000: 23%|██▎ | 459/2000 [02:27<08:11, 3.13it/s, loss=0.644]" ] }, { @@ -8052,7 +8052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 459/2000 [02:29<08:08, 3.15it/s, loss=0.641]" + "training until 2000: 23%|██▎ | 459/2000 [02:27<08:11, 3.13it/s, loss=0.666]" ] }, { @@ -8060,7 +8060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 460/2000 [02:30<08:11, 3.14it/s, loss=0.641]" + "training until 2000: 23%|██▎ | 460/2000 [02:28<08:10, 3.14it/s, loss=0.666]" ] }, { @@ -8068,7 +8068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 460/2000 [02:30<08:11, 3.14it/s, loss=0.572]" + "training until 2000: 23%|██▎ | 460/2000 [02:28<08:10, 3.14it/s, loss=0.67] " ] }, { @@ -8076,7 +8076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 461/2000 [02:30<08:09, 3.15it/s, loss=0.572]" + "training until 2000: 23%|██▎ | 461/2000 [02:28<08:10, 3.14it/s, loss=0.67]" ] }, { @@ -8084,7 +8084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 461/2000 [02:30<08:09, 3.15it/s, loss=0.538]" + "training until 2000: 23%|██▎ | 461/2000 [02:28<08:10, 3.14it/s, loss=0.734]" ] }, { @@ -8092,7 +8092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 462/2000 [02:30<08:16, 3.10it/s, loss=0.538]" + "training until 2000: 23%|██▎ | 462/2000 [02:28<08:05, 3.17it/s, loss=0.734]" ] }, { @@ -8100,7 +8100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 462/2000 [02:30<08:16, 3.10it/s, loss=0.543]" + "training until 2000: 23%|██▎ | 462/2000 [02:28<08:05, 3.17it/s, loss=0.682]" ] }, { @@ -8108,7 +8108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 463/2000 [02:30<08:08, 3.15it/s, loss=0.543]" + "training until 2000: 23%|██▎ | 463/2000 [02:29<08:00, 3.20it/s, loss=0.682]" ] }, { @@ -8116,7 +8116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 463/2000 [02:30<08:08, 3.15it/s, loss=0.571]" + "training until 2000: 23%|██▎ | 463/2000 [02:29<08:00, 3.20it/s, loss=0.684]" ] }, { @@ -8124,7 +8124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 464/2000 [02:31<08:09, 3.13it/s, loss=0.571]" + "training until 2000: 23%|██▎ | 464/2000 [02:29<08:01, 3.19it/s, loss=0.684]" ] }, { @@ -8132,7 +8132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 464/2000 [02:31<08:09, 3.13it/s, loss=0.558]" + "training until 2000: 23%|██▎ | 464/2000 [02:29<08:01, 3.19it/s, loss=0.654]" ] }, { @@ -8140,7 +8140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 465/2000 [02:31<08:09, 3.14it/s, loss=0.558]" + "training until 2000: 23%|██▎ | 465/2000 [02:29<07:58, 3.21it/s, loss=0.654]" ] }, { @@ -8148,7 +8148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 465/2000 [02:31<08:09, 3.14it/s, loss=0.589]" + "training until 2000: 23%|██▎ | 465/2000 [02:29<07:58, 3.21it/s, loss=0.648]" ] }, { @@ -8156,7 +8156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 466/2000 [02:31<08:04, 3.16it/s, loss=0.589]" + "training until 2000: 23%|██▎ | 466/2000 [02:30<08:03, 3.17it/s, loss=0.648]" ] }, { @@ -8164,7 +8164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 466/2000 [02:31<08:04, 3.16it/s, loss=0.608]" + "training until 2000: 23%|██▎ | 466/2000 [02:30<08:03, 3.17it/s, loss=0.617]" ] }, { @@ -8172,7 +8172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 467/2000 [02:32<08:07, 3.14it/s, loss=0.608]" + "training until 2000: 23%|██▎ | 467/2000 [02:30<08:04, 3.16it/s, loss=0.617]" ] }, { @@ -8180,7 +8180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 467/2000 [02:32<08:07, 3.14it/s, loss=0.536]" + "training until 2000: 23%|██▎ | 467/2000 [02:30<08:04, 3.16it/s, loss=0.696]" ] }, { @@ -8188,7 +8188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 468/2000 [02:32<10:02, 2.54it/s, loss=0.536]" + "training until 2000: 23%|██▎ | 468/2000 [02:30<09:57, 2.57it/s, loss=0.696]" ] }, { @@ -8196,7 +8196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 468/2000 [02:32<10:02, 2.54it/s, loss=0.738]" + "training until 2000: 23%|██▎ | 468/2000 [02:30<09:57, 2.57it/s, loss=0.704]" ] }, { @@ -8204,7 +8204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 469/2000 [02:33<09:34, 2.67it/s, loss=0.738]" + "training until 2000: 23%|██▎ | 469/2000 [02:31<09:27, 2.70it/s, loss=0.704]" ] }, { @@ -8212,7 +8212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 23%|██▎ | 469/2000 [02:33<09:34, 2.67it/s, loss=0.513]" + "training until 2000: 23%|██▎ | 469/2000 [02:31<09:27, 2.70it/s, loss=0.645]" ] }, { @@ -8220,7 +8220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 470/2000 [02:33<09:12, 2.77it/s, loss=0.513]" + "training until 2000: 24%|██▎ | 470/2000 [02:31<09:03, 2.81it/s, loss=0.645]" ] }, { @@ -8228,7 +8228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 470/2000 [02:33<09:12, 2.77it/s, loss=0.516]" + "training until 2000: 24%|██▎ | 470/2000 [02:31<09:03, 2.81it/s, loss=0.64] " ] }, { @@ -8236,7 +8236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 471/2000 [02:33<08:55, 2.86it/s, loss=0.516]" + "training until 2000: 24%|██▎ | 471/2000 [02:31<08:48, 2.89it/s, loss=0.64]" ] }, { @@ -8244,7 +8244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 471/2000 [02:33<08:55, 2.86it/s, loss=0.531]" + "training until 2000: 24%|██▎ | 471/2000 [02:31<08:48, 2.89it/s, loss=0.662]" ] }, { @@ -8252,7 +8252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 472/2000 [02:34<08:39, 2.94it/s, loss=0.531]" + "training until 2000: 24%|██▎ | 472/2000 [02:32<08:36, 2.96it/s, loss=0.662]" ] }, { @@ -8260,7 +8260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 472/2000 [02:34<08:39, 2.94it/s, loss=0.578]" + "training until 2000: 24%|██▎ | 472/2000 [02:32<08:36, 2.96it/s, loss=0.695]" ] }, { @@ -8268,7 +8268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 473/2000 [02:34<08:30, 2.99it/s, loss=0.578]" + "training until 2000: 24%|██▎ | 473/2000 [02:32<08:23, 3.03it/s, loss=0.695]" ] }, { @@ -8276,7 +8276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 473/2000 [02:34<08:30, 2.99it/s, loss=0.603]" + "training until 2000: 24%|██▎ | 473/2000 [02:32<08:23, 3.03it/s, loss=0.739]" ] }, { @@ -8284,7 +8284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 474/2000 [02:34<08:33, 2.97it/s, loss=0.603]" + "training until 2000: 24%|██▎ | 474/2000 [02:32<08:14, 3.09it/s, loss=0.739]" ] }, { @@ -8292,7 +8292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▎ | 474/2000 [02:34<08:33, 2.97it/s, loss=0.566]" + "training until 2000: 24%|██▎ | 474/2000 [02:32<08:14, 3.09it/s, loss=0.65] " ] }, { @@ -8300,7 +8300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 475/2000 [02:35<08:25, 3.02it/s, loss=0.566]" + "training until 2000: 24%|██▍ | 475/2000 [02:33<08:12, 3.10it/s, loss=0.65]" ] }, { @@ -8308,7 +8308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 475/2000 [02:35<08:25, 3.02it/s, loss=0.551]" + "training until 2000: 24%|██▍ | 475/2000 [02:33<08:12, 3.10it/s, loss=0.648]" ] }, { @@ -8316,7 +8316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 476/2000 [02:35<08:18, 3.06it/s, loss=0.551]" + "training until 2000: 24%|██▍ | 476/2000 [02:33<08:07, 3.13it/s, loss=0.648]" ] }, { @@ -8324,7 +8324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 476/2000 [02:35<08:18, 3.06it/s, loss=0.574]" + "training until 2000: 24%|██▍ | 476/2000 [02:33<08:07, 3.13it/s, loss=0.684]" ] }, { @@ -8332,7 +8332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 477/2000 [02:35<08:15, 3.07it/s, loss=0.574]" + "training until 2000: 24%|██▍ | 477/2000 [02:33<08:01, 3.16it/s, loss=0.684]" ] }, { @@ -8340,7 +8340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 477/2000 [02:35<08:15, 3.07it/s, loss=0.561]" + "training until 2000: 24%|██▍ | 477/2000 [02:33<08:01, 3.16it/s, loss=0.627]" ] }, { @@ -8348,7 +8348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 478/2000 [02:36<08:16, 3.07it/s, loss=0.561]" + "training until 2000: 24%|██▍ | 478/2000 [02:34<07:59, 3.17it/s, loss=0.627]" ] }, { @@ -8356,7 +8356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 478/2000 [02:36<08:16, 3.07it/s, loss=0.541]" + "training until 2000: 24%|██▍ | 478/2000 [02:34<07:59, 3.17it/s, loss=0.654]" ] }, { @@ -8364,7 +8364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 479/2000 [02:36<08:11, 3.09it/s, loss=0.541]" + "training until 2000: 24%|██▍ | 479/2000 [02:34<07:56, 3.19it/s, loss=0.654]" ] }, { @@ -8372,7 +8372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 479/2000 [02:36<08:11, 3.09it/s, loss=0.692]" + "training until 2000: 24%|██▍ | 479/2000 [02:34<07:56, 3.19it/s, loss=0.706]" ] }, { @@ -8380,7 +8380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 480/2000 [02:36<08:14, 3.08it/s, loss=0.692]" + "training until 2000: 24%|██▍ | 480/2000 [02:34<07:55, 3.20it/s, loss=0.706]" ] }, { @@ -8388,7 +8388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 480/2000 [02:36<08:14, 3.08it/s, loss=0.708]" + "training until 2000: 24%|██▍ | 480/2000 [02:34<07:55, 3.20it/s, loss=0.721]" ] }, { @@ -8396,7 +8396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 481/2000 [02:37<08:07, 3.12it/s, loss=0.708]" + "training until 2000: 24%|██▍ | 481/2000 [02:35<07:57, 3.18it/s, loss=0.721]" ] }, { @@ -8404,7 +8404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 481/2000 [02:37<08:07, 3.12it/s, loss=0.568]" + "training until 2000: 24%|██▍ | 481/2000 [02:35<07:57, 3.18it/s, loss=0.665]" ] }, { @@ -8412,7 +8412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 482/2000 [02:37<08:04, 3.14it/s, loss=0.568]" + "training until 2000: 24%|██▍ | 482/2000 [02:35<07:59, 3.16it/s, loss=0.665]" ] }, { @@ -8420,7 +8420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 482/2000 [02:37<08:04, 3.14it/s, loss=0.517]" + "training until 2000: 24%|██▍ | 482/2000 [02:35<07:59, 3.16it/s, loss=0.613]" ] }, { @@ -8428,7 +8428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 483/2000 [02:37<08:06, 3.12it/s, loss=0.517]" + "training until 2000: 24%|██▍ | 483/2000 [02:35<08:04, 3.13it/s, loss=0.613]" ] }, { @@ -8436,7 +8436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 483/2000 [02:37<08:06, 3.12it/s, loss=0.588]" + "training until 2000: 24%|██▍ | 483/2000 [02:35<08:04, 3.13it/s, loss=0.66] " ] }, { @@ -8444,7 +8444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 484/2000 [02:38<08:05, 3.12it/s, loss=0.588]" + "training until 2000: 24%|██▍ | 484/2000 [02:36<08:01, 3.15it/s, loss=0.66]" ] }, { @@ -8452,7 +8452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 484/2000 [02:38<08:05, 3.12it/s, loss=0.556]" + "training until 2000: 24%|██▍ | 484/2000 [02:36<08:01, 3.15it/s, loss=0.72]" ] }, { @@ -8460,7 +8460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 485/2000 [02:38<08:02, 3.14it/s, loss=0.556]" + "training until 2000: 24%|██▍ | 485/2000 [02:36<07:57, 3.17it/s, loss=0.72]" ] }, { @@ -8468,7 +8468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 485/2000 [02:38<08:02, 3.14it/s, loss=0.539]" + "training until 2000: 24%|██▍ | 485/2000 [02:36<07:57, 3.17it/s, loss=0.61]" ] }, { @@ -8476,7 +8476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 486/2000 [02:38<08:03, 3.13it/s, loss=0.539]" + "training until 2000: 24%|██▍ | 486/2000 [02:36<08:02, 3.14it/s, loss=0.61]" ] }, { @@ -8484,7 +8484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 486/2000 [02:38<08:03, 3.13it/s, loss=0.582]" + "training until 2000: 24%|██▍ | 486/2000 [02:36<08:02, 3.14it/s, loss=0.642]" ] }, { @@ -8492,7 +8492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 487/2000 [02:38<07:55, 3.18it/s, loss=0.582]" + "training until 2000: 24%|██▍ | 487/2000 [02:36<08:00, 3.15it/s, loss=0.642]" ] }, { @@ -8500,7 +8500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 487/2000 [02:38<07:55, 3.18it/s, loss=0.601]" + "training until 2000: 24%|██▍ | 487/2000 [02:36<08:00, 3.15it/s, loss=0.646]" ] }, { @@ -8508,7 +8508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 488/2000 [02:39<08:02, 3.13it/s, loss=0.601]" + "training until 2000: 24%|██▍ | 488/2000 [02:37<08:00, 3.15it/s, loss=0.646]" ] }, { @@ -8516,7 +8516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 488/2000 [02:39<08:02, 3.13it/s, loss=0.632]" + "training until 2000: 24%|██▍ | 488/2000 [02:37<08:00, 3.15it/s, loss=0.615]" ] }, { @@ -8524,7 +8524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 489/2000 [02:39<08:08, 3.10it/s, loss=0.632]" + "training until 2000: 24%|██▍ | 489/2000 [02:37<08:01, 3.14it/s, loss=0.615]" ] }, { @@ -8532,7 +8532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 489/2000 [02:39<08:08, 3.10it/s, loss=0.643]" + "training until 2000: 24%|██▍ | 489/2000 [02:37<08:01, 3.14it/s, loss=0.665]" ] }, { @@ -8540,7 +8540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 490/2000 [02:39<08:09, 3.09it/s, loss=0.643]" + "training until 2000: 24%|██▍ | 490/2000 [02:37<08:05, 3.11it/s, loss=0.665]" ] }, { @@ -8548,7 +8548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 24%|██▍ | 490/2000 [02:39<08:09, 3.09it/s, loss=0.516]" + "training until 2000: 24%|██▍ | 490/2000 [02:37<08:05, 3.11it/s, loss=0.631]" ] }, { @@ -8556,7 +8556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 491/2000 [02:40<08:27, 2.97it/s, loss=0.516]" + "training until 2000: 25%|██▍ | 491/2000 [02:38<08:02, 3.13it/s, loss=0.631]" ] }, { @@ -8564,7 +8564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 491/2000 [02:40<08:27, 2.97it/s, loss=0.544]" + "training until 2000: 25%|██▍ | 491/2000 [02:38<08:02, 3.13it/s, loss=0.67] " ] }, { @@ -8572,7 +8572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 492/2000 [02:40<08:20, 3.01it/s, loss=0.544]" + "training until 2000: 25%|██▍ | 492/2000 [02:38<08:02, 3.13it/s, loss=0.67]" ] }, { @@ -8580,7 +8580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 492/2000 [02:40<08:20, 3.01it/s, loss=0.572]" + "training until 2000: 25%|██▍ | 492/2000 [02:38<08:02, 3.13it/s, loss=0.618]" ] }, { @@ -8588,7 +8588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 493/2000 [02:40<08:17, 3.03it/s, loss=0.572]" + "training until 2000: 25%|██▍ | 493/2000 [02:38<07:57, 3.16it/s, loss=0.618]" ] }, { @@ -8596,7 +8596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 493/2000 [02:40<08:17, 3.03it/s, loss=0.565]" + "training until 2000: 25%|██▍ | 493/2000 [02:38<07:57, 3.16it/s, loss=0.673]" ] }, { @@ -8604,7 +8604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 494/2000 [02:41<08:07, 3.09it/s, loss=0.565]" + "training until 2000: 25%|██▍ | 494/2000 [02:39<08:02, 3.12it/s, loss=0.673]" ] }, { @@ -8612,7 +8612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 494/2000 [02:41<08:07, 3.09it/s, loss=0.585]" + "training until 2000: 25%|██▍ | 494/2000 [02:39<08:02, 3.12it/s, loss=0.597]" ] }, { @@ -8620,7 +8620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 495/2000 [02:41<08:06, 3.09it/s, loss=0.585]" + "training until 2000: 25%|██▍ | 495/2000 [02:39<08:04, 3.11it/s, loss=0.597]" ] }, { @@ -8628,7 +8628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 495/2000 [02:41<08:06, 3.09it/s, loss=0.671]" + "training until 2000: 25%|██▍ | 495/2000 [02:39<08:04, 3.11it/s, loss=0.687]" ] }, { @@ -8636,7 +8636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 496/2000 [02:41<08:05, 3.10it/s, loss=0.671]" + "training until 2000: 25%|██▍ | 496/2000 [02:39<08:05, 3.10it/s, loss=0.687]" ] }, { @@ -8644,7 +8644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 496/2000 [02:41<08:05, 3.10it/s, loss=0.607]" + "training until 2000: 25%|██▍ | 496/2000 [02:39<08:05, 3.10it/s, loss=0.683]" ] }, { @@ -8652,7 +8652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 497/2000 [02:42<08:01, 3.12it/s, loss=0.607]" + "training until 2000: 25%|██▍ | 497/2000 [02:40<08:00, 3.13it/s, loss=0.683]" ] }, { @@ -8660,7 +8660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 497/2000 [02:42<08:01, 3.12it/s, loss=0.543]" + "training until 2000: 25%|██▍ | 497/2000 [02:40<08:00, 3.13it/s, loss=0.657]" ] }, { @@ -8668,7 +8668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 498/2000 [02:42<08:03, 3.11it/s, loss=0.543]" + "training until 2000: 25%|██▍ | 498/2000 [02:40<07:59, 3.13it/s, loss=0.657]" ] }, { @@ -8676,7 +8676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 498/2000 [02:42<08:03, 3.11it/s, loss=0.532]" + "training until 2000: 25%|██▍ | 498/2000 [02:40<07:59, 3.13it/s, loss=0.62] " ] }, { @@ -8684,7 +8684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 499/2000 [02:42<08:02, 3.11it/s, loss=0.532]" + "training until 2000: 25%|██▍ | 499/2000 [02:40<07:58, 3.14it/s, loss=0.62]" ] }, { @@ -8692,7 +8692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▍ | 499/2000 [02:42<08:02, 3.11it/s, loss=0.624]" + "training until 2000: 25%|██▍ | 499/2000 [02:40<07:58, 3.14it/s, loss=0.629]" ] }, { @@ -8700,7 +8700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 500/2000 [02:43<08:01, 3.12it/s, loss=0.624]" + "training until 2000: 25%|██▌ | 500/2000 [02:41<08:03, 3.10it/s, loss=0.629]" ] }, { @@ -8708,7 +8708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 500/2000 [02:43<08:01, 3.12it/s, loss=0.57] " + "training until 2000: 25%|██▌ | 500/2000 [02:41<08:03, 3.10it/s, loss=0.624]" ] }, { @@ -8796,7 +8796,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:12, 16.91blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:10, 20.49blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" ] }, { @@ -8818,7 +8818,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:23, 9.31blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:20, 10.52blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" ] }, { @@ -8840,7 +8840,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:11, 18.41blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 20.70blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" ] }, { @@ -8862,7 +8862,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:11, 18.41blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:15, 13.71blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" ] }, { @@ -8884,7 +8884,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:11, 18.41blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.44blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" ] }, { @@ -8906,7 +8906,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:11, 18.41blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.44blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" ] }, { @@ -8928,7 +8928,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:11, 18.41blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.44blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" ] }, { @@ -8950,7 +8950,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 17.87blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.44blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" ] }, { @@ -8972,7 +8972,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 17.87blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.44blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" ] }, { @@ -8994,7 +8994,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 17.87blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.44blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" ] }, { @@ -9016,7 +9016,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:11, 17.87blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.44blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -9038,7 +9038,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:11, 17.87blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 20.45blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -9060,7 +9060,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.30blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 20.45blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" ] }, { @@ -9082,7 +9082,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.30blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 20.45blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" ] }, { @@ -9104,7 +9104,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.30blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 20.45blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" ] }, { @@ -9126,7 +9126,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.30blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 20.45blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" ] }, { @@ -9148,7 +9148,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.30blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 20.45blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" ] }, { @@ -9170,7 +9170,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 18.74blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 20.45blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" ] }, { @@ -9192,7 +9192,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 18.74blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.90blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" ] }, { @@ -9214,7 +9214,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 18.74blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.90blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" ] }, { @@ -9236,7 +9236,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:11, 18.74blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.90blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" ] }, { @@ -9258,7 +9258,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:11, 18.74blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 19.90blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" ] }, { @@ -9280,7 +9280,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.48blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 19.90blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" ] }, { @@ -9302,7 +9302,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.48blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.63blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" ] }, { @@ -9324,7 +9324,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.48blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.63blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" ] }, { @@ -9346,7 +9346,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:11, 18.48blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.63blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" ] }, { @@ -9368,7 +9368,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:11, 18.48blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 19.63blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" ] }, { @@ -9390,7 +9390,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.83blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 19.63blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" ] }, { @@ -9412,7 +9412,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.83blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.55blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" ] }, { @@ -9434,7 +9434,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.83blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.55blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" ] }, { @@ -9456,7 +9456,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 18.83blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.55blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" ] }, { @@ -9478,7 +9478,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 18.83blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 19.55blocks/s, ⧗=0, ▶=1, ✔=14, ✗=0, ∅=0]" ] }, { @@ -9500,7 +9500,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 18.69blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 19.55blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" ] }, { @@ -9522,7 +9522,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 18.69blocks/s, ⧗=0, ▶=1, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 18.07blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" ] }, { @@ -9544,7 +9544,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 18.69blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 18.07blocks/s, ⧗=0, ▶=1, ✔=15, ✗=0, ∅=0]" ] }, { @@ -9566,7 +9566,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:10, 18.69blocks/s, ⧗=0, ▶=1, ✔=15, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 18.07blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" ] }, { @@ -9588,7 +9588,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:10, 18.69blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 18.07blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" ] }, { @@ -9610,7 +9610,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.99blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 18.07blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" ] }, { @@ -9632,7 +9632,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.99blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" ] }, { @@ -9654,7 +9654,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.99blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" ] }, { @@ -9676,7 +9676,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.99blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" ] }, { @@ -9698,7 +9698,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.99blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" ] }, { @@ -9720,7 +9720,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:00<00:11, 17.93blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.76blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" ] }, { @@ -9742,7 +9742,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:00<00:11, 17.93blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:10, 18.11blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" ] }, { @@ -9764,7 +9764,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.93blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:10, 18.11blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" ] }, { @@ -9786,7 +9786,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:10, 18.11blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" ] }, { @@ -9808,7 +9808,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:10, 18.11blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" ] }, { @@ -9830,7 +9830,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.24blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:10, 18.11blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" ] }, { @@ -9852,7 +9852,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.24blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:10, 18.17blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" ] }, { @@ -9874,7 +9874,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.24blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:10, 18.17blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" ] }, { @@ -9896,7 +9896,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 17.24blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:10, 18.17blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" ] }, { @@ -9918,7 +9918,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 17.24blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 18.17blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" ] }, { @@ -9940,7 +9940,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 16.99blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 18.17blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" ] }, { @@ -9962,7 +9962,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 16.99blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 18.18blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" ] }, { @@ -9984,7 +9984,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 16.99blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 18.18blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" ] }, { @@ -10006,7 +10006,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:11, 16.99blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 18.18blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" ] }, { @@ -10028,7 +10028,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:11, 16.99blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 18.18blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" ] }, { @@ -10050,7 +10050,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.53blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 18.18blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" ] }, { @@ -10072,7 +10072,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.53blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 18.64blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" ] }, { @@ -10094,7 +10094,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.53blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 18.64blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" ] }, { @@ -10116,7 +10116,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.53blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 18.64blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" ] }, { @@ -10138,7 +10138,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.53blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.64blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" ] }, { @@ -10160,7 +10160,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.08blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.64blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" ] }, { @@ -10182,7 +10182,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.08blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.62blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" ] }, { @@ -10204,7 +10204,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.08blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.62blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" ] }, { @@ -10226,7 +10226,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.08blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.62blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" ] }, { @@ -10248,7 +10248,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.08blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.62blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" ] }, { @@ -10270,7 +10270,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.09blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.62blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" ] }, { @@ -10292,7 +10292,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.09blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.35blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" ] }, { @@ -10314,7 +10314,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.09blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.35blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" ] }, { @@ -10336,7 +10336,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 18.09blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.35blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" ] }, { @@ -10358,7 +10358,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 18.09blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.35blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" ] }, { @@ -10380,7 +10380,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.49blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.35blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" ] }, { @@ -10402,7 +10402,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.49blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 17.36blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" ] }, { @@ -10424,7 +10424,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.49blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 17.36blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" ] }, { @@ -10446,7 +10446,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.49blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 17.36blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" ] }, { @@ -10468,7 +10468,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.49blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 17.36blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" ] }, { @@ -10490,7 +10490,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.76blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 17.36blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" ] }, { @@ -10512,7 +10512,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.76blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 16.84blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" ] }, { @@ -10534,7 +10534,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.76blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 16.84blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" ] }, { @@ -10556,7 +10556,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.76blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 16.84blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" ] }, { @@ -10578,7 +10578,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.76blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 16.84blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" ] }, { @@ -10600,7 +10600,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.31blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 16.84blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" ] }, { @@ -10622,7 +10622,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.31blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 17.43blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" ] }, { @@ -10644,7 +10644,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.31blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 17.43blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" ] }, { @@ -10666,7 +10666,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.31blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 17.43blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" ] }, { @@ -10688,7 +10688,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.31blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:10, 17.43blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" ] }, { @@ -10710,7 +10710,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.75blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:10, 17.43blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" ] }, { @@ -10732,7 +10732,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.75blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.03blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" ] }, { @@ -10754,7 +10754,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.75blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.03blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" ] }, { @@ -10776,7 +10776,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.75blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.03blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" ] }, { @@ -10798,7 +10798,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.75blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.03blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" ] }, { @@ -10820,7 +10820,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 17.99blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.03blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" ] }, { @@ -10842,7 +10842,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 17.99blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" ] }, { @@ -10864,7 +10864,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 17.99blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" ] }, { @@ -10886,7 +10886,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 17.99blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" ] }, { @@ -10908,7 +10908,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 17.99blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" ] }, { @@ -10930,7 +10930,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 17.79blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" ] }, { @@ -10952,7 +10952,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 17.79blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.04blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" ] }, { @@ -10974,7 +10974,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 17.79blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.04blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" ] }, { @@ -10996,7 +10996,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 17.79blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.04blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" ] }, { @@ -11018,7 +11018,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 17.79blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.04blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" ] }, { @@ -11040,7 +11040,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 17.65blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.04blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" ] }, { @@ -11062,7 +11062,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 17.65blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.28blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" ] }, { @@ -11084,7 +11084,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 17.65blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.28blocks/s, ⧗=0, ▶=1, ✔=43, ✗=0, ∅=0]" ] }, { @@ -11106,7 +11106,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 17.65blocks/s, ⧗=0, ▶=1, ✔=43, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.28blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" ] }, { @@ -11128,7 +11128,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 17.65blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.28blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" ] }, { @@ -11150,7 +11150,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.77blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.28blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" ] }, { @@ -11172,7 +11172,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.77blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.55blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" ] }, { @@ -11194,7 +11194,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.77blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.55blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" ] }, { @@ -11216,7 +11216,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.77blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.55blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" ] }, { @@ -11238,7 +11238,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.77blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.55blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" ] }, { @@ -11260,7 +11260,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.03blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.55blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" ] }, { @@ -11282,7 +11282,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.03blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.57blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" ] }, { @@ -11304,7 +11304,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.03blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.57blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" ] }, { @@ -11326,7 +11326,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.03blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.57blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" ] }, { @@ -11348,7 +11348,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.03blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.57blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" ] }, { @@ -11370,7 +11370,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 16.98blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.57blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" ] }, { @@ -11392,7 +11392,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 16.98blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.46blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" ] }, { @@ -11414,7 +11414,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 16.98blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.46blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" ] }, { @@ -11436,7 +11436,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 16.98blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.46blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" ] }, { @@ -11458,7 +11458,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 16.98blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:09, 17.46blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" ] }, { @@ -11480,7 +11480,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:09, 17.46blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" ] }, { @@ -11502,7 +11502,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" ] }, { @@ -11524,7 +11524,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" ] }, { @@ -11546,7 +11546,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" ] }, { @@ -11568,7 +11568,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" ] }, { @@ -11590,7 +11590,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:11, 14.77blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" ] }, { @@ -11612,7 +11612,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:11, 14.77blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:02<00:10, 15.92blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" ] }, { @@ -11634,7 +11634,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:03<00:11, 14.77blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:02<00:10, 15.92blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" ] }, { @@ -11656,7 +11656,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:11, 14.77blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.92blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" ] }, { @@ -11678,7 +11678,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:11, 14.77blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.92blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" ] }, { @@ -11700,7 +11700,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 14.97blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.92blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" ] }, { @@ -11722,7 +11722,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 14.97blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:09, 16.28blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" ] }, { @@ -11744,7 +11744,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 14.97blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:09, 16.28blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" ] }, { @@ -11766,7 +11766,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 14.97blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:09, 16.28blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" ] }, { @@ -11788,7 +11788,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 14.97blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.28blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" ] }, { @@ -11810,7 +11810,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.28blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" ] }, { @@ -11832,7 +11832,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" ] }, { @@ -11854,7 +11854,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" ] }, { @@ -11876,7 +11876,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" ] }, { @@ -11898,7 +11898,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 15.84blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" ] }, { @@ -11920,7 +11920,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.43blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 15.84blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" ] }, { @@ -11942,7 +11942,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.43blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:09, 16.25blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" ] }, { @@ -11964,7 +11964,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.43blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:09, 16.25blocks/s, ⧗=0, ▶=1, ✔=59, ✗=0, ∅=0]" ] }, { @@ -11986,7 +11986,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:10, 15.43blocks/s, ⧗=0, ▶=1, ✔=59, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:09, 16.25blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" ] }, { @@ -12008,7 +12008,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:10, 15.43blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:09, 16.25blocks/s, ⧗=0, ▶=1, ✔=60, ✗=0, ∅=0]" ] }, { @@ -12030,7 +12030,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:09, 16.25blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" ] }, { @@ -12052,7 +12052,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=1, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 16.86blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" ] }, { @@ -12074,7 +12074,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.52blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 16.86blocks/s, ⧗=0, ▶=1, ✔=61, ✗=0, ∅=0]" ] }, { @@ -12096,7 +12096,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 15.52blocks/s, ⧗=0, ▶=1, ✔=61, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 16.86blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" ] }, { @@ -12118,7 +12118,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 15.52blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.86blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" ] }, { @@ -12140,7 +12140,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.78blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.86blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" ] }, { @@ -12162,7 +12162,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.78blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" ] }, { @@ -12184,7 +12184,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.78blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" ] }, { @@ -12206,7 +12206,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.78blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" ] }, { @@ -12228,7 +12228,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.78blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=1, ✔=64, ✗=0, ∅=0]" ] }, { @@ -12250,7 +12250,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.84blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" ] }, { @@ -12272,7 +12272,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.84blocks/s, ⧗=0, ▶=1, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 16.52blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" ] }, { @@ -12294,7 +12294,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.84blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 16.52blocks/s, ⧗=0, ▶=1, ✔=65, ✗=0, ∅=0]" ] }, { @@ -12316,7 +12316,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 15.84blocks/s, ⧗=0, ▶=1, ✔=65, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 16.52blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" ] }, { @@ -12338,7 +12338,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 15.84blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.52blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" ] }, { @@ -12360,7 +12360,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.52blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" ] }, { @@ -12382,7 +12382,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.82blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" ] }, { @@ -12404,7 +12404,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.82blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" ] }, { @@ -12426,7 +12426,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.82blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" ] }, { @@ -12448,7 +12448,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:09, 16.14blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.82blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" ] }, { @@ -12470,7 +12470,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.69blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.82blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" ] }, { @@ -12492,7 +12492,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.69blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" ] }, { @@ -12514,7 +12514,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:08, 16.69blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" ] }, { @@ -12536,7 +12536,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 16.69blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" ] }, { @@ -12558,7 +12558,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 16.69blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 16.12blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" ] }, { @@ -12580,7 +12580,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.59blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" ] }, { @@ -12602,7 +12602,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.59blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.34blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" ] }, { @@ -12624,7 +12624,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.59blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.34blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" ] }, { @@ -12646,7 +12646,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.59blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.34blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" ] }, { @@ -12668,7 +12668,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.59blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.34blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" ] }, { @@ -12690,7 +12690,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.07blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.34blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" ] }, { @@ -12712,7 +12712,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.07blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.14blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" ] }, { @@ -12734,7 +12734,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.07blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.14blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" ] }, { @@ -12756,7 +12756,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.07blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.14blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" ] }, { @@ -12778,7 +12778,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.07blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.14blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" ] }, { @@ -12800,7 +12800,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.14blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" ] }, { @@ -12822,7 +12822,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.42blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" ] }, { @@ -12844,7 +12844,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.42blocks/s, ⧗=0, ▶=1, ✔=75, ✗=0, ∅=0]" ] }, { @@ -12866,7 +12866,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=1, ✔=75, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.42blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" ] }, { @@ -12888,7 +12888,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 17.42blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" ] }, { @@ -12910,7 +12910,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 17.42blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" ] }, { @@ -12932,7 +12932,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" ] }, { @@ -12954,7 +12954,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" ] }, { @@ -12976,7 +12976,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" ] }, { @@ -12998,7 +12998,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=1, ✔=78, ✗=0, ∅=0]" ] }, { @@ -13020,7 +13020,7 @@ "output_type": "stream", "text": [ "\r", - 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"predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.91blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.17blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" ] }, { @@ -13702,7 +13680,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.91blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.17blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" ] }, { @@ -13724,7 +13702,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.91blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" ] }, { @@ -13746,7 +13724,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.91blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" ] }, { @@ -13768,7 +13746,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.91blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" ] }, { @@ -13790,7 +13768,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" ] }, { @@ -13812,7 +13790,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" ] }, { @@ -13834,7 +13812,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" ] }, { @@ -13856,7 +13834,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" ] }, { @@ -13878,7 +13856,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" ] }, { @@ -13900,7 +13878,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.07blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" ] }, { @@ -13922,7 +13900,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.07blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" ] }, { @@ -13944,7 +13922,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.07blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" ] }, { @@ -13966,7 +13944,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 17.07blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" ] }, { @@ -13988,7 +13966,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 17.07blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" ] }, { @@ -14010,7 +13988,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.49blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" ] }, { @@ -14032,7 +14010,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.49blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.58blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" ] }, { @@ -14054,7 +14032,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.49blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 18.12blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" ] }, { @@ -14076,7 +14054,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.49blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 18.12blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" ] }, { @@ -14098,7 +14076,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.49blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 18.12blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" ] }, { @@ -14120,7 +14098,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.46blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 18.12blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" ] }, { @@ -14142,7 +14120,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.46blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 18.12blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" ] }, { @@ -14164,7 +14142,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.46blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.73blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" ] }, { @@ -14186,7 +14164,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.46blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.73blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" ] }, { @@ -14208,7 +14186,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.46blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.73blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" ] }, { @@ -14230,7 +14208,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.93blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 17.73blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" ] }, { @@ -14252,7 +14230,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.93blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 17.73blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" ] }, { @@ -14274,7 +14252,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.93blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.64blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" ] }, { @@ -14296,7 +14274,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.93blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.64blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" ] }, { @@ -14318,7 +14296,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.93blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.64blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" ] }, { @@ -14340,7 +14318,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.17blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.64blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" ] }, { @@ -14362,7 +14340,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.17blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.64blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" ] }, { @@ -14384,7 +14362,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.17blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 17.94blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" ] }, { @@ -14406,7 +14384,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 17.17blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 17.94blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" ] }, { @@ -14428,7 +14406,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 17.17blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 17.94blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" ] }, { @@ -14450,7 +14428,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.30blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:05<00:06, 17.94blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" ] }, { @@ -14472,7 +14450,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.30blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:05<00:06, 17.94blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" ] }, { @@ -14494,7 +14472,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.30blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:05<00:06, 17.75blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" ] }, { @@ -14516,7 +14494,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.30blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:05<00:06, 17.75blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" ] }, { @@ -14538,7 +14516,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.30blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" ] }, { @@ -14560,7 +14538,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.49blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" ] }, { @@ -14582,7 +14560,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.49blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" ] }, { @@ -14604,7 +14582,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.49blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.95blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" ] }, { @@ -14626,7 +14604,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.49blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.95blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" ] }, { @@ -14648,7 +14626,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.49blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.95blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" ] }, { @@ -14670,7 +14648,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.96blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.95blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" ] }, { @@ -14692,7 +14670,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.96blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.95blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" ] }, { @@ -14714,7 +14692,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.96blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 17.87blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" ] }, { @@ -14736,7 +14714,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 17.87blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" ] }, { @@ -14758,7 +14736,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 17.87blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" ] }, { @@ -14780,7 +14758,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.87blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" ] }, { @@ -14802,7 +14780,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.87blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" ] }, { @@ -14824,7 +14802,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.79blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 17.91blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" ] }, { @@ -14846,7 +14824,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.79blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 17.91blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" ] }, { @@ -14868,7 +14846,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.79blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 17.91blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" ] }, { @@ -14890,7 +14868,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.79blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 17.91blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" ] }, { @@ -14912,7 +14890,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.79blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 17.91blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" ] }, { @@ -14934,7 +14912,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.07blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 17.91blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" ] }, { @@ -14956,7 +14934,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.07blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" ] }, { @@ -14978,7 +14956,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.07blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" ] }, { @@ -15000,7 +14978,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 19.07blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" ] }, { @@ -15022,7 +15000,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 19.07blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" ] }, { @@ -15044,7 +15022,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 19.07blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" ] }, { @@ -15066,7 +15044,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 19.26blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" ] }, { @@ -15088,7 +15066,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 19.26blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 19.11blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" ] }, { @@ -15110,7 +15088,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 19.26blocks/s, ⧗=0, ▶=1, ✔=116, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 19.11blocks/s, ⧗=0, ▶=1, ✔=116, ✗=0, ∅=0]" ] }, { @@ -15132,7 +15110,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 19.26blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 19.11blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" ] }, { @@ -15154,7 +15132,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 19.15blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 19.11blocks/s, ⧗=0, ▶=1, ✔=117, ✗=0, ∅=0]" ] }, { @@ -15176,7 +15154,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 19.15blocks/s, ⧗=0, ▶=1, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 19.11blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" ] }, { @@ -15198,7 +15176,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 19.15blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" ] }, { @@ -15220,7 +15198,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 19.15blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" ] }, { @@ -15242,7 +15220,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 19.15blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" ] }, { @@ -15264,7 +15242,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 19.19blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" ] }, { @@ -15286,7 +15264,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 19.19blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" ] }, { @@ -15308,7 +15286,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 19.19blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 19.04blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" ] }, { @@ -15330,7 +15308,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 19.19blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 19.04blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" ] }, { @@ -15352,7 +15330,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 19.19blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 19.04blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" ] }, { @@ -15374,7 +15352,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:04, 19.26blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:04, 19.04blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" ] }, { @@ -15396,7 +15374,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:04, 19.26blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:04, 19.04blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" ] }, { @@ -15418,7 +15396,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:04, 19.26blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:06<00:05, 18.68blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" ] }, { @@ -15440,7 +15418,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:06<00:04, 19.26blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:06<00:05, 18.68blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" ] }, { @@ -15462,7 +15440,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:04, 19.26blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:06<00:05, 18.68blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" ] }, { @@ -15484,7 +15462,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.45blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:06<00:04, 18.68blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" ] }, { @@ -15506,7 +15484,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.45blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:04, 18.68blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" ] }, { @@ -15528,7 +15506,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.45blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.09blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" ] }, { @@ -15550,7 +15528,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:04, 18.45blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.09blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" ] }, { @@ -15572,7 +15550,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:04, 18.45blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.09blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" ] }, { @@ -15594,7 +15572,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 18.13blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.09blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" ] }, { @@ -15616,7 +15594,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 18.13blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.09blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" ] }, { @@ -15638,7 +15616,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 18.13blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 16.76blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" ] }, { @@ -15660,7 +15638,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:04, 18.13blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 16.76blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" ] }, { @@ -15682,7 +15660,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:04, 18.13blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 16.76blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" ] }, { @@ -15704,7 +15682,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 18.43blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 16.76blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" ] }, { @@ -15726,7 +15704,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 18.43blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 16.76blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" ] }, { @@ -15748,7 +15726,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 18.43blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 17.42blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" ] }, { @@ -15770,7 +15748,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:04, 18.43blocks/s, ⧗=0, ▶=1, ✔=128, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 17.42blocks/s, ⧗=0, ▶=1, ✔=128, ✗=0, ∅=0]" ] }, { @@ -15792,7 +15770,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:04, 18.43blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 17.42blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" ] }, { @@ -15814,7 +15792,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 18.55blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 17.42blocks/s, ⧗=0, ▶=1, ✔=129, ✗=0, ∅=0]" ] }, { @@ -15836,7 +15814,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 18.55blocks/s, ⧗=0, ▶=1, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 17.42blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" ] }, { @@ -15858,7 +15836,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 18.55blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" ] }, { @@ -15880,7 +15858,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 18.55blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" ] }, { @@ -15902,7 +15880,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 18.55blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" ] }, { @@ -15924,7 +15902,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 18.57blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" ] }, { @@ -15946,7 +15924,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 18.57blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" ] }, { @@ -15968,7 +15946,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 18.57blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.14blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" ] }, { @@ -15990,7 +15968,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.57blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.14blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" ] }, { @@ -16012,7 +15990,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.57blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.14blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" ] }, { @@ -16034,7 +16012,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.14blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" ] }, { @@ -16056,7 +16034,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.14blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" ] }, { @@ -16078,7 +16056,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.53blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" ] }, { @@ -16100,7 +16078,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.53blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" ] }, { @@ -16122,7 +16100,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.53blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" ] }, { @@ -16144,7 +16122,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.53blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" ] }, { @@ -16166,7 +16144,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.53blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" ] }, { @@ -16188,7 +16166,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.40blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" ] }, { @@ -16210,7 +16188,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.40blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" ] }, { @@ -16232,7 +16210,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.40blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" ] }, { @@ -16254,7 +16232,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.40blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" ] }, { @@ -16276,7 +16254,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.40blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" ] }, { @@ -16298,7 +16276,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 17.85blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" ] }, { @@ -16320,7 +16298,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 17.85blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" ] }, { @@ -16342,7 +16320,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 17.85blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" ] }, { @@ -16364,7 +16342,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 18.89blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 17.85blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" ] }, { @@ -16386,7 +16364,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 18.89blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 17.85blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" ] }, { @@ -16408,7 +16386,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 18.89blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:07<00:04, 17.81blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" ] }, { @@ -16430,7 +16408,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:07<00:04, 18.89blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:07<00:04, 17.81blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" ] }, { @@ -16452,7 +16430,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 18.89blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:07<00:04, 17.81blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" ] }, { @@ -16474,7 +16452,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.47blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:07<00:04, 17.81blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" ] }, { @@ -16496,7 +16474,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.47blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.81blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" ] }, { @@ -16518,7 +16496,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.47blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 17.15blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" ] }, { @@ -16540,7 +16518,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 17.47blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 17.15blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" ] }, { @@ -16562,7 +16540,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 17.47blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 17.15blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" ] }, { @@ -16584,7 +16562,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 17.15blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" ] }, { @@ -16606,7 +16584,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 17.15blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" ] }, { @@ -16628,7 +16606,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.60blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" ] }, { @@ -16650,7 +16628,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.60blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" ] }, { @@ -16672,7 +16650,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.60blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" ] }, { @@ -16694,7 +16672,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.36blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:04, 17.60blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" ] }, { @@ -16716,7 +16694,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.36blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:04, 17.60blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" ] }, { @@ -16738,7 +16716,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.36blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 17.81blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" ] }, { @@ -16760,7 +16738,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.36blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 17.81blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" ] }, { @@ -16782,7 +16760,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.36blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 17.81blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" ] }, { @@ -16804,7 +16782,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.06blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.81blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" ] }, { @@ -16826,7 +16804,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.06blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.81blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" ] }, { @@ -16848,7 +16826,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.06blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.45blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" ] }, { @@ -16870,7 +16848,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 18.06blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.45blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" ] }, { @@ -16892,7 +16870,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 18.06blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.45blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" ] }, { @@ -16914,7 +16892,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.66blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.45blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" ] }, { @@ -16936,7 +16914,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.66blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.45blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" ] }, { @@ -16958,7 +16936,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.66blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" ] }, { @@ -16980,7 +16958,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.66blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" ] }, { @@ -17002,7 +16980,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.66blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" ] }, { @@ -17024,7 +17002,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.28blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" ] }, { @@ -17046,7 +17024,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.28blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" ] }, { @@ -17068,7 +17046,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.28blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.73blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" ] }, { @@ -17090,7 +17068,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.28blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.73blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" ] }, { @@ -17112,7 +17090,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.28blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.73blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" ] }, { @@ -17134,7 +17112,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.88blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.73blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" ] }, { @@ -17156,7 +17134,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.88blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.73blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" ] }, { @@ -17178,7 +17156,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.88blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.23blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" ] }, { @@ -17200,7 +17178,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.88blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.23blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" ] }, { @@ -17222,7 +17200,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.88blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.23blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" ] }, { @@ -17244,7 +17222,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.86blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.23blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" ] }, { @@ -17266,7 +17244,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.86blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.23blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" ] }, { @@ -17288,7 +17266,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.86blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.56blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" ] }, { @@ -17310,7 +17288,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.86blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.56blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" ] }, { @@ -17332,7 +17310,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.86blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.56blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" ] }, { @@ -17354,7 +17332,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:08<00:03, 16.39blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:08<00:03, 16.56blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" ] }, { @@ -17376,7 +17354,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:08<00:03, 16.39blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.56blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" ] }, { @@ -17398,7 +17376,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.39blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 15.07blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" ] }, { @@ -17420,7 +17398,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.39blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 15.07blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" ] }, { @@ -17442,7 +17420,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.39blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 15.07blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" ] }, { @@ -17464,7 +17442,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.07blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" ] }, { @@ -17486,7 +17464,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.07blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" ] }, { @@ -17508,7 +17486,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" ] }, { @@ -17530,7 +17508,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" ] }, { @@ -17552,7 +17530,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" ] }, { @@ -17574,7 +17552,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" ] }, { @@ -17596,7 +17574,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" ] }, { @@ -17618,7 +17596,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" ] }, { @@ -17640,7 +17618,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" ] }, { @@ -17662,7 +17640,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" ] }, { @@ -17684,7 +17662,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.58blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" ] }, { @@ -17706,7 +17684,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.58blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 14.41blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" ] }, { @@ -17728,7 +17706,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.58blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" ] }, { @@ -17750,7 +17728,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.58blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" ] }, { @@ -17772,7 +17750,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.58blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" ] }, { @@ -17794,7 +17772,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" ] }, { @@ -17816,7 +17794,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" ] }, { @@ -17838,7 +17816,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.19blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" ] }, { @@ -17860,7 +17838,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.19blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" ] }, { @@ -17882,7 +17860,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.19blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" ] }, { @@ -17904,7 +17882,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 16.25blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.19blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" ] }, { @@ -17926,7 +17904,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 16.25blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.19blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" ] }, { @@ -17948,7 +17926,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 16.25blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" ] }, { @@ -17970,7 +17948,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:02, 16.25blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" ] }, { @@ -17992,7 +17970,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:02, 16.25blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" ] }, { @@ -18014,7 +17992,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 16.71blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" ] }, { @@ -18036,7 +18014,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 16.71blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" ] }, { @@ -18058,7 +18036,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 16.71blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.80blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" ] }, { @@ -18080,7 +18058,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 16.71blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.80blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" ] }, { @@ -18102,7 +18080,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 16.71blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.80blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" ] }, { @@ -18124,7 +18102,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 16.52blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.80blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" ] }, { @@ -18146,7 +18124,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 16.52blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.80blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" ] }, { @@ -18168,7 +18146,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 16.52blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:09<00:02, 14.80blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" ] }, { @@ -18190,7 +18168,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:09<00:02, 16.52blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:09<00:02, 14.80blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" ] }, { @@ -18212,7 +18190,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:09<00:02, 16.52blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 14.80blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" ] }, { @@ -18234,7 +18212,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:09<00:02, 16.45blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 14.80blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" ] }, { @@ -18256,7 +18234,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:09<00:02, 16.45blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 14.80blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" ] }, { @@ -18278,7 +18256,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 14.92blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" ] }, { @@ -18300,7 +18278,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 14.92blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" ] }, { @@ -18322,7 +18300,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 14.92blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" ] }, { @@ -18344,7 +18322,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.66blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 14.92blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" ] }, { @@ -18366,7 +18344,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.66blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 14.92blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" ] }, { @@ -18388,7 +18366,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.66blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" ] }, { @@ -18410,7 +18388,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.66blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" ] }, { @@ -18432,7 +18410,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.66blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" ] }, { @@ -18454,7 +18432,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" ] }, { @@ -18476,7 +18454,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" ] }, { @@ -18498,7 +18476,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" ] }, { @@ -18520,7 +18498,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" ] }, { @@ -18542,7 +18520,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" ] }, { @@ -18564,7 +18542,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.34blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" ] }, { @@ -18586,7 +18564,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.34blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" ] }, { @@ -18608,7 +18586,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.34blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" ] }, { @@ -18630,7 +18608,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.34blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" ] }, { @@ -18652,7 +18630,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.34blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" ] }, { @@ -18674,7 +18652,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 17.06blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" ] }, { @@ -18696,7 +18674,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 17.06blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" ] }, { @@ -18718,7 +18696,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 17.06blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.16blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" ] }, { @@ -18740,7 +18718,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.06blocks/s, ⧗=0, ▶=1, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.16blocks/s, ⧗=0, ▶=1, ✔=182, ✗=0, ∅=0]" ] }, { @@ -18762,7 +18740,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.06blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.16blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" ] }, { @@ -18784,7 +18762,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.02blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.16blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" ] }, { @@ -18806,7 +18784,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.02blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.16blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" ] }, { @@ -18828,7 +18806,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.02blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 16.86blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" ] }, { @@ -18850,7 +18828,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.02blocks/s, ⧗=0, ▶=1, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 16.86blocks/s, ⧗=0, ▶=1, ✔=184, ✗=0, ∅=0]" ] }, { @@ -18872,7 +18850,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.02blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 16.86blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" ] }, { @@ -18894,7 +18872,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 16.86blocks/s, ⧗=0, ▶=1, ✔=185, ✗=0, ∅=0]" ] }, { @@ -18916,7 +18894,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=1, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 16.86blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" ] }, { @@ -18938,7 +18916,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.37blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" ] }, { @@ -18960,7 +18938,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.37blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" ] }, { @@ -18982,7 +18960,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.37blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" ] }, { @@ -19004,7 +18982,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 16.72blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.37blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" ] }, { @@ -19026,7 +19004,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 16.72blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.37blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" ] }, { @@ -19048,7 +19026,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 16.72blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 17.17blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" ] }, { @@ -19070,7 +19048,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 16.72blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 17.17blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" ] }, { @@ -19092,7 +19070,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 16.72blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 17.17blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" ] }, { @@ -19114,7 +19092,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 16.56blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 17.17blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" ] }, { @@ -19136,7 +19114,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 16.56blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.17blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" ] }, { @@ -19158,7 +19136,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 16.56blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" ] }, { @@ -19180,7 +19158,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:10<00:01, 16.56blocks/s, ⧗=0, ▶=1, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=1, ✔=190, ✗=0, ∅=0]" ] }, { @@ -19202,7 +19180,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 16.56blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" ] }, { @@ -19224,7 +19202,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.51blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" ] }, { @@ -19246,7 +19224,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.51blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" ] }, { @@ -19268,7 +19246,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.51blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.46blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" ] }, { @@ -19290,7 +19268,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.51blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.46blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" ] }, { @@ -19312,7 +19290,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.51blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.46blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" ] }, { @@ -19334,7 +19312,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.04blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.46blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" ] }, { @@ -19356,7 +19334,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.04blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.46blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" ] }, { @@ -19378,7 +19356,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.04blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.72blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" ] }, { @@ -19400,7 +19378,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.04blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.72blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" ] }, { @@ -19422,7 +19400,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.04blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.72blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" ] }, { @@ -19444,7 +19422,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 14.90blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 16.72blocks/s, ⧗=0, ▶=1, ✔=195, ✗=0, ∅=0]" ] }, { @@ -19466,7 +19444,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 14.90blocks/s, ⧗=0, ▶=1, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 16.72blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" ] }, { @@ -19488,7 +19466,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 14.90blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" ] }, { @@ -19510,7 +19488,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 14.90blocks/s, ⧗=0, ▶=1, ✔=196, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=1, ✔=196, ✗=0, ∅=0]" ] }, { @@ -19532,7 +19510,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 14.90blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" ] }, { @@ -19554,7 +19532,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 14.91blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" ] }, { @@ -19576,7 +19554,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 14.91blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.07blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" ] }, { @@ -19598,7 +19576,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 14.91blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" ] }, { @@ -19620,7 +19598,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 14.91blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" ] }, { @@ -19642,7 +19620,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 14.91blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" ] }, { @@ -19664,7 +19642,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 15.45blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" ] }, { @@ -19686,7 +19664,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 15.45blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" ] }, { @@ -19708,7 +19686,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 15.45blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.14blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" ] }, { @@ -19730,7 +19708,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:01, 15.45blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.14blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" ] }, { @@ -19752,7 +19730,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:01, 15.45blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.14blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" ] }, { @@ -19774,7 +19752,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 15.60blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.14blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" ] }, { @@ -19796,7 +19774,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 15.60blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.14blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" ] }, { @@ -19818,7 +19796,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 15.60blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" ] }, { @@ -19840,7 +19818,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 15.60blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" ] }, { @@ -19862,7 +19840,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 15.60blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" ] }, { @@ -19884,7 +19862,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.31blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" ] }, { @@ -19906,7 +19884,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.31blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" ] }, { @@ -19928,7 +19906,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.31blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.80blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" ] }, { @@ -19950,7 +19928,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 16.31blocks/s, ⧗=0, ▶=1, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.80blocks/s, ⧗=0, ▶=1, ✔=204, ✗=0, ∅=0]" ] }, { @@ -19972,7 +19950,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 16.31blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.80blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" ] }, { @@ -19994,7 +19972,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.11blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.80blocks/s, ⧗=0, ▶=1, ✔=205, ✗=0, ∅=0]" ] }, { @@ -20016,7 +19994,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.11blocks/s, ⧗=0, ▶=1, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.80blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" ] }, { @@ -20038,7 +20016,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.11blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:11<00:00, 18.02blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" ] }, { @@ -20060,7 +20038,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:11<00:00, 17.11blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:11<00:00, 18.02blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" ] }, { @@ -20082,7 +20060,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.11blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 18.02blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" ] }, { @@ -20104,7 +20082,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 16.58blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 18.02blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" ] }, { @@ -20126,7 +20104,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 16.58blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 18.02blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" ] }, { @@ -20148,7 +20126,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 16.58blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.62blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" ] }, { @@ -20170,7 +20148,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 16.58blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.62blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" ] }, { @@ -20192,7 +20170,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 16.58blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.62blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" ] }, { @@ -20214,7 +20192,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.04blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.62blocks/s, ⧗=0, ▶=1, ✔=209, ✗=0, ∅=0]" ] }, { @@ -20236,7 +20214,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.04blocks/s, ⧗=0, ▶=1, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.62blocks/s, ⧗=0, ▶=0, ✔=210, ✗=0, ∅=0]" ] }, { @@ -20258,7 +20236,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.04blocks/s, ⧗=0, ▶=0, ✔=210, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.95blocks/s, ⧗=0, ▶=0, ✔=210, ✗=0, ∅=0]" ] }, { @@ -20280,7 +20258,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.04blocks/s, ⧗=0, ▶=1, ✔=210, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.95blocks/s, ⧗=0, ▶=1, ✔=210, ✗=0, ∅=0]" ] }, { @@ -20302,7 +20280,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.04blocks/s, ⧗=0, ▶=0, ✔=211, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.95blocks/s, ⧗=0, ▶=0, ✔=211, ✗=0, ∅=0]" ] }, { @@ -20324,7 +20302,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 17.11blocks/s, ⧗=0, ▶=0, ✔=211, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 17.95blocks/s, ⧗=0, ▶=1, ✔=211, ✗=0, ∅=0]" ] }, { @@ -20346,7 +20324,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 17.11blocks/s, ⧗=0, ▶=1, ✔=211, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 17.95blocks/s, ⧗=0, ▶=0, ✔=212, ✗=0, ∅=0]" ] }, { @@ -20368,7 +20346,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 17.11blocks/s, ⧗=0, ▶=0, ✔=212, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 18.21blocks/s, ⧗=0, ▶=0, ✔=212, ✗=0, ∅=0]" ] }, { @@ -20390,7 +20368,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 17.11blocks/s, ⧗=0, ▶=1, ✔=212, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 18.21blocks/s, ⧗=0, ▶=1, ✔=212, ✗=0, ∅=0]" ] }, { @@ -20412,7 +20390,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 17.11blocks/s, ⧗=0, ▶=0, ✔=213, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 18.21blocks/s, ⧗=0, ▶=0, ✔=213, ✗=0, ∅=0]" ] }, { @@ -20434,7 +20412,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 17.35blocks/s, ⧗=0, ▶=0, ✔=213, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 18.21blocks/s, ⧗=0, ▶=1, ✔=213, ✗=0, ∅=0]" ] }, { @@ -20456,7 +20434,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 17.35blocks/s, ⧗=0, ▶=1, ✔=213, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 18.21blocks/s, ⧗=0, ▶=0, ✔=214, ✗=0, ∅=0]" ] }, { @@ -20478,7 +20456,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 17.35blocks/s, ⧗=0, ▶=0, ✔=214, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 17.19blocks/s, ⧗=0, ▶=0, ✔=214, ✗=0, ∅=0]" ] }, { @@ -20500,7 +20478,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 17.35blocks/s, ⧗=0, ▶=1, ✔=214, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 17.19blocks/s, ⧗=0, ▶=1, ✔=214, ✗=0, ∅=0]" ] }, { @@ -20522,7 +20500,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 17.35blocks/s, ⧗=0, ▶=0, ✔=215, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 17.19blocks/s, ⧗=0, ▶=0, ✔=215, ✗=0, ∅=0]" ] }, { @@ -20544,7 +20522,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.78blocks/s, ⧗=0, ▶=0, ✔=215, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.19blocks/s, ⧗=0, ▶=1, ✔=215, ✗=0, ∅=0]" ] }, { @@ -20566,7 +20544,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.78blocks/s, ⧗=0, ▶=1, ✔=215, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.19blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -20588,7 +20566,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.78blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ▶: 100%|██████████| 216/216 [00:12<00:00, 17.15blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -20610,7 +20588,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.78blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.15blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -20625,7 +20603,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.23blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.24blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -20673,7 +20651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 501/2000 [03:17<4:21:24, 10.46s/it, loss=0.57]" + "training until 2000: 25%|██▌ | 501/2000 [03:14<4:14:33, 10.19s/it, loss=0.624]" ] }, { @@ -20681,7 +20659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 501/2000 [03:17<4:21:24, 10.46s/it, loss=0.513]" + "training until 2000: 25%|██▌ | 501/2000 [03:14<4:14:33, 10.19s/it, loss=0.709]" ] }, { @@ -20689,7 +20667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 502/2000 [03:17<3:05:20, 7.42s/it, loss=0.513]" + "training until 2000: 25%|██▌ | 502/2000 [03:14<3:00:22, 7.22s/it, loss=0.709]" ] }, { @@ -20697,7 +20675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 502/2000 [03:17<3:05:20, 7.42s/it, loss=0.58] " + "training until 2000: 25%|██▌ | 502/2000 [03:14<3:00:22, 7.22s/it, loss=0.654]" ] }, { @@ -20705,7 +20683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 503/2000 [03:17<2:12:01, 5.29s/it, loss=0.58]" + "training until 2000: 25%|██▌ | 503/2000 [03:14<2:08:34, 5.15s/it, loss=0.654]" ] }, { @@ -20713,7 +20691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 503/2000 [03:17<2:12:01, 5.29s/it, loss=0.547]" + "training until 2000: 25%|██▌ | 503/2000 [03:14<2:08:34, 5.15s/it, loss=0.71] " ] }, { @@ -20721,7 +20699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 504/2000 [03:18<1:34:46, 3.80s/it, loss=0.547]" + "training until 2000: 25%|██▌ | 504/2000 [03:15<1:32:17, 3.70s/it, loss=0.71]" ] }, { @@ -20729,7 +20707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 504/2000 [03:18<1:34:46, 3.80s/it, loss=0.553]" + "training until 2000: 25%|██▌ | 504/2000 [03:15<1:32:17, 3.70s/it, loss=0.659]" ] }, { @@ -20737,7 +20715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 505/2000 [03:18<1:08:40, 2.76s/it, loss=0.553]" + "training until 2000: 25%|██▌ | 505/2000 [03:15<1:06:53, 2.68s/it, loss=0.659]" ] }, { @@ -20745,7 +20723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 505/2000 [03:18<1:08:40, 2.76s/it, loss=0.557]" + "training until 2000: 25%|██▌ | 505/2000 [03:15<1:06:53, 2.68s/it, loss=0.697]" ] }, { @@ -20753,7 +20731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 506/2000 [03:18<50:25, 2.03s/it, loss=0.557] " + "training until 2000: 25%|██▌ | 506/2000 [03:15<49:11, 1.98s/it, loss=0.697] " ] }, { @@ -20761,7 +20739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 506/2000 [03:18<50:25, 2.03s/it, loss=0.575]" + "training until 2000: 25%|██▌ | 506/2000 [03:15<49:11, 1.98s/it, loss=0.736]" ] }, { @@ -20769,7 +20747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 507/2000 [03:19<37:37, 1.51s/it, loss=0.575]" + "training until 2000: 25%|██▌ | 507/2000 [03:16<36:50, 1.48s/it, loss=0.736]" ] }, { @@ -20777,7 +20755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 507/2000 [03:19<37:37, 1.51s/it, loss=0.601]" + "training until 2000: 25%|██▌ | 507/2000 [03:16<36:50, 1.48s/it, loss=0.618]" ] }, { @@ -20785,7 +20763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 508/2000 [03:19<28:39, 1.15s/it, loss=0.601]" + "training until 2000: 25%|██▌ | 508/2000 [03:16<28:07, 1.13s/it, loss=0.618]" ] }, { @@ -20793,7 +20771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 508/2000 [03:19<28:39, 1.15s/it, loss=0.564]" + "training until 2000: 25%|██▌ | 508/2000 [03:16<28:07, 1.13s/it, loss=0.633]" ] }, { @@ -20801,7 +20779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 509/2000 [03:19<22:24, 1.11it/s, loss=0.564]" + "training until 2000: 25%|██▌ | 509/2000 [03:16<22:03, 1.13it/s, loss=0.633]" ] }, { @@ -20809,7 +20787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 25%|██▌ | 509/2000 [03:19<22:24, 1.11it/s, loss=0.622]" + "training until 2000: 25%|██▌ | 509/2000 [03:16<22:03, 1.13it/s, loss=0.674]" ] }, { @@ -20817,7 +20795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 510/2000 [03:20<18:01, 1.38it/s, loss=0.622]" + "training until 2000: 26%|██▌ | 510/2000 [03:17<17:50, 1.39it/s, loss=0.674]" ] }, { @@ -20825,7 +20803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 510/2000 [03:20<18:01, 1.38it/s, loss=0.542]" + "training until 2000: 26%|██▌ | 510/2000 [03:17<17:50, 1.39it/s, loss=0.621]" ] }, { @@ -20833,7 +20811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 511/2000 [03:20<14:54, 1.67it/s, loss=0.542]" + "training until 2000: 26%|██▌ | 511/2000 [03:17<14:47, 1.68it/s, loss=0.621]" ] }, { @@ -20841,7 +20819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 511/2000 [03:20<14:54, 1.67it/s, loss=0.539]" + "training until 2000: 26%|██▌ | 511/2000 [03:17<14:47, 1.68it/s, loss=0.699]" ] }, { @@ -20849,7 +20827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 512/2000 [03:20<12:45, 1.94it/s, loss=0.539]" + "training until 2000: 26%|██▌ | 512/2000 [03:17<12:47, 1.94it/s, loss=0.699]" ] }, { @@ -20857,7 +20835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 512/2000 [03:20<12:45, 1.94it/s, loss=0.538]" + "training until 2000: 26%|██▌ | 512/2000 [03:17<12:47, 1.94it/s, loss=0.656]" ] }, { @@ -20865,7 +20843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 513/2000 [03:21<11:20, 2.18it/s, loss=0.538]" + "training until 2000: 26%|██▌ | 513/2000 [03:18<11:14, 2.20it/s, loss=0.656]" ] }, { @@ -20873,7 +20851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 513/2000 [03:21<11:20, 2.18it/s, loss=0.542]" + "training until 2000: 26%|██▌ | 513/2000 [03:18<11:14, 2.20it/s, loss=0.631]" ] }, { @@ -20881,7 +20859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 514/2000 [03:21<10:19, 2.40it/s, loss=0.542]" + "training until 2000: 26%|██▌ | 514/2000 [03:18<10:11, 2.43it/s, loss=0.631]" ] }, { @@ -20889,7 +20867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 514/2000 [03:21<10:19, 2.40it/s, loss=0.581]" + "training until 2000: 26%|██▌ | 514/2000 [03:18<10:11, 2.43it/s, loss=0.602]" ] }, { @@ -20897,7 +20875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 515/2000 [03:21<09:31, 2.60it/s, loss=0.581]" + "training until 2000: 26%|██▌ | 515/2000 [03:18<09:29, 2.61it/s, loss=0.602]" ] }, { @@ -20905,7 +20883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 515/2000 [03:21<09:31, 2.60it/s, loss=0.575]" + "training until 2000: 26%|██▌ | 515/2000 [03:18<09:29, 2.61it/s, loss=0.662]" ] }, { @@ -20913,7 +20891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 516/2000 [03:22<08:54, 2.77it/s, loss=0.575]" + "training until 2000: 26%|██▌ | 516/2000 [03:19<09:01, 2.74it/s, loss=0.662]" ] }, { @@ -20921,7 +20899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 516/2000 [03:22<08:54, 2.77it/s, loss=0.736]" + "training until 2000: 26%|██▌ | 516/2000 [03:19<09:01, 2.74it/s, loss=0.671]" ] }, { @@ -20929,7 +20907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 517/2000 [03:22<08:33, 2.89it/s, loss=0.736]" + "training until 2000: 26%|██▌ | 517/2000 [03:19<08:36, 2.87it/s, loss=0.671]" ] }, { @@ -20937,7 +20915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 517/2000 [03:22<08:33, 2.89it/s, loss=0.6] " + "training until 2000: 26%|██▌ | 517/2000 [03:19<08:36, 2.87it/s, loss=0.636]" ] }, { @@ -20945,7 +20923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 518/2000 [03:22<08:29, 2.91it/s, loss=0.6]" + "training until 2000: 26%|██▌ | 518/2000 [03:19<08:21, 2.96it/s, loss=0.636]" ] }, { @@ -20953,7 +20931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 518/2000 [03:22<08:29, 2.91it/s, loss=0.608]" + "training until 2000: 26%|██▌ | 518/2000 [03:19<08:21, 2.96it/s, loss=0.689]" ] }, { @@ -20961,7 +20939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 519/2000 [03:23<08:12, 3.01it/s, loss=0.608]" + "training until 2000: 26%|██▌ | 519/2000 [03:20<08:10, 3.02it/s, loss=0.689]" ] }, { @@ -20969,7 +20947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 519/2000 [03:23<08:12, 3.01it/s, loss=0.619]" + "training until 2000: 26%|██▌ | 519/2000 [03:20<08:10, 3.02it/s, loss=0.647]" ] }, { @@ -20977,7 +20955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 520/2000 [03:23<08:09, 3.02it/s, loss=0.619]" + "training until 2000: 26%|██▌ | 520/2000 [03:20<08:04, 3.05it/s, loss=0.647]" ] }, { @@ -20985,7 +20963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 520/2000 [03:23<08:09, 3.02it/s, loss=0.537]" + "training until 2000: 26%|██▌ | 520/2000 [03:20<08:04, 3.05it/s, loss=0.705]" ] }, { @@ -20993,7 +20971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 521/2000 [03:23<08:02, 3.07it/s, loss=0.537]" + "training until 2000: 26%|██▌ | 521/2000 [03:20<08:03, 3.06it/s, loss=0.705]" ] }, { @@ -21001,7 +20979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 521/2000 [03:23<08:02, 3.07it/s, loss=0.554]" + "training until 2000: 26%|██▌ | 521/2000 [03:20<08:03, 3.06it/s, loss=0.656]" ] }, { @@ -21009,7 +20987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 522/2000 [03:23<07:59, 3.08it/s, loss=0.554]" + "training until 2000: 26%|██▌ | 522/2000 [03:21<08:03, 3.06it/s, loss=0.656]" ] }, { @@ -21017,7 +20995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 522/2000 [03:23<07:59, 3.08it/s, loss=0.669]" + "training until 2000: 26%|██▌ | 522/2000 [03:21<08:03, 3.06it/s, loss=0.658]" ] }, { @@ -21025,7 +21003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 523/2000 [03:24<08:01, 3.07it/s, loss=0.669]" + "training until 2000: 26%|██▌ | 523/2000 [03:21<07:58, 3.08it/s, loss=0.658]" ] }, { @@ -21033,7 +21011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 523/2000 [03:24<08:01, 3.07it/s, loss=0.567]" + "training until 2000: 26%|██▌ | 523/2000 [03:21<07:58, 3.08it/s, loss=0.637]" ] }, { @@ -21041,7 +21019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 524/2000 [03:24<08:03, 3.05it/s, loss=0.567]" + "training until 2000: 26%|██▌ | 524/2000 [03:21<07:54, 3.11it/s, loss=0.637]" ] }, { @@ -21049,7 +21027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▌ | 524/2000 [03:24<08:03, 3.05it/s, loss=0.583]" + "training until 2000: 26%|██▌ | 524/2000 [03:21<07:54, 3.11it/s, loss=0.673]" ] }, { @@ -21057,7 +21035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 525/2000 [03:24<08:02, 3.06it/s, loss=0.583]" + "training until 2000: 26%|██▋ | 525/2000 [03:21<07:49, 3.14it/s, loss=0.673]" ] }, { @@ -21065,7 +21043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 525/2000 [03:24<08:02, 3.06it/s, loss=0.554]" + "training until 2000: 26%|██▋ | 525/2000 [03:21<07:49, 3.14it/s, loss=0.64] " ] }, { @@ -21073,7 +21051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 526/2000 [03:25<08:01, 3.06it/s, loss=0.554]" + "training until 2000: 26%|██▋ | 526/2000 [03:22<07:53, 3.11it/s, loss=0.64]" ] }, { @@ -21081,7 +21059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 526/2000 [03:25<08:01, 3.06it/s, loss=0.53] " + "training until 2000: 26%|██▋ | 526/2000 [03:22<07:53, 3.11it/s, loss=0.607]" ] }, { @@ -21089,7 +21067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 527/2000 [03:25<07:58, 3.08it/s, loss=0.53]" + "training until 2000: 26%|██▋ | 527/2000 [03:22<07:53, 3.11it/s, loss=0.607]" ] }, { @@ -21097,7 +21075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 527/2000 [03:25<07:58, 3.08it/s, loss=0.575]" + "training until 2000: 26%|██▋ | 527/2000 [03:22<07:53, 3.11it/s, loss=0.678]" ] }, { @@ -21105,7 +21083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 528/2000 [03:25<07:51, 3.12it/s, loss=0.575]" + "training until 2000: 26%|██▋ | 528/2000 [03:23<09:45, 2.51it/s, loss=0.678]" ] }, { @@ -21113,7 +21091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 528/2000 [03:25<07:51, 3.12it/s, loss=0.563]" + "training until 2000: 26%|██▋ | 528/2000 [03:23<09:45, 2.51it/s, loss=0.612]" ] }, { @@ -21121,7 +21099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 529/2000 [03:26<07:54, 3.10it/s, loss=0.563]" + "training until 2000: 26%|██▋ | 529/2000 [03:23<09:11, 2.67it/s, loss=0.612]" ] }, { @@ -21129,7 +21107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 529/2000 [03:26<07:54, 3.10it/s, loss=0.568]" + "training until 2000: 26%|██▋ | 529/2000 [03:23<09:11, 2.67it/s, loss=0.717]" ] }, { @@ -21137,7 +21115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 530/2000 [03:26<09:45, 2.51it/s, loss=0.568]" + "training until 2000: 26%|██▋ | 530/2000 [03:23<08:42, 2.81it/s, loss=0.717]" ] }, { @@ -21145,7 +21123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 26%|██▋ | 530/2000 [03:26<09:45, 2.51it/s, loss=0.551]" + "training until 2000: 26%|██▋ | 530/2000 [03:23<08:42, 2.81it/s, loss=0.714]" ] }, { @@ -21153,7 +21131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 531/2000 [03:27<09:12, 2.66it/s, loss=0.551]" + "training until 2000: 27%|██▋ | 531/2000 [03:24<08:30, 2.88it/s, loss=0.714]" ] }, { @@ -21161,7 +21139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 531/2000 [03:27<09:12, 2.66it/s, loss=0.58] " + "training until 2000: 27%|██▋ | 531/2000 [03:24<08:30, 2.88it/s, loss=0.646]" ] }, { @@ -21169,7 +21147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 532/2000 [03:27<08:56, 2.74it/s, loss=0.58]" + "training until 2000: 27%|██▋ | 532/2000 [03:24<08:24, 2.91it/s, loss=0.646]" ] }, { @@ -21177,7 +21155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 532/2000 [03:27<08:56, 2.74it/s, loss=0.645]" + "training until 2000: 27%|██▋ | 532/2000 [03:24<08:24, 2.91it/s, loss=0.605]" ] }, { @@ -21185,7 +21163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 533/2000 [03:27<08:44, 2.80it/s, loss=0.645]" + "training until 2000: 27%|██▋ | 533/2000 [03:24<08:13, 2.97it/s, loss=0.605]" ] }, { @@ -21193,7 +21171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 533/2000 [03:27<08:44, 2.80it/s, loss=0.533]" + "training until 2000: 27%|██▋ | 533/2000 [03:24<08:13, 2.97it/s, loss=0.687]" ] }, { @@ -21201,7 +21179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 534/2000 [03:28<08:33, 2.86it/s, loss=0.533]" + "training until 2000: 27%|██▋ | 534/2000 [03:25<08:03, 3.03it/s, loss=0.687]" ] }, { @@ -21209,7 +21187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 534/2000 [03:28<08:33, 2.86it/s, loss=0.51] " + "training until 2000: 27%|██▋ | 534/2000 [03:25<08:03, 3.03it/s, loss=0.725]" ] }, { @@ -21217,7 +21195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 535/2000 [03:28<08:19, 2.93it/s, loss=0.51]" + "training until 2000: 27%|██▋ | 535/2000 [03:25<07:57, 3.07it/s, loss=0.725]" ] }, { @@ -21225,7 +21203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 535/2000 [03:28<08:19, 2.93it/s, loss=0.594]" + "training until 2000: 27%|██▋ | 535/2000 [03:25<07:57, 3.07it/s, loss=0.673]" ] }, { @@ -21233,7 +21211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 536/2000 [03:28<08:09, 2.99it/s, loss=0.594]" + "training until 2000: 27%|██▋ | 536/2000 [03:25<07:54, 3.09it/s, loss=0.673]" ] }, { @@ -21241,7 +21219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 536/2000 [03:28<08:09, 2.99it/s, loss=0.549]" + "training until 2000: 27%|██▋ | 536/2000 [03:25<07:54, 3.09it/s, loss=0.633]" ] }, { @@ -21249,7 +21227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 537/2000 [03:29<08:01, 3.04it/s, loss=0.549]" + "training until 2000: 27%|██▋ | 537/2000 [03:26<07:53, 3.09it/s, loss=0.633]" ] }, { @@ -21257,7 +21235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 537/2000 [03:29<08:01, 3.04it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 537/2000 [03:26<07:53, 3.09it/s, loss=0.607]" ] }, { @@ -21265,7 +21243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 538/2000 [03:29<07:55, 3.07it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 538/2000 [03:26<07:57, 3.06it/s, loss=0.607]" ] }, { @@ -21273,7 +21251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 538/2000 [03:29<07:55, 3.07it/s, loss=0.519]" + "training until 2000: 27%|██▋ | 538/2000 [03:26<07:57, 3.06it/s, loss=0.583]" ] }, { @@ -21281,7 +21259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 539/2000 [03:29<08:00, 3.04it/s, loss=0.519]" + "training until 2000: 27%|██▋ | 539/2000 [03:26<07:54, 3.08it/s, loss=0.583]" ] }, { @@ -21289,7 +21267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 539/2000 [03:29<08:00, 3.04it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 539/2000 [03:26<07:54, 3.08it/s, loss=0.58] " ] }, { @@ -21297,7 +21275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 540/2000 [03:30<08:02, 3.02it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 540/2000 [03:27<07:50, 3.11it/s, loss=0.58]" ] }, { @@ -21305,7 +21283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 540/2000 [03:30<08:02, 3.02it/s, loss=0.63] " + "training until 2000: 27%|██▋ | 540/2000 [03:27<07:50, 3.11it/s, loss=0.661]" ] }, { @@ -21313,7 +21291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 541/2000 [03:30<07:59, 3.04it/s, loss=0.63]" + "training until 2000: 27%|██▋ | 541/2000 [03:27<07:58, 3.05it/s, loss=0.661]" ] }, { @@ -21321,7 +21299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 541/2000 [03:30<07:59, 3.04it/s, loss=0.53]" + "training until 2000: 27%|██▋ | 541/2000 [03:27<07:58, 3.05it/s, loss=0.64] " ] }, { @@ -21329,7 +21307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 542/2000 [03:30<08:01, 3.03it/s, loss=0.53]" + "training until 2000: 27%|██▋ | 542/2000 [03:27<07:58, 3.05it/s, loss=0.64]" ] }, { @@ -21337,7 +21315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 542/2000 [03:30<08:01, 3.03it/s, loss=0.529]" + "training until 2000: 27%|██▋ | 542/2000 [03:27<07:58, 3.05it/s, loss=0.642]" ] }, { @@ -21345,7 +21323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 543/2000 [03:31<08:00, 3.03it/s, loss=0.529]" + "training until 2000: 27%|██▋ | 543/2000 [03:28<07:50, 3.10it/s, loss=0.642]" ] }, { @@ -21353,7 +21331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 543/2000 [03:31<08:00, 3.03it/s, loss=0.541]" + "training until 2000: 27%|██▋ | 543/2000 [03:28<07:50, 3.10it/s, loss=0.667]" ] }, { @@ -21361,7 +21339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 544/2000 [03:31<07:54, 3.07it/s, loss=0.541]" + "training until 2000: 27%|██▋ | 544/2000 [03:28<07:47, 3.11it/s, loss=0.667]" ] }, { @@ -21369,7 +21347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 544/2000 [03:31<07:54, 3.07it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 544/2000 [03:28<07:47, 3.11it/s, loss=0.608]" ] }, { @@ -21377,7 +21355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 545/2000 [03:31<07:56, 3.05it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 545/2000 [03:28<07:42, 3.14it/s, loss=0.608]" ] }, { @@ -21385,7 +21363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 545/2000 [03:31<07:56, 3.05it/s, loss=0.52] " + "training until 2000: 27%|██▋ | 545/2000 [03:28<07:42, 3.14it/s, loss=0.68] " ] }, { @@ -21393,7 +21371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 546/2000 [03:32<07:54, 3.06it/s, loss=0.52]" + "training until 2000: 27%|██▋ | 546/2000 [03:28<07:36, 3.19it/s, loss=0.68]" ] }, { @@ -21401,7 +21379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 546/2000 [03:32<07:54, 3.06it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 546/2000 [03:28<07:36, 3.19it/s, loss=0.637]" ] }, { @@ -21409,7 +21387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 547/2000 [03:32<07:54, 3.07it/s, loss=0.543]" + "training until 2000: 27%|██▋ | 547/2000 [03:29<07:35, 3.19it/s, loss=0.637]" ] }, { @@ -21417,7 +21395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 547/2000 [03:32<07:54, 3.07it/s, loss=0.528]" + "training until 2000: 27%|██▋ | 547/2000 [03:29<07:35, 3.19it/s, loss=0.647]" ] }, { @@ -21425,7 +21403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 548/2000 [03:32<07:55, 3.05it/s, loss=0.528]" + "training until 2000: 27%|██▋ | 548/2000 [03:29<07:35, 3.19it/s, loss=0.647]" ] }, { @@ -21433,7 +21411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 548/2000 [03:32<07:55, 3.05it/s, loss=0.548]" + "training until 2000: 27%|██▋ | 548/2000 [03:29<07:35, 3.19it/s, loss=0.614]" ] }, { @@ -21441,7 +21419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 549/2000 [03:33<07:51, 3.08it/s, loss=0.548]" + "training until 2000: 27%|██▋ | 549/2000 [03:29<07:42, 3.14it/s, loss=0.614]" ] }, { @@ -21449,7 +21427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 27%|██▋ | 549/2000 [03:33<07:51, 3.08it/s, loss=0.613]" + "training until 2000: 27%|██▋ | 549/2000 [03:29<07:42, 3.14it/s, loss=0.654]" ] }, { @@ -21457,7 +21435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 550/2000 [03:33<07:46, 3.11it/s, loss=0.613]" + "training until 2000: 28%|██▊ | 550/2000 [03:30<07:43, 3.13it/s, loss=0.654]" ] }, { @@ -21465,7 +21443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 550/2000 [03:33<07:46, 3.11it/s, loss=0.594]" + "training until 2000: 28%|██▊ | 550/2000 [03:30<07:43, 3.13it/s, loss=0.655]" ] }, { @@ -21473,7 +21451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 551/2000 [03:33<07:48, 3.09it/s, loss=0.594]" + "training until 2000: 28%|██▊ | 551/2000 [03:30<07:40, 3.15it/s, loss=0.655]" ] }, { @@ -21481,7 +21459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 551/2000 [03:33<07:48, 3.09it/s, loss=0.565]" + "training until 2000: 28%|██▊ | 551/2000 [03:30<07:40, 3.15it/s, loss=0.645]" ] }, { @@ -21489,7 +21467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 552/2000 [03:34<07:53, 3.06it/s, loss=0.565]" + "training until 2000: 28%|██▊ | 552/2000 [03:30<07:45, 3.11it/s, loss=0.645]" ] }, { @@ -21497,7 +21475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 552/2000 [03:34<07:53, 3.06it/s, loss=0.586]" + "training until 2000: 28%|██▊ | 552/2000 [03:30<07:45, 3.11it/s, loss=0.644]" ] }, { @@ -21505,7 +21483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 553/2000 [03:34<07:52, 3.06it/s, loss=0.586]" + "training until 2000: 28%|██▊ | 553/2000 [03:31<07:48, 3.09it/s, loss=0.644]" ] }, { @@ -21513,7 +21491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 553/2000 [03:34<07:52, 3.06it/s, loss=0.563]" + "training until 2000: 28%|██▊ | 553/2000 [03:31<07:48, 3.09it/s, loss=0.592]" ] }, { @@ -21521,7 +21499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 554/2000 [03:34<07:51, 3.07it/s, loss=0.563]" + "training until 2000: 28%|██▊ | 554/2000 [03:31<07:50, 3.07it/s, loss=0.592]" ] }, { @@ -21529,7 +21507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 554/2000 [03:34<07:51, 3.07it/s, loss=0.632]" + "training until 2000: 28%|██▊ | 554/2000 [03:31<07:50, 3.07it/s, loss=0.717]" ] }, { @@ -21537,7 +21515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 555/2000 [03:34<07:53, 3.05it/s, loss=0.632]" + "training until 2000: 28%|██▊ | 555/2000 [03:31<07:49, 3.08it/s, loss=0.717]" ] }, { @@ -21545,7 +21523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 555/2000 [03:34<07:53, 3.05it/s, loss=0.532]" + "training until 2000: 28%|██▊ | 555/2000 [03:31<07:49, 3.08it/s, loss=0.61] " ] }, { @@ -21553,7 +21531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 556/2000 [03:35<07:46, 3.09it/s, loss=0.532]" + "training until 2000: 28%|██▊ | 556/2000 [03:32<07:47, 3.09it/s, loss=0.61]" ] }, { @@ -21561,7 +21539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 556/2000 [03:35<07:46, 3.09it/s, loss=0.562]" + "training until 2000: 28%|██▊ | 556/2000 [03:32<07:47, 3.09it/s, loss=0.61]" ] }, { @@ -21569,7 +21547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 557/2000 [03:35<07:43, 3.11it/s, loss=0.562]" + "training until 2000: 28%|██▊ | 557/2000 [03:32<07:44, 3.10it/s, loss=0.61]" ] }, { @@ -21577,7 +21555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 557/2000 [03:35<07:43, 3.11it/s, loss=0.568]" + "training until 2000: 28%|██▊ | 557/2000 [03:32<07:44, 3.10it/s, loss=0.699]" ] }, { @@ -21585,7 +21563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 558/2000 [03:35<07:48, 3.08it/s, loss=0.568]" + "training until 2000: 28%|██▊ | 558/2000 [03:32<07:42, 3.12it/s, loss=0.699]" ] }, { @@ -21593,7 +21571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 558/2000 [03:35<07:48, 3.08it/s, loss=0.557]" + "training until 2000: 28%|██▊ | 558/2000 [03:32<07:42, 3.12it/s, loss=0.651]" ] }, { @@ -21601,7 +21579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 559/2000 [03:36<07:44, 3.10it/s, loss=0.557]" + "training until 2000: 28%|██▊ | 559/2000 [03:33<07:37, 3.15it/s, loss=0.651]" ] }, { @@ -21609,7 +21587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 559/2000 [03:36<07:44, 3.10it/s, loss=0.583]" + "training until 2000: 28%|██▊ | 559/2000 [03:33<07:37, 3.15it/s, loss=0.656]" ] }, { @@ -21617,7 +21595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 560/2000 [03:36<07:49, 3.06it/s, loss=0.583]" + "training until 2000: 28%|██▊ | 560/2000 [03:33<07:40, 3.13it/s, loss=0.656]" ] }, { @@ -21625,7 +21603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 560/2000 [03:36<07:49, 3.06it/s, loss=0.52] " + "training until 2000: 28%|██▊ | 560/2000 [03:33<07:40, 3.13it/s, loss=0.661]" ] }, { @@ -21633,7 +21611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 561/2000 [03:36<07:52, 3.05it/s, loss=0.52]" + "training until 2000: 28%|██▊ | 561/2000 [03:33<07:36, 3.15it/s, loss=0.661]" ] }, { @@ -21641,7 +21619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 561/2000 [03:36<07:52, 3.05it/s, loss=0.547]" + "training until 2000: 28%|██▊ | 561/2000 [03:33<07:36, 3.15it/s, loss=0.601]" ] }, { @@ -21649,7 +21627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 562/2000 [03:37<07:49, 3.06it/s, loss=0.547]" + "training until 2000: 28%|██▊ | 562/2000 [03:34<07:36, 3.15it/s, loss=0.601]" ] }, { @@ -21657,7 +21635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 562/2000 [03:37<07:49, 3.06it/s, loss=0.526]" + "training until 2000: 28%|██▊ | 562/2000 [03:34<07:36, 3.15it/s, loss=0.68] " ] }, { @@ -21665,7 +21643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 563/2000 [03:37<07:47, 3.07it/s, loss=0.526]" + "training until 2000: 28%|██▊ | 563/2000 [03:34<07:35, 3.16it/s, loss=0.68]" ] }, { @@ -21673,7 +21651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 563/2000 [03:37<07:47, 3.07it/s, loss=0.557]" + "training until 2000: 28%|██▊ | 563/2000 [03:34<07:35, 3.16it/s, loss=0.672]" ] }, { @@ -21681,7 +21659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 564/2000 [03:37<07:47, 3.07it/s, loss=0.557]" + "training until 2000: 28%|██▊ | 564/2000 [03:34<07:40, 3.12it/s, loss=0.672]" ] }, { @@ -21689,7 +21667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 564/2000 [03:37<07:47, 3.07it/s, loss=0.71] " + "training until 2000: 28%|██▊ | 564/2000 [03:34<07:40, 3.12it/s, loss=0.612]" ] }, { @@ -21697,7 +21675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 565/2000 [03:38<07:53, 3.03it/s, loss=0.71]" + "training until 2000: 28%|██▊ | 565/2000 [03:35<07:38, 3.13it/s, loss=0.612]" ] }, { @@ -21705,7 +21683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 565/2000 [03:38<07:53, 3.03it/s, loss=0.557]" + "training until 2000: 28%|██▊ | 565/2000 [03:35<07:38, 3.13it/s, loss=0.641]" ] }, { @@ -21713,7 +21691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 566/2000 [03:38<08:00, 2.98it/s, loss=0.557]" + "training until 2000: 28%|██▊ | 566/2000 [03:35<07:36, 3.14it/s, loss=0.641]" ] }, { @@ -21721,7 +21699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 566/2000 [03:38<08:00, 2.98it/s, loss=0.548]" + "training until 2000: 28%|██▊ | 566/2000 [03:35<07:36, 3.14it/s, loss=0.624]" ] }, { @@ -21729,7 +21707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 567/2000 [03:38<08:02, 2.97it/s, loss=0.548]" + "training until 2000: 28%|██▊ | 567/2000 [03:35<07:40, 3.11it/s, loss=0.624]" ] }, { @@ -21737,7 +21715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 567/2000 [03:38<08:02, 2.97it/s, loss=0.545]" + "training until 2000: 28%|██▊ | 567/2000 [03:35<07:40, 3.11it/s, loss=0.642]" ] }, { @@ -21745,7 +21723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 568/2000 [03:39<07:50, 3.04it/s, loss=0.545]" + "training until 2000: 28%|██▊ | 568/2000 [03:35<07:37, 3.13it/s, loss=0.642]" ] }, { @@ -21753,7 +21731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 568/2000 [03:39<07:50, 3.04it/s, loss=0.54] " + "training until 2000: 28%|██▊ | 568/2000 [03:35<07:37, 3.13it/s, loss=0.598]" ] }, { @@ -21761,7 +21739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 569/2000 [03:39<07:49, 3.05it/s, loss=0.54]" + "training until 2000: 28%|██▊ | 569/2000 [03:36<07:35, 3.14it/s, loss=0.598]" ] }, { @@ -21769,7 +21747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 569/2000 [03:39<07:49, 3.05it/s, loss=0.551]" + "training until 2000: 28%|██▊ | 569/2000 [03:36<07:35, 3.14it/s, loss=0.587]" ] }, { @@ -21777,7 +21755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 570/2000 [03:39<07:45, 3.07it/s, loss=0.551]" + "training until 2000: 28%|██▊ | 570/2000 [03:36<07:29, 3.18it/s, loss=0.587]" ] }, { @@ -21785,7 +21763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 28%|██▊ | 570/2000 [03:39<07:45, 3.07it/s, loss=0.605]" + "training until 2000: 28%|██▊ | 570/2000 [03:36<07:29, 3.18it/s, loss=0.619]" ] }, { @@ -21793,7 +21771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 571/2000 [03:40<07:42, 3.09it/s, loss=0.605]" + "training until 2000: 29%|██▊ | 571/2000 [03:36<07:30, 3.17it/s, loss=0.619]" ] }, { @@ -21801,7 +21779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 571/2000 [03:40<07:42, 3.09it/s, loss=0.537]" + "training until 2000: 29%|██▊ | 571/2000 [03:36<07:30, 3.17it/s, loss=0.621]" ] }, { @@ -21809,7 +21787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 572/2000 [03:40<07:36, 3.13it/s, loss=0.537]" + "training until 2000: 29%|██▊ | 572/2000 [03:37<07:32, 3.15it/s, loss=0.621]" ] }, { @@ -21817,7 +21795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 572/2000 [03:40<07:36, 3.13it/s, loss=0.54] " + "training until 2000: 29%|██▊ | 572/2000 [03:37<07:32, 3.15it/s, loss=0.664]" ] }, { @@ -21825,7 +21803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 573/2000 [03:40<07:32, 3.15it/s, loss=0.54]" + "training until 2000: 29%|██▊ | 573/2000 [03:37<07:29, 3.17it/s, loss=0.664]" ] }, { @@ -21833,7 +21811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 573/2000 [03:40<07:32, 3.15it/s, loss=0.555]" + "training until 2000: 29%|██▊ | 573/2000 [03:37<07:29, 3.17it/s, loss=0.672]" ] }, { @@ -21841,7 +21819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 574/2000 [03:41<07:36, 3.12it/s, loss=0.555]" + "training until 2000: 29%|██▊ | 574/2000 [03:37<07:28, 3.18it/s, loss=0.672]" ] }, { @@ -21849,7 +21827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▊ | 574/2000 [03:41<07:36, 3.12it/s, loss=0.591]" + "training until 2000: 29%|██▊ | 574/2000 [03:37<07:28, 3.18it/s, loss=0.684]" ] }, { @@ -21857,7 +21835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 575/2000 [03:41<07:38, 3.11it/s, loss=0.591]" + "training until 2000: 29%|██▉ | 575/2000 [03:38<07:28, 3.18it/s, loss=0.684]" ] }, { @@ -21865,7 +21843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 575/2000 [03:41<07:38, 3.11it/s, loss=0.651]" + "training until 2000: 29%|██▉ | 575/2000 [03:38<07:28, 3.18it/s, loss=0.612]" ] }, { @@ -21873,7 +21851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 576/2000 [03:41<07:40, 3.09it/s, loss=0.651]" + "training until 2000: 29%|██▉ | 576/2000 [03:38<07:30, 3.16it/s, loss=0.612]" ] }, { @@ -21881,7 +21859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 576/2000 [03:41<07:40, 3.09it/s, loss=0.523]" + "training until 2000: 29%|██▉ | 576/2000 [03:38<07:30, 3.16it/s, loss=0.715]" ] }, { @@ -21889,7 +21867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 577/2000 [03:42<07:36, 3.12it/s, loss=0.523]" + "training until 2000: 29%|██▉ | 577/2000 [03:38<07:33, 3.14it/s, loss=0.715]" ] }, { @@ -21897,7 +21875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 577/2000 [03:42<07:36, 3.12it/s, loss=0.576]" + "training until 2000: 29%|██▉ | 577/2000 [03:38<07:33, 3.14it/s, loss=0.701]" ] }, { @@ -21905,7 +21883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 578/2000 [03:42<07:32, 3.15it/s, loss=0.576]" + "training until 2000: 29%|██▉ | 578/2000 [03:39<07:33, 3.14it/s, loss=0.701]" ] }, { @@ -21913,7 +21891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 578/2000 [03:42<07:32, 3.15it/s, loss=0.564]" + "training until 2000: 29%|██▉ | 578/2000 [03:39<07:33, 3.14it/s, loss=0.7] " ] }, { @@ -21921,7 +21899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 579/2000 [03:42<07:25, 3.19it/s, loss=0.564]" + "training until 2000: 29%|██▉ | 579/2000 [03:39<07:39, 3.09it/s, loss=0.7]" ] }, { @@ -21929,7 +21907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 579/2000 [03:42<07:25, 3.19it/s, loss=0.557]" + "training until 2000: 29%|██▉ | 579/2000 [03:39<07:39, 3.09it/s, loss=0.639]" ] }, { @@ -21937,7 +21915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 580/2000 [03:43<07:24, 3.20it/s, loss=0.557]" + "training until 2000: 29%|██▉ | 580/2000 [03:39<07:41, 3.08it/s, loss=0.639]" ] }, { @@ -21945,7 +21923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 580/2000 [03:43<07:24, 3.20it/s, loss=0.549]" + "training until 2000: 29%|██▉ | 580/2000 [03:39<07:41, 3.08it/s, loss=0.692]" ] }, { @@ -21953,7 +21931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 581/2000 [03:43<07:23, 3.20it/s, loss=0.549]" + "training until 2000: 29%|██▉ | 581/2000 [03:40<07:41, 3.08it/s, loss=0.692]" ] }, { @@ -21961,7 +21939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 581/2000 [03:43<07:23, 3.20it/s, loss=0.562]" + "training until 2000: 29%|██▉ | 581/2000 [03:40<07:41, 3.08it/s, loss=0.651]" ] }, { @@ -21969,7 +21947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 582/2000 [03:43<07:23, 3.20it/s, loss=0.562]" + "training until 2000: 29%|██▉ | 582/2000 [03:40<07:42, 3.06it/s, loss=0.651]" ] }, { @@ -21977,7 +21955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 582/2000 [03:43<07:23, 3.20it/s, loss=0.516]" + "training until 2000: 29%|██▉ | 582/2000 [03:40<07:42, 3.06it/s, loss=0.685]" ] }, { @@ -21985,7 +21963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 583/2000 [03:44<07:31, 3.14it/s, loss=0.516]" + "training until 2000: 29%|██▉ | 583/2000 [03:40<07:36, 3.10it/s, loss=0.685]" ] }, { @@ -21993,7 +21971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 583/2000 [03:44<07:31, 3.14it/s, loss=0.636]" + "training until 2000: 29%|██▉ | 583/2000 [03:40<07:36, 3.10it/s, loss=0.559]" ] }, { @@ -22001,7 +21979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 584/2000 [03:44<07:27, 3.17it/s, loss=0.636]" + "training until 2000: 29%|██▉ | 584/2000 [03:41<07:28, 3.16it/s, loss=0.559]" ] }, { @@ -22009,7 +21987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 584/2000 [03:44<07:27, 3.17it/s, loss=0.553]" + "training until 2000: 29%|██▉ | 584/2000 [03:41<07:28, 3.16it/s, loss=0.581]" ] }, { @@ -22017,7 +21995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 585/2000 [03:44<07:24, 3.18it/s, loss=0.553]" + "training until 2000: 29%|██▉ | 585/2000 [03:41<07:27, 3.16it/s, loss=0.581]" ] }, { @@ -22025,7 +22003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 585/2000 [03:44<07:24, 3.18it/s, loss=0.523]" + "training until 2000: 29%|██▉ | 585/2000 [03:41<07:27, 3.16it/s, loss=0.695]" ] }, { @@ -22033,7 +22011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 586/2000 [03:44<07:22, 3.20it/s, loss=0.523]" + "training until 2000: 29%|██▉ | 586/2000 [03:41<07:26, 3.17it/s, loss=0.695]" ] }, { @@ -22041,7 +22019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 586/2000 [03:44<07:22, 3.20it/s, loss=0.549]" + "training until 2000: 29%|██▉ | 586/2000 [03:41<07:26, 3.17it/s, loss=0.636]" ] }, { @@ -22049,7 +22027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 587/2000 [03:45<07:26, 3.16it/s, loss=0.549]" + "training until 2000: 29%|██▉ | 587/2000 [03:42<07:25, 3.17it/s, loss=0.636]" ] }, { @@ -22057,7 +22035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 587/2000 [03:45<07:26, 3.16it/s, loss=0.573]" + "training until 2000: 29%|██▉ | 587/2000 [03:42<07:25, 3.17it/s, loss=0.631]" ] }, { @@ -22065,7 +22043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 588/2000 [03:45<07:29, 3.14it/s, loss=0.573]" + "training until 2000: 29%|██▉ | 588/2000 [03:42<07:26, 3.17it/s, loss=0.631]" ] }, { @@ -22073,7 +22051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 588/2000 [03:45<07:29, 3.14it/s, loss=0.532]" + "training until 2000: 29%|██▉ | 588/2000 [03:42<07:26, 3.17it/s, loss=0.661]" ] }, { @@ -22081,7 +22059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 589/2000 [03:45<07:29, 3.14it/s, loss=0.532]" + "training until 2000: 29%|██▉ | 589/2000 [03:42<07:22, 3.19it/s, loss=0.661]" ] }, { @@ -22089,7 +22067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 29%|██▉ | 589/2000 [03:45<07:29, 3.14it/s, loss=0.536]" + "training until 2000: 29%|██▉ | 589/2000 [03:42<07:22, 3.19it/s, loss=0.672]" ] }, { @@ -22097,7 +22075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 590/2000 [03:46<07:33, 3.11it/s, loss=0.536]" + "training until 2000: 30%|██▉ | 590/2000 [03:42<07:27, 3.15it/s, loss=0.672]" ] }, { @@ -22105,7 +22083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 590/2000 [03:46<07:33, 3.11it/s, loss=0.56] " + "training until 2000: 30%|██▉ | 590/2000 [03:42<07:27, 3.15it/s, loss=0.597]" ] }, { @@ -22113,7 +22091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 591/2000 [03:46<07:34, 3.10it/s, loss=0.56]" + "training until 2000: 30%|██▉ | 591/2000 [03:43<07:23, 3.18it/s, loss=0.597]" ] }, { @@ -22121,7 +22099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 591/2000 [03:46<07:34, 3.10it/s, loss=0.573]" + "training until 2000: 30%|██▉ | 591/2000 [03:43<07:23, 3.18it/s, loss=0.603]" ] }, { @@ -22129,7 +22107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 592/2000 [03:46<07:30, 3.12it/s, loss=0.573]" + "training until 2000: 30%|██▉ | 592/2000 [03:43<07:27, 3.15it/s, loss=0.603]" ] }, { @@ -22137,7 +22115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 592/2000 [03:46<07:30, 3.12it/s, loss=0.55] " + "training until 2000: 30%|██▉ | 592/2000 [03:43<07:27, 3.15it/s, loss=0.621]" ] }, { @@ -22145,7 +22123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 593/2000 [03:47<07:31, 3.12it/s, loss=0.55]" + "training until 2000: 30%|██▉ | 593/2000 [03:44<09:13, 2.54it/s, loss=0.621]" ] }, { @@ -22153,7 +22131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 593/2000 [03:47<07:31, 3.12it/s, loss=0.605]" + "training until 2000: 30%|██▉ | 593/2000 [03:44<09:13, 2.54it/s, loss=0.57] " ] }, { @@ -22161,7 +22139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 594/2000 [03:47<09:02, 2.59it/s, loss=0.605]" + "training until 2000: 30%|██▉ | 594/2000 [03:44<08:41, 2.70it/s, loss=0.57]" ] }, { @@ -22169,7 +22147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 594/2000 [03:47<09:02, 2.59it/s, loss=0.555]" + "training until 2000: 30%|██▉ | 594/2000 [03:44<08:41, 2.70it/s, loss=0.561]" ] }, { @@ -22177,7 +22155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 595/2000 [03:48<08:31, 2.75it/s, loss=0.555]" + "training until 2000: 30%|██▉ | 595/2000 [03:44<08:19, 2.81it/s, loss=0.561]" ] }, { @@ -22185,7 +22163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 595/2000 [03:48<08:31, 2.75it/s, loss=0.52] " + "training until 2000: 30%|██▉ | 595/2000 [03:44<08:19, 2.81it/s, loss=0.581]" ] }, { @@ -22193,7 +22171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 596/2000 [03:48<08:19, 2.81it/s, loss=0.52]" + "training until 2000: 30%|██▉ | 596/2000 [03:45<08:02, 2.91it/s, loss=0.581]" ] }, { @@ -22201,7 +22179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 596/2000 [03:48<08:19, 2.81it/s, loss=0.667]" + "training until 2000: 30%|██▉ | 596/2000 [03:45<08:02, 2.91it/s, loss=0.593]" ] }, { @@ -22209,7 +22187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 597/2000 [03:48<08:01, 2.91it/s, loss=0.667]" + "training until 2000: 30%|██▉ | 597/2000 [03:45<07:53, 2.96it/s, loss=0.593]" ] }, { @@ -22217,7 +22195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 597/2000 [03:48<08:01, 2.91it/s, loss=0.535]" + "training until 2000: 30%|██▉ | 597/2000 [03:45<07:53, 2.96it/s, loss=0.693]" ] }, { @@ -22225,7 +22203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 598/2000 [03:49<07:58, 2.93it/s, loss=0.535]" + "training until 2000: 30%|██▉ | 598/2000 [03:45<07:47, 3.00it/s, loss=0.693]" ] }, { @@ -22233,7 +22211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 598/2000 [03:49<07:58, 2.93it/s, loss=0.578]" + "training until 2000: 30%|██▉ | 598/2000 [03:45<07:47, 3.00it/s, loss=0.652]" ] }, { @@ -22241,7 +22219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 599/2000 [03:49<07:50, 2.98it/s, loss=0.578]" + "training until 2000: 30%|██▉ | 599/2000 [03:46<07:42, 3.03it/s, loss=0.652]" ] }, { @@ -22249,7 +22227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|██▉ | 599/2000 [03:49<07:50, 2.98it/s, loss=0.567]" + "training until 2000: 30%|██▉ | 599/2000 [03:46<07:42, 3.03it/s, loss=0.58] " ] }, { @@ -22257,7 +22235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 600/2000 [03:49<07:45, 3.00it/s, loss=0.567]" + "training until 2000: 30%|███ | 600/2000 [03:46<07:40, 3.04it/s, loss=0.58]" ] }, { @@ -22265,7 +22243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 600/2000 [03:49<07:45, 3.00it/s, loss=0.528]" + "training until 2000: 30%|███ | 600/2000 [03:46<07:40, 3.04it/s, loss=0.681]" ] }, { @@ -22273,7 +22251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 601/2000 [03:50<07:39, 3.05it/s, loss=0.528]" + "training until 2000: 30%|███ | 601/2000 [03:46<07:40, 3.04it/s, loss=0.681]" ] }, { @@ -22281,7 +22259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 601/2000 [03:50<07:39, 3.05it/s, loss=0.534]" + "training until 2000: 30%|███ | 601/2000 [03:46<07:40, 3.04it/s, loss=0.649]" ] }, { @@ -22289,7 +22267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 602/2000 [03:50<07:35, 3.07it/s, loss=0.534]" + "training until 2000: 30%|███ | 602/2000 [03:47<07:36, 3.07it/s, loss=0.649]" ] }, { @@ -22297,7 +22275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 602/2000 [03:50<07:35, 3.07it/s, loss=0.639]" + "training until 2000: 30%|███ | 602/2000 [03:47<07:36, 3.07it/s, loss=0.624]" ] }, { @@ -22305,7 +22283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 603/2000 [03:50<07:35, 3.06it/s, loss=0.639]" + "training until 2000: 30%|███ | 603/2000 [03:47<07:32, 3.09it/s, loss=0.624]" ] }, { @@ -22313,7 +22291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 603/2000 [03:50<07:35, 3.06it/s, loss=0.558]" + "training until 2000: 30%|███ | 603/2000 [03:47<07:32, 3.09it/s, loss=0.662]" ] }, { @@ -22321,7 +22299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 604/2000 [03:50<07:33, 3.08it/s, loss=0.558]" + "training until 2000: 30%|███ | 604/2000 [03:47<07:29, 3.11it/s, loss=0.662]" ] }, { @@ -22329,7 +22307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 604/2000 [03:50<07:33, 3.08it/s, loss=0.584]" + "training until 2000: 30%|███ | 604/2000 [03:47<07:29, 3.11it/s, loss=0.689]" ] }, { @@ -22337,7 +22315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 605/2000 [03:51<07:30, 3.10it/s, loss=0.584]" + "training until 2000: 30%|███ | 605/2000 [03:48<07:22, 3.15it/s, loss=0.689]" ] }, { @@ -22345,7 +22323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 605/2000 [03:51<07:30, 3.10it/s, loss=0.509]" + "training until 2000: 30%|███ | 605/2000 [03:48<07:22, 3.15it/s, loss=0.682]" ] }, { @@ -22353,7 +22331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 606/2000 [03:51<07:26, 3.12it/s, loss=0.509]" + "training until 2000: 30%|███ | 606/2000 [03:48<07:24, 3.14it/s, loss=0.682]" ] }, { @@ -22361,7 +22339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 606/2000 [03:51<07:26, 3.12it/s, loss=0.524]" + "training until 2000: 30%|███ | 606/2000 [03:48<07:24, 3.14it/s, loss=0.626]" ] }, { @@ -22369,7 +22347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 607/2000 [03:51<07:29, 3.10it/s, loss=0.524]" + "training until 2000: 30%|███ | 607/2000 [03:48<07:27, 3.11it/s, loss=0.626]" ] }, { @@ -22377,7 +22355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 607/2000 [03:51<07:29, 3.10it/s, loss=0.541]" + "training until 2000: 30%|███ | 607/2000 [03:48<07:27, 3.11it/s, loss=0.652]" ] }, { @@ -22385,7 +22363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 608/2000 [03:52<07:32, 3.08it/s, loss=0.541]" + "training until 2000: 30%|███ | 608/2000 [03:49<07:31, 3.08it/s, loss=0.652]" ] }, { @@ -22393,7 +22371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 608/2000 [03:52<07:32, 3.08it/s, loss=0.526]" + "training until 2000: 30%|███ | 608/2000 [03:49<07:31, 3.08it/s, loss=0.58] " ] }, { @@ -22401,7 +22379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 609/2000 [03:52<07:32, 3.07it/s, loss=0.526]" + "training until 2000: 30%|███ | 609/2000 [03:49<07:30, 3.09it/s, loss=0.58]" ] }, { @@ -22409,7 +22387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 609/2000 [03:52<07:32, 3.07it/s, loss=0.58] " + "training until 2000: 30%|███ | 609/2000 [03:49<07:30, 3.09it/s, loss=0.585]" ] }, { @@ -22417,7 +22395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 610/2000 [03:52<07:26, 3.11it/s, loss=0.58]" + "training until 2000: 30%|███ | 610/2000 [03:49<07:28, 3.10it/s, loss=0.585]" ] }, { @@ -22425,7 +22403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 30%|███ | 610/2000 [03:52<07:26, 3.11it/s, loss=0.56]" + "training until 2000: 30%|███ | 610/2000 [03:49<07:28, 3.10it/s, loss=0.592]" ] }, { @@ -22433,7 +22411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 611/2000 [03:53<07:26, 3.11it/s, loss=0.56]" + "training until 2000: 31%|███ | 611/2000 [03:49<07:28, 3.10it/s, loss=0.592]" ] }, { @@ -22441,7 +22419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 611/2000 [03:53<07:26, 3.11it/s, loss=0.576]" + "training until 2000: 31%|███ | 611/2000 [03:49<07:28, 3.10it/s, loss=0.73] " ] }, { @@ -22449,7 +22427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 612/2000 [03:53<07:22, 3.14it/s, loss=0.576]" + "training until 2000: 31%|███ | 612/2000 [03:50<07:24, 3.12it/s, loss=0.73]" ] }, { @@ -22457,7 +22435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 612/2000 [03:53<07:22, 3.14it/s, loss=0.578]" + "training until 2000: 31%|███ | 612/2000 [03:50<07:24, 3.12it/s, loss=0.635]" ] }, { @@ -22465,7 +22443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 613/2000 [03:53<07:19, 3.15it/s, loss=0.578]" + "training until 2000: 31%|███ | 613/2000 [03:50<07:26, 3.11it/s, loss=0.635]" ] }, { @@ -22473,7 +22451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 613/2000 [03:53<07:19, 3.15it/s, loss=0.558]" + "training until 2000: 31%|███ | 613/2000 [03:50<07:26, 3.11it/s, loss=0.622]" ] }, { @@ -22481,7 +22459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 614/2000 [03:54<07:15, 3.19it/s, loss=0.558]" + "training until 2000: 31%|███ | 614/2000 [03:50<07:25, 3.11it/s, loss=0.622]" ] }, { @@ -22489,7 +22467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 614/2000 [03:54<07:15, 3.19it/s, loss=0.508]" + "training until 2000: 31%|███ | 614/2000 [03:50<07:25, 3.11it/s, loss=0.706]" ] }, { @@ -22497,7 +22475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 615/2000 [03:54<07:18, 3.16it/s, loss=0.508]" + "training until 2000: 31%|███ | 615/2000 [03:51<07:24, 3.12it/s, loss=0.706]" ] }, { @@ -22505,7 +22483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 615/2000 [03:54<07:18, 3.16it/s, loss=0.519]" + "training until 2000: 31%|███ | 615/2000 [03:51<07:24, 3.12it/s, loss=0.645]" ] }, { @@ -22513,7 +22491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 616/2000 [03:54<07:16, 3.17it/s, loss=0.519]" + "training until 2000: 31%|███ | 616/2000 [03:51<07:26, 3.10it/s, loss=0.645]" ] }, { @@ -22521,7 +22499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 616/2000 [03:54<07:16, 3.17it/s, loss=0.511]" + "training until 2000: 31%|███ | 616/2000 [03:51<07:26, 3.10it/s, loss=0.56] " ] }, { @@ -22529,7 +22507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 617/2000 [03:55<07:16, 3.17it/s, loss=0.511]" + "training until 2000: 31%|███ | 617/2000 [03:51<07:34, 3.04it/s, loss=0.56]" ] }, { @@ -22537,7 +22515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 617/2000 [03:55<07:16, 3.17it/s, loss=0.537]" + "training until 2000: 31%|███ | 617/2000 [03:51<07:34, 3.04it/s, loss=0.643]" ] }, { @@ -22545,7 +22523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 618/2000 [03:55<07:18, 3.15it/s, loss=0.537]" + "training until 2000: 31%|███ | 618/2000 [03:52<07:29, 3.07it/s, loss=0.643]" ] }, { @@ -22553,7 +22531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 618/2000 [03:55<07:18, 3.15it/s, loss=0.566]" + "training until 2000: 31%|███ | 618/2000 [03:52<07:29, 3.07it/s, loss=0.625]" ] }, { @@ -22561,7 +22539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 619/2000 [03:55<07:15, 3.17it/s, loss=0.566]" + "training until 2000: 31%|███ | 619/2000 [03:52<07:22, 3.12it/s, loss=0.625]" ] }, { @@ -22569,7 +22547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 619/2000 [03:55<07:15, 3.17it/s, loss=0.531]" + "training until 2000: 31%|███ | 619/2000 [03:52<07:22, 3.12it/s, loss=0.722]" ] }, { @@ -22577,7 +22555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 620/2000 [03:56<07:14, 3.18it/s, loss=0.531]" + "training until 2000: 31%|███ | 620/2000 [03:52<07:21, 3.13it/s, loss=0.722]" ] }, { @@ -22585,7 +22563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 620/2000 [03:56<07:14, 3.18it/s, loss=0.585]" + "training until 2000: 31%|███ | 620/2000 [03:52<07:21, 3.13it/s, loss=0.599]" ] }, { @@ -22593,7 +22571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 621/2000 [03:56<07:14, 3.17it/s, loss=0.585]" + "training until 2000: 31%|███ | 621/2000 [03:53<07:17, 3.15it/s, loss=0.599]" ] }, { @@ -22601,7 +22579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 621/2000 [03:56<07:14, 3.17it/s, loss=0.571]" + "training until 2000: 31%|███ | 621/2000 [03:53<07:17, 3.15it/s, loss=0.679]" ] }, { @@ -22609,7 +22587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 622/2000 [03:56<07:18, 3.14it/s, loss=0.571]" + "training until 2000: 31%|███ | 622/2000 [03:53<07:17, 3.15it/s, loss=0.679]" ] }, { @@ -22617,7 +22595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 622/2000 [03:56<07:18, 3.14it/s, loss=0.526]" + "training until 2000: 31%|███ | 622/2000 [03:53<07:17, 3.15it/s, loss=0.674]" ] }, { @@ -22625,7 +22603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 623/2000 [03:57<07:22, 3.11it/s, loss=0.526]" + "training until 2000: 31%|███ | 623/2000 [03:53<07:15, 3.16it/s, loss=0.674]" ] }, { @@ -22633,7 +22611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 623/2000 [03:57<07:22, 3.11it/s, loss=0.56] " + "training until 2000: 31%|███ | 623/2000 [03:53<07:15, 3.16it/s, loss=0.663]" ] }, { @@ -22641,7 +22619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 624/2000 [03:57<07:18, 3.14it/s, loss=0.56]" + "training until 2000: 31%|███ | 624/2000 [03:54<07:14, 3.17it/s, loss=0.663]" ] }, { @@ -22649,7 +22627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███ | 624/2000 [03:57<07:18, 3.14it/s, loss=0.512]" + "training until 2000: 31%|███ | 624/2000 [03:54<07:14, 3.17it/s, loss=0.554]" ] }, { @@ -22657,7 +22635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 625/2000 [03:57<07:16, 3.15it/s, loss=0.512]" + "training until 2000: 31%|███▏ | 625/2000 [03:54<07:08, 3.21it/s, loss=0.554]" ] }, { @@ -22665,7 +22643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 625/2000 [03:57<07:16, 3.15it/s, loss=0.545]" + "training until 2000: 31%|███▏ | 625/2000 [03:54<07:08, 3.21it/s, loss=0.635]" ] }, { @@ -22673,7 +22651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 626/2000 [03:57<07:10, 3.19it/s, loss=0.545]" + "training until 2000: 31%|███▏ | 626/2000 [03:54<07:09, 3.20it/s, loss=0.635]" ] }, { @@ -22681,7 +22659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 626/2000 [03:57<07:10, 3.19it/s, loss=0.56] " + "training until 2000: 31%|███▏ | 626/2000 [03:54<07:09, 3.20it/s, loss=0.719]" ] }, { @@ -22689,7 +22667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 627/2000 [03:58<07:09, 3.19it/s, loss=0.56]" + "training until 2000: 31%|███▏ | 627/2000 [03:55<07:12, 3.17it/s, loss=0.719]" ] }, { @@ -22697,7 +22675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 627/2000 [03:58<07:09, 3.19it/s, loss=0.529]" + "training until 2000: 31%|███▏ | 627/2000 [03:55<07:12, 3.17it/s, loss=0.572]" ] }, { @@ -22705,7 +22683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 628/2000 [03:58<07:12, 3.17it/s, loss=0.529]" + "training until 2000: 31%|███▏ | 628/2000 [03:55<07:17, 3.14it/s, loss=0.572]" ] }, { @@ -22713,7 +22691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 628/2000 [03:58<07:12, 3.17it/s, loss=0.557]" + "training until 2000: 31%|███▏ | 628/2000 [03:55<07:17, 3.14it/s, loss=0.655]" ] }, { @@ -22721,7 +22699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 629/2000 [03:58<07:12, 3.17it/s, loss=0.557]" + "training until 2000: 31%|███▏ | 629/2000 [03:55<07:18, 3.13it/s, loss=0.655]" ] }, { @@ -22729,7 +22707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 31%|███▏ | 629/2000 [03:58<07:12, 3.17it/s, loss=0.55] " + "training until 2000: 31%|███▏ | 629/2000 [03:55<07:18, 3.13it/s, loss=0.573]" ] }, { @@ -22737,7 +22715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 630/2000 [03:59<07:11, 3.17it/s, loss=0.55]" + "training until 2000: 32%|███▏ | 630/2000 [03:56<07:19, 3.12it/s, loss=0.573]" ] }, { @@ -22745,7 +22723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 630/2000 [03:59<07:11, 3.17it/s, loss=0.525]" + "training until 2000: 32%|███▏ | 630/2000 [03:56<07:19, 3.12it/s, loss=0.634]" ] }, { @@ -22753,7 +22731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 631/2000 [03:59<07:14, 3.15it/s, loss=0.525]" + "training until 2000: 32%|███▏ | 631/2000 [03:56<07:18, 3.12it/s, loss=0.634]" ] }, { @@ -22761,7 +22739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 631/2000 [03:59<07:14, 3.15it/s, loss=0.541]" + "training until 2000: 32%|███▏ | 631/2000 [03:56<07:18, 3.12it/s, loss=0.572]" ] }, { @@ -22769,7 +22747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 632/2000 [03:59<07:14, 3.15it/s, loss=0.541]" + "training until 2000: 32%|███▏ | 632/2000 [03:56<07:22, 3.09it/s, loss=0.572]" ] }, { @@ -22777,7 +22755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 632/2000 [03:59<07:14, 3.15it/s, loss=0.645]" + "training until 2000: 32%|███▏ | 632/2000 [03:56<07:22, 3.09it/s, loss=0.6] " ] }, { @@ -22785,7 +22763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 633/2000 [04:00<07:16, 3.13it/s, loss=0.645]" + "training until 2000: 32%|███▏ | 633/2000 [03:57<07:23, 3.09it/s, loss=0.6]" ] }, { @@ -22793,7 +22771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 633/2000 [04:00<07:16, 3.13it/s, loss=0.62] " + "training until 2000: 32%|███▏ | 633/2000 [03:57<07:23, 3.09it/s, loss=0.622]" ] }, { @@ -22801,7 +22779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 634/2000 [04:00<07:13, 3.15it/s, loss=0.62]" + "training until 2000: 32%|███▏ | 634/2000 [03:57<07:25, 3.07it/s, loss=0.622]" ] }, { @@ -22809,7 +22787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 634/2000 [04:00<07:13, 3.15it/s, loss=0.552]" + "training until 2000: 32%|███▏ | 634/2000 [03:57<07:25, 3.07it/s, loss=0.64] " ] }, { @@ -22817,7 +22795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 635/2000 [04:00<07:12, 3.16it/s, loss=0.552]" + "training until 2000: 32%|███▏ | 635/2000 [03:57<07:21, 3.09it/s, loss=0.64]" ] }, { @@ -22825,7 +22803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 635/2000 [04:00<07:12, 3.16it/s, loss=0.617]" + "training until 2000: 32%|███▏ | 635/2000 [03:57<07:21, 3.09it/s, loss=0.604]" ] }, { @@ -22833,7 +22811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 636/2000 [04:01<07:12, 3.16it/s, loss=0.617]" + "training until 2000: 32%|███▏ | 636/2000 [03:58<07:26, 3.06it/s, loss=0.604]" ] }, { @@ -22841,7 +22819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 636/2000 [04:01<07:12, 3.16it/s, loss=0.602]" + "training until 2000: 32%|███▏ | 636/2000 [03:58<07:26, 3.06it/s, loss=0.63] " ] }, { @@ -22849,7 +22827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 637/2000 [04:01<07:15, 3.13it/s, loss=0.602]" + "training until 2000: 32%|███▏ | 637/2000 [03:58<07:20, 3.09it/s, loss=0.63]" ] }, { @@ -22857,7 +22835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 637/2000 [04:01<07:15, 3.13it/s, loss=0.539]" + "training until 2000: 32%|███▏ | 637/2000 [03:58<07:20, 3.09it/s, loss=0.651]" ] }, { @@ -22865,7 +22843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 638/2000 [04:01<07:15, 3.13it/s, loss=0.539]" + "training until 2000: 32%|███▏ | 638/2000 [03:58<07:19, 3.10it/s, loss=0.651]" ] }, { @@ -22873,7 +22851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 638/2000 [04:01<07:15, 3.13it/s, loss=0.552]" + "training until 2000: 32%|███▏ | 638/2000 [03:58<07:19, 3.10it/s, loss=0.606]" ] }, { @@ -22881,7 +22859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 639/2000 [04:02<07:11, 3.15it/s, loss=0.552]" + "training until 2000: 32%|███▏ | 639/2000 [03:58<07:19, 3.10it/s, loss=0.606]" ] }, { @@ -22889,7 +22867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 639/2000 [04:02<07:11, 3.15it/s, loss=0.524]" + "training until 2000: 32%|███▏ | 639/2000 [03:58<07:19, 3.10it/s, loss=0.596]" ] }, { @@ -22897,7 +22875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 640/2000 [04:02<07:11, 3.15it/s, loss=0.524]" + "training until 2000: 32%|███▏ | 640/2000 [03:59<07:19, 3.10it/s, loss=0.596]" ] }, { @@ -22905,7 +22883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 640/2000 [04:02<07:11, 3.15it/s, loss=0.621]" + "training until 2000: 32%|███▏ | 640/2000 [03:59<07:19, 3.10it/s, loss=0.613]" ] }, { @@ -22913,7 +22891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 641/2000 [04:02<07:12, 3.14it/s, loss=0.621]" + "training until 2000: 32%|███▏ | 641/2000 [03:59<07:14, 3.13it/s, loss=0.613]" ] }, { @@ -22921,7 +22899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 641/2000 [04:02<07:12, 3.14it/s, loss=0.534]" + "training until 2000: 32%|███▏ | 641/2000 [03:59<07:14, 3.13it/s, loss=0.543]" ] }, { @@ -22929,7 +22907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 642/2000 [04:03<07:11, 3.15it/s, loss=0.534]" + "training until 2000: 32%|███▏ | 642/2000 [03:59<07:13, 3.13it/s, loss=0.543]" ] }, { @@ -22937,7 +22915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 642/2000 [04:03<07:11, 3.15it/s, loss=0.626]" + "training until 2000: 32%|███▏ | 642/2000 [03:59<07:13, 3.13it/s, loss=0.584]" ] }, { @@ -22945,7 +22923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 643/2000 [04:03<07:08, 3.17it/s, loss=0.626]" + "training until 2000: 32%|███▏ | 643/2000 [04:00<07:06, 3.18it/s, loss=0.584]" ] }, { @@ -22953,7 +22931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 643/2000 [04:03<07:08, 3.17it/s, loss=0.559]" + "training until 2000: 32%|███▏ | 643/2000 [04:00<07:06, 3.18it/s, loss=0.673]" ] }, { @@ -22961,7 +22939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 644/2000 [04:03<07:05, 3.19it/s, loss=0.559]" + "training until 2000: 32%|███▏ | 644/2000 [04:00<07:06, 3.18it/s, loss=0.673]" ] }, { @@ -22969,7 +22947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 644/2000 [04:03<07:05, 3.19it/s, loss=0.593]" + "training until 2000: 32%|███▏ | 644/2000 [04:00<07:06, 3.18it/s, loss=0.645]" ] }, { @@ -22977,7 +22955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 645/2000 [04:03<07:06, 3.18it/s, loss=0.593]" + "training until 2000: 32%|███▏ | 645/2000 [04:00<07:21, 3.07it/s, loss=0.645]" ] }, { @@ -22985,7 +22963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 645/2000 [04:03<07:06, 3.18it/s, loss=0.536]" + "training until 2000: 32%|███▏ | 645/2000 [04:00<07:21, 3.07it/s, loss=0.571]" ] }, { @@ -22993,7 +22971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 646/2000 [04:04<07:07, 3.17it/s, loss=0.536]" + "training until 2000: 32%|███▏ | 646/2000 [04:01<07:16, 3.11it/s, loss=0.571]" ] }, { @@ -23001,7 +22979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 646/2000 [04:04<07:07, 3.17it/s, loss=0.556]" + "training until 2000: 32%|███▏ | 646/2000 [04:01<07:16, 3.11it/s, loss=0.621]" ] }, { @@ -23009,7 +22987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 647/2000 [04:04<07:10, 3.14it/s, loss=0.556]" + "training until 2000: 32%|███▏ | 647/2000 [04:01<07:21, 3.07it/s, loss=0.621]" ] }, { @@ -23017,7 +22995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 647/2000 [04:04<07:10, 3.14it/s, loss=0.538]" + "training until 2000: 32%|███▏ | 647/2000 [04:01<07:21, 3.07it/s, loss=0.635]" ] }, { @@ -23025,7 +23003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 648/2000 [04:04<07:13, 3.12it/s, loss=0.538]" + "training until 2000: 32%|███▏ | 648/2000 [04:01<07:18, 3.08it/s, loss=0.635]" ] }, { @@ -23033,7 +23011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 648/2000 [04:04<07:13, 3.12it/s, loss=0.526]" + "training until 2000: 32%|███▏ | 648/2000 [04:01<07:18, 3.08it/s, loss=0.599]" ] }, { @@ -23041,7 +23019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 649/2000 [04:05<07:16, 3.10it/s, loss=0.526]" + "training until 2000: 32%|███▏ | 649/2000 [04:02<07:12, 3.12it/s, loss=0.599]" ] }, { @@ -23049,7 +23027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▏ | 649/2000 [04:05<07:16, 3.10it/s, loss=0.575]" + "training until 2000: 32%|███▏ | 649/2000 [04:02<07:12, 3.12it/s, loss=0.719]" ] }, { @@ -23057,7 +23035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▎ | 650/2000 [04:05<07:13, 3.11it/s, loss=0.575]" + "training until 2000: 32%|███▎ | 650/2000 [04:02<07:11, 3.13it/s, loss=0.719]" ] }, { @@ -23065,7 +23043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 32%|███▎ | 650/2000 [04:05<07:13, 3.11it/s, loss=0.538]" + "training until 2000: 32%|███▎ | 650/2000 [04:02<07:11, 3.13it/s, loss=0.645]" ] }, { @@ -23073,7 +23051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 651/2000 [04:05<07:12, 3.12it/s, loss=0.538]" + "training until 2000: 33%|███▎ | 651/2000 [04:02<07:08, 3.15it/s, loss=0.645]" ] }, { @@ -23081,7 +23059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 651/2000 [04:05<07:12, 3.12it/s, loss=0.559]" + "training until 2000: 33%|███▎ | 651/2000 [04:02<07:08, 3.15it/s, loss=0.579]" ] }, { @@ -23089,7 +23067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 652/2000 [04:06<07:17, 3.08it/s, loss=0.559]" + "training until 2000: 33%|███▎ | 652/2000 [04:03<07:08, 3.15it/s, loss=0.579]" ] }, { @@ -23097,7 +23075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 652/2000 [04:06<07:17, 3.08it/s, loss=0.534]" + "training until 2000: 33%|███▎ | 652/2000 [04:03<07:08, 3.15it/s, loss=0.597]" ] }, { @@ -23105,7 +23083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 653/2000 [04:06<07:17, 3.08it/s, loss=0.534]" + "training until 2000: 33%|███▎ | 653/2000 [04:03<07:10, 3.13it/s, loss=0.597]" ] }, { @@ -23113,7 +23091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 653/2000 [04:06<07:17, 3.08it/s, loss=0.578]" + "training until 2000: 33%|███▎ | 653/2000 [04:03<07:10, 3.13it/s, loss=0.662]" ] }, { @@ -23121,7 +23099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 654/2000 [04:06<07:14, 3.10it/s, loss=0.578]" + "training until 2000: 33%|███▎ | 654/2000 [04:03<07:18, 3.07it/s, loss=0.662]" ] }, { @@ -23129,7 +23107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 654/2000 [04:06<07:14, 3.10it/s, loss=0.567]" + "training until 2000: 33%|███▎ | 654/2000 [04:03<07:18, 3.07it/s, loss=0.657]" ] }, { @@ -23137,7 +23115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 655/2000 [04:07<07:08, 3.14it/s, loss=0.567]" + "training until 2000: 33%|███▎ | 655/2000 [04:04<07:09, 3.13it/s, loss=0.657]" ] }, { @@ -23145,7 +23123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 655/2000 [04:07<07:08, 3.14it/s, loss=0.53] " + "training until 2000: 33%|███▎ | 655/2000 [04:04<07:09, 3.13it/s, loss=0.637]" ] }, { @@ -23153,7 +23131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 656/2000 [04:07<07:09, 3.13it/s, loss=0.53]" + "training until 2000: 33%|███▎ | 656/2000 [04:04<08:43, 2.57it/s, loss=0.637]" ] }, { @@ -23161,7 +23139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 656/2000 [04:07<07:09, 3.13it/s, loss=0.554]" + "training until 2000: 33%|███▎ | 656/2000 [04:04<08:43, 2.57it/s, loss=0.648]" ] }, { @@ -23169,7 +23147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 657/2000 [04:07<07:08, 3.14it/s, loss=0.554]" + "training until 2000: 33%|███▎ | 657/2000 [04:04<08:15, 2.71it/s, loss=0.648]" ] }, { @@ -23177,7 +23155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 657/2000 [04:07<07:08, 3.14it/s, loss=0.554]" + "training until 2000: 33%|███▎ | 657/2000 [04:04<08:15, 2.71it/s, loss=0.616]" ] }, { @@ -23185,7 +23163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 658/2000 [04:08<07:07, 3.14it/s, loss=0.554]" + "training until 2000: 33%|███▎ | 658/2000 [04:05<07:54, 2.83it/s, loss=0.616]" ] }, { @@ -23193,7 +23171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 658/2000 [04:08<07:07, 3.14it/s, loss=0.548]" + "training until 2000: 33%|███▎ | 658/2000 [04:05<07:54, 2.83it/s, loss=0.607]" ] }, { @@ -23201,7 +23179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 659/2000 [04:08<08:42, 2.57it/s, loss=0.548]" + "training until 2000: 33%|███▎ | 659/2000 [04:05<07:36, 2.94it/s, loss=0.607]" ] }, { @@ -23209,7 +23187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 659/2000 [04:08<08:42, 2.57it/s, loss=0.505]" + "training until 2000: 33%|███▎ | 659/2000 [04:05<07:36, 2.94it/s, loss=0.591]" ] }, { @@ -23217,7 +23195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 660/2000 [04:09<08:14, 2.71it/s, loss=0.505]" + "training until 2000: 33%|███▎ | 660/2000 [04:05<07:22, 3.03it/s, loss=0.591]" ] }, { @@ -23225,7 +23203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 660/2000 [04:09<08:14, 2.71it/s, loss=0.567]" + "training until 2000: 33%|███▎ | 660/2000 [04:05<07:22, 3.03it/s, loss=0.565]" ] }, { @@ -23233,7 +23211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 661/2000 [04:09<07:53, 2.83it/s, loss=0.567]" + "training until 2000: 33%|███▎ | 661/2000 [04:06<07:18, 3.05it/s, loss=0.565]" ] }, { @@ -23241,7 +23219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 661/2000 [04:09<07:53, 2.83it/s, loss=0.518]" + "training until 2000: 33%|███▎ | 661/2000 [04:06<07:18, 3.05it/s, loss=0.599]" ] }, { @@ -23249,7 +23227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 662/2000 [04:09<07:37, 2.93it/s, loss=0.518]" + "training until 2000: 33%|███▎ | 662/2000 [04:06<07:13, 3.09it/s, loss=0.599]" ] }, { @@ -23257,7 +23235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 662/2000 [04:09<07:37, 2.93it/s, loss=0.563]" + "training until 2000: 33%|███▎ | 662/2000 [04:06<07:13, 3.09it/s, loss=0.597]" ] }, { @@ -23265,7 +23243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 663/2000 [04:09<07:23, 3.01it/s, loss=0.563]" + "training until 2000: 33%|███▎ | 663/2000 [04:06<07:11, 3.10it/s, loss=0.597]" ] }, { @@ -23273,7 +23251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 663/2000 [04:09<07:23, 3.01it/s, loss=0.553]" + "training until 2000: 33%|███▎ | 663/2000 [04:06<07:11, 3.10it/s, loss=0.571]" ] }, { @@ -23281,7 +23259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 664/2000 [04:10<07:19, 3.04it/s, loss=0.553]" + "training until 2000: 33%|███▎ | 664/2000 [04:07<07:12, 3.09it/s, loss=0.571]" ] }, { @@ -23289,7 +23267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 664/2000 [04:10<07:19, 3.04it/s, loss=0.56] " + "training until 2000: 33%|███▎ | 664/2000 [04:07<07:12, 3.09it/s, loss=0.604]" ] }, { @@ -23297,7 +23275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 665/2000 [04:10<07:18, 3.04it/s, loss=0.56]" + "training until 2000: 33%|███▎ | 665/2000 [04:07<07:10, 3.10it/s, loss=0.604]" ] }, { @@ -23305,7 +23283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 665/2000 [04:10<07:18, 3.04it/s, loss=0.531]" + "training until 2000: 33%|███▎ | 665/2000 [04:07<07:10, 3.10it/s, loss=0.667]" ] }, { @@ -23313,7 +23291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 666/2000 [04:10<07:12, 3.08it/s, loss=0.531]" + "training until 2000: 33%|███▎ | 666/2000 [04:07<07:06, 3.12it/s, loss=0.667]" ] }, { @@ -23321,7 +23299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 666/2000 [04:10<07:12, 3.08it/s, loss=0.576]" + "training until 2000: 33%|███▎ | 666/2000 [04:07<07:06, 3.12it/s, loss=0.616]" ] }, { @@ -23329,7 +23307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 667/2000 [04:11<07:11, 3.09it/s, loss=0.576]" + "training until 2000: 33%|███▎ | 667/2000 [04:08<07:06, 3.12it/s, loss=0.616]" ] }, { @@ -23337,7 +23315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 667/2000 [04:11<07:11, 3.09it/s, loss=0.542]" + "training until 2000: 33%|███▎ | 667/2000 [04:08<07:06, 3.12it/s, loss=0.628]" ] }, { @@ -23345,7 +23323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 668/2000 [04:11<07:09, 3.10it/s, loss=0.542]" + "training until 2000: 33%|███▎ | 668/2000 [04:08<07:02, 3.15it/s, loss=0.628]" ] }, { @@ -23353,7 +23331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 668/2000 [04:11<07:09, 3.10it/s, loss=0.548]" + "training until 2000: 33%|███▎ | 668/2000 [04:08<07:02, 3.15it/s, loss=0.66] " ] }, { @@ -23361,7 +23339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 669/2000 [04:11<07:05, 3.13it/s, loss=0.548]" + "training until 2000: 33%|███▎ | 669/2000 [04:08<07:03, 3.14it/s, loss=0.66]" ] }, { @@ -23369,7 +23347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 33%|███▎ | 669/2000 [04:11<07:05, 3.13it/s, loss=0.569]" + "training until 2000: 33%|███▎ | 669/2000 [04:08<07:03, 3.14it/s, loss=0.553]" ] }, { @@ -23377,7 +23355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 670/2000 [04:12<07:01, 3.15it/s, loss=0.569]" + "training until 2000: 34%|███▎ | 670/2000 [04:09<07:04, 3.13it/s, loss=0.553]" ] }, { @@ -23385,7 +23363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 670/2000 [04:12<07:01, 3.15it/s, loss=0.578]" + "training until 2000: 34%|███▎ | 670/2000 [04:09<07:04, 3.13it/s, loss=0.59] " ] }, { @@ -23393,7 +23371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 671/2000 [04:12<06:57, 3.18it/s, loss=0.578]" + "training until 2000: 34%|███▎ | 671/2000 [04:09<07:01, 3.15it/s, loss=0.59]" ] }, { @@ -23401,7 +23379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 671/2000 [04:12<06:57, 3.18it/s, loss=0.54] " + "training until 2000: 34%|███▎ | 671/2000 [04:09<07:01, 3.15it/s, loss=0.639]" ] }, { @@ -23409,7 +23387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 672/2000 [04:12<07:01, 3.15it/s, loss=0.54]" + "training until 2000: 34%|███▎ | 672/2000 [04:09<07:00, 3.16it/s, loss=0.639]" ] }, { @@ -23417,7 +23395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 672/2000 [04:12<07:01, 3.15it/s, loss=0.51]" + "training until 2000: 34%|███▎ | 672/2000 [04:09<07:00, 3.16it/s, loss=0.595]" ] }, { @@ -23425,7 +23403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 673/2000 [04:13<07:01, 3.15it/s, loss=0.51]" + "training until 2000: 34%|███▎ | 673/2000 [04:10<07:06, 3.11it/s, loss=0.595]" ] }, { @@ -23433,7 +23411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 673/2000 [04:13<07:01, 3.15it/s, loss=0.671]" + "training until 2000: 34%|███▎ | 673/2000 [04:10<07:06, 3.11it/s, loss=0.58] " ] }, { @@ -23441,7 +23419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 674/2000 [04:13<06:58, 3.17it/s, loss=0.671]" + "training until 2000: 34%|███▎ | 674/2000 [04:10<07:02, 3.14it/s, loss=0.58]" ] }, { @@ -23449,7 +23427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▎ | 674/2000 [04:13<06:58, 3.17it/s, loss=0.537]" + "training until 2000: 34%|███▎ | 674/2000 [04:10<07:02, 3.14it/s, loss=0.688]" ] }, { @@ -23457,7 +23435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 675/2000 [04:13<07:03, 3.13it/s, loss=0.537]" + "training until 2000: 34%|███▍ | 675/2000 [04:10<07:05, 3.11it/s, loss=0.688]" ] }, { @@ -23465,7 +23443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 675/2000 [04:13<07:03, 3.13it/s, loss=0.573]" + "training until 2000: 34%|███▍ | 675/2000 [04:10<07:05, 3.11it/s, loss=0.61] " ] }, { @@ -23473,7 +23451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 676/2000 [04:14<07:02, 3.14it/s, loss=0.573]" + "training until 2000: 34%|███▍ | 676/2000 [04:10<07:03, 3.13it/s, loss=0.61]" ] }, { @@ -23481,7 +23459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 676/2000 [04:14<07:02, 3.14it/s, loss=0.615]" + "training until 2000: 34%|███▍ | 676/2000 [04:10<07:03, 3.13it/s, loss=0.576]" ] }, { @@ -23489,7 +23467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 677/2000 [04:14<07:03, 3.12it/s, loss=0.615]" + "training until 2000: 34%|███▍ | 677/2000 [04:11<07:01, 3.14it/s, loss=0.576]" ] }, { @@ -23497,7 +23475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 677/2000 [04:14<07:03, 3.12it/s, loss=0.565]" + "training until 2000: 34%|███▍ | 677/2000 [04:11<07:01, 3.14it/s, loss=0.607]" ] }, { @@ -23505,7 +23483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 678/2000 [04:14<07:04, 3.12it/s, loss=0.565]" + "training until 2000: 34%|███▍ | 678/2000 [04:11<06:57, 3.16it/s, loss=0.607]" ] }, { @@ -23513,7 +23491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 678/2000 [04:14<07:04, 3.12it/s, loss=0.511]" + "training until 2000: 34%|███▍ | 678/2000 [04:11<06:57, 3.16it/s, loss=0.603]" ] }, { @@ -23521,7 +23499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 679/2000 [04:15<07:05, 3.11it/s, loss=0.511]" + "training until 2000: 34%|███▍ | 679/2000 [04:11<06:52, 3.21it/s, loss=0.603]" ] }, { @@ -23529,7 +23507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 679/2000 [04:15<07:05, 3.11it/s, loss=0.59] " + "training until 2000: 34%|███▍ | 679/2000 [04:11<06:52, 3.21it/s, loss=0.537]" ] }, { @@ -23537,7 +23515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 680/2000 [04:15<07:08, 3.08it/s, loss=0.59]" + "training until 2000: 34%|███▍ | 680/2000 [04:12<06:54, 3.18it/s, loss=0.537]" ] }, { @@ -23545,7 +23523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 680/2000 [04:15<07:08, 3.08it/s, loss=0.572]" + "training until 2000: 34%|███▍ | 680/2000 [04:12<06:54, 3.18it/s, loss=0.658]" ] }, { @@ -23553,7 +23531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 681/2000 [04:15<07:06, 3.09it/s, loss=0.572]" + "training until 2000: 34%|███▍ | 681/2000 [04:12<06:52, 3.20it/s, loss=0.658]" ] }, { @@ -23561,7 +23539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 681/2000 [04:15<07:06, 3.09it/s, loss=0.601]" + "training until 2000: 34%|███▍ | 681/2000 [04:12<06:52, 3.20it/s, loss=0.585]" ] }, { @@ -23569,7 +23547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 682/2000 [04:16<07:05, 3.10it/s, loss=0.601]" + "training until 2000: 34%|███▍ | 682/2000 [04:12<06:52, 3.20it/s, loss=0.585]" ] }, { @@ -23577,7 +23555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 682/2000 [04:16<07:05, 3.10it/s, loss=0.59] " + "training until 2000: 34%|███▍ | 682/2000 [04:12<06:52, 3.20it/s, loss=0.599]" ] }, { @@ -23585,7 +23563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 683/2000 [04:16<07:03, 3.11it/s, loss=0.59]" + "training until 2000: 34%|███▍ | 683/2000 [04:13<06:56, 3.16it/s, loss=0.599]" ] }, { @@ -23593,7 +23571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 683/2000 [04:16<07:03, 3.11it/s, loss=0.531]" + "training until 2000: 34%|███▍ | 683/2000 [04:13<06:56, 3.16it/s, loss=0.594]" ] }, { @@ -23601,7 +23579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 684/2000 [04:16<07:01, 3.12it/s, loss=0.531]" + "training until 2000: 34%|███▍ | 684/2000 [04:13<07:01, 3.13it/s, loss=0.594]" ] }, { @@ -23609,7 +23587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 684/2000 [04:16<07:01, 3.12it/s, loss=0.574]" + "training until 2000: 34%|███▍ | 684/2000 [04:13<07:01, 3.13it/s, loss=0.625]" ] }, { @@ -23617,7 +23595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 685/2000 [04:17<06:59, 3.13it/s, loss=0.574]" + "training until 2000: 34%|███▍ | 685/2000 [04:13<06:57, 3.15it/s, loss=0.625]" ] }, { @@ -23625,7 +23603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 685/2000 [04:17<06:59, 3.13it/s, loss=0.548]" + "training until 2000: 34%|███▍ | 685/2000 [04:13<06:57, 3.15it/s, loss=0.591]" ] }, { @@ -23633,7 +23611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 686/2000 [04:17<07:00, 3.12it/s, loss=0.548]" + "training until 2000: 34%|███▍ | 686/2000 [04:14<06:55, 3.16it/s, loss=0.591]" ] }, { @@ -23641,7 +23619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 686/2000 [04:17<07:00, 3.12it/s, loss=0.57] " + "training until 2000: 34%|███▍ | 686/2000 [04:14<06:55, 3.16it/s, loss=0.631]" ] }, { @@ -23649,7 +23627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 687/2000 [04:17<07:00, 3.13it/s, loss=0.57]" + "training until 2000: 34%|███▍ | 687/2000 [04:14<06:51, 3.19it/s, loss=0.631]" ] }, { @@ -23657,7 +23635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 687/2000 [04:17<07:00, 3.13it/s, loss=0.553]" + "training until 2000: 34%|███▍ | 687/2000 [04:14<06:51, 3.19it/s, loss=0.657]" ] }, { @@ -23665,7 +23643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 688/2000 [04:17<07:03, 3.09it/s, loss=0.553]" + "training until 2000: 34%|███▍ | 688/2000 [04:14<06:49, 3.21it/s, loss=0.657]" ] }, { @@ -23673,7 +23651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 688/2000 [04:17<07:03, 3.09it/s, loss=0.551]" + "training until 2000: 34%|███▍ | 688/2000 [04:14<06:49, 3.21it/s, loss=0.57] " ] }, { @@ -23681,7 +23659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 689/2000 [04:18<07:04, 3.09it/s, loss=0.551]" + "training until 2000: 34%|███▍ | 689/2000 [04:15<06:49, 3.20it/s, loss=0.57]" ] }, { @@ -23689,7 +23667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 689/2000 [04:18<07:04, 3.09it/s, loss=0.58] " + "training until 2000: 34%|███▍ | 689/2000 [04:15<06:49, 3.20it/s, loss=0.588]" ] }, { @@ -23697,7 +23675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 690/2000 [04:18<07:00, 3.12it/s, loss=0.58]" + "training until 2000: 34%|███▍ | 690/2000 [04:15<06:53, 3.17it/s, loss=0.588]" ] }, { @@ -23705,7 +23683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 34%|███▍ | 690/2000 [04:18<07:00, 3.12it/s, loss=0.539]" + "training until 2000: 34%|███▍ | 690/2000 [04:15<06:53, 3.17it/s, loss=0.591]" ] }, { @@ -23713,7 +23691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 691/2000 [04:18<06:59, 3.12it/s, loss=0.539]" + "training until 2000: 35%|███▍ | 691/2000 [04:15<06:54, 3.16it/s, loss=0.591]" ] }, { @@ -23721,7 +23699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 691/2000 [04:18<06:59, 3.12it/s, loss=0.526]" + "training until 2000: 35%|███▍ | 691/2000 [04:15<06:54, 3.16it/s, loss=0.545]" ] }, { @@ -23729,7 +23707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 692/2000 [04:19<07:01, 3.10it/s, loss=0.526]" + "training until 2000: 35%|███▍ | 692/2000 [04:16<06:50, 3.18it/s, loss=0.545]" ] }, { @@ -23737,7 +23715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 692/2000 [04:19<07:01, 3.10it/s, loss=0.5] " + "training until 2000: 35%|███▍ | 692/2000 [04:16<06:50, 3.18it/s, loss=0.718]" ] }, { @@ -23745,7 +23723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 693/2000 [04:19<06:56, 3.14it/s, loss=0.5]" + "training until 2000: 35%|███▍ | 693/2000 [04:16<06:51, 3.18it/s, loss=0.718]" ] }, { @@ -23753,7 +23731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 693/2000 [04:19<06:56, 3.14it/s, loss=0.501]" + "training until 2000: 35%|███▍ | 693/2000 [04:16<06:51, 3.18it/s, loss=0.579]" ] }, { @@ -23761,7 +23739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 694/2000 [04:19<06:58, 3.12it/s, loss=0.501]" + "training until 2000: 35%|███▍ | 694/2000 [04:16<06:49, 3.19it/s, loss=0.579]" ] }, { @@ -23769,7 +23747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 694/2000 [04:19<06:58, 3.12it/s, loss=0.601]" + "training until 2000: 35%|███▍ | 694/2000 [04:16<06:49, 3.19it/s, loss=0.661]" ] }, { @@ -23777,7 +23755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 695/2000 [04:20<07:00, 3.10it/s, loss=0.601]" + "training until 2000: 35%|███▍ | 695/2000 [04:16<06:47, 3.20it/s, loss=0.661]" ] }, { @@ -23785,7 +23763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 695/2000 [04:20<07:00, 3.10it/s, loss=0.507]" + "training until 2000: 35%|███▍ | 695/2000 [04:16<06:47, 3.20it/s, loss=0.632]" ] }, { @@ -23793,7 +23771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 696/2000 [04:20<07:04, 3.08it/s, loss=0.507]" + "training until 2000: 35%|███▍ | 696/2000 [04:17<06:49, 3.19it/s, loss=0.632]" ] }, { @@ -23801,7 +23779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 696/2000 [04:20<07:04, 3.08it/s, loss=0.532]" + "training until 2000: 35%|███▍ | 696/2000 [04:17<06:49, 3.19it/s, loss=0.627]" ] }, { @@ -23809,7 +23787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 697/2000 [04:20<07:03, 3.08it/s, loss=0.532]" + "training until 2000: 35%|███▍ | 697/2000 [04:17<06:46, 3.20it/s, loss=0.627]" ] }, { @@ -23817,7 +23795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 697/2000 [04:20<07:03, 3.08it/s, loss=0.528]" + "training until 2000: 35%|███▍ | 697/2000 [04:17<06:46, 3.20it/s, loss=0.657]" ] }, { @@ -23825,7 +23803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 698/2000 [04:21<07:09, 3.03it/s, loss=0.528]" + "training until 2000: 35%|███▍ | 698/2000 [04:17<06:45, 3.21it/s, loss=0.657]" ] }, { @@ -23833,7 +23811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 698/2000 [04:21<07:09, 3.03it/s, loss=0.61] " + "training until 2000: 35%|███▍ | 698/2000 [04:17<06:45, 3.21it/s, loss=0.563]" ] }, { @@ -23841,7 +23819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 699/2000 [04:21<07:05, 3.05it/s, loss=0.61]" + "training until 2000: 35%|███▍ | 699/2000 [04:18<06:51, 3.16it/s, loss=0.563]" ] }, { @@ -23849,7 +23827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▍ | 699/2000 [04:21<07:05, 3.05it/s, loss=0.507]" + "training until 2000: 35%|███▍ | 699/2000 [04:18<06:51, 3.16it/s, loss=0.565]" ] }, { @@ -23857,7 +23835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 700/2000 [04:21<07:12, 3.01it/s, loss=0.507]" + "training until 2000: 35%|███▌ | 700/2000 [04:18<06:56, 3.12it/s, loss=0.565]" ] }, { @@ -23865,7 +23843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 700/2000 [04:21<07:12, 3.01it/s, loss=0.532]" + "training until 2000: 35%|███▌ | 700/2000 [04:18<06:56, 3.12it/s, loss=0.569]" ] }, { @@ -23873,7 +23851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 701/2000 [04:22<07:09, 3.02it/s, loss=0.532]" + "training until 2000: 35%|███▌ | 701/2000 [04:18<06:55, 3.13it/s, loss=0.569]" ] }, { @@ -23881,7 +23859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 701/2000 [04:22<07:09, 3.02it/s, loss=0.54] " + "training until 2000: 35%|███▌ | 701/2000 [04:18<06:55, 3.13it/s, loss=0.636]" ] }, { @@ -23889,7 +23867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 702/2000 [04:22<07:06, 3.04it/s, loss=0.54]" + "training until 2000: 35%|███▌ | 702/2000 [04:19<07:01, 3.08it/s, loss=0.636]" ] }, { @@ -23897,7 +23875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 702/2000 [04:22<07:06, 3.04it/s, loss=0.529]" + "training until 2000: 35%|███▌ | 702/2000 [04:19<07:01, 3.08it/s, loss=0.571]" ] }, { @@ -23905,7 +23883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 703/2000 [04:22<07:06, 3.04it/s, loss=0.529]" + "training until 2000: 35%|███▌ | 703/2000 [04:19<06:55, 3.12it/s, loss=0.571]" ] }, { @@ -23913,7 +23891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 703/2000 [04:22<07:06, 3.04it/s, loss=0.555]" + "training until 2000: 35%|███▌ | 703/2000 [04:19<06:55, 3.12it/s, loss=0.617]" ] }, { @@ -23921,7 +23899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 704/2000 [04:23<07:03, 3.06it/s, loss=0.555]" + "training until 2000: 35%|███▌ | 704/2000 [04:19<06:55, 3.12it/s, loss=0.617]" ] }, { @@ -23929,7 +23907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 704/2000 [04:23<07:03, 3.06it/s, loss=0.564]" + "training until 2000: 35%|███▌ | 704/2000 [04:19<06:55, 3.12it/s, loss=0.543]" ] }, { @@ -23937,7 +23915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 705/2000 [04:23<07:02, 3.06it/s, loss=0.564]" + "training until 2000: 35%|███▌ | 705/2000 [04:20<06:52, 3.14it/s, loss=0.543]" ] }, { @@ -23945,7 +23923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 705/2000 [04:23<07:02, 3.06it/s, loss=0.539]" + "training until 2000: 35%|███▌ | 705/2000 [04:20<06:52, 3.14it/s, loss=0.621]" ] }, { @@ -23953,7 +23931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 706/2000 [04:23<07:00, 3.07it/s, loss=0.539]" + "training until 2000: 35%|███▌ | 706/2000 [04:20<06:54, 3.12it/s, loss=0.621]" ] }, { @@ -23961,7 +23939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 706/2000 [04:23<07:00, 3.07it/s, loss=0.511]" + "training until 2000: 35%|███▌ | 706/2000 [04:20<06:54, 3.12it/s, loss=0.534]" ] }, { @@ -23969,7 +23947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 707/2000 [04:24<06:58, 3.09it/s, loss=0.511]" + "training until 2000: 35%|███▌ | 707/2000 [04:20<06:56, 3.11it/s, loss=0.534]" ] }, { @@ -23977,7 +23955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 707/2000 [04:24<06:58, 3.09it/s, loss=0.493]" + "training until 2000: 35%|███▌ | 707/2000 [04:20<06:56, 3.11it/s, loss=0.548]" ] }, { @@ -23985,7 +23963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 708/2000 [04:24<07:01, 3.07it/s, loss=0.493]" + "training until 2000: 35%|███▌ | 708/2000 [04:21<06:51, 3.14it/s, loss=0.548]" ] }, { @@ -23993,7 +23971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 708/2000 [04:24<07:01, 3.07it/s, loss=0.523]" + "training until 2000: 35%|███▌ | 708/2000 [04:21<06:51, 3.14it/s, loss=0.582]" ] }, { @@ -24001,7 +23979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 709/2000 [04:24<06:59, 3.08it/s, loss=0.523]" + "training until 2000: 35%|███▌ | 709/2000 [04:21<06:51, 3.14it/s, loss=0.582]" ] }, { @@ -24009,7 +23987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 35%|███▌ | 709/2000 [04:24<06:59, 3.08it/s, loss=0.596]" + "training until 2000: 35%|███▌ | 709/2000 [04:21<06:51, 3.14it/s, loss=0.572]" ] }, { @@ -24017,7 +23995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 710/2000 [04:25<07:00, 3.07it/s, loss=0.596]" + "training until 2000: 36%|███▌ | 710/2000 [04:21<06:52, 3.13it/s, loss=0.572]" ] }, { @@ -24025,7 +24003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 710/2000 [04:25<07:00, 3.07it/s, loss=0.581]" + "training until 2000: 36%|███▌ | 710/2000 [04:21<06:52, 3.13it/s, loss=0.688]" ] }, { @@ -24033,7 +24011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 711/2000 [04:25<07:01, 3.06it/s, loss=0.581]" + "training until 2000: 36%|███▌ | 711/2000 [04:22<06:56, 3.10it/s, loss=0.688]" ] }, { @@ -24041,7 +24019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 711/2000 [04:25<07:01, 3.06it/s, loss=0.526]" + "training until 2000: 36%|███▌ | 711/2000 [04:22<06:56, 3.10it/s, loss=0.606]" ] }, { @@ -24049,7 +24027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 712/2000 [04:25<06:58, 3.08it/s, loss=0.526]" + "training until 2000: 36%|███▌ | 712/2000 [04:22<06:59, 3.07it/s, loss=0.606]" ] }, { @@ -24057,7 +24035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 712/2000 [04:25<06:58, 3.08it/s, loss=0.518]" + "training until 2000: 36%|███▌ | 712/2000 [04:22<06:59, 3.07it/s, loss=0.563]" ] }, { @@ -24065,7 +24043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 713/2000 [04:26<07:04, 3.03it/s, loss=0.518]" + "training until 2000: 36%|███▌ | 713/2000 [04:22<06:59, 3.06it/s, loss=0.563]" ] }, { @@ -24073,7 +24051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 713/2000 [04:26<07:04, 3.03it/s, loss=0.511]" + "training until 2000: 36%|███▌ | 713/2000 [04:22<06:59, 3.06it/s, loss=0.59] " ] }, { @@ -24081,7 +24059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 714/2000 [04:26<07:03, 3.03it/s, loss=0.511]" + "training until 2000: 36%|███▌ | 714/2000 [04:23<06:53, 3.11it/s, loss=0.59]" ] }, { @@ -24089,7 +24067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 714/2000 [04:26<07:03, 3.03it/s, loss=0.549]" + "training until 2000: 36%|███▌ | 714/2000 [04:23<06:53, 3.11it/s, loss=0.623]" ] }, { @@ -24097,7 +24075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 715/2000 [04:26<06:58, 3.07it/s, loss=0.549]" + "training until 2000: 36%|███▌ | 715/2000 [04:23<06:48, 3.14it/s, loss=0.623]" ] }, { @@ -24105,7 +24083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 715/2000 [04:26<06:58, 3.07it/s, loss=0.624]" + "training until 2000: 36%|███▌ | 715/2000 [04:23<06:48, 3.14it/s, loss=0.577]" ] }, { @@ -24113,7 +24091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 716/2000 [04:27<07:00, 3.05it/s, loss=0.624]" + "training until 2000: 36%|███▌ | 716/2000 [04:23<06:48, 3.14it/s, loss=0.577]" ] }, { @@ -24121,7 +24099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 716/2000 [04:27<07:00, 3.05it/s, loss=0.536]" + "training until 2000: 36%|███▌ | 716/2000 [04:23<06:48, 3.14it/s, loss=0.608]" ] }, { @@ -24129,7 +24107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 717/2000 [04:27<06:58, 3.07it/s, loss=0.536]" + "training until 2000: 36%|███▌ | 717/2000 [04:24<06:49, 3.13it/s, loss=0.608]" ] }, { @@ -24137,7 +24115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 717/2000 [04:27<06:58, 3.07it/s, loss=0.509]" + "training until 2000: 36%|███▌ | 717/2000 [04:24<06:49, 3.13it/s, loss=0.569]" ] }, { @@ -24145,7 +24123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 718/2000 [04:27<07:00, 3.05it/s, loss=0.509]" + "training until 2000: 36%|███▌ | 718/2000 [04:24<06:46, 3.16it/s, loss=0.569]" ] }, { @@ -24153,7 +24131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 718/2000 [04:27<07:00, 3.05it/s, loss=0.552]" + "training until 2000: 36%|███▌ | 718/2000 [04:24<06:46, 3.16it/s, loss=0.578]" ] }, { @@ -24161,7 +24139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 719/2000 [04:28<06:59, 3.05it/s, loss=0.552]" + "training until 2000: 36%|███▌ | 719/2000 [04:24<06:39, 3.21it/s, loss=0.578]" ] }, { @@ -24169,7 +24147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 719/2000 [04:28<06:59, 3.05it/s, loss=0.543]" + "training until 2000: 36%|███▌ | 719/2000 [04:24<06:39, 3.21it/s, loss=0.698]" ] }, { @@ -24177,7 +24155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 720/2000 [04:28<06:55, 3.08it/s, loss=0.543]" + "training until 2000: 36%|███▌ | 720/2000 [04:25<08:13, 2.59it/s, loss=0.698]" ] }, { @@ -24185,7 +24163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 720/2000 [04:28<06:55, 3.08it/s, loss=0.52] " + "training until 2000: 36%|███▌ | 720/2000 [04:25<08:13, 2.59it/s, loss=0.66] " ] }, { @@ -24193,7 +24171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 721/2000 [04:28<06:53, 3.09it/s, loss=0.52]" + "training until 2000: 36%|███▌ | 721/2000 [04:25<07:53, 2.70it/s, loss=0.66]" ] }, { @@ -24201,7 +24179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 721/2000 [04:28<06:53, 3.09it/s, loss=0.544]" + "training until 2000: 36%|███▌ | 721/2000 [04:25<07:53, 2.70it/s, loss=0.575]" ] }, { @@ -24209,7 +24187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 722/2000 [04:29<06:55, 3.07it/s, loss=0.544]" + "training until 2000: 36%|███▌ | 722/2000 [04:25<07:41, 2.77it/s, loss=0.575]" ] }, { @@ -24217,7 +24195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 722/2000 [04:29<06:55, 3.07it/s, loss=0.537]" + "training until 2000: 36%|███▌ | 722/2000 [04:25<07:41, 2.77it/s, loss=0.613]" ] }, { @@ -24225,7 +24203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 723/2000 [04:29<06:59, 3.04it/s, loss=0.537]" + "training until 2000: 36%|███▌ | 723/2000 [04:26<07:26, 2.86it/s, loss=0.613]" ] }, { @@ -24233,7 +24211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 723/2000 [04:29<06:59, 3.04it/s, loss=0.669]" + "training until 2000: 36%|███▌ | 723/2000 [04:26<07:26, 2.86it/s, loss=0.636]" ] }, { @@ -24241,7 +24219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 724/2000 [04:29<08:31, 2.50it/s, loss=0.669]" + "training until 2000: 36%|███▌ | 724/2000 [04:26<07:15, 2.93it/s, loss=0.636]" ] }, { @@ -24249,7 +24227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▌ | 724/2000 [04:29<08:31, 2.50it/s, loss=0.534]" + "training until 2000: 36%|███▌ | 724/2000 [04:26<07:15, 2.93it/s, loss=0.709]" ] }, { @@ -24257,7 +24235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 725/2000 [04:30<08:02, 2.64it/s, loss=0.534]" + "training until 2000: 36%|███▋ | 725/2000 [04:26<07:09, 2.97it/s, loss=0.709]" ] }, { @@ -24265,7 +24243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 725/2000 [04:30<08:02, 2.64it/s, loss=0.694]" + "training until 2000: 36%|███▋ | 725/2000 [04:26<07:09, 2.97it/s, loss=0.539]" ] }, { @@ -24273,7 +24251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 726/2000 [04:30<07:40, 2.77it/s, loss=0.694]" + "training until 2000: 36%|███▋ | 726/2000 [04:27<07:04, 3.00it/s, loss=0.539]" ] }, { @@ -24281,7 +24259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 726/2000 [04:30<07:40, 2.77it/s, loss=0.529]" + "training until 2000: 36%|███▋ | 726/2000 [04:27<07:04, 3.00it/s, loss=0.576]" ] }, { @@ -24289,7 +24267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 727/2000 [04:30<07:25, 2.86it/s, loss=0.529]" + "training until 2000: 36%|███▋ | 727/2000 [04:27<07:02, 3.02it/s, loss=0.576]" ] }, { @@ -24297,7 +24275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 727/2000 [04:30<07:25, 2.86it/s, loss=0.589]" + "training until 2000: 36%|███▋ | 727/2000 [04:27<07:02, 3.02it/s, loss=0.569]" ] }, { @@ -24305,7 +24283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 728/2000 [04:31<07:13, 2.93it/s, loss=0.589]" + "training until 2000: 36%|███▋ | 728/2000 [04:27<07:01, 3.02it/s, loss=0.569]" ] }, { @@ -24313,7 +24291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 728/2000 [04:31<07:13, 2.93it/s, loss=0.573]" + "training until 2000: 36%|███▋ | 728/2000 [04:27<07:01, 3.02it/s, loss=0.597]" ] }, { @@ -24321,7 +24299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 729/2000 [04:31<07:07, 2.97it/s, loss=0.573]" + "training until 2000: 36%|███▋ | 729/2000 [04:28<06:58, 3.04it/s, loss=0.597]" ] }, { @@ -24329,7 +24307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 729/2000 [04:31<07:07, 2.97it/s, loss=0.533]" + "training until 2000: 36%|███▋ | 729/2000 [04:28<06:58, 3.04it/s, loss=0.632]" ] }, { @@ -24337,7 +24315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 730/2000 [04:31<07:04, 2.99it/s, loss=0.533]" + "training until 2000: 36%|███▋ | 730/2000 [04:28<06:55, 3.06it/s, loss=0.632]" ] }, { @@ -24345,7 +24323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 36%|███▋ | 730/2000 [04:31<07:04, 2.99it/s, loss=0.529]" + "training until 2000: 36%|███▋ | 730/2000 [04:28<06:55, 3.06it/s, loss=0.669]" ] }, { @@ -24353,7 +24331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 731/2000 [04:32<06:57, 3.04it/s, loss=0.529]" + "training until 2000: 37%|███▋ | 731/2000 [04:28<06:55, 3.06it/s, loss=0.669]" ] }, { @@ -24361,7 +24339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 731/2000 [04:32<06:57, 3.04it/s, loss=0.543]" + "training until 2000: 37%|███▋ | 731/2000 [04:28<06:55, 3.06it/s, loss=0.584]" ] }, { @@ -24369,7 +24347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 732/2000 [04:32<06:48, 3.10it/s, loss=0.543]" + "training until 2000: 37%|███▋ | 732/2000 [04:29<06:50, 3.09it/s, loss=0.584]" ] }, { @@ -24377,7 +24355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 732/2000 [04:32<06:48, 3.10it/s, loss=0.549]" + "training until 2000: 37%|███▋ | 732/2000 [04:29<06:50, 3.09it/s, loss=0.64] " ] }, { @@ -24385,7 +24363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 733/2000 [04:32<06:45, 3.12it/s, loss=0.549]" + "training until 2000: 37%|███▋ | 733/2000 [04:29<06:58, 3.03it/s, loss=0.64]" ] }, { @@ -24393,7 +24371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 733/2000 [04:32<06:45, 3.12it/s, loss=0.571]" + "training until 2000: 37%|███▋ | 733/2000 [04:29<06:58, 3.03it/s, loss=0.585]" ] }, { @@ -24401,7 +24379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 734/2000 [04:33<06:45, 3.12it/s, loss=0.571]" + "training until 2000: 37%|███▋ | 734/2000 [04:29<06:49, 3.09it/s, loss=0.585]" ] }, { @@ -24409,7 +24387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 734/2000 [04:33<06:45, 3.12it/s, loss=0.509]" + "training until 2000: 37%|███▋ | 734/2000 [04:29<06:49, 3.09it/s, loss=0.508]" ] }, { @@ -24417,7 +24395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 735/2000 [04:33<06:42, 3.14it/s, loss=0.509]" + "training until 2000: 37%|███▋ | 735/2000 [04:30<06:45, 3.12it/s, loss=0.508]" ] }, { @@ -24425,7 +24403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 735/2000 [04:33<06:42, 3.14it/s, loss=0.531]" + "training until 2000: 37%|███▋ | 735/2000 [04:30<06:45, 3.12it/s, loss=0.715]" ] }, { @@ -24433,7 +24411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 736/2000 [04:33<06:43, 3.13it/s, loss=0.531]" + "training until 2000: 37%|███▋ | 736/2000 [04:30<06:46, 3.11it/s, loss=0.715]" ] }, { @@ -24441,7 +24419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 736/2000 [04:33<06:43, 3.13it/s, loss=0.551]" + "training until 2000: 37%|███▋ | 736/2000 [04:30<06:46, 3.11it/s, loss=0.563]" ] }, { @@ -24449,7 +24427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 737/2000 [04:34<06:45, 3.12it/s, loss=0.551]" + "training until 2000: 37%|███▋ | 737/2000 [04:30<06:48, 3.09it/s, loss=0.563]" ] }, { @@ -24457,7 +24435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 737/2000 [04:34<06:45, 3.12it/s, loss=0.52] " + "training until 2000: 37%|███▋ | 737/2000 [04:30<06:48, 3.09it/s, loss=0.629]" ] }, { @@ -24465,7 +24443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 738/2000 [04:34<06:47, 3.10it/s, loss=0.52]" + "training until 2000: 37%|███▋ | 738/2000 [04:31<06:52, 3.06it/s, loss=0.629]" ] }, { @@ -24473,7 +24451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 738/2000 [04:34<06:47, 3.10it/s, loss=0.498]" + "training until 2000: 37%|███▋ | 738/2000 [04:31<06:52, 3.06it/s, loss=0.552]" ] }, { @@ -24481,7 +24459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 739/2000 [04:34<06:43, 3.12it/s, loss=0.498]" + "training until 2000: 37%|███▋ | 739/2000 [04:31<06:53, 3.05it/s, loss=0.552]" ] }, { @@ -24489,7 +24467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 739/2000 [04:34<06:43, 3.12it/s, loss=0.538]" + "training until 2000: 37%|███▋ | 739/2000 [04:31<06:53, 3.05it/s, loss=0.611]" ] }, { @@ -24497,7 +24475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 740/2000 [04:35<06:45, 3.11it/s, loss=0.538]" + "training until 2000: 37%|███▋ | 740/2000 [04:31<06:47, 3.09it/s, loss=0.611]" ] }, { @@ -24505,7 +24483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 740/2000 [04:35<06:45, 3.11it/s, loss=0.563]" + "training until 2000: 37%|███▋ | 740/2000 [04:31<06:47, 3.09it/s, loss=0.511]" ] }, { @@ -24513,7 +24491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 741/2000 [04:35<06:50, 3.07it/s, loss=0.563]" + "training until 2000: 37%|███▋ | 741/2000 [04:32<06:44, 3.12it/s, loss=0.511]" ] }, { @@ -24521,7 +24499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 741/2000 [04:35<06:50, 3.07it/s, loss=0.527]" + "training until 2000: 37%|███▋ | 741/2000 [04:32<06:44, 3.12it/s, loss=0.54] " ] }, { @@ -24529,7 +24507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 742/2000 [04:35<06:43, 3.12it/s, loss=0.527]" + "training until 2000: 37%|███▋ | 742/2000 [04:32<06:44, 3.11it/s, loss=0.54]" ] }, { @@ -24537,7 +24515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 742/2000 [04:35<06:43, 3.12it/s, loss=0.543]" + "training until 2000: 37%|███▋ | 742/2000 [04:32<06:44, 3.11it/s, loss=0.626]" ] }, { @@ -24545,7 +24523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 743/2000 [04:36<06:42, 3.12it/s, loss=0.543]" + "training until 2000: 37%|███▋ | 743/2000 [04:32<06:40, 3.14it/s, loss=0.626]" ] }, { @@ -24553,7 +24531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 743/2000 [04:36<06:42, 3.12it/s, loss=0.641]" + "training until 2000: 37%|███▋ | 743/2000 [04:32<06:40, 3.14it/s, loss=0.556]" ] }, { @@ -24561,7 +24539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 744/2000 [04:36<06:45, 3.10it/s, loss=0.641]" + "training until 2000: 37%|███▋ | 744/2000 [04:32<06:35, 3.17it/s, loss=0.556]" ] }, { @@ -24569,7 +24547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 744/2000 [04:36<06:45, 3.10it/s, loss=0.501]" + "training until 2000: 37%|███▋ | 744/2000 [04:32<06:35, 3.17it/s, loss=0.598]" ] }, { @@ -24577,7 +24555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 745/2000 [04:36<06:40, 3.14it/s, loss=0.501]" + "training until 2000: 37%|███▋ | 745/2000 [04:33<06:35, 3.17it/s, loss=0.598]" ] }, { @@ -24585,7 +24563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 745/2000 [04:36<06:40, 3.14it/s, loss=0.571]" + "training until 2000: 37%|███▋ | 745/2000 [04:33<06:35, 3.17it/s, loss=0.572]" ] }, { @@ -24593,7 +24571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 746/2000 [04:37<06:40, 3.13it/s, loss=0.571]" + "training until 2000: 37%|███▋ | 746/2000 [04:33<06:36, 3.17it/s, loss=0.572]" ] }, { @@ -24601,7 +24579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 746/2000 [04:37<06:40, 3.13it/s, loss=0.54] " + "training until 2000: 37%|███▋ | 746/2000 [04:33<06:36, 3.17it/s, loss=0.55] " ] }, { @@ -24609,7 +24587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 747/2000 [04:37<06:37, 3.15it/s, loss=0.54]" + "training until 2000: 37%|███▋ | 747/2000 [04:33<06:37, 3.15it/s, loss=0.55]" ] }, { @@ -24617,7 +24595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 747/2000 [04:37<06:37, 3.15it/s, loss=0.531]" + "training until 2000: 37%|███▋ | 747/2000 [04:33<06:37, 3.15it/s, loss=0.696]" ] }, { @@ -24625,7 +24603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 748/2000 [04:37<06:38, 3.14it/s, loss=0.531]" + "training until 2000: 37%|███▋ | 748/2000 [04:34<06:34, 3.17it/s, loss=0.696]" ] }, { @@ -24633,7 +24611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 748/2000 [04:37<06:38, 3.14it/s, loss=0.528]" + "training until 2000: 37%|███▋ | 748/2000 [04:34<06:34, 3.17it/s, loss=0.525]" ] }, { @@ -24641,7 +24619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 749/2000 [04:37<06:34, 3.17it/s, loss=0.528]" + "training until 2000: 37%|███▋ | 749/2000 [04:34<06:37, 3.15it/s, loss=0.525]" ] }, { @@ -24649,7 +24627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 37%|███▋ | 749/2000 [04:37<06:34, 3.17it/s, loss=0.515]" + "training until 2000: 37%|███▋ | 749/2000 [04:34<06:37, 3.15it/s, loss=0.591]" ] }, { @@ -24657,7 +24635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 750/2000 [04:38<06:34, 3.17it/s, loss=0.515]" + "training until 2000: 38%|███▊ | 750/2000 [04:34<06:39, 3.13it/s, loss=0.591]" ] }, { @@ -24665,7 +24643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 750/2000 [04:38<06:34, 3.17it/s, loss=0.507]" + "training until 2000: 38%|███▊ | 750/2000 [04:34<06:39, 3.13it/s, loss=0.584]" ] }, { @@ -24673,7 +24651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 751/2000 [04:38<06:34, 3.16it/s, loss=0.507]" + "training until 2000: 38%|███▊ | 751/2000 [04:35<06:38, 3.13it/s, loss=0.584]" ] }, { @@ -24681,7 +24659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 751/2000 [04:38<06:34, 3.16it/s, loss=0.518]" + "training until 2000: 38%|███▊ | 751/2000 [04:35<06:38, 3.13it/s, loss=0.628]" ] }, { @@ -24689,7 +24667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 752/2000 [04:38<06:34, 3.16it/s, loss=0.518]" + "training until 2000: 38%|███▊ | 752/2000 [04:35<06:38, 3.13it/s, loss=0.628]" ] }, { @@ -24697,7 +24675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 752/2000 [04:38<06:34, 3.16it/s, loss=0.492]" + "training until 2000: 38%|███▊ | 752/2000 [04:35<06:38, 3.13it/s, loss=0.577]" ] }, { @@ -24705,7 +24683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 753/2000 [04:39<06:36, 3.15it/s, loss=0.492]" + "training until 2000: 38%|███▊ | 753/2000 [04:35<06:36, 3.15it/s, loss=0.577]" ] }, { @@ -24713,7 +24691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 753/2000 [04:39<06:36, 3.15it/s, loss=0.49] " + "training until 2000: 38%|███▊ | 753/2000 [04:35<06:36, 3.15it/s, loss=0.565]" ] }, { @@ -24721,7 +24699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 754/2000 [04:39<06:30, 3.19it/s, loss=0.49]" + "training until 2000: 38%|███▊ | 754/2000 [04:36<06:39, 3.12it/s, loss=0.565]" ] }, { @@ -24729,7 +24707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 754/2000 [04:39<06:30, 3.19it/s, loss=0.49]" + "training until 2000: 38%|███▊ | 754/2000 [04:36<06:39, 3.12it/s, loss=0.667]" ] }, { @@ -24737,7 +24715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 755/2000 [04:39<06:30, 3.19it/s, loss=0.49]" + "training until 2000: 38%|███▊ | 755/2000 [04:36<06:33, 3.16it/s, loss=0.667]" ] }, { @@ -24745,7 +24723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 755/2000 [04:39<06:30, 3.19it/s, loss=0.529]" + "training until 2000: 38%|███▊ | 755/2000 [04:36<06:33, 3.16it/s, loss=0.585]" ] }, { @@ -24753,7 +24731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 756/2000 [04:40<06:33, 3.16it/s, loss=0.529]" + "training until 2000: 38%|███▊ | 756/2000 [04:36<06:35, 3.15it/s, loss=0.585]" ] }, { @@ -24761,7 +24739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 756/2000 [04:40<06:33, 3.16it/s, loss=0.502]" + "training until 2000: 38%|███▊ | 756/2000 [04:36<06:35, 3.15it/s, loss=0.563]" ] }, { @@ -24769,7 +24747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 757/2000 [04:40<06:33, 3.16it/s, loss=0.502]" + "training until 2000: 38%|███▊ | 757/2000 [04:37<06:30, 3.18it/s, loss=0.563]" ] }, { @@ -24777,7 +24755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 757/2000 [04:40<06:33, 3.16it/s, loss=0.519]" + "training until 2000: 38%|███▊ | 757/2000 [04:37<06:30, 3.18it/s, loss=0.595]" ] }, { @@ -24785,7 +24763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 758/2000 [04:40<06:32, 3.16it/s, loss=0.519]" + "training until 2000: 38%|███▊ | 758/2000 [04:37<06:32, 3.16it/s, loss=0.595]" ] }, { @@ -24793,7 +24771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 758/2000 [04:40<06:32, 3.16it/s, loss=0.604]" + "training until 2000: 38%|███▊ | 758/2000 [04:37<06:32, 3.16it/s, loss=0.645]" ] }, { @@ -24801,7 +24779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 759/2000 [04:41<06:29, 3.19it/s, loss=0.604]" + "training until 2000: 38%|███▊ | 759/2000 [04:37<06:35, 3.13it/s, loss=0.645]" ] }, { @@ -24809,7 +24787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 759/2000 [04:41<06:29, 3.19it/s, loss=0.609]" + "training until 2000: 38%|███▊ | 759/2000 [04:37<06:35, 3.13it/s, loss=0.592]" ] }, { @@ -24817,7 +24795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 760/2000 [04:41<06:32, 3.16it/s, loss=0.609]" + "training until 2000: 38%|███▊ | 760/2000 [04:38<06:38, 3.11it/s, loss=0.592]" ] }, { @@ -24825,7 +24803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 760/2000 [04:41<06:32, 3.16it/s, loss=0.591]" + "training until 2000: 38%|███▊ | 760/2000 [04:38<06:38, 3.11it/s, loss=0.497]" ] }, { @@ -24833,7 +24811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 761/2000 [04:41<06:30, 3.17it/s, loss=0.591]" + "training until 2000: 38%|███▊ | 761/2000 [04:38<06:39, 3.10it/s, loss=0.497]" ] }, { @@ -24841,7 +24819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 761/2000 [04:41<06:30, 3.17it/s, loss=0.611]" + "training until 2000: 38%|███▊ | 761/2000 [04:38<06:39, 3.10it/s, loss=0.605]" ] }, { @@ -24849,7 +24827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 762/2000 [04:42<06:29, 3.18it/s, loss=0.611]" + "training until 2000: 38%|███▊ | 762/2000 [04:38<06:39, 3.10it/s, loss=0.605]" ] }, { @@ -24857,7 +24835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 762/2000 [04:42<06:29, 3.18it/s, loss=0.55] " + "training until 2000: 38%|███▊ | 762/2000 [04:38<06:39, 3.10it/s, loss=0.617]" ] }, { @@ -24865,7 +24843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 763/2000 [04:42<06:34, 3.13it/s, loss=0.55]" + "training until 2000: 38%|███▊ | 763/2000 [04:39<06:36, 3.12it/s, loss=0.617]" ] }, { @@ -24873,7 +24851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 763/2000 [04:42<06:34, 3.13it/s, loss=0.511]" + "training until 2000: 38%|███▊ | 763/2000 [04:39<06:36, 3.12it/s, loss=0.562]" ] }, { @@ -24881,7 +24859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 764/2000 [04:42<06:39, 3.09it/s, loss=0.511]" + "training until 2000: 38%|███▊ | 764/2000 [04:39<06:34, 3.13it/s, loss=0.562]" ] }, { @@ -24889,7 +24867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 764/2000 [04:42<06:39, 3.09it/s, loss=0.546]" + "training until 2000: 38%|███▊ | 764/2000 [04:39<06:34, 3.13it/s, loss=0.558]" ] }, { @@ -24897,7 +24875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 765/2000 [04:43<06:46, 3.04it/s, loss=0.546]" + "training until 2000: 38%|███▊ | 765/2000 [04:39<06:34, 3.13it/s, loss=0.558]" ] }, { @@ -24905,7 +24883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 765/2000 [04:43<06:46, 3.04it/s, loss=0.547]" + "training until 2000: 38%|███▊ | 765/2000 [04:39<06:34, 3.13it/s, loss=0.552]" ] }, { @@ -24913,7 +24891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 766/2000 [04:43<06:47, 3.03it/s, loss=0.547]" + "training until 2000: 38%|███▊ | 766/2000 [04:39<06:37, 3.10it/s, loss=0.552]" ] }, { @@ -24921,7 +24899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 766/2000 [04:43<06:47, 3.03it/s, loss=0.531]" + "training until 2000: 38%|███▊ | 766/2000 [04:39<06:37, 3.10it/s, loss=0.583]" ] }, { @@ -24929,7 +24907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 767/2000 [04:43<06:51, 3.00it/s, loss=0.531]" + "training until 2000: 38%|███▊ | 767/2000 [04:40<06:41, 3.07it/s, loss=0.583]" ] }, { @@ -24937,7 +24915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 767/2000 [04:43<06:51, 3.00it/s, loss=0.542]" + "training until 2000: 38%|███▊ | 767/2000 [04:40<06:41, 3.07it/s, loss=0.639]" ] }, { @@ -24945,7 +24923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 768/2000 [04:44<06:43, 3.05it/s, loss=0.542]" + "training until 2000: 38%|███▊ | 768/2000 [04:40<06:44, 3.05it/s, loss=0.639]" ] }, { @@ -24953,7 +24931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 768/2000 [04:44<06:43, 3.05it/s, loss=0.583]" + "training until 2000: 38%|███▊ | 768/2000 [04:40<06:44, 3.05it/s, loss=0.528]" ] }, { @@ -24961,7 +24939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 769/2000 [04:44<06:47, 3.02it/s, loss=0.583]" + "training until 2000: 38%|███▊ | 769/2000 [04:40<06:45, 3.04it/s, loss=0.528]" ] }, { @@ -24969,7 +24947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 769/2000 [04:44<06:47, 3.02it/s, loss=0.533]" + "training until 2000: 38%|███▊ | 769/2000 [04:40<06:45, 3.04it/s, loss=0.567]" ] }, { @@ -24977,7 +24955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 770/2000 [04:44<06:43, 3.05it/s, loss=0.533]" + "training until 2000: 38%|███▊ | 770/2000 [04:41<06:40, 3.07it/s, loss=0.567]" ] }, { @@ -24985,7 +24963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 38%|███▊ | 770/2000 [04:44<06:43, 3.05it/s, loss=0.52] " + "training until 2000: 38%|███▊ | 770/2000 [04:41<06:40, 3.07it/s, loss=0.593]" ] }, { @@ -24993,7 +24971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 771/2000 [04:45<06:40, 3.07it/s, loss=0.52]" + "training until 2000: 39%|███▊ | 771/2000 [04:41<06:38, 3.08it/s, loss=0.593]" ] }, { @@ -25001,7 +24979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 771/2000 [04:45<06:40, 3.07it/s, loss=0.525]" + "training until 2000: 39%|███▊ | 771/2000 [04:41<06:38, 3.08it/s, loss=0.571]" ] }, { @@ -25009,7 +24987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 772/2000 [04:45<06:38, 3.08it/s, loss=0.525]" + "training until 2000: 39%|███▊ | 772/2000 [04:41<06:36, 3.09it/s, loss=0.571]" ] }, { @@ -25017,7 +24995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 772/2000 [04:45<06:38, 3.08it/s, loss=0.579]" + "training until 2000: 39%|███▊ | 772/2000 [04:41<06:36, 3.09it/s, loss=0.611]" ] }, { @@ -25025,7 +25003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 773/2000 [04:45<06:36, 3.09it/s, loss=0.579]" + "training until 2000: 39%|███▊ | 773/2000 [04:42<06:38, 3.08it/s, loss=0.611]" ] }, { @@ -25033,7 +25011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 773/2000 [04:45<06:36, 3.09it/s, loss=0.514]" + "training until 2000: 39%|███▊ | 773/2000 [04:42<06:38, 3.08it/s, loss=0.563]" ] }, { @@ -25041,7 +25019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 774/2000 [04:45<06:35, 3.10it/s, loss=0.514]" + "training until 2000: 39%|███▊ | 774/2000 [04:42<06:35, 3.10it/s, loss=0.563]" ] }, { @@ -25049,7 +25027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▊ | 774/2000 [04:45<06:35, 3.10it/s, loss=0.607]" + "training until 2000: 39%|███▊ | 774/2000 [04:42<06:35, 3.10it/s, loss=0.573]" ] }, { @@ -25057,7 +25035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 775/2000 [04:46<06:40, 3.06it/s, loss=0.607]" + "training until 2000: 39%|███▉ | 775/2000 [04:42<06:33, 3.12it/s, loss=0.573]" ] }, { @@ -25065,7 +25043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 775/2000 [04:46<06:40, 3.06it/s, loss=0.544]" + "training until 2000: 39%|███▉ | 775/2000 [04:42<06:33, 3.12it/s, loss=0.531]" ] }, { @@ -25073,7 +25051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 776/2000 [04:46<06:37, 3.08it/s, loss=0.544]" + "training until 2000: 39%|███▉ | 776/2000 [04:43<06:28, 3.15it/s, loss=0.531]" ] }, { @@ -25081,7 +25059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 776/2000 [04:46<06:37, 3.08it/s, loss=0.529]" + "training until 2000: 39%|███▉ | 776/2000 [04:43<06:28, 3.15it/s, loss=0.581]" ] }, { @@ -25089,7 +25067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 777/2000 [04:46<06:37, 3.07it/s, loss=0.529]" + "training until 2000: 39%|███▉ | 777/2000 [04:43<06:27, 3.15it/s, loss=0.581]" ] }, { @@ -25097,7 +25075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 777/2000 [04:46<06:37, 3.07it/s, loss=0.507]" + "training until 2000: 39%|███▉ | 777/2000 [04:43<06:27, 3.15it/s, loss=0.565]" ] }, { @@ -25105,7 +25083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 778/2000 [04:47<06:40, 3.05it/s, loss=0.507]" + "training until 2000: 39%|███▉ | 778/2000 [04:43<06:27, 3.16it/s, loss=0.565]" ] }, { @@ -25113,7 +25091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 778/2000 [04:47<06:40, 3.05it/s, loss=0.529]" + "training until 2000: 39%|███▉ | 778/2000 [04:43<06:27, 3.16it/s, loss=0.614]" ] }, { @@ -25121,7 +25099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 779/2000 [04:47<06:34, 3.10it/s, loss=0.529]" + "training until 2000: 39%|███▉ | 779/2000 [04:44<06:23, 3.18it/s, loss=0.614]" ] }, { @@ -25129,7 +25107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 779/2000 [04:47<06:34, 3.10it/s, loss=0.504]" + "training until 2000: 39%|███▉ | 779/2000 [04:44<06:23, 3.18it/s, loss=0.651]" ] }, { @@ -25137,7 +25115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 780/2000 [04:47<06:32, 3.11it/s, loss=0.504]" + "training until 2000: 39%|███▉ | 780/2000 [04:44<06:21, 3.19it/s, loss=0.651]" ] }, { @@ -25145,7 +25123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 780/2000 [04:47<06:32, 3.11it/s, loss=0.554]" + "training until 2000: 39%|███▉ | 780/2000 [04:44<06:21, 3.19it/s, loss=0.571]" ] }, { @@ -25153,7 +25131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 781/2000 [04:48<06:29, 3.13it/s, loss=0.554]" + "training until 2000: 39%|███▉ | 781/2000 [04:44<06:28, 3.14it/s, loss=0.571]" ] }, { @@ -25161,7 +25139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 781/2000 [04:48<06:29, 3.13it/s, loss=0.614]" + "training until 2000: 39%|███▉ | 781/2000 [04:44<06:28, 3.14it/s, loss=0.557]" ] }, { @@ -25169,7 +25147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 782/2000 [04:48<06:30, 3.12it/s, loss=0.614]" + "training until 2000: 39%|███▉ | 782/2000 [04:45<06:23, 3.18it/s, loss=0.557]" ] }, { @@ -25177,7 +25155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 782/2000 [04:48<06:30, 3.12it/s, loss=0.536]" + "training until 2000: 39%|███▉ | 782/2000 [04:45<06:23, 3.18it/s, loss=0.609]" ] }, { @@ -25185,7 +25163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 783/2000 [04:48<06:29, 3.12it/s, loss=0.536]" + "training until 2000: 39%|███▉ | 783/2000 [04:45<07:52, 2.58it/s, loss=0.609]" ] }, { @@ -25193,7 +25171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 783/2000 [04:48<06:29, 3.12it/s, loss=0.503]" + "training until 2000: 39%|███▉ | 783/2000 [04:45<07:52, 2.58it/s, loss=0.562]" ] }, { @@ -25201,7 +25179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 784/2000 [04:49<06:29, 3.12it/s, loss=0.503]" + "training until 2000: 39%|███▉ | 784/2000 [04:45<07:29, 2.71it/s, loss=0.562]" ] }, { @@ -25209,7 +25187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 784/2000 [04:49<06:29, 3.12it/s, loss=0.504]" + "training until 2000: 39%|███▉ | 784/2000 [04:45<07:29, 2.71it/s, loss=0.541]" ] }, { @@ -25217,7 +25195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 785/2000 [04:49<06:29, 3.12it/s, loss=0.504]" + "training until 2000: 39%|███▉ | 785/2000 [04:46<07:08, 2.84it/s, loss=0.541]" ] }, { @@ -25225,7 +25203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 785/2000 [04:49<06:29, 3.12it/s, loss=0.567]" + "training until 2000: 39%|███▉ | 785/2000 [04:46<07:08, 2.84it/s, loss=0.56] " ] }, { @@ -25233,7 +25211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 786/2000 [04:49<06:29, 3.12it/s, loss=0.567]" + "training until 2000: 39%|███▉ | 786/2000 [04:46<06:56, 2.92it/s, loss=0.56]" ] }, { @@ -25241,7 +25219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 786/2000 [04:49<06:29, 3.12it/s, loss=0.594]" + "training until 2000: 39%|███▉ | 786/2000 [04:46<06:56, 2.92it/s, loss=0.59]" ] }, { @@ -25249,7 +25227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 787/2000 [04:50<06:26, 3.14it/s, loss=0.594]" + "training until 2000: 39%|███▉ | 787/2000 [04:46<06:45, 2.99it/s, loss=0.59]" ] }, { @@ -25257,7 +25235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 787/2000 [04:50<06:26, 3.14it/s, loss=0.565]" + "training until 2000: 39%|███▉ | 787/2000 [04:46<06:45, 2.99it/s, loss=0.504]" ] }, { @@ -25265,7 +25243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 788/2000 [04:50<06:28, 3.12it/s, loss=0.565]" + "training until 2000: 39%|███▉ | 788/2000 [04:47<06:41, 3.02it/s, loss=0.504]" ] }, { @@ -25273,7 +25251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 788/2000 [04:50<06:28, 3.12it/s, loss=0.499]" + "training until 2000: 39%|███▉ | 788/2000 [04:47<06:41, 3.02it/s, loss=0.533]" ] }, { @@ -25281,7 +25259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 789/2000 [04:50<06:27, 3.12it/s, loss=0.499]" + "training until 2000: 39%|███▉ | 789/2000 [04:47<06:32, 3.08it/s, loss=0.533]" ] }, { @@ -25289,7 +25267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 39%|███▉ | 789/2000 [04:50<06:27, 3.12it/s, loss=0.544]" + "training until 2000: 39%|███▉ | 789/2000 [04:47<06:32, 3.08it/s, loss=0.555]" ] }, { @@ -25297,7 +25275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 790/2000 [04:51<07:57, 2.53it/s, loss=0.544]" + "training until 2000: 40%|███▉ | 790/2000 [04:47<06:31, 3.09it/s, loss=0.555]" ] }, { @@ -25305,7 +25283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 790/2000 [04:51<07:57, 2.53it/s, loss=0.51] " + "training until 2000: 40%|███▉ | 790/2000 [04:47<06:31, 3.09it/s, loss=0.577]" ] }, { @@ -25313,7 +25291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 791/2000 [04:51<07:33, 2.67it/s, loss=0.51]" + "training until 2000: 40%|███▉ | 791/2000 [04:48<06:27, 3.12it/s, loss=0.577]" ] }, { @@ -25321,7 +25299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 791/2000 [04:51<07:33, 2.67it/s, loss=0.516]" + "training until 2000: 40%|███▉ | 791/2000 [04:48<06:27, 3.12it/s, loss=0.584]" ] }, { @@ -25329,7 +25307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 792/2000 [04:52<07:14, 2.78it/s, loss=0.516]" + "training until 2000: 40%|███▉ | 792/2000 [04:48<06:25, 3.14it/s, loss=0.584]" ] }, { @@ -25337,7 +25315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 792/2000 [04:52<07:14, 2.78it/s, loss=0.524]" + "training until 2000: 40%|███▉ | 792/2000 [04:48<06:25, 3.14it/s, loss=0.507]" ] }, { @@ -25345,7 +25323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 793/2000 [04:52<07:02, 2.85it/s, loss=0.524]" + "training until 2000: 40%|███▉ | 793/2000 [04:48<06:26, 3.12it/s, loss=0.507]" ] }, { @@ -25353,7 +25331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 793/2000 [04:52<07:02, 2.85it/s, loss=0.563]" + "training until 2000: 40%|███▉ | 793/2000 [04:48<06:26, 3.12it/s, loss=0.535]" ] }, { @@ -25361,7 +25339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 794/2000 [04:52<06:54, 2.91it/s, loss=0.563]" + "training until 2000: 40%|███▉ | 794/2000 [04:49<06:24, 3.14it/s, loss=0.535]" ] }, { @@ -25369,7 +25347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 794/2000 [04:52<06:54, 2.91it/s, loss=0.587]" + "training until 2000: 40%|███▉ | 794/2000 [04:49<06:24, 3.14it/s, loss=0.606]" ] }, { @@ -25377,7 +25355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 795/2000 [04:53<06:48, 2.95it/s, loss=0.587]" + "training until 2000: 40%|███▉ | 795/2000 [04:49<06:22, 3.15it/s, loss=0.606]" ] }, { @@ -25385,7 +25363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 795/2000 [04:53<06:48, 2.95it/s, loss=0.562]" + "training until 2000: 40%|███▉ | 795/2000 [04:49<06:22, 3.15it/s, loss=0.541]" ] }, { @@ -25393,7 +25371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 796/2000 [04:53<06:41, 3.00it/s, loss=0.562]" + "training until 2000: 40%|███▉ | 796/2000 [04:49<06:23, 3.14it/s, loss=0.541]" ] }, { @@ -25401,7 +25379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 796/2000 [04:53<06:41, 3.00it/s, loss=0.526]" + "training until 2000: 40%|███▉ | 796/2000 [04:49<06:23, 3.14it/s, loss=0.525]" ] }, { @@ -25409,7 +25387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 797/2000 [04:53<06:40, 3.00it/s, loss=0.526]" + "training until 2000: 40%|███▉ | 797/2000 [04:50<06:24, 3.13it/s, loss=0.525]" ] }, { @@ -25417,7 +25395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 797/2000 [04:53<06:40, 3.00it/s, loss=0.536]" + "training until 2000: 40%|███▉ | 797/2000 [04:50<06:24, 3.13it/s, loss=0.625]" ] }, { @@ -25425,7 +25403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 798/2000 [04:54<06:41, 3.00it/s, loss=0.536]" + "training until 2000: 40%|███▉ | 798/2000 [04:50<06:21, 3.15it/s, loss=0.625]" ] }, { @@ -25433,7 +25411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 798/2000 [04:54<06:41, 3.00it/s, loss=0.604]" + "training until 2000: 40%|███▉ | 798/2000 [04:50<06:21, 3.15it/s, loss=0.574]" ] }, { @@ -25441,7 +25419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 799/2000 [04:54<06:41, 2.99it/s, loss=0.604]" + "training until 2000: 40%|███▉ | 799/2000 [04:50<06:16, 3.19it/s, loss=0.574]" ] }, { @@ -25449,7 +25427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|███▉ | 799/2000 [04:54<06:41, 2.99it/s, loss=0.494]" + "training until 2000: 40%|███▉ | 799/2000 [04:50<06:16, 3.19it/s, loss=0.549]" ] }, { @@ -25457,7 +25435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 800/2000 [04:54<06:37, 3.02it/s, loss=0.494]" + "training until 2000: 40%|████ | 800/2000 [04:51<06:25, 3.11it/s, loss=0.549]" ] }, { @@ -25465,7 +25443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 800/2000 [04:54<06:37, 3.02it/s, loss=0.561]" + "training until 2000: 40%|████ | 800/2000 [04:51<06:25, 3.11it/s, loss=0.612]" ] }, { @@ -25473,7 +25451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 801/2000 [04:55<06:36, 3.03it/s, loss=0.561]" + "training until 2000: 40%|████ | 801/2000 [04:51<06:31, 3.06it/s, loss=0.612]" ] }, { @@ -25481,7 +25459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 801/2000 [04:55<06:36, 3.03it/s, loss=0.53] " + "training until 2000: 40%|████ | 801/2000 [04:51<06:31, 3.06it/s, loss=0.606]" ] }, { @@ -25489,7 +25467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 802/2000 [04:55<06:30, 3.06it/s, loss=0.53]" + "training until 2000: 40%|████ | 802/2000 [04:51<06:27, 3.09it/s, loss=0.606]" ] }, { @@ -25497,7 +25475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 802/2000 [04:55<06:30, 3.06it/s, loss=0.54]" + "training until 2000: 40%|████ | 802/2000 [04:51<06:27, 3.09it/s, loss=0.591]" ] }, { @@ -25505,7 +25483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 803/2000 [04:55<06:33, 3.04it/s, loss=0.54]" + "training until 2000: 40%|████ | 803/2000 [04:52<06:27, 3.09it/s, loss=0.591]" ] }, { @@ -25513,7 +25491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 803/2000 [04:55<06:33, 3.04it/s, loss=0.524]" + "training until 2000: 40%|████ | 803/2000 [04:52<06:27, 3.09it/s, loss=0.553]" ] }, { @@ -25521,7 +25499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 804/2000 [04:55<06:30, 3.06it/s, loss=0.524]" + "training until 2000: 40%|████ | 804/2000 [04:52<06:23, 3.12it/s, loss=0.553]" ] }, { @@ -25529,7 +25507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 804/2000 [04:55<06:30, 3.06it/s, loss=0.52] " + "training until 2000: 40%|████ | 804/2000 [04:52<06:23, 3.12it/s, loss=0.489]" ] }, { @@ -25537,7 +25515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 805/2000 [04:56<06:29, 3.07it/s, loss=0.52]" + "training until 2000: 40%|████ | 805/2000 [04:52<06:24, 3.11it/s, loss=0.489]" ] }, { @@ -25545,7 +25523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 805/2000 [04:56<06:29, 3.07it/s, loss=0.569]" + "training until 2000: 40%|████ | 805/2000 [04:52<06:24, 3.11it/s, loss=0.554]" ] }, { @@ -25553,7 +25531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 806/2000 [04:56<06:26, 3.09it/s, loss=0.569]" + "training until 2000: 40%|████ | 806/2000 [04:52<06:23, 3.12it/s, loss=0.554]" ] }, { @@ -25561,7 +25539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 806/2000 [04:56<06:26, 3.09it/s, loss=0.515]" + "training until 2000: 40%|████ | 806/2000 [04:52<06:23, 3.12it/s, loss=0.539]" ] }, { @@ -25569,7 +25547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 807/2000 [04:56<06:25, 3.09it/s, loss=0.515]" + "training until 2000: 40%|████ | 807/2000 [04:53<06:19, 3.15it/s, loss=0.539]" ] }, { @@ -25577,7 +25555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 807/2000 [04:56<06:25, 3.09it/s, loss=0.5] " + "training until 2000: 40%|████ | 807/2000 [04:53<06:19, 3.15it/s, loss=0.563]" ] }, { @@ -25585,7 +25563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 808/2000 [04:57<06:24, 3.10it/s, loss=0.5]" + "training until 2000: 40%|████ | 808/2000 [04:53<06:20, 3.14it/s, loss=0.563]" ] }, { @@ -25593,7 +25571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 808/2000 [04:57<06:24, 3.10it/s, loss=0.5]" + "training until 2000: 40%|████ | 808/2000 [04:53<06:20, 3.14it/s, loss=0.61] " ] }, { @@ -25601,7 +25579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 809/2000 [04:57<06:21, 3.12it/s, loss=0.5]" + "training until 2000: 40%|████ | 809/2000 [04:53<06:19, 3.13it/s, loss=0.61]" ] }, { @@ -25609,7 +25587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 809/2000 [04:57<06:21, 3.12it/s, loss=0.526]" + "training until 2000: 40%|████ | 809/2000 [04:53<06:19, 3.13it/s, loss=0.573]" ] }, { @@ -25617,7 +25595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 810/2000 [04:57<06:23, 3.11it/s, loss=0.526]" + "training until 2000: 40%|████ | 810/2000 [04:54<06:20, 3.13it/s, loss=0.573]" ] }, { @@ -25625,7 +25603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 40%|████ | 810/2000 [04:57<06:23, 3.11it/s, loss=0.554]" + "training until 2000: 40%|████ | 810/2000 [04:54<06:20, 3.13it/s, loss=0.567]" ] }, { @@ -25633,7 +25611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 811/2000 [04:58<06:22, 3.11it/s, loss=0.554]" + "training until 2000: 41%|████ | 811/2000 [04:54<06:16, 3.16it/s, loss=0.567]" ] }, { @@ -25641,7 +25619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 811/2000 [04:58<06:22, 3.11it/s, loss=0.523]" + "training until 2000: 41%|████ | 811/2000 [04:54<06:16, 3.16it/s, loss=0.513]" ] }, { @@ -25649,7 +25627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 812/2000 [04:58<06:27, 3.07it/s, loss=0.523]" + "training until 2000: 41%|████ | 812/2000 [04:54<06:18, 3.14it/s, loss=0.513]" ] }, { @@ -25657,7 +25635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 812/2000 [04:58<06:27, 3.07it/s, loss=0.525]" + "training until 2000: 41%|████ | 812/2000 [04:54<06:18, 3.14it/s, loss=0.545]" ] }, { @@ -25665,7 +25643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 813/2000 [04:58<06:23, 3.10it/s, loss=0.525]" + "training until 2000: 41%|████ | 813/2000 [04:55<06:16, 3.15it/s, loss=0.545]" ] }, { @@ -25673,7 +25651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 813/2000 [04:58<06:23, 3.10it/s, loss=0.533]" + "training until 2000: 41%|████ | 813/2000 [04:55<06:16, 3.15it/s, loss=0.547]" ] }, { @@ -25681,7 +25659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 814/2000 [04:59<06:20, 3.12it/s, loss=0.533]" + "training until 2000: 41%|████ | 814/2000 [04:55<06:16, 3.15it/s, loss=0.547]" ] }, { @@ -25689,7 +25667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 814/2000 [04:59<06:20, 3.12it/s, loss=0.543]" + "training until 2000: 41%|████ | 814/2000 [04:55<06:16, 3.15it/s, loss=0.526]" ] }, { @@ -25697,7 +25675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 815/2000 [04:59<06:14, 3.17it/s, loss=0.543]" + "training until 2000: 41%|████ | 815/2000 [04:55<06:15, 3.15it/s, loss=0.526]" ] }, { @@ -25705,7 +25683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 815/2000 [04:59<06:14, 3.17it/s, loss=0.503]" + "training until 2000: 41%|████ | 815/2000 [04:55<06:15, 3.15it/s, loss=0.595]" ] }, { @@ -25713,7 +25691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 816/2000 [04:59<06:16, 3.14it/s, loss=0.503]" + "training until 2000: 41%|████ | 816/2000 [04:56<06:15, 3.15it/s, loss=0.595]" ] }, { @@ -25721,7 +25699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 816/2000 [04:59<06:16, 3.14it/s, loss=0.565]" + "training until 2000: 41%|████ | 816/2000 [04:56<06:15, 3.15it/s, loss=0.562]" ] }, { @@ -25729,7 +25707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 817/2000 [05:00<06:17, 3.14it/s, loss=0.565]" + "training until 2000: 41%|████ | 817/2000 [04:56<06:13, 3.17it/s, loss=0.562]" ] }, { @@ -25737,7 +25715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 817/2000 [05:00<06:17, 3.14it/s, loss=0.565]" + "training until 2000: 41%|████ | 817/2000 [04:56<06:13, 3.17it/s, loss=0.541]" ] }, { @@ -25745,7 +25723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 818/2000 [05:00<06:20, 3.10it/s, loss=0.565]" + "training until 2000: 41%|████ | 818/2000 [04:56<06:09, 3.20it/s, loss=0.541]" ] }, { @@ -25753,7 +25731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 818/2000 [05:00<06:20, 3.10it/s, loss=0.565]" + "training until 2000: 41%|████ | 818/2000 [04:56<06:09, 3.20it/s, loss=0.557]" ] }, { @@ -25761,7 +25739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 819/2000 [05:00<06:21, 3.10it/s, loss=0.565]" + "training until 2000: 41%|████ | 819/2000 [04:57<06:09, 3.20it/s, loss=0.557]" ] }, { @@ -25769,7 +25747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 819/2000 [05:00<06:21, 3.10it/s, loss=0.578]" + "training until 2000: 41%|████ | 819/2000 [04:57<06:09, 3.20it/s, loss=0.603]" ] }, { @@ -25777,7 +25755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 820/2000 [05:01<06:18, 3.12it/s, loss=0.578]" + "training until 2000: 41%|████ | 820/2000 [04:57<06:11, 3.18it/s, loss=0.603]" ] }, { @@ -25785,7 +25763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 820/2000 [05:01<06:18, 3.12it/s, loss=0.483]" + "training until 2000: 41%|████ | 820/2000 [04:57<06:11, 3.18it/s, loss=0.546]" ] }, { @@ -25793,7 +25771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 821/2000 [05:01<06:13, 3.15it/s, loss=0.483]" + "training until 2000: 41%|████ | 821/2000 [04:57<06:14, 3.14it/s, loss=0.546]" ] }, { @@ -25801,7 +25779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 821/2000 [05:01<06:13, 3.15it/s, loss=0.542]" + "training until 2000: 41%|████ | 821/2000 [04:57<06:14, 3.14it/s, loss=0.555]" ] }, { @@ -25809,7 +25787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 822/2000 [05:01<06:12, 3.17it/s, loss=0.542]" + "training until 2000: 41%|████ | 822/2000 [04:58<06:17, 3.12it/s, loss=0.555]" ] }, { @@ -25817,7 +25795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 822/2000 [05:01<06:12, 3.17it/s, loss=0.548]" + "training until 2000: 41%|████ | 822/2000 [04:58<06:17, 3.12it/s, loss=0.522]" ] }, { @@ -25825,7 +25803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 823/2000 [05:02<06:11, 3.17it/s, loss=0.548]" + "training until 2000: 41%|████ | 823/2000 [04:58<06:16, 3.13it/s, loss=0.522]" ] }, { @@ -25833,7 +25811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 823/2000 [05:02<06:11, 3.17it/s, loss=0.499]" + "training until 2000: 41%|████ | 823/2000 [04:58<06:16, 3.13it/s, loss=0.518]" ] }, { @@ -25841,7 +25819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 824/2000 [05:02<06:11, 3.16it/s, loss=0.499]" + "training until 2000: 41%|████ | 824/2000 [04:58<06:15, 3.13it/s, loss=0.518]" ] }, { @@ -25849,7 +25827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████ | 824/2000 [05:02<06:11, 3.16it/s, loss=0.533]" + "training until 2000: 41%|████ | 824/2000 [04:58<06:15, 3.13it/s, loss=0.607]" ] }, { @@ -25857,7 +25835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 825/2000 [05:02<06:20, 3.09it/s, loss=0.533]" + "training until 2000: 41%|████▏ | 825/2000 [04:59<06:10, 3.17it/s, loss=0.607]" ] }, { @@ -25865,7 +25843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 825/2000 [05:02<06:20, 3.09it/s, loss=0.506]" + "training until 2000: 41%|████▏ | 825/2000 [04:59<06:10, 3.17it/s, loss=0.503]" ] }, { @@ -25873,7 +25851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 826/2000 [05:03<06:21, 3.08it/s, loss=0.506]" + "training until 2000: 41%|████▏ | 826/2000 [04:59<06:10, 3.17it/s, loss=0.503]" ] }, { @@ -25881,7 +25859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 826/2000 [05:03<06:21, 3.08it/s, loss=0.54] " + "training until 2000: 41%|████▏ | 826/2000 [04:59<06:10, 3.17it/s, loss=0.546]" ] }, { @@ -25889,7 +25867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 827/2000 [05:03<06:19, 3.09it/s, loss=0.54]" + "training until 2000: 41%|████▏ | 827/2000 [04:59<06:07, 3.19it/s, loss=0.546]" ] }, { @@ -25897,7 +25875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 827/2000 [05:03<06:19, 3.09it/s, loss=0.547]" + "training until 2000: 41%|████▏ | 827/2000 [04:59<06:07, 3.19it/s, loss=0.521]" ] }, { @@ -25905,7 +25883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 828/2000 [05:03<06:20, 3.08it/s, loss=0.547]" + "training until 2000: 41%|████▏ | 828/2000 [04:59<06:05, 3.21it/s, loss=0.521]" ] }, { @@ -25913,7 +25891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 828/2000 [05:03<06:20, 3.08it/s, loss=0.503]" + "training until 2000: 41%|████▏ | 828/2000 [04:59<06:05, 3.21it/s, loss=0.598]" ] }, { @@ -25921,7 +25899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 829/2000 [05:04<06:21, 3.07it/s, loss=0.503]" + "training until 2000: 41%|████▏ | 829/2000 [05:00<06:07, 3.19it/s, loss=0.598]" ] }, { @@ -25929,7 +25907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 41%|████▏ | 829/2000 [05:04<06:21, 3.07it/s, loss=0.537]" + "training until 2000: 41%|████▏ | 829/2000 [05:00<06:07, 3.19it/s, loss=0.546]" ] }, { @@ -25937,7 +25915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 830/2000 [05:04<06:21, 3.07it/s, loss=0.537]" + "training until 2000: 42%|████▏ | 830/2000 [05:00<06:03, 3.22it/s, loss=0.546]" ] }, { @@ -25945,7 +25923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 830/2000 [05:04<06:21, 3.07it/s, loss=0.502]" + "training until 2000: 42%|████▏ | 830/2000 [05:00<06:03, 3.22it/s, loss=0.576]" ] }, { @@ -25953,7 +25931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 831/2000 [05:04<06:25, 3.03it/s, loss=0.502]" + "training until 2000: 42%|████▏ | 831/2000 [05:00<06:04, 3.21it/s, loss=0.576]" ] }, { @@ -25961,7 +25939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 831/2000 [05:04<06:25, 3.03it/s, loss=0.531]" + "training until 2000: 42%|████▏ | 831/2000 [05:00<06:04, 3.21it/s, loss=0.537]" ] }, { @@ -25969,7 +25947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 832/2000 [05:04<06:22, 3.06it/s, loss=0.531]" + "training until 2000: 42%|████▏ | 832/2000 [05:01<06:06, 3.18it/s, loss=0.537]" ] }, { @@ -25977,7 +25955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 832/2000 [05:04<06:22, 3.06it/s, loss=0.561]" + "training until 2000: 42%|████▏ | 832/2000 [05:01<06:06, 3.18it/s, loss=0.601]" ] }, { @@ -25985,7 +25963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 833/2000 [05:05<06:18, 3.08it/s, loss=0.561]" + "training until 2000: 42%|████▏ | 833/2000 [05:01<06:09, 3.16it/s, loss=0.601]" ] }, { @@ -25993,7 +25971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 833/2000 [05:05<06:18, 3.08it/s, loss=0.499]" + "training until 2000: 42%|████▏ | 833/2000 [05:01<06:09, 3.16it/s, loss=0.528]" ] }, { @@ -26001,7 +25979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 834/2000 [05:05<06:16, 3.10it/s, loss=0.499]" + "training until 2000: 42%|████▏ | 834/2000 [05:01<06:09, 3.15it/s, loss=0.528]" ] }, { @@ -26009,7 +25987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 834/2000 [05:05<06:16, 3.10it/s, loss=0.543]" + "training until 2000: 42%|████▏ | 834/2000 [05:01<06:09, 3.15it/s, loss=0.578]" ] }, { @@ -26017,7 +25995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 835/2000 [05:05<06:19, 3.07it/s, loss=0.543]" + "training until 2000: 42%|████▏ | 835/2000 [05:02<06:09, 3.15it/s, loss=0.578]" ] }, { @@ -26025,7 +26003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 835/2000 [05:05<06:19, 3.07it/s, loss=0.495]" + "training until 2000: 42%|████▏ | 835/2000 [05:02<06:09, 3.15it/s, loss=0.533]" ] }, { @@ -26033,7 +26011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 836/2000 [05:06<06:19, 3.06it/s, loss=0.495]" + "training until 2000: 42%|████▏ | 836/2000 [05:02<06:09, 3.15it/s, loss=0.533]" ] }, { @@ -26041,7 +26019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 836/2000 [05:06<06:19, 3.06it/s, loss=0.595]" + "training until 2000: 42%|████▏ | 836/2000 [05:02<06:09, 3.15it/s, loss=0.624]" ] }, { @@ -26049,7 +26027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 837/2000 [05:06<06:17, 3.08it/s, loss=0.595]" + "training until 2000: 42%|████▏ | 837/2000 [05:02<06:08, 3.16it/s, loss=0.624]" ] }, { @@ -26057,7 +26035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 837/2000 [05:06<06:17, 3.08it/s, loss=0.564]" + "training until 2000: 42%|████▏ | 837/2000 [05:02<06:08, 3.16it/s, loss=0.545]" ] }, { @@ -26065,7 +26043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 838/2000 [05:06<06:19, 3.07it/s, loss=0.564]" + "training until 2000: 42%|████▏ | 838/2000 [05:03<06:09, 3.15it/s, loss=0.545]" ] }, { @@ -26073,7 +26051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 838/2000 [05:06<06:19, 3.07it/s, loss=0.527]" + "training until 2000: 42%|████▏ | 838/2000 [05:03<06:09, 3.15it/s, loss=0.559]" ] }, { @@ -26081,7 +26059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 839/2000 [05:07<06:14, 3.10it/s, loss=0.527]" + "training until 2000: 42%|████▏ | 839/2000 [05:03<06:08, 3.15it/s, loss=0.559]" ] }, { @@ -26089,7 +26067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 839/2000 [05:07<06:14, 3.10it/s, loss=0.503]" + "training until 2000: 42%|████▏ | 839/2000 [05:03<06:08, 3.15it/s, loss=0.68] " ] }, { @@ -26097,7 +26075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 840/2000 [05:07<06:15, 3.09it/s, loss=0.503]" + "training until 2000: 42%|████▏ | 840/2000 [05:03<06:10, 3.13it/s, loss=0.68]" ] }, { @@ -26105,7 +26083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 840/2000 [05:07<06:15, 3.09it/s, loss=0.558]" + "training until 2000: 42%|████▏ | 840/2000 [05:03<06:10, 3.13it/s, loss=0.553]" ] }, { @@ -26113,7 +26091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 841/2000 [05:07<06:16, 3.08it/s, loss=0.558]" + "training until 2000: 42%|████▏ | 841/2000 [05:04<06:11, 3.12it/s, loss=0.553]" ] }, { @@ -26121,7 +26099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 841/2000 [05:07<06:16, 3.08it/s, loss=0.473]" + "training until 2000: 42%|████▏ | 841/2000 [05:04<06:11, 3.12it/s, loss=0.545]" ] }, { @@ -26129,7 +26107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 842/2000 [05:08<06:15, 3.08it/s, loss=0.473]" + "training until 2000: 42%|████▏ | 842/2000 [05:04<06:13, 3.10it/s, loss=0.545]" ] }, { @@ -26137,7 +26115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 842/2000 [05:08<06:15, 3.08it/s, loss=0.525]" + "training until 2000: 42%|████▏ | 842/2000 [05:04<06:13, 3.10it/s, loss=0.52] " ] }, { @@ -26145,7 +26123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 843/2000 [05:08<06:14, 3.09it/s, loss=0.525]" + "training until 2000: 42%|████▏ | 843/2000 [05:04<06:09, 3.13it/s, loss=0.52]" ] }, { @@ -26153,7 +26131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 843/2000 [05:08<06:14, 3.09it/s, loss=0.523]" + "training until 2000: 42%|████▏ | 843/2000 [05:04<06:09, 3.13it/s, loss=0.516]" ] }, { @@ -26161,7 +26139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 844/2000 [05:08<06:14, 3.09it/s, loss=0.523]" + "training until 2000: 42%|████▏ | 844/2000 [05:05<06:06, 3.15it/s, loss=0.516]" ] }, { @@ -26169,7 +26147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 844/2000 [05:08<06:14, 3.09it/s, loss=0.49] " + "training until 2000: 42%|████▏ | 844/2000 [05:05<06:06, 3.15it/s, loss=0.548]" ] }, { @@ -26177,7 +26155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 845/2000 [05:09<06:11, 3.11it/s, loss=0.49]" + "training until 2000: 42%|████▏ | 845/2000 [05:05<06:02, 3.18it/s, loss=0.548]" ] }, { @@ -26185,7 +26163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 845/2000 [05:09<06:11, 3.11it/s, loss=0.572]" + "training until 2000: 42%|████▏ | 845/2000 [05:05<06:02, 3.18it/s, loss=0.675]" ] }, { @@ -26193,7 +26171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 846/2000 [05:09<06:09, 3.12it/s, loss=0.572]" + "training until 2000: 42%|████▏ | 846/2000 [05:05<06:02, 3.18it/s, loss=0.675]" ] }, { @@ -26201,7 +26179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 846/2000 [05:09<06:09, 3.12it/s, loss=0.52] " + "training until 2000: 42%|████▏ | 846/2000 [05:05<06:02, 3.18it/s, loss=0.546]" ] }, { @@ -26209,7 +26187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 847/2000 [05:09<06:10, 3.11it/s, loss=0.52]" + "training until 2000: 42%|████▏ | 847/2000 [05:05<06:01, 3.19it/s, loss=0.546]" ] }, { @@ -26217,7 +26195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 847/2000 [05:09<06:10, 3.11it/s, loss=0.634]" + "training until 2000: 42%|████▏ | 847/2000 [05:05<06:01, 3.19it/s, loss=0.514]" ] }, { @@ -26225,7 +26203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 848/2000 [05:10<06:10, 3.11it/s, loss=0.634]" + "training until 2000: 42%|████▏ | 848/2000 [05:06<06:02, 3.18it/s, loss=0.514]" ] }, { @@ -26233,7 +26211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 848/2000 [05:10<06:10, 3.11it/s, loss=0.649]" + "training until 2000: 42%|████▏ | 848/2000 [05:06<06:02, 3.18it/s, loss=0.634]" ] }, { @@ -26241,7 +26219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 849/2000 [05:10<06:09, 3.11it/s, loss=0.649]" + "training until 2000: 42%|████▏ | 849/2000 [05:06<07:27, 2.57it/s, loss=0.634]" ] }, { @@ -26249,7 +26227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▏ | 849/2000 [05:10<06:09, 3.11it/s, loss=0.629]" + "training until 2000: 42%|████▏ | 849/2000 [05:06<07:27, 2.57it/s, loss=0.506]" ] }, { @@ -26257,7 +26235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▎ | 850/2000 [05:10<06:15, 3.06it/s, loss=0.629]" + "training until 2000: 42%|████▎ | 850/2000 [05:07<07:02, 2.72it/s, loss=0.506]" ] }, { @@ -26265,7 +26243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 42%|████▎ | 850/2000 [05:10<06:15, 3.06it/s, loss=0.571]" + "training until 2000: 42%|████▎ | 850/2000 [05:07<07:02, 2.72it/s, loss=0.581]" ] }, { @@ -26273,7 +26251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 851/2000 [05:11<06:11, 3.09it/s, loss=0.571]" + "training until 2000: 43%|████▎ | 851/2000 [05:07<06:47, 2.82it/s, loss=0.581]" ] }, { @@ -26281,7 +26259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 851/2000 [05:11<06:11, 3.09it/s, loss=0.537]" + "training until 2000: 43%|████▎ | 851/2000 [05:07<06:47, 2.82it/s, loss=0.527]" ] }, { @@ -26289,7 +26267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 852/2000 [05:11<06:07, 3.12it/s, loss=0.537]" + "training until 2000: 43%|████▎ | 852/2000 [05:07<06:37, 2.89it/s, loss=0.527]" ] }, { @@ -26297,7 +26275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 852/2000 [05:11<06:07, 3.12it/s, loss=0.528]" + "training until 2000: 43%|████▎ | 852/2000 [05:07<06:37, 2.89it/s, loss=0.561]" ] }, { @@ -26305,7 +26283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 853/2000 [05:11<06:04, 3.14it/s, loss=0.528]" + "training until 2000: 43%|████▎ | 853/2000 [05:08<06:29, 2.94it/s, loss=0.561]" ] }, { @@ -26313,7 +26291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 853/2000 [05:11<06:04, 3.14it/s, loss=0.587]" + "training until 2000: 43%|████▎ | 853/2000 [05:08<06:29, 2.94it/s, loss=0.565]" ] }, { @@ -26321,7 +26299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 854/2000 [05:12<06:10, 3.09it/s, loss=0.587]" + "training until 2000: 43%|████▎ | 854/2000 [05:08<06:23, 2.99it/s, loss=0.565]" ] }, { @@ -26329,7 +26307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 854/2000 [05:12<06:10, 3.09it/s, loss=0.488]" + "training until 2000: 43%|████▎ | 854/2000 [05:08<06:23, 2.99it/s, loss=0.614]" ] }, { @@ -26337,7 +26315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 855/2000 [05:12<06:09, 3.10it/s, loss=0.488]" + "training until 2000: 43%|████▎ | 855/2000 [05:08<06:18, 3.03it/s, loss=0.614]" ] }, { @@ -26345,7 +26323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 855/2000 [05:12<06:09, 3.10it/s, loss=0.555]" + "training until 2000: 43%|████▎ | 855/2000 [05:08<06:18, 3.03it/s, loss=0.518]" ] }, { @@ -26353,7 +26331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 856/2000 [05:12<07:32, 2.53it/s, loss=0.555]" + "training until 2000: 43%|████▎ | 856/2000 [05:09<06:18, 3.02it/s, loss=0.518]" ] }, { @@ -26361,7 +26339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 856/2000 [05:12<07:32, 2.53it/s, loss=0.539]" + "training until 2000: 43%|████▎ | 856/2000 [05:09<06:18, 3.02it/s, loss=0.565]" ] }, { @@ -26369,7 +26347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 857/2000 [05:13<07:11, 2.65it/s, loss=0.539]" + "training until 2000: 43%|████▎ | 857/2000 [05:09<06:14, 3.05it/s, loss=0.565]" ] }, { @@ -26377,7 +26355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 857/2000 [05:13<07:11, 2.65it/s, loss=0.508]" + "training until 2000: 43%|████▎ | 857/2000 [05:09<06:14, 3.05it/s, loss=0.579]" ] }, { @@ -26385,7 +26363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 858/2000 [05:13<06:54, 2.76it/s, loss=0.508]" + "training until 2000: 43%|████▎ | 858/2000 [05:09<06:14, 3.05it/s, loss=0.579]" ] }, { @@ -26393,7 +26371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 858/2000 [05:13<06:54, 2.76it/s, loss=0.512]" + "training until 2000: 43%|████▎ | 858/2000 [05:09<06:14, 3.05it/s, loss=0.537]" ] }, { @@ -26401,7 +26379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 859/2000 [05:13<06:42, 2.83it/s, loss=0.512]" + "training until 2000: 43%|████▎ | 859/2000 [05:10<06:10, 3.08it/s, loss=0.537]" ] }, { @@ -26409,7 +26387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 859/2000 [05:13<06:42, 2.83it/s, loss=0.538]" + "training until 2000: 43%|████▎ | 859/2000 [05:10<06:10, 3.08it/s, loss=0.565]" ] }, { @@ -26417,7 +26395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 860/2000 [05:14<06:34, 2.89it/s, loss=0.538]" + "training until 2000: 43%|████▎ | 860/2000 [05:10<06:11, 3.07it/s, loss=0.565]" ] }, { @@ -26425,7 +26403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 860/2000 [05:14<06:34, 2.89it/s, loss=0.528]" + "training until 2000: 43%|████▎ | 860/2000 [05:10<06:11, 3.07it/s, loss=0.557]" ] }, { @@ -26433,7 +26411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 861/2000 [05:14<06:26, 2.95it/s, loss=0.528]" + "training until 2000: 43%|████▎ | 861/2000 [05:10<06:04, 3.13it/s, loss=0.557]" ] }, { @@ -26441,7 +26419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 861/2000 [05:14<06:26, 2.95it/s, loss=0.626]" + "training until 2000: 43%|████▎ | 861/2000 [05:10<06:04, 3.13it/s, loss=0.502]" ] }, { @@ -26449,7 +26427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 862/2000 [05:14<06:22, 2.98it/s, loss=0.626]" + "training until 2000: 43%|████▎ | 862/2000 [05:11<06:06, 3.11it/s, loss=0.502]" ] }, { @@ -26457,7 +26435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 862/2000 [05:14<06:22, 2.98it/s, loss=0.591]" + "training until 2000: 43%|████▎ | 862/2000 [05:11<06:06, 3.11it/s, loss=0.522]" ] }, { @@ -26465,7 +26443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 863/2000 [05:15<06:17, 3.02it/s, loss=0.591]" + "training until 2000: 43%|████▎ | 863/2000 [05:11<06:05, 3.11it/s, loss=0.522]" ] }, { @@ -26473,7 +26451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 863/2000 [05:15<06:17, 3.02it/s, loss=0.505]" + "training until 2000: 43%|████▎ | 863/2000 [05:11<06:05, 3.11it/s, loss=0.517]" ] }, { @@ -26481,7 +26459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 864/2000 [05:15<06:18, 3.00it/s, loss=0.505]" + "training until 2000: 43%|████▎ | 864/2000 [05:11<06:09, 3.08it/s, loss=0.517]" ] }, { @@ -26489,7 +26467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 864/2000 [05:15<06:18, 3.00it/s, loss=0.5] " + "training until 2000: 43%|████▎ | 864/2000 [05:11<06:09, 3.08it/s, loss=0.49] " ] }, { @@ -26497,7 +26475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 865/2000 [05:15<06:11, 3.05it/s, loss=0.5]" + "training until 2000: 43%|████▎ | 865/2000 [05:11<06:05, 3.11it/s, loss=0.49]" ] }, { @@ -26505,7 +26483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 865/2000 [05:15<06:11, 3.05it/s, loss=0.498]" + "training until 2000: 43%|████▎ | 865/2000 [05:12<06:05, 3.11it/s, loss=0.744]" ] }, { @@ -26513,7 +26491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 866/2000 [05:16<06:13, 3.04it/s, loss=0.498]" + "training until 2000: 43%|████▎ | 866/2000 [05:12<06:05, 3.10it/s, loss=0.744]" ] }, { @@ -26521,7 +26499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 866/2000 [05:16<06:13, 3.04it/s, loss=0.609]" + "training until 2000: 43%|████▎ | 866/2000 [05:12<06:05, 3.10it/s, loss=0.628]" ] }, { @@ -26529,7 +26507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 867/2000 [05:16<06:09, 3.06it/s, loss=0.609]" + "training until 2000: 43%|████▎ | 867/2000 [05:12<05:58, 3.16it/s, loss=0.628]" ] }, { @@ -26537,7 +26515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 867/2000 [05:16<06:09, 3.06it/s, loss=0.555]" + "training until 2000: 43%|████▎ | 867/2000 [05:12<05:58, 3.16it/s, loss=0.481]" ] }, { @@ -26545,7 +26523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 868/2000 [05:16<06:12, 3.04it/s, loss=0.555]" + "training until 2000: 43%|████▎ | 868/2000 [05:12<06:01, 3.13it/s, loss=0.481]" ] }, { @@ -26553,7 +26531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 868/2000 [05:16<06:12, 3.04it/s, loss=0.502]" + "training until 2000: 43%|████▎ | 868/2000 [05:12<06:01, 3.13it/s, loss=0.58] " ] }, { @@ -26561,7 +26539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 869/2000 [05:17<06:14, 3.02it/s, loss=0.502]" + "training until 2000: 43%|████▎ | 869/2000 [05:13<05:56, 3.17it/s, loss=0.58]" ] }, { @@ -26569,7 +26547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 43%|████▎ | 869/2000 [05:17<06:14, 3.02it/s, loss=0.51] " + "training until 2000: 43%|████▎ | 869/2000 [05:13<05:56, 3.17it/s, loss=0.497]" ] }, { @@ -26577,7 +26555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 870/2000 [05:17<06:15, 3.01it/s, loss=0.51]" + "training until 2000: 44%|████▎ | 870/2000 [05:13<05:54, 3.18it/s, loss=0.497]" ] }, { @@ -26585,7 +26563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 870/2000 [05:17<06:15, 3.01it/s, loss=0.49]" + "training until 2000: 44%|████▎ | 870/2000 [05:13<05:54, 3.18it/s, loss=0.632]" ] }, { @@ -26593,7 +26571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 871/2000 [05:17<06:10, 3.05it/s, loss=0.49]" + "training until 2000: 44%|████▎ | 871/2000 [05:13<05:57, 3.16it/s, loss=0.632]" ] }, { @@ -26601,7 +26579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 871/2000 [05:17<06:10, 3.05it/s, loss=0.487]" + "training until 2000: 44%|████▎ | 871/2000 [05:13<05:57, 3.16it/s, loss=0.618]" ] }, { @@ -26609,7 +26587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 872/2000 [05:18<06:10, 3.05it/s, loss=0.487]" + "training until 2000: 44%|████▎ | 872/2000 [05:14<05:54, 3.18it/s, loss=0.618]" ] }, { @@ -26617,7 +26595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 872/2000 [05:18<06:10, 3.05it/s, loss=0.485]" + "training until 2000: 44%|████▎ | 872/2000 [05:14<05:54, 3.18it/s, loss=0.525]" ] }, { @@ -26625,7 +26603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 873/2000 [05:18<06:09, 3.05it/s, loss=0.485]" + "training until 2000: 44%|████▎ | 873/2000 [05:14<05:53, 3.18it/s, loss=0.525]" ] }, { @@ -26633,7 +26611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 873/2000 [05:18<06:09, 3.05it/s, loss=0.545]" + "training until 2000: 44%|████▎ | 873/2000 [05:14<05:53, 3.18it/s, loss=0.482]" ] }, { @@ -26641,7 +26619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 874/2000 [05:18<06:03, 3.09it/s, loss=0.545]" + "training until 2000: 44%|████▎ | 874/2000 [05:14<05:55, 3.17it/s, loss=0.482]" ] }, { @@ -26649,7 +26627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▎ | 874/2000 [05:18<06:03, 3.09it/s, loss=0.514]" + "training until 2000: 44%|████▎ | 874/2000 [05:14<05:55, 3.17it/s, loss=0.54] " ] }, { @@ -26657,7 +26635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 875/2000 [05:19<06:02, 3.10it/s, loss=0.514]" + "training until 2000: 44%|████▍ | 875/2000 [05:15<05:54, 3.18it/s, loss=0.54]" ] }, { @@ -26665,7 +26643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 875/2000 [05:19<06:02, 3.10it/s, loss=0.483]" + "training until 2000: 44%|████▍ | 875/2000 [05:15<05:54, 3.18it/s, loss=0.562]" ] }, { @@ -26673,7 +26651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 876/2000 [05:19<05:58, 3.14it/s, loss=0.483]" + "training until 2000: 44%|████▍ | 876/2000 [05:15<05:54, 3.17it/s, loss=0.562]" ] }, { @@ -26681,7 +26659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 876/2000 [05:19<05:58, 3.14it/s, loss=0.514]" + "training until 2000: 44%|████▍ | 876/2000 [05:15<05:54, 3.17it/s, loss=0.552]" ] }, { @@ -26689,7 +26667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 877/2000 [05:19<05:59, 3.12it/s, loss=0.514]" + "training until 2000: 44%|████▍ | 877/2000 [05:15<05:55, 3.16it/s, loss=0.552]" ] }, { @@ -26697,7 +26675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 877/2000 [05:19<05:59, 3.12it/s, loss=0.508]" + "training until 2000: 44%|████▍ | 877/2000 [05:15<05:55, 3.16it/s, loss=0.614]" ] }, { @@ -26705,7 +26683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 878/2000 [05:20<05:59, 3.12it/s, loss=0.508]" + "training until 2000: 44%|████▍ | 878/2000 [05:16<05:55, 3.16it/s, loss=0.614]" ] }, { @@ -26713,7 +26691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 878/2000 [05:20<05:59, 3.12it/s, loss=0.506]" + "training until 2000: 44%|████▍ | 878/2000 [05:16<05:55, 3.16it/s, loss=0.545]" ] }, { @@ -26721,7 +26699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 879/2000 [05:20<06:01, 3.10it/s, loss=0.506]" + "training until 2000: 44%|████▍ | 879/2000 [05:16<05:56, 3.14it/s, loss=0.545]" ] }, { @@ -26729,7 +26707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 879/2000 [05:20<06:01, 3.10it/s, loss=0.526]" + "training until 2000: 44%|████▍ | 879/2000 [05:16<05:56, 3.14it/s, loss=0.598]" ] }, { @@ -26737,7 +26715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 880/2000 [05:20<05:59, 3.12it/s, loss=0.526]" + "training until 2000: 44%|████▍ | 880/2000 [05:16<05:54, 3.16it/s, loss=0.598]" ] }, { @@ -26745,7 +26723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 880/2000 [05:20<05:59, 3.12it/s, loss=0.526]" + "training until 2000: 44%|████▍ | 880/2000 [05:16<05:54, 3.16it/s, loss=0.553]" ] }, { @@ -26753,7 +26731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 881/2000 [05:21<06:03, 3.08it/s, loss=0.526]" + "training until 2000: 44%|████▍ | 881/2000 [05:17<05:57, 3.13it/s, loss=0.553]" ] }, { @@ -26761,7 +26739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 881/2000 [05:21<06:03, 3.08it/s, loss=0.533]" + "training until 2000: 44%|████▍ | 881/2000 [05:17<05:57, 3.13it/s, loss=0.537]" ] }, { @@ -26769,7 +26747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 882/2000 [05:21<05:59, 3.11it/s, loss=0.533]" + "training until 2000: 44%|████▍ | 882/2000 [05:17<05:55, 3.14it/s, loss=0.537]" ] }, { @@ -26777,7 +26755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 882/2000 [05:21<05:59, 3.11it/s, loss=0.509]" + "training until 2000: 44%|████▍ | 882/2000 [05:17<05:55, 3.14it/s, loss=0.516]" ] }, { @@ -26785,7 +26763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 883/2000 [05:21<05:58, 3.12it/s, loss=0.509]" + "training until 2000: 44%|████▍ | 883/2000 [05:17<05:58, 3.12it/s, loss=0.516]" ] }, { @@ -26793,7 +26771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 883/2000 [05:21<05:58, 3.12it/s, loss=0.533]" + "training until 2000: 44%|████▍ | 883/2000 [05:17<05:58, 3.12it/s, loss=0.666]" ] }, { @@ -26801,7 +26779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 884/2000 [05:22<06:03, 3.07it/s, loss=0.533]" + "training until 2000: 44%|████▍ | 884/2000 [05:18<05:55, 3.14it/s, loss=0.666]" ] }, { @@ -26809,7 +26787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 884/2000 [05:22<06:03, 3.07it/s, loss=0.544]" + "training until 2000: 44%|████▍ | 884/2000 [05:18<05:55, 3.14it/s, loss=0.559]" ] }, { @@ -26817,7 +26795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 885/2000 [05:22<06:02, 3.07it/s, loss=0.544]" + "training until 2000: 44%|████▍ | 885/2000 [05:18<05:56, 3.13it/s, loss=0.559]" ] }, { @@ -26825,7 +26803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 885/2000 [05:22<06:02, 3.07it/s, loss=0.516]" + "training until 2000: 44%|████▍ | 885/2000 [05:18<05:56, 3.13it/s, loss=0.558]" ] }, { @@ -26833,7 +26811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 886/2000 [05:22<06:02, 3.07it/s, loss=0.516]" + "training until 2000: 44%|████▍ | 886/2000 [05:18<05:57, 3.12it/s, loss=0.558]" ] }, { @@ -26841,7 +26819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 886/2000 [05:22<06:02, 3.07it/s, loss=0.484]" + "training until 2000: 44%|████▍ | 886/2000 [05:18<05:57, 3.12it/s, loss=0.563]" ] }, { @@ -26849,7 +26827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 887/2000 [05:23<06:01, 3.08it/s, loss=0.484]" + "training until 2000: 44%|████▍ | 887/2000 [05:18<05:58, 3.10it/s, loss=0.563]" ] }, { @@ -26857,7 +26835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 887/2000 [05:23<06:01, 3.08it/s, loss=0.499]" + "training until 2000: 44%|████▍ | 887/2000 [05:18<05:58, 3.10it/s, loss=0.566]" ] }, { @@ -26865,7 +26843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 888/2000 [05:23<06:02, 3.06it/s, loss=0.499]" + "training until 2000: 44%|████▍ | 888/2000 [05:19<05:53, 3.14it/s, loss=0.566]" ] }, { @@ -26873,7 +26851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 888/2000 [05:23<06:02, 3.06it/s, loss=0.504]" + "training until 2000: 44%|████▍ | 888/2000 [05:19<05:53, 3.14it/s, loss=0.563]" ] }, { @@ -26881,7 +26859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 889/2000 [05:23<06:05, 3.04it/s, loss=0.504]" + "training until 2000: 44%|████▍ | 889/2000 [05:19<05:53, 3.14it/s, loss=0.563]" ] }, { @@ -26889,7 +26867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 889/2000 [05:23<06:05, 3.04it/s, loss=0.508]" + "training until 2000: 44%|████▍ | 889/2000 [05:19<05:53, 3.14it/s, loss=0.583]" ] }, { @@ -26897,7 +26875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 890/2000 [05:24<06:03, 3.05it/s, loss=0.508]" + "training until 2000: 44%|████▍ | 890/2000 [05:19<05:54, 3.13it/s, loss=0.583]" ] }, { @@ -26905,7 +26883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 44%|████▍ | 890/2000 [05:24<06:03, 3.05it/s, loss=0.533]" + "training until 2000: 44%|████▍ | 890/2000 [05:19<05:54, 3.13it/s, loss=0.531]" ] }, { @@ -26913,7 +26891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 891/2000 [05:24<05:57, 3.10it/s, loss=0.533]" + "training until 2000: 45%|████▍ | 891/2000 [05:20<05:58, 3.10it/s, loss=0.531]" ] }, { @@ -26921,7 +26899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 891/2000 [05:24<05:57, 3.10it/s, loss=0.479]" + "training until 2000: 45%|████▍ | 891/2000 [05:20<05:58, 3.10it/s, loss=0.563]" ] }, { @@ -26929,7 +26907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 892/2000 [05:24<05:58, 3.09it/s, loss=0.479]" + "training until 2000: 45%|████▍ | 892/2000 [05:20<05:55, 3.12it/s, loss=0.563]" ] }, { @@ -26937,7 +26915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 892/2000 [05:24<05:58, 3.09it/s, loss=0.522]" + "training until 2000: 45%|████▍ | 892/2000 [05:20<05:55, 3.12it/s, loss=0.536]" ] }, { @@ -26945,7 +26923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 893/2000 [05:25<05:58, 3.09it/s, loss=0.522]" + "training until 2000: 45%|████▍ | 893/2000 [05:20<05:48, 3.17it/s, loss=0.536]" ] }, { @@ -26953,7 +26931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 893/2000 [05:25<05:58, 3.09it/s, loss=0.48] " + "training until 2000: 45%|████▍ | 893/2000 [05:20<05:48, 3.17it/s, loss=0.654]" ] }, { @@ -26961,7 +26939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 894/2000 [05:25<05:55, 3.11it/s, loss=0.48]" + "training until 2000: 45%|████▍ | 894/2000 [05:21<05:49, 3.16it/s, loss=0.654]" ] }, { @@ -26969,7 +26947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 894/2000 [05:25<05:55, 3.11it/s, loss=0.473]" + "training until 2000: 45%|████▍ | 894/2000 [05:21<05:49, 3.16it/s, loss=0.496]" ] }, { @@ -26977,7 +26955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 895/2000 [05:25<05:53, 3.12it/s, loss=0.473]" + "training until 2000: 45%|████▍ | 895/2000 [05:21<05:51, 3.15it/s, loss=0.496]" ] }, { @@ -26985,7 +26963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 895/2000 [05:25<05:53, 3.12it/s, loss=0.527]" + "training until 2000: 45%|████▍ | 895/2000 [05:21<05:51, 3.15it/s, loss=0.499]" ] }, { @@ -26993,7 +26971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 896/2000 [05:25<05:50, 3.15it/s, loss=0.527]" + "training until 2000: 45%|████▍ | 896/2000 [05:21<05:47, 3.18it/s, loss=0.499]" ] }, { @@ -27001,7 +26979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 896/2000 [05:25<05:50, 3.15it/s, loss=0.512]" + "training until 2000: 45%|████▍ | 896/2000 [05:21<05:47, 3.18it/s, loss=0.66] " ] }, { @@ -27009,7 +26987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 897/2000 [05:26<05:51, 3.14it/s, loss=0.512]" + "training until 2000: 45%|████▍ | 897/2000 [05:22<05:45, 3.19it/s, loss=0.66]" ] }, { @@ -27017,7 +26995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 897/2000 [05:26<05:51, 3.14it/s, loss=0.473]" + "training until 2000: 45%|████▍ | 897/2000 [05:22<05:45, 3.19it/s, loss=0.491]" ] }, { @@ -27025,7 +27003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 898/2000 [05:26<05:50, 3.14it/s, loss=0.473]" + "training until 2000: 45%|████▍ | 898/2000 [05:22<05:48, 3.17it/s, loss=0.491]" ] }, { @@ -27033,7 +27011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 898/2000 [05:26<05:50, 3.14it/s, loss=0.513]" + "training until 2000: 45%|████▍ | 898/2000 [05:22<05:48, 3.17it/s, loss=0.612]" ] }, { @@ -27041,7 +27019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 899/2000 [05:26<05:50, 3.14it/s, loss=0.513]" + "training until 2000: 45%|████▍ | 899/2000 [05:22<05:46, 3.18it/s, loss=0.612]" ] }, { @@ -27049,7 +27027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▍ | 899/2000 [05:26<05:50, 3.14it/s, loss=0.559]" + "training until 2000: 45%|████▍ | 899/2000 [05:22<05:46, 3.18it/s, loss=0.497]" ] }, { @@ -27057,7 +27035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 900/2000 [05:27<05:53, 3.11it/s, loss=0.559]" + "training until 2000: 45%|████▌ | 900/2000 [05:23<05:48, 3.16it/s, loss=0.497]" ] }, { @@ -27065,7 +27043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 900/2000 [05:27<05:53, 3.11it/s, loss=0.529]" + "training until 2000: 45%|████▌ | 900/2000 [05:23<05:48, 3.16it/s, loss=0.523]" ] }, { @@ -27073,7 +27051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 901/2000 [05:27<05:55, 3.09it/s, loss=0.529]" + "training until 2000: 45%|████▌ | 901/2000 [05:23<05:45, 3.18it/s, loss=0.523]" ] }, { @@ -27081,7 +27059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 901/2000 [05:27<05:55, 3.09it/s, loss=0.523]" + "training until 2000: 45%|████▌ | 901/2000 [05:23<05:45, 3.18it/s, loss=0.509]" ] }, { @@ -27089,7 +27067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 902/2000 [05:27<05:54, 3.10it/s, loss=0.523]" + "training until 2000: 45%|████▌ | 902/2000 [05:23<05:46, 3.17it/s, loss=0.509]" ] }, { @@ -27097,7 +27075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 902/2000 [05:27<05:54, 3.10it/s, loss=0.476]" + "training until 2000: 45%|████▌ | 902/2000 [05:23<05:46, 3.17it/s, loss=0.554]" ] }, { @@ -27105,7 +27083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 903/2000 [05:28<06:00, 3.04it/s, loss=0.476]" + "training until 2000: 45%|████▌ | 903/2000 [05:24<05:49, 3.14it/s, loss=0.554]" ] }, { @@ -27113,7 +27091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 903/2000 [05:28<06:00, 3.04it/s, loss=0.51] " + "training until 2000: 45%|████▌ | 903/2000 [05:24<05:49, 3.14it/s, loss=0.498]" ] }, { @@ -27121,7 +27099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 904/2000 [05:28<06:01, 3.03it/s, loss=0.51]" + "training until 2000: 45%|████▌ | 904/2000 [05:24<05:46, 3.17it/s, loss=0.498]" ] }, { @@ -27129,7 +27107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 904/2000 [05:28<06:01, 3.03it/s, loss=0.5] " + "training until 2000: 45%|████▌ | 904/2000 [05:24<05:46, 3.17it/s, loss=0.546]" ] }, { @@ -27137,7 +27115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 905/2000 [05:28<06:04, 3.01it/s, loss=0.5]" + "training until 2000: 45%|████▌ | 905/2000 [05:24<05:47, 3.15it/s, loss=0.546]" ] }, { @@ -27145,7 +27123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 905/2000 [05:28<06:04, 3.01it/s, loss=0.512]" + "training until 2000: 45%|████▌ | 905/2000 [05:24<05:47, 3.15it/s, loss=0.506]" ] }, { @@ -27153,7 +27131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 906/2000 [05:29<06:03, 3.01it/s, loss=0.512]" + "training until 2000: 45%|████▌ | 906/2000 [05:24<05:47, 3.15it/s, loss=0.506]" ] }, { @@ -27161,7 +27139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 906/2000 [05:29<06:03, 3.01it/s, loss=0.481]" + "training until 2000: 45%|████▌ | 906/2000 [05:24<05:47, 3.15it/s, loss=0.681]" ] }, { @@ -27169,7 +27147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 907/2000 [05:29<05:59, 3.04it/s, loss=0.481]" + "training until 2000: 45%|████▌ | 907/2000 [05:25<05:50, 3.12it/s, loss=0.681]" ] }, { @@ -27177,7 +27155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 907/2000 [05:29<05:59, 3.04it/s, loss=0.501]" + "training until 2000: 45%|████▌ | 907/2000 [05:25<05:50, 3.12it/s, loss=0.566]" ] }, { @@ -27185,7 +27163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 908/2000 [05:29<05:59, 3.04it/s, loss=0.501]" + "training until 2000: 45%|████▌ | 908/2000 [05:25<05:49, 3.13it/s, loss=0.566]" ] }, { @@ -27193,7 +27171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 908/2000 [05:29<05:59, 3.04it/s, loss=0.484]" + "training until 2000: 45%|████▌ | 908/2000 [05:25<05:49, 3.13it/s, loss=0.668]" ] }, { @@ -27201,7 +27179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 909/2000 [05:30<05:59, 3.03it/s, loss=0.484]" + "training until 2000: 45%|████▌ | 909/2000 [05:25<05:54, 3.08it/s, loss=0.668]" ] }, { @@ -27209,7 +27187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 45%|████▌ | 909/2000 [05:30<05:59, 3.03it/s, loss=0.5] " + "training until 2000: 45%|████▌ | 909/2000 [05:25<05:54, 3.08it/s, loss=0.525]" ] }, { @@ -27217,7 +27195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 910/2000 [05:30<06:04, 2.99it/s, loss=0.5]" + "training until 2000: 46%|████▌ | 910/2000 [05:26<05:50, 3.11it/s, loss=0.525]" ] }, { @@ -27225,7 +27203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 910/2000 [05:30<06:04, 2.99it/s, loss=0.513]" + "training until 2000: 46%|████▌ | 910/2000 [05:26<05:50, 3.11it/s, loss=0.525]" ] }, { @@ -27233,7 +27211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 911/2000 [05:30<06:04, 2.99it/s, loss=0.513]" + "training until 2000: 46%|████▌ | 911/2000 [05:26<05:53, 3.08it/s, loss=0.525]" ] }, { @@ -27241,7 +27219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 911/2000 [05:30<06:04, 2.99it/s, loss=0.588]" + "training until 2000: 46%|████▌ | 911/2000 [05:26<05:53, 3.08it/s, loss=0.535]" ] }, { @@ -27249,7 +27227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 912/2000 [05:31<06:02, 3.01it/s, loss=0.588]" + "training until 2000: 46%|████▌ | 912/2000 [05:27<07:11, 2.52it/s, loss=0.535]" ] }, { @@ -27257,7 +27235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 912/2000 [05:31<06:02, 3.01it/s, loss=0.493]" + "training until 2000: 46%|████▌ | 912/2000 [05:27<07:11, 2.52it/s, loss=0.584]" ] }, { @@ -27265,7 +27243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 913/2000 [05:31<06:01, 3.00it/s, loss=0.493]" + "training until 2000: 46%|████▌ | 913/2000 [05:27<06:49, 2.66it/s, loss=0.584]" ] }, { @@ -27273,7 +27251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 913/2000 [05:31<06:01, 3.00it/s, loss=0.521]" + "training until 2000: 46%|████▌ | 913/2000 [05:27<06:49, 2.66it/s, loss=0.671]" ] }, { @@ -27281,7 +27259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 914/2000 [05:31<05:56, 3.05it/s, loss=0.521]" + "training until 2000: 46%|████▌ | 914/2000 [05:27<06:29, 2.79it/s, loss=0.671]" ] }, { @@ -27289,7 +27267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 914/2000 [05:31<05:56, 3.05it/s, loss=0.586]" + "training until 2000: 46%|████▌ | 914/2000 [05:27<06:29, 2.79it/s, loss=0.568]" ] }, { @@ -27297,7 +27275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 915/2000 [05:32<05:52, 3.08it/s, loss=0.586]" + "training until 2000: 46%|████▌ | 915/2000 [05:28<06:12, 2.91it/s, loss=0.568]" ] }, { @@ -27305,7 +27283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 915/2000 [05:32<05:52, 3.08it/s, loss=0.494]" + "training until 2000: 46%|████▌ | 915/2000 [05:28<06:12, 2.91it/s, loss=0.511]" ] }, { @@ -27313,7 +27291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 916/2000 [05:32<05:50, 3.09it/s, loss=0.494]" + "training until 2000: 46%|████▌ | 916/2000 [05:28<06:05, 2.97it/s, loss=0.511]" ] }, { @@ -27321,7 +27299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 916/2000 [05:32<05:50, 3.09it/s, loss=0.503]" + "training until 2000: 46%|████▌ | 916/2000 [05:28<06:05, 2.97it/s, loss=0.531]" ] }, { @@ -27329,7 +27307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 917/2000 [05:32<05:50, 3.09it/s, loss=0.503]" + "training until 2000: 46%|████▌ | 917/2000 [05:28<05:55, 3.04it/s, loss=0.531]" ] }, { @@ -27337,7 +27315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 917/2000 [05:32<05:50, 3.09it/s, loss=0.544]" + "training until 2000: 46%|████▌ | 917/2000 [05:28<05:55, 3.04it/s, loss=0.54] " ] }, { @@ -27345,7 +27323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 918/2000 [05:33<05:49, 3.10it/s, loss=0.544]" + "training until 2000: 46%|████▌ | 918/2000 [05:29<05:50, 3.09it/s, loss=0.54]" ] }, { @@ -27353,7 +27331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 918/2000 [05:33<05:49, 3.10it/s, loss=0.652]" + "training until 2000: 46%|████▌ | 918/2000 [05:29<05:50, 3.09it/s, loss=0.522]" ] }, { @@ -27361,7 +27339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 919/2000 [05:33<05:50, 3.08it/s, loss=0.652]" + "training until 2000: 46%|████▌ | 919/2000 [05:29<05:50, 3.08it/s, loss=0.522]" ] }, { @@ -27369,7 +27347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 919/2000 [05:33<05:50, 3.08it/s, loss=0.509]" + "training until 2000: 46%|████▌ | 919/2000 [05:29<05:50, 3.08it/s, loss=0.598]" ] }, { @@ -27377,7 +27355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 920/2000 [05:33<05:48, 3.10it/s, loss=0.509]" + "training until 2000: 46%|████▌ | 920/2000 [05:29<05:51, 3.07it/s, loss=0.598]" ] }, { @@ -27385,7 +27363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 920/2000 [05:33<05:48, 3.10it/s, loss=0.51] " + "training until 2000: 46%|████▌ | 920/2000 [05:29<05:51, 3.07it/s, loss=0.497]" ] }, { @@ -27393,7 +27371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 921/2000 [05:34<07:10, 2.51it/s, loss=0.51]" + "training until 2000: 46%|████▌ | 921/2000 [05:30<05:48, 3.10it/s, loss=0.497]" ] }, { @@ -27401,7 +27379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 921/2000 [05:34<07:10, 2.51it/s, loss=0.514]" + "training until 2000: 46%|████▌ | 921/2000 [05:30<05:48, 3.10it/s, loss=0.496]" ] }, { @@ -27409,7 +27387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 922/2000 [05:34<06:45, 2.66it/s, loss=0.514]" + "training until 2000: 46%|████▌ | 922/2000 [05:30<05:49, 3.08it/s, loss=0.496]" ] }, { @@ -27417,7 +27395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 922/2000 [05:34<06:45, 2.66it/s, loss=0.561]" + "training until 2000: 46%|████▌ | 922/2000 [05:30<05:49, 3.08it/s, loss=0.585]" ] }, { @@ -27425,7 +27403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 923/2000 [05:35<06:31, 2.75it/s, loss=0.561]" + "training until 2000: 46%|████▌ | 923/2000 [05:30<05:45, 3.12it/s, loss=0.585]" ] }, { @@ -27433,7 +27411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 923/2000 [05:35<06:31, 2.75it/s, loss=0.499]" + "training until 2000: 46%|████▌ | 923/2000 [05:30<05:45, 3.12it/s, loss=0.683]" ] }, { @@ -27441,7 +27419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 924/2000 [05:35<06:18, 2.84it/s, loss=0.499]" + "training until 2000: 46%|████▌ | 924/2000 [05:31<05:44, 3.13it/s, loss=0.683]" ] }, { @@ -27449,7 +27427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▌ | 924/2000 [05:35<06:18, 2.84it/s, loss=0.57] " + "training until 2000: 46%|████▌ | 924/2000 [05:31<05:44, 3.13it/s, loss=0.563]" ] }, { @@ -27457,7 +27435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 925/2000 [05:35<06:09, 2.91it/s, loss=0.57]" + "training until 2000: 46%|████▋ | 925/2000 [05:31<05:41, 3.15it/s, loss=0.563]" ] }, { @@ -27465,7 +27443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 925/2000 [05:35<06:09, 2.91it/s, loss=0.545]" + "training until 2000: 46%|████▋ | 925/2000 [05:31<05:41, 3.15it/s, loss=0.625]" ] }, { @@ -27473,7 +27451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 926/2000 [05:36<06:01, 2.97it/s, loss=0.545]" + "training until 2000: 46%|████▋ | 926/2000 [05:31<05:42, 3.14it/s, loss=0.625]" ] }, { @@ -27481,7 +27459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 926/2000 [05:36<06:01, 2.97it/s, loss=0.476]" + "training until 2000: 46%|████▋ | 926/2000 [05:31<05:42, 3.14it/s, loss=0.497]" ] }, { @@ -27489,7 +27467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 927/2000 [05:36<05:58, 3.00it/s, loss=0.476]" + "training until 2000: 46%|████▋ | 927/2000 [05:31<05:45, 3.10it/s, loss=0.497]" ] }, { @@ -27497,7 +27475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 927/2000 [05:36<05:58, 3.00it/s, loss=0.493]" + "training until 2000: 46%|████▋ | 927/2000 [05:31<05:45, 3.10it/s, loss=0.552]" ] }, { @@ -27505,7 +27483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 928/2000 [05:36<05:56, 3.01it/s, loss=0.493]" + "training until 2000: 46%|████▋ | 928/2000 [05:32<05:45, 3.11it/s, loss=0.552]" ] }, { @@ -27513,7 +27491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 928/2000 [05:36<05:56, 3.01it/s, loss=0.466]" + "training until 2000: 46%|████▋ | 928/2000 [05:32<05:45, 3.11it/s, loss=0.541]" ] }, { @@ -27521,7 +27499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 929/2000 [05:37<05:57, 3.00it/s, loss=0.466]" + "training until 2000: 46%|████▋ | 929/2000 [05:32<05:44, 3.11it/s, loss=0.541]" ] }, { @@ -27529,7 +27507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 929/2000 [05:37<05:57, 3.00it/s, loss=0.5] " + "training until 2000: 46%|████▋ | 929/2000 [05:32<05:44, 3.11it/s, loss=0.693]" ] }, { @@ -27537,7 +27515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 930/2000 [05:37<05:54, 3.02it/s, loss=0.5]" + "training until 2000: 46%|████▋ | 930/2000 [05:32<05:46, 3.09it/s, loss=0.693]" ] }, { @@ -27545,7 +27523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 46%|████▋ | 930/2000 [05:37<05:54, 3.02it/s, loss=0.489]" + "training until 2000: 46%|████▋ | 930/2000 [05:32<05:46, 3.09it/s, loss=0.548]" ] }, { @@ -27553,7 +27531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 931/2000 [05:37<05:50, 3.05it/s, loss=0.489]" + "training until 2000: 47%|████▋ | 931/2000 [05:33<05:45, 3.10it/s, loss=0.548]" ] }, { @@ -27561,7 +27539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 931/2000 [05:37<05:50, 3.05it/s, loss=0.5] " + "training until 2000: 47%|████▋ | 931/2000 [05:33<05:45, 3.10it/s, loss=0.486]" ] }, { @@ -27569,7 +27547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 932/2000 [05:37<05:50, 3.05it/s, loss=0.5]" + "training until 2000: 47%|████▋ | 932/2000 [05:33<05:41, 3.13it/s, loss=0.486]" ] }, { @@ -27577,7 +27555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 932/2000 [05:37<05:50, 3.05it/s, loss=0.531]" + "training until 2000: 47%|████▋ | 932/2000 [05:33<05:41, 3.13it/s, loss=0.488]" ] }, { @@ -27585,7 +27563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 933/2000 [05:38<05:51, 3.03it/s, loss=0.531]" + "training until 2000: 47%|████▋ | 933/2000 [05:33<05:40, 3.14it/s, loss=0.488]" ] }, { @@ -27593,7 +27571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 933/2000 [05:38<05:51, 3.03it/s, loss=0.521]" + "training until 2000: 47%|████▋ | 933/2000 [05:33<05:40, 3.14it/s, loss=0.512]" ] }, { @@ -27601,7 +27579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 934/2000 [05:38<05:51, 3.03it/s, loss=0.521]" + "training until 2000: 47%|████▋ | 934/2000 [05:34<05:38, 3.15it/s, loss=0.512]" ] }, { @@ -27609,7 +27587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 934/2000 [05:38<05:51, 3.03it/s, loss=0.486]" + "training until 2000: 47%|████▋ | 934/2000 [05:34<05:38, 3.15it/s, loss=0.56] " ] }, { @@ -27617,7 +27595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 935/2000 [05:38<05:50, 3.04it/s, loss=0.486]" + "training until 2000: 47%|████▋ | 935/2000 [05:34<05:40, 3.12it/s, loss=0.56]" ] }, { @@ -27625,7 +27603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 935/2000 [05:38<05:50, 3.04it/s, loss=0.566]" + "training until 2000: 47%|████▋ | 935/2000 [05:34<05:40, 3.12it/s, loss=0.477]" ] }, { @@ -27633,7 +27611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 936/2000 [05:39<05:55, 2.99it/s, loss=0.566]" + "training until 2000: 47%|████▋ | 936/2000 [05:34<05:40, 3.12it/s, loss=0.477]" ] }, { @@ -27641,7 +27619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 936/2000 [05:39<05:55, 2.99it/s, loss=0.521]" + "training until 2000: 47%|████▋ | 936/2000 [05:34<05:40, 3.12it/s, loss=0.507]" ] }, { @@ -27649,7 +27627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 937/2000 [05:39<05:57, 2.97it/s, loss=0.521]" + "training until 2000: 47%|████▋ | 937/2000 [05:35<05:41, 3.12it/s, loss=0.507]" ] }, { @@ -27657,7 +27635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 937/2000 [05:39<05:57, 2.97it/s, loss=0.513]" + "training until 2000: 47%|████▋ | 937/2000 [05:35<05:41, 3.12it/s, loss=0.522]" ] }, { @@ -27665,7 +27643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 938/2000 [05:40<05:59, 2.96it/s, loss=0.513]" + "training until 2000: 47%|████▋ | 938/2000 [05:35<05:40, 3.12it/s, loss=0.522]" ] }, { @@ -27673,7 +27651,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 938/2000 [05:40<05:59, 2.96it/s, loss=0.487]" + "training until 2000: 47%|████▋ | 938/2000 [05:35<05:40, 3.12it/s, loss=0.573]" ] }, { @@ -27681,7 +27659,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 939/2000 [05:40<05:57, 2.97it/s, loss=0.487]" + "training until 2000: 47%|████▋ | 939/2000 [05:35<05:48, 3.05it/s, loss=0.573]" ] }, { @@ -27689,7 +27667,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 939/2000 [05:40<05:57, 2.97it/s, loss=0.559]" + "training until 2000: 47%|████▋ | 939/2000 [05:35<05:48, 3.05it/s, loss=0.483]" ] }, { @@ -27697,7 +27675,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 940/2000 [05:40<05:52, 3.01it/s, loss=0.559]" + "training until 2000: 47%|████▋ | 940/2000 [05:36<05:43, 3.08it/s, loss=0.483]" ] }, { @@ -27705,7 +27683,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 940/2000 [05:40<05:52, 3.01it/s, loss=0.588]" + "training until 2000: 47%|████▋ | 940/2000 [05:36<05:43, 3.08it/s, loss=0.508]" ] }, { @@ -27713,7 +27691,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 941/2000 [05:40<05:49, 3.03it/s, loss=0.588]" + "training until 2000: 47%|████▋ | 941/2000 [05:36<05:43, 3.08it/s, loss=0.508]" ] }, { @@ -27721,7 +27699,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 941/2000 [05:40<05:49, 3.03it/s, loss=0.523]" + "training until 2000: 47%|████▋ | 941/2000 [05:36<05:43, 3.08it/s, loss=0.475]" ] }, { @@ -27729,7 +27707,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 942/2000 [05:41<05:54, 2.99it/s, loss=0.523]" + "training until 2000: 47%|████▋ | 942/2000 [05:36<05:49, 3.03it/s, loss=0.475]" ] }, { @@ -27737,7 +27715,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 942/2000 [05:41<05:54, 2.99it/s, loss=0.54] " + "training until 2000: 47%|████▋ | 942/2000 [05:36<05:49, 3.03it/s, loss=0.495]" ] }, { @@ -27745,7 +27723,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 943/2000 [05:41<05:55, 2.97it/s, loss=0.54]" + "training until 2000: 47%|████▋ | 943/2000 [05:37<05:46, 3.05it/s, loss=0.495]" ] }, { @@ -27753,7 +27731,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 943/2000 [05:41<05:55, 2.97it/s, loss=0.514]" + "training until 2000: 47%|████▋ | 943/2000 [05:37<05:46, 3.05it/s, loss=0.533]" ] }, { @@ -27761,7 +27739,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 944/2000 [05:42<05:54, 2.98it/s, loss=0.514]" + "training until 2000: 47%|████▋ | 944/2000 [05:37<05:42, 3.08it/s, loss=0.533]" ] }, { @@ -27769,7 +27747,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 944/2000 [05:42<05:54, 2.98it/s, loss=0.481]" + "training until 2000: 47%|████▋ | 944/2000 [05:37<05:42, 3.08it/s, loss=0.687]" ] }, { @@ -27777,7 +27755,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 945/2000 [05:42<05:50, 3.01it/s, loss=0.481]" + "training until 2000: 47%|████▋ | 945/2000 [05:37<05:44, 3.06it/s, loss=0.687]" ] }, { @@ -27785,7 +27763,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 945/2000 [05:42<05:50, 3.01it/s, loss=0.563]" + "training until 2000: 47%|████▋ | 945/2000 [05:37<05:44, 3.06it/s, loss=0.478]" ] }, { @@ -27793,7 +27771,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 946/2000 [05:42<05:53, 2.98it/s, loss=0.563]" + "training until 2000: 47%|████▋ | 946/2000 [05:38<05:41, 3.09it/s, loss=0.478]" ] }, { @@ -27801,7 +27779,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 946/2000 [05:42<05:53, 2.98it/s, loss=0.498]" + "training until 2000: 47%|████▋ | 946/2000 [05:38<05:41, 3.09it/s, loss=0.472]" ] }, { @@ -27809,7 +27787,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 947/2000 [05:43<05:57, 2.95it/s, loss=0.498]" + "training until 2000: 47%|████▋ | 947/2000 [05:38<05:44, 3.05it/s, loss=0.472]" ] }, { @@ -27817,7 +27795,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 947/2000 [05:43<05:57, 2.95it/s, loss=0.523]" + "training until 2000: 47%|████▋ | 947/2000 [05:38<05:44, 3.05it/s, loss=0.503]" ] }, { @@ -27825,7 +27803,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 948/2000 [05:43<05:53, 2.98it/s, loss=0.523]" + "training until 2000: 47%|████▋ | 948/2000 [05:38<05:40, 3.09it/s, loss=0.503]" ] }, { @@ -27833,7 +27811,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 948/2000 [05:43<05:53, 2.98it/s, loss=0.486]" + "training until 2000: 47%|████▋ | 948/2000 [05:38<05:40, 3.09it/s, loss=0.504]" ] }, { @@ -27841,7 +27819,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 949/2000 [05:43<05:50, 3.00it/s, loss=0.486]" + "training until 2000: 47%|████▋ | 949/2000 [05:39<05:39, 3.10it/s, loss=0.504]" ] }, { @@ -27849,7 +27827,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 47%|████▋ | 949/2000 [05:43<05:50, 3.00it/s, loss=0.647]" + "training until 2000: 47%|████▋ | 949/2000 [05:39<05:39, 3.10it/s, loss=0.502]" ] }, { @@ -27857,7 +27835,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 950/2000 [05:44<05:47, 3.02it/s, loss=0.647]" + "training until 2000: 48%|████▊ | 950/2000 [05:39<05:36, 3.12it/s, loss=0.502]" ] }, { @@ -27865,7 +27843,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 950/2000 [05:44<05:47, 3.02it/s, loss=0.527]" + "training until 2000: 48%|████▊ | 950/2000 [05:39<05:36, 3.12it/s, loss=0.519]" ] }, { @@ -27873,7 +27851,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 951/2000 [05:44<05:48, 3.01it/s, loss=0.527]" + "training until 2000: 48%|████▊ | 951/2000 [05:39<05:35, 3.13it/s, loss=0.519]" ] }, { @@ -27881,7 +27859,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 951/2000 [05:44<05:48, 3.01it/s, loss=0.497]" + "training until 2000: 48%|████▊ | 951/2000 [05:39<05:35, 3.13it/s, loss=0.532]" ] }, { @@ -27889,7 +27867,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 952/2000 [05:44<05:57, 2.93it/s, loss=0.497]" + "training until 2000: 48%|████▊ | 952/2000 [05:40<05:36, 3.12it/s, loss=0.532]" ] }, { @@ -27897,7 +27875,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 952/2000 [05:44<05:57, 2.93it/s, loss=0.506]" + "training until 2000: 48%|████▊ | 952/2000 [05:40<05:36, 3.12it/s, loss=0.51] " ] }, { @@ -27905,7 +27883,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 953/2000 [05:45<05:54, 2.96it/s, loss=0.506]" + "training until 2000: 48%|████▊ | 953/2000 [05:40<05:39, 3.09it/s, loss=0.51]" ] }, { @@ -27913,7 +27891,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 953/2000 [05:45<05:54, 2.96it/s, loss=0.528]" + "training until 2000: 48%|████▊ | 953/2000 [05:40<05:39, 3.09it/s, loss=0.467]" ] }, { @@ -27921,7 +27899,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 954/2000 [05:45<05:55, 2.94it/s, loss=0.528]" + "training until 2000: 48%|████▊ | 954/2000 [05:40<05:36, 3.11it/s, loss=0.467]" ] }, { @@ -27929,7 +27907,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 954/2000 [05:45<05:55, 2.94it/s, loss=0.599]" + "training until 2000: 48%|████▊ | 954/2000 [05:40<05:36, 3.11it/s, loss=0.457]" ] }, { @@ -27937,7 +27915,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 955/2000 [05:45<05:55, 2.94it/s, loss=0.599]" + "training until 2000: 48%|████▊ | 955/2000 [05:41<05:33, 3.13it/s, loss=0.457]" ] }, { @@ -27945,7 +27923,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 955/2000 [05:45<05:55, 2.94it/s, loss=0.493]" + "training until 2000: 48%|████▊ | 955/2000 [05:41<05:33, 3.13it/s, loss=0.599]" ] }, { @@ -27953,7 +27931,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 956/2000 [05:46<05:55, 2.94it/s, loss=0.493]" + "training until 2000: 48%|████▊ | 956/2000 [05:41<05:28, 3.18it/s, loss=0.599]" ] }, { @@ -27961,7 +27939,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 956/2000 [05:46<05:55, 2.94it/s, loss=0.491]" + "training until 2000: 48%|████▊ | 956/2000 [05:41<05:28, 3.18it/s, loss=0.51] " ] }, { @@ -27969,7 +27947,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 957/2000 [05:46<05:55, 2.93it/s, loss=0.491]" + "training until 2000: 48%|████▊ | 957/2000 [05:41<05:28, 3.17it/s, loss=0.51]" ] }, { @@ -27977,7 +27955,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 957/2000 [05:46<05:55, 2.93it/s, loss=0.49] " + "training until 2000: 48%|████▊ | 957/2000 [05:41<05:28, 3.17it/s, loss=0.652]" ] }, { @@ -27985,7 +27963,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 958/2000 [05:46<05:46, 3.01it/s, loss=0.49]" + "training until 2000: 48%|████▊ | 958/2000 [05:41<05:28, 3.17it/s, loss=0.652]" ] }, { @@ -27993,7 +27971,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 958/2000 [05:46<05:46, 3.01it/s, loss=0.601]" + "training until 2000: 48%|████▊ | 958/2000 [05:41<05:28, 3.17it/s, loss=0.473]" ] }, { @@ -28001,7 +27979,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 959/2000 [05:47<05:43, 3.03it/s, loss=0.601]" + "training until 2000: 48%|████▊ | 959/2000 [05:42<05:29, 3.16it/s, loss=0.473]" ] }, { @@ -28009,7 +27987,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 959/2000 [05:47<05:43, 3.03it/s, loss=0.5] " + "training until 2000: 48%|████▊ | 959/2000 [05:42<05:29, 3.16it/s, loss=0.547]" ] }, { @@ -28017,7 +27995,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 960/2000 [05:47<05:41, 3.05it/s, loss=0.5]" + "training until 2000: 48%|████▊ | 960/2000 [05:42<05:31, 3.14it/s, loss=0.547]" ] }, { @@ -28025,7 +28003,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 960/2000 [05:47<05:41, 3.05it/s, loss=0.563]" + "training until 2000: 48%|████▊ | 960/2000 [05:42<05:31, 3.14it/s, loss=0.589]" ] }, { @@ -28033,7 +28011,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 961/2000 [05:47<05:38, 3.07it/s, loss=0.563]" + "training until 2000: 48%|████▊ | 961/2000 [05:42<05:31, 3.13it/s, loss=0.589]" ] }, { @@ -28041,7 +28019,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 961/2000 [05:47<05:38, 3.07it/s, loss=0.537]" + "training until 2000: 48%|████▊ | 961/2000 [05:42<05:31, 3.13it/s, loss=0.543]" ] }, { @@ -28049,7 +28027,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 962/2000 [05:48<05:40, 3.05it/s, loss=0.537]" + "training until 2000: 48%|████▊ | 962/2000 [05:43<05:29, 3.15it/s, loss=0.543]" ] }, { @@ -28057,7 +28035,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 962/2000 [05:48<05:40, 3.05it/s, loss=0.464]" + "training until 2000: 48%|████▊ | 962/2000 [05:43<05:29, 3.15it/s, loss=0.484]" ] }, { @@ -28065,7 +28043,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 963/2000 [05:48<05:35, 3.09it/s, loss=0.464]" + "training until 2000: 48%|████▊ | 963/2000 [05:43<05:30, 3.14it/s, loss=0.484]" ] }, { @@ -28073,7 +28051,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 963/2000 [05:48<05:35, 3.09it/s, loss=0.558]" + "training until 2000: 48%|████▊ | 963/2000 [05:43<05:30, 3.14it/s, loss=0.508]" ] }, { @@ -28081,7 +28059,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 964/2000 [05:48<05:34, 3.09it/s, loss=0.558]" + "training until 2000: 48%|████▊ | 964/2000 [05:43<05:33, 3.11it/s, loss=0.508]" ] }, { @@ -28089,7 +28067,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 964/2000 [05:48<05:34, 3.09it/s, loss=0.602]" + "training until 2000: 48%|████▊ | 964/2000 [05:43<05:33, 3.11it/s, loss=0.579]" ] }, { @@ -28097,7 +28075,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 965/2000 [05:48<05:30, 3.13it/s, loss=0.602]" + "training until 2000: 48%|████▊ | 965/2000 [05:44<05:32, 3.11it/s, loss=0.579]" ] }, { @@ -28105,7 +28083,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 965/2000 [05:48<05:30, 3.13it/s, loss=0.526]" + "training until 2000: 48%|████▊ | 965/2000 [05:44<05:32, 3.11it/s, loss=0.599]" ] }, { @@ -28113,7 +28091,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 966/2000 [05:49<05:33, 3.10it/s, loss=0.526]" + "training until 2000: 48%|████▊ | 966/2000 [05:44<05:31, 3.12it/s, loss=0.599]" ] }, { @@ -28121,7 +28099,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 966/2000 [05:49<05:33, 3.10it/s, loss=0.495]" + "training until 2000: 48%|████▊ | 966/2000 [05:44<05:31, 3.12it/s, loss=0.516]" ] }, { @@ -28129,7 +28107,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 967/2000 [05:49<05:31, 3.12it/s, loss=0.495]" + "training until 2000: 48%|████▊ | 967/2000 [05:44<05:35, 3.08it/s, loss=0.516]" ] }, { @@ -28137,7 +28115,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 967/2000 [05:49<05:31, 3.12it/s, loss=0.5] " + "training until 2000: 48%|████▊ | 967/2000 [05:44<05:35, 3.08it/s, loss=0.496]" ] }, { @@ -28145,7 +28123,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 968/2000 [05:49<05:31, 3.12it/s, loss=0.5]" + "training until 2000: 48%|████▊ | 968/2000 [05:45<05:36, 3.06it/s, loss=0.496]" ] }, { @@ -28153,7 +28131,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 968/2000 [05:49<05:31, 3.12it/s, loss=0.474]" + "training until 2000: 48%|████▊ | 968/2000 [05:45<05:36, 3.06it/s, loss=0.496]" ] }, { @@ -28161,7 +28139,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 969/2000 [05:50<05:31, 3.11it/s, loss=0.474]" + "training until 2000: 48%|████▊ | 969/2000 [05:45<05:35, 3.07it/s, loss=0.496]" ] }, { @@ -28169,7 +28147,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 969/2000 [05:50<05:31, 3.11it/s, loss=0.489]" + "training until 2000: 48%|████▊ | 969/2000 [05:45<05:35, 3.07it/s, loss=0.499]" ] }, { @@ -28177,7 +28155,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 970/2000 [05:50<05:29, 3.13it/s, loss=0.489]" + "training until 2000: 48%|████▊ | 970/2000 [05:45<05:33, 3.09it/s, loss=0.499]" ] }, { @@ -28185,7 +28163,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 48%|████▊ | 970/2000 [05:50<05:29, 3.13it/s, loss=0.541]" + "training until 2000: 48%|████▊ | 970/2000 [05:45<05:33, 3.09it/s, loss=0.516]" ] }, { @@ -28193,7 +28171,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 971/2000 [05:50<05:31, 3.10it/s, loss=0.541]" + "training until 2000: 49%|████▊ | 971/2000 [05:46<05:30, 3.11it/s, loss=0.516]" ] }, { @@ -28201,7 +28179,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 971/2000 [05:50<05:31, 3.10it/s, loss=0.486]" + "training until 2000: 49%|████▊ | 971/2000 [05:46<05:30, 3.11it/s, loss=0.547]" ] }, { @@ -28209,7 +28187,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 972/2000 [05:51<05:28, 3.13it/s, loss=0.486]" + "training until 2000: 49%|████▊ | 972/2000 [05:46<05:32, 3.10it/s, loss=0.547]" ] }, { @@ -28217,7 +28195,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 972/2000 [05:51<05:28, 3.13it/s, loss=0.577]" + "training until 2000: 49%|████▊ | 972/2000 [05:46<05:32, 3.10it/s, loss=0.551]" ] }, { @@ -28225,7 +28203,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 973/2000 [05:51<05:29, 3.12it/s, loss=0.577]" + "training until 2000: 49%|████▊ | 973/2000 [05:46<05:31, 3.10it/s, loss=0.551]" ] }, { @@ -28233,7 +28211,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 973/2000 [05:51<05:29, 3.12it/s, loss=0.519]" + "training until 2000: 49%|████▊ | 973/2000 [05:46<05:31, 3.10it/s, loss=0.608]" ] }, { @@ -28241,7 +28219,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 974/2000 [05:51<05:28, 3.12it/s, loss=0.519]" + "training until 2000: 49%|████▊ | 974/2000 [05:47<05:29, 3.11it/s, loss=0.608]" ] }, { @@ -28249,7 +28227,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▊ | 974/2000 [05:51<05:28, 3.12it/s, loss=0.499]" + "training until 2000: 49%|████▊ | 974/2000 [05:47<05:29, 3.11it/s, loss=0.654]" ] }, { @@ -28257,7 +28235,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 975/2000 [05:52<05:28, 3.12it/s, loss=0.499]" + "training until 2000: 49%|████▉ | 975/2000 [05:47<05:25, 3.15it/s, loss=0.654]" ] }, { @@ -28265,7 +28243,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 975/2000 [05:52<05:28, 3.12it/s, loss=0.571]" + "training until 2000: 49%|████▉ | 975/2000 [05:47<05:25, 3.15it/s, loss=0.635]" ] }, { @@ -28273,7 +28251,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 976/2000 [05:52<05:31, 3.09it/s, loss=0.571]" + "training until 2000: 49%|████▉ | 976/2000 [05:47<05:24, 3.16it/s, loss=0.635]" ] }, { @@ -28281,7 +28259,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 976/2000 [05:52<05:31, 3.09it/s, loss=0.483]" + "training until 2000: 49%|████▉ | 976/2000 [05:47<05:24, 3.16it/s, loss=0.494]" ] }, { @@ -28289,7 +28267,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 977/2000 [05:52<05:34, 3.06it/s, loss=0.483]" + "training until 2000: 49%|████▉ | 977/2000 [05:48<05:23, 3.17it/s, loss=0.494]" ] }, { @@ -28297,7 +28275,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 977/2000 [05:52<05:34, 3.06it/s, loss=0.561]" + "training until 2000: 49%|████▉ | 977/2000 [05:48<05:23, 3.17it/s, loss=0.479]" ] }, { @@ -28305,7 +28283,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 978/2000 [05:53<05:32, 3.08it/s, loss=0.561]" + "training until 2000: 49%|████▉ | 978/2000 [05:48<06:39, 2.56it/s, loss=0.479]" ] }, { @@ -28313,7 +28291,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 978/2000 [05:53<05:32, 3.08it/s, loss=0.565]" + "training until 2000: 49%|████▉ | 978/2000 [05:48<06:39, 2.56it/s, loss=0.513]" ] }, { @@ -28321,7 +28299,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 979/2000 [05:53<05:27, 3.12it/s, loss=0.565]" + "training until 2000: 49%|████▉ | 979/2000 [05:48<06:20, 2.68it/s, loss=0.513]" ] }, { @@ -28329,7 +28307,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 979/2000 [05:53<05:27, 3.12it/s, loss=0.56] " + "training until 2000: 49%|████▉ | 979/2000 [05:48<06:20, 2.68it/s, loss=0.503]" ] }, { @@ -28337,7 +28315,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 980/2000 [05:53<05:31, 3.08it/s, loss=0.56]" + "training until 2000: 49%|████▉ | 980/2000 [05:49<06:03, 2.80it/s, loss=0.503]" ] }, { @@ -28345,7 +28323,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 980/2000 [05:53<05:31, 3.08it/s, loss=0.474]" + "training until 2000: 49%|████▉ | 980/2000 [05:49<06:03, 2.80it/s, loss=0.514]" ] }, { @@ -28353,7 +28331,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 981/2000 [05:54<05:31, 3.08it/s, loss=0.474]" + "training until 2000: 49%|████▉ | 981/2000 [05:49<05:53, 2.88it/s, loss=0.514]" ] }, { @@ -28361,7 +28339,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 981/2000 [05:54<05:31, 3.08it/s, loss=0.482]" + "training until 2000: 49%|████▉ | 981/2000 [05:49<05:53, 2.88it/s, loss=0.493]" ] }, { @@ -28369,7 +28347,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 982/2000 [05:54<05:30, 3.08it/s, loss=0.482]" + "training until 2000: 49%|████▉ | 982/2000 [05:49<05:43, 2.97it/s, loss=0.493]" ] }, { @@ -28377,7 +28355,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 982/2000 [05:54<05:30, 3.08it/s, loss=0.564]" + "training until 2000: 49%|████▉ | 982/2000 [05:49<05:43, 2.97it/s, loss=0.485]" ] }, { @@ -28385,7 +28363,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 983/2000 [05:54<05:29, 3.09it/s, loss=0.564]" + "training until 2000: 49%|████▉ | 983/2000 [05:50<05:42, 2.97it/s, loss=0.485]" ] }, { @@ -28393,7 +28371,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 983/2000 [05:54<05:29, 3.09it/s, loss=0.556]" + "training until 2000: 49%|████▉ | 983/2000 [05:50<05:42, 2.97it/s, loss=0.515]" ] }, { @@ -28401,7 +28379,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 984/2000 [05:55<05:29, 3.08it/s, loss=0.556]" + "training until 2000: 49%|████▉ | 984/2000 [05:50<05:36, 3.02it/s, loss=0.515]" ] }, { @@ -28409,7 +28387,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 984/2000 [05:55<05:29, 3.08it/s, loss=0.479]" + "training until 2000: 49%|████▉ | 984/2000 [05:50<05:36, 3.02it/s, loss=0.48] " ] }, { @@ -28417,7 +28395,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 985/2000 [05:55<06:45, 2.50it/s, loss=0.479]" + "training until 2000: 49%|████▉ | 985/2000 [05:50<05:34, 3.03it/s, loss=0.48]" ] }, { @@ -28425,7 +28403,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 985/2000 [05:55<06:45, 2.50it/s, loss=0.49] " + "training until 2000: 49%|████▉ | 985/2000 [05:50<05:34, 3.03it/s, loss=0.516]" ] }, { @@ -28433,7 +28411,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 986/2000 [05:55<06:21, 2.66it/s, loss=0.49]" + "training until 2000: 49%|████▉ | 986/2000 [05:51<05:31, 3.06it/s, loss=0.516]" ] }, { @@ -28441,7 +28419,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 986/2000 [05:55<06:21, 2.66it/s, loss=0.518]" + "training until 2000: 49%|████▉ | 986/2000 [05:51<05:31, 3.06it/s, loss=0.531]" ] }, { @@ -28449,7 +28427,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 987/2000 [05:56<06:03, 2.78it/s, loss=0.518]" + "training until 2000: 49%|████▉ | 987/2000 [05:51<05:24, 3.12it/s, loss=0.531]" ] }, { @@ -28457,7 +28435,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 987/2000 [05:56<06:03, 2.78it/s, loss=0.529]" + "training until 2000: 49%|████▉ | 987/2000 [05:51<05:24, 3.12it/s, loss=0.48] " ] }, { @@ -28465,7 +28443,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 988/2000 [05:56<05:50, 2.88it/s, loss=0.529]" + "training until 2000: 49%|████▉ | 988/2000 [05:51<05:22, 3.14it/s, loss=0.48]" ] }, { @@ -28473,7 +28451,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 988/2000 [05:56<05:50, 2.88it/s, loss=0.466]" + "training until 2000: 49%|████▉ | 988/2000 [05:51<05:22, 3.14it/s, loss=0.527]" ] }, { @@ -28481,7 +28459,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 989/2000 [05:56<05:43, 2.94it/s, loss=0.466]" + "training until 2000: 49%|████▉ | 989/2000 [05:52<05:19, 3.16it/s, loss=0.527]" ] }, { @@ -28489,7 +28467,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 49%|████▉ | 989/2000 [05:56<05:43, 2.94it/s, loss=0.571]" + "training until 2000: 49%|████▉ | 989/2000 [05:52<05:19, 3.16it/s, loss=0.498]" ] }, { @@ -28497,7 +28475,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 990/2000 [05:57<05:39, 2.98it/s, loss=0.571]" + "training until 2000: 50%|████▉ | 990/2000 [05:52<05:19, 3.16it/s, loss=0.498]" ] }, { @@ -28505,7 +28483,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 990/2000 [05:57<05:39, 2.98it/s, loss=0.511]" + "training until 2000: 50%|████▉ | 990/2000 [05:52<05:19, 3.16it/s, loss=0.535]" ] }, { @@ -28513,7 +28491,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 991/2000 [05:57<05:33, 3.03it/s, loss=0.511]" + "training until 2000: 50%|████▉ | 991/2000 [05:52<05:21, 3.14it/s, loss=0.535]" ] }, { @@ -28521,7 +28499,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 991/2000 [05:57<05:33, 3.03it/s, loss=0.599]" + "training until 2000: 50%|████▉ | 991/2000 [05:52<05:21, 3.14it/s, loss=0.47] " ] }, { @@ -28529,7 +28507,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 992/2000 [05:57<05:35, 3.00it/s, loss=0.599]" + "training until 2000: 50%|████▉ | 992/2000 [05:53<05:17, 3.17it/s, loss=0.47]" ] }, { @@ -28537,7 +28515,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 992/2000 [05:57<05:35, 3.00it/s, loss=0.488]" + "training until 2000: 50%|████▉ | 992/2000 [05:53<05:17, 3.17it/s, loss=0.47]" ] }, { @@ -28545,7 +28523,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 993/2000 [05:58<05:30, 3.05it/s, loss=0.488]" + "training until 2000: 50%|████▉ | 993/2000 [05:53<05:20, 3.15it/s, loss=0.47]" ] }, { @@ -28553,7 +28531,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 993/2000 [05:58<05:30, 3.05it/s, loss=0.526]" + "training until 2000: 50%|████▉ | 993/2000 [05:53<05:20, 3.15it/s, loss=0.647]" ] }, { @@ -28561,7 +28539,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 994/2000 [05:58<05:24, 3.10it/s, loss=0.526]" + "training until 2000: 50%|████▉ | 994/2000 [05:53<05:15, 3.19it/s, loss=0.647]" ] }, { @@ -28569,7 +28547,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 994/2000 [05:58<05:24, 3.10it/s, loss=0.514]" + "training until 2000: 50%|████▉ | 994/2000 [05:53<05:15, 3.19it/s, loss=0.494]" ] }, { @@ -28577,7 +28555,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 995/2000 [05:58<05:28, 3.06it/s, loss=0.514]" + "training until 2000: 50%|████▉ | 995/2000 [05:54<05:15, 3.19it/s, loss=0.494]" ] }, { @@ -28585,7 +28563,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 995/2000 [05:58<05:28, 3.06it/s, loss=0.484]" + "training until 2000: 50%|████▉ | 995/2000 [05:54<05:15, 3.19it/s, loss=0.555]" ] }, { @@ -28593,7 +28571,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 996/2000 [05:59<05:28, 3.06it/s, loss=0.484]" + "training until 2000: 50%|████▉ | 996/2000 [05:54<05:15, 3.18it/s, loss=0.555]" ] }, { @@ -28601,7 +28579,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 996/2000 [05:59<05:28, 3.06it/s, loss=0.511]" + "training until 2000: 50%|████▉ | 996/2000 [05:54<05:15, 3.18it/s, loss=0.543]" ] }, { @@ -28609,7 +28587,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 997/2000 [05:59<05:31, 3.03it/s, loss=0.511]" + "training until 2000: 50%|████▉ | 997/2000 [05:54<05:14, 3.19it/s, loss=0.543]" ] }, { @@ -28617,7 +28595,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 997/2000 [05:59<05:31, 3.03it/s, loss=0.5] " + "training until 2000: 50%|████▉ | 997/2000 [05:54<05:14, 3.19it/s, loss=0.563]" ] }, { @@ -28625,7 +28603,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 998/2000 [05:59<05:32, 3.01it/s, loss=0.5]" + "training until 2000: 50%|████▉ | 998/2000 [05:54<05:11, 3.21it/s, loss=0.563]" ] }, { @@ -28633,7 +28611,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 998/2000 [05:59<05:32, 3.01it/s, loss=0.506]" + "training until 2000: 50%|████▉ | 998/2000 [05:54<05:11, 3.21it/s, loss=0.526]" ] }, { @@ -28641,7 +28619,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 999/2000 [06:00<05:30, 3.03it/s, loss=0.506]" + "training until 2000: 50%|████▉ | 999/2000 [05:55<05:13, 3.19it/s, loss=0.526]" ] }, { @@ -28649,7 +28627,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|████▉ | 999/2000 [06:00<05:30, 3.03it/s, loss=0.487]" + "training until 2000: 50%|████▉ | 999/2000 [05:55<05:13, 3.19it/s, loss=0.529]" ] }, { @@ -28657,7 +28635,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1000/2000 [06:00<05:29, 3.04it/s, loss=0.487]" + "training until 2000: 50%|█████ | 1000/2000 [05:55<05:16, 3.16it/s, loss=0.529]" ] }, { @@ -28665,7 +28643,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1000/2000 [06:00<05:29, 3.04it/s, loss=0.539]" + "training until 2000: 50%|█████ | 1000/2000 [05:55<05:16, 3.16it/s, loss=0.538]" ] }, { @@ -28753,7 +28731,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:10, 20.63blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:10, 20.66blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" ] }, { @@ -28775,7 +28753,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:20, 10.45blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:20, 10.50blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" ] }, { @@ -28797,7 +28775,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 20.56blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 20.66blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" ] }, { @@ -28819,7 +28797,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:15, 13.65blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:15, 13.78blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" ] }, { @@ -28841,7 +28819,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.31blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.54blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" ] }, { @@ -28863,7 +28841,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.31blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.54blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" ] }, { @@ -28885,7 +28863,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.31blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.54blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" ] }, { @@ -28907,7 +28885,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.31blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.54blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" ] }, { @@ -28929,7 +28907,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.31blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.54blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" ] }, { @@ -28951,7 +28929,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.31blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.54blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" ] }, { @@ -28973,7 +28951,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.31blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.54blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -28995,7 +28973,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.88blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 20.05blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -29017,7 +28995,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.88blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 20.05blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" ] }, { @@ -29039,7 +29017,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.88blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 20.05blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" ] }, { @@ -29061,7 +29039,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.88blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 20.05blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" ] }, { @@ -29083,7 +29061,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.88blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 20.05blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" ] }, { @@ -29105,7 +29083,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 17.97blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 20.05blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" ] }, { @@ -29127,7 +29105,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 17.97blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 20.05blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" ] }, { @@ -29149,7 +29127,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 17.97blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" ] }, { @@ -29171,7 +29149,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:11, 17.97blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" ] }, { @@ -29193,7 +29171,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:11, 17.97blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" ] }, { @@ -29215,7 +29193,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.05blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" ] }, { @@ -29237,7 +29215,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.05blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" ] }, { @@ -29259,7 +29237,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.05blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.79blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" ] }, { @@ -29281,7 +29259,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:11, 18.05blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.79blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" ] }, { @@ -29303,7 +29281,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:11, 18.05blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.79blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" ] }, { @@ -29325,7 +29303,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 19.79blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" ] }, { @@ -29347,7 +29325,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 19.79blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" ] }, { @@ -29369,7 +29347,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.65blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" ] }, { @@ -29391,7 +29369,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.65blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" ] }, { @@ -29413,7 +29391,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.65blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" ] }, { @@ -29435,7 +29413,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:11, 18.32blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 19.65blocks/s, ⧗=0, ▶=1, ✔=14, ✗=0, ∅=0]" ] }, { @@ -29457,7 +29435,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:11, 18.32blocks/s, ⧗=0, ▶=1, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 19.65blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" ] }, { @@ -29479,7 +29457,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:11, 18.32blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 18.23blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" ] }, { @@ -29501,7 +29479,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:10, 18.32blocks/s, ⧗=0, ▶=1, ✔=15, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 18.23blocks/s, ⧗=0, ▶=1, ✔=15, ✗=0, ∅=0]" ] }, { @@ -29523,7 +29501,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:10, 18.32blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 18.23blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" ] }, { @@ -29545,7 +29523,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 16.86blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:10, 18.23blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" ] }, { @@ -29567,7 +29545,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 16.86blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:10, 18.23blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" ] }, { @@ -29589,7 +29567,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 16.86blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.86blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" ] }, { @@ -29611,7 +29589,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 16.86blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.86blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" ] }, { @@ -29633,7 +29611,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:01<00:11, 16.86blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.86blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" ] }, { @@ -29655,7 +29633,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:00<00:11, 17.86blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" ] }, { @@ -29677,7 +29655,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.86blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" ] }, { @@ -29699,7 +29677,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.52blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" ] }, { @@ -29721,7 +29699,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.52blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" ] }, { @@ -29743,7 +29721,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.52blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" ] }, { @@ -29765,7 +29743,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 16.92blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.52blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" ] }, { @@ -29787,7 +29765,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 16.92blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.52blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" ] }, { @@ -29809,7 +29787,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 16.92blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 16.51blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" ] }, { @@ -29831,7 +29809,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 16.92blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 16.51blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" ] }, { @@ -29853,7 +29831,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 16.92blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 16.51blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" ] }, { @@ -29875,7 +29853,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 17.15blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 16.51blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" ] }, { @@ -29897,7 +29875,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 17.15blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 16.51blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" ] }, { @@ -29919,7 +29897,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:11, 17.15blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:11, 16.56blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" ] }, { @@ -29941,7 +29919,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:11, 17.15blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:11, 16.56blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" ] }, { @@ -29963,7 +29941,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:11, 17.15blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:11, 16.56blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" ] }, { @@ -29985,7 +29963,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.50blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.56blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" ] }, { @@ -30007,7 +29985,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.50blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.56blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" ] }, { @@ -30029,7 +30007,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.50blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:11, 16.67blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" ] }, { @@ -30051,7 +30029,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.50blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:11, 16.67blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" ] }, { @@ -30073,7 +30051,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.50blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:11, 16.67blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" ] }, { @@ -30095,7 +30073,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:11, 16.67blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" ] }, { @@ -30117,7 +30095,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:11, 16.67blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" ] }, { @@ -30139,7 +30117,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:11, 16.44blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" ] }, { @@ -30161,7 +30139,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:11, 16.44blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" ] }, { @@ -30183,7 +30161,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:11, 16.44blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" ] }, { @@ -30205,7 +30183,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:11, 16.44blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" ] }, { @@ -30227,7 +30205,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:11, 16.44blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" ] }, { @@ -30249,7 +30227,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:11, 16.23blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" ] }, { @@ -30271,7 +30249,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:11, 16.23blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" ] }, { @@ -30293,7 +30271,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.93blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:11, 16.23blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" ] }, { @@ -30315,7 +30293,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.05blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:11, 16.23blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" ] }, { @@ -30337,7 +30315,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.05blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:11, 16.23blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" ] }, { @@ -30359,7 +30337,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.05blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:11, 16.70blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" ] }, { @@ -30381,7 +30359,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.05blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:11, 16.70blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" ] }, { @@ -30403,7 +30381,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.05blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:11, 16.70blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" ] }, { @@ -30425,7 +30403,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.47blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:11, 16.70blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" ] }, { @@ -30447,7 +30425,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.47blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:11, 16.70blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" ] }, { @@ -30469,7 +30447,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.47blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 17.09blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" ] }, { @@ -30491,7 +30469,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.47blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 17.09blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" ] }, { @@ -30513,7 +30491,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.47blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 17.09blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" ] }, { @@ -30535,7 +30513,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.37blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 17.09blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" ] }, { @@ -30557,7 +30535,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.37blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 17.09blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" ] }, { @@ -30579,7 +30557,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.37blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 17.68blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" ] }, { @@ -30601,7 +30579,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.37blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 17.68blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" ] }, { @@ -30623,7 +30601,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.37blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:02<00:10, 17.68blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" ] }, { @@ -30645,7 +30623,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.80blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:10, 17.68blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" ] }, { @@ -30667,7 +30645,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.80blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:10, 17.68blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" ] }, { @@ -30689,7 +30667,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.80blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" ] }, { @@ -30711,7 +30689,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.80blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" ] }, { @@ -30733,7 +30711,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.80blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" ] }, { @@ -30755,7 +30733,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.74blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" ] }, { @@ -30777,7 +30755,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.74blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" ] }, { @@ -30799,7 +30777,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.74blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.09blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" ] }, { @@ -30821,7 +30799,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.74blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.09blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" ] }, { @@ -30843,7 +30821,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.74blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.09blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" ] }, { @@ -30865,7 +30843,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.18blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.09blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" ] }, { @@ -30887,7 +30865,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.18blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.09blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" ] }, { @@ -30909,7 +30887,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.18blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.21blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" ] }, { @@ -30931,7 +30909,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.18blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.21blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" ] }, { @@ -30953,7 +30931,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.18blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.21blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" ] }, { @@ -30975,7 +30953,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.01blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.21blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" ] }, { @@ -30997,7 +30975,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.01blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.21blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" ] }, { @@ -31019,7 +30997,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.01blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.33blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" ] }, { @@ -31041,7 +31019,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.01blocks/s, ⧗=0, ▶=1, ✔=43, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.33blocks/s, ⧗=0, ▶=1, ✔=43, ✗=0, ∅=0]" ] }, { @@ -31063,7 +31041,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.01blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.33blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" ] }, { @@ -31085,7 +31063,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.92blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.33blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" ] }, { @@ -31107,7 +31085,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.92blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.33blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" ] }, { @@ -31129,7 +31107,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.92blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.62blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" ] }, { @@ -31151,7 +31129,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.92blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.62blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" ] }, { @@ -31173,7 +31151,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.92blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.62blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" ] }, { @@ -31195,7 +31173,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.22blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.62blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" ] }, { @@ -31217,7 +31195,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.22blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.62blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" ] }, { @@ -31239,7 +31217,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.22blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.48blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" ] }, { @@ -31261,7 +31239,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.22blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.48blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" ] }, { @@ -31283,7 +31261,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.22blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.48blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" ] }, { @@ -31305,7 +31283,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.37blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.48blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" ] }, { @@ -31327,7 +31305,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.37blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.48blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" ] }, { @@ -31349,7 +31327,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.37blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.32blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" ] }, { @@ -31371,7 +31349,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.37blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.32blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" ] }, { @@ -31393,7 +31371,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.37blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.32blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" ] }, { @@ -31415,7 +31393,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:09, 16.88blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:09, 17.32blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" ] }, { @@ -31437,7 +31415,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:09, 16.88blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:09, 17.32blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" ] }, { @@ -31459,7 +31437,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:09, 16.88blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 15.87blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" ] }, { @@ -31481,7 +31459,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:09, 16.88blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 15.87blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" ] }, { @@ -31503,7 +31481,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:09, 16.88blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 15.87blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" ] }, { @@ -31525,7 +31503,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.87blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" ] }, { @@ -31547,7 +31525,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.84blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:03<00:10, 15.87blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" ] }, { @@ -31569,7 +31547,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.13blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" ] }, { @@ -31591,7 +31569,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.13blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" ] }, { @@ -31613,7 +31591,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.13blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" ] }, { @@ -31635,7 +31613,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.13blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" ] }, { @@ -31657,7 +31635,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.13blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" ] }, { @@ -31679,7 +31657,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:11, 14.60blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" ] }, { @@ -31701,7 +31679,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:11, 14.60blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" ] }, { @@ -31723,7 +31701,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 15.84blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:11, 14.60blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" ] }, { @@ -31745,7 +31723,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.44blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 14.60blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" ] }, { @@ -31767,7 +31745,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.44blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 14.60blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" ] }, { @@ -31789,7 +31767,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.44blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 14.93blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" ] }, { @@ -31811,7 +31789,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:09, 16.44blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 14.93blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" ] }, { @@ -31833,7 +31811,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:09, 16.44blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 14.93blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" ] }, { @@ -31855,7 +31833,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 16.07blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 14.93blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" ] }, { @@ -31877,7 +31855,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 16.07blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 14.93blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" ] }, { @@ -31899,7 +31877,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 16.07blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:10, 15.28blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" ] }, { @@ -31921,7 +31899,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:09, 16.07blocks/s, ⧗=0, ▶=1, ✔=59, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:10, 15.28blocks/s, ⧗=0, ▶=1, ✔=59, ✗=0, ∅=0]" ] }, { @@ -31943,7 +31921,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:09, 16.07blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:10, 15.28blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" ] }, { @@ -31965,7 +31943,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.41blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.28blocks/s, ⧗=0, ▶=1, ✔=60, ✗=0, ∅=0]" ] }, { @@ -31987,7 +31965,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.41blocks/s, ⧗=0, ▶=1, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.28blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" ] }, { @@ -32009,7 +31987,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.41blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" ] }, { @@ -32031,7 +32009,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:10, 15.41blocks/s, ⧗=0, ▶=1, ✔=61, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=1, ✔=61, ✗=0, ∅=0]" ] }, { @@ -32053,7 +32031,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:10, 15.41blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" ] }, { @@ -32075,7 +32053,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.89blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" ] }, { @@ -32097,7 +32075,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.89blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" ] }, { @@ -32119,7 +32097,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.89blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" ] }, { @@ -32141,7 +32119,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.89blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" ] }, { @@ -32163,7 +32141,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.89blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" ] }, { @@ -32185,7 +32163,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.88blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=1, ✔=64, ✗=0, ∅=0]" ] }, { @@ -32207,7 +32185,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.88blocks/s, ⧗=0, ▶=1, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 16.12blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" ] }, { @@ -32229,7 +32207,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.88blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 16.55blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" ] }, { @@ -32251,7 +32229,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 15.88blocks/s, ⧗=0, ▶=1, ✔=65, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 16.55blocks/s, ⧗=0, ▶=1, ✔=65, ✗=0, ∅=0]" ] }, { @@ -32273,7 +32251,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 15.88blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:09, 16.55blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" ] }, { @@ -32295,7 +32273,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.43blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.55blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" ] }, { @@ -32317,7 +32295,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.43blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.55blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" ] }, { @@ -32339,7 +32317,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.43blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.66blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" ] }, { @@ -32361,7 +32339,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:09, 16.43blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.66blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" ] }, { @@ -32383,7 +32361,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:09, 16.43blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.66blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" ] }, { @@ -32405,7 +32383,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.92blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.66blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" ] }, { @@ -32427,7 +32405,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.92blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:08, 16.66blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" ] }, { @@ -32449,7 +32427,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 16.92blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 16.48blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" ] }, { @@ -32471,7 +32449,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:03<00:08, 16.92blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 16.48blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" ] }, { @@ -32493,7 +32471,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 16.92blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 16.48blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" ] }, { @@ -32515,7 +32493,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.85blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.48blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" ] }, { @@ -32537,7 +32515,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.85blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.48blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" ] }, { @@ -32559,7 +32537,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.85blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.95blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" ] }, { @@ -32581,7 +32559,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.85blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.95blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" ] }, { @@ -32603,7 +32581,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.85blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.95blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" ] }, { @@ -32625,7 +32603,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.23blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.95blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" ] }, { @@ -32647,7 +32625,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.23blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.95blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" ] }, { @@ -32669,7 +32647,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.23blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" ] }, { @@ -32691,7 +32669,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.23blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" ] }, { @@ -32713,7 +32691,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.23blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" ] }, { @@ -32735,7 +32713,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.26blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" ] }, { @@ -32757,7 +32735,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.26blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" ] }, { @@ -32779,7 +32757,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.26blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.47blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" ] }, { @@ -32801,7 +32779,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.26blocks/s, ⧗=0, ▶=1, ✔=75, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.47blocks/s, ⧗=0, ▶=1, ✔=75, ✗=0, ∅=0]" ] }, { @@ -32823,7 +32801,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.26blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.47blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" ] }, { @@ -32845,7 +32823,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 17.38blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 17.47blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" ] }, { @@ -32867,7 +32845,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 17.38blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 17.47blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" ] }, { @@ -32889,7 +32867,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 17.38blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:08, 16.73blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" ] }, { @@ -32911,7 +32889,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.38blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:08, 16.73blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" ] }, { @@ -32933,7 +32911,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.38blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:08, 16.73blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" ] }, { @@ -32955,7 +32933,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.91blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:08, 16.73blocks/s, ⧗=0, ▶=1, ✔=78, ✗=0, ∅=0]" ] }, { @@ -32977,7 +32955,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.91blocks/s, ⧗=0, ▶=1, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:08, 16.73blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" ] }, { @@ -32999,7 +32977,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.91blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.46blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" ] }, { @@ -33021,7 +32999,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.91blocks/s, ⧗=0, ▶=1, ✔=79, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.46blocks/s, ⧗=0, ▶=1, ✔=79, ✗=0, ∅=0]" ] }, { @@ -33043,7 +33021,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.91blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.46blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" ] }, { @@ -33065,7 +33043,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 18.18blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.46blocks/s, ⧗=0, ▶=1, ✔=80, ✗=0, ∅=0]" ] }, { @@ -33087,7 +33065,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 18.18blocks/s, ⧗=0, ▶=1, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.46blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" ] }, { @@ -33109,7 +33087,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 18.18blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 17.28blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" ] }, { @@ -33131,7 +33109,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 18.18blocks/s, ⧗=0, ▶=1, ✔=81, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 17.28blocks/s, ⧗=0, ▶=1, ✔=81, ✗=0, ∅=0]" ] }, { @@ -33153,7 +33131,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 18.18blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 17.28blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" ] }, { @@ -33175,7 +33153,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 18.00blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.28blocks/s, ⧗=0, ▶=1, ✔=82, ✗=0, ∅=0]" ] }, { @@ -33197,7 +33175,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 18.00blocks/s, ⧗=0, ▶=1, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.28blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" ] }, { @@ -33219,7 +33197,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 18.00blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.56blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" ] }, { @@ -33241,7 +33219,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 18.00blocks/s, ⧗=0, ▶=1, ✔=83, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.56blocks/s, ⧗=0, ▶=1, ✔=83, ✗=0, ∅=0]" ] }, { @@ -33263,7 +33241,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 18.00blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.56blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" ] }, { @@ -33285,7 +33263,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 18.27blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.56blocks/s, ⧗=0, ▶=1, ✔=84, ✗=0, ∅=0]" ] }, { @@ -33307,7 +33285,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 18.27blocks/s, ⧗=0, ▶=1, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.56blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" ] }, { @@ -33329,7 +33307,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 18.27blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.87blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" ] }, { @@ -33351,7 +33329,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 18.27blocks/s, ⧗=0, ▶=1, ✔=85, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.87blocks/s, ⧗=0, ▶=1, ✔=85, ✗=0, ∅=0]" ] }, { @@ -33373,7 +33351,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 18.27blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:05<00:07, 17.87blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" ] }, { @@ -33395,7 +33373,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:04<00:07, 17.98blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.87blocks/s, ⧗=0, ▶=1, ✔=86, ✗=0, ∅=0]" ] }, { @@ -33417,7 +33395,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:04<00:07, 17.98blocks/s, ⧗=0, ▶=1, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.87blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" ] }, { @@ -33439,7 +33417,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.98blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.27blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" ] }, { @@ -33461,7 +33439,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.98blocks/s, ⧗=0, ▶=1, ✔=87, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.27blocks/s, ⧗=0, ▶=1, ✔=87, ✗=0, ∅=0]" ] }, { @@ -33483,7 +33461,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.98blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.27blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" ] }, { @@ -33505,7 +33483,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 17.11blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 17.27blocks/s, ⧗=0, ▶=1, ✔=88, ✗=0, ∅=0]" ] }, { @@ -33527,7 +33505,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 17.11blocks/s, ⧗=0, ▶=1, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 17.27blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" ] }, { @@ -33549,7 +33527,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 17.11blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.57blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" ] }, { @@ -33571,7 +33549,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 17.11blocks/s, ⧗=0, ▶=1, ✔=89, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.57blocks/s, ⧗=0, ▶=1, ✔=89, ✗=0, ∅=0]" ] }, { @@ -33593,7 +33571,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 17.11blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.57blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" ] }, { @@ -33615,7 +33593,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.57blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" ] }, { @@ -33637,7 +33615,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.57blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" ] }, { @@ -33659,7 +33637,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" ] }, { @@ -33681,7 +33659,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" ] }, { @@ -33703,7 +33681,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" ] }, { @@ -33725,7 +33703,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.46blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" ] }, { @@ -33747,7 +33725,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.46blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" ] }, { @@ -33769,7 +33747,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.46blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 16.25blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" ] }, { @@ -33791,7 +33769,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.46blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 16.25blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" ] }, { @@ -33813,7 +33791,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.46blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 16.25blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" ] }, { @@ -33835,7 +33813,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.35blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 16.25blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" ] }, { @@ -33857,7 +33835,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.35blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 16.25blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" ] }, { @@ -33879,7 +33857,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.35blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 16.31blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" ] }, { @@ -33901,7 +33879,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.35blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 16.31blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" ] }, { @@ -33923,7 +33901,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.35blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 16.31blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" ] }, { @@ -33945,7 +33923,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 16.85blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 16.31blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" ] }, { @@ -33967,7 +33945,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 16.85blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 16.31blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" ] }, { @@ -33989,7 +33967,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 16.85blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:07, 16.81blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" ] }, { @@ -34011,7 +33989,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:07, 16.85blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:07, 16.81blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" ] }, { @@ -34033,7 +34011,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:07, 16.85blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:07, 16.81blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" ] }, { @@ -34055,7 +34033,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 16.96blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:07, 16.81blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" ] }, { @@ -34077,7 +34055,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 16.96blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:07, 16.81blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" ] }, { @@ -34099,7 +34077,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 16.96blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 16.79blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" ] }, { @@ -34121,7 +34099,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 16.96blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 16.79blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" ] }, { @@ -34143,7 +34121,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 16.96blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 16.79blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" ] }, { @@ -34165,7 +34143,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.97blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.79blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" ] }, { @@ -34187,7 +34165,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.97blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.79blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" ] }, { @@ -34209,7 +34187,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.97blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.20blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" ] }, { @@ -34231,7 +34209,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.97blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.20blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" ] }, { @@ -34253,7 +34231,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.97blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.20blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" ] }, { @@ -34275,7 +34253,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.47blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.20blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" ] }, { @@ -34297,7 +34275,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.47blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:06<00:06, 17.20blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" ] }, { @@ -34319,7 +34297,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.47blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" ] }, { @@ -34341,7 +34319,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 17.47blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" ] }, { @@ -34363,7 +34341,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 17.47blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" ] }, { @@ -34385,7 +34363,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:05<00:06, 17.72blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" ] }, { @@ -34407,7 +34385,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:05<00:06, 17.72blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.75blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" ] }, { @@ -34429,7 +34407,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.72blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.73blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" ] }, { @@ -34451,7 +34429,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.72blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.73blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" ] }, { @@ -34473,7 +34451,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.72blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.73blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" ] }, { @@ -34495,7 +34473,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.27blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.73blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" ] }, { @@ -34517,7 +34495,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.27blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.73blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" ] }, { @@ -34539,7 +34517,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.27blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 18.05blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" ] }, { @@ -34561,7 +34539,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.27blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 18.05blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" ] }, { @@ -34583,7 +34561,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.27blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 18.05blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" ] }, { @@ -34605,7 +34583,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.71blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:05, 18.05blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" ] }, { @@ -34627,7 +34605,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.71blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:05, 18.05blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" ] }, { @@ -34649,7 +34627,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.71blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 18.05blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" ] }, { @@ -34671,7 +34649,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:06, 17.71blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 18.56blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" ] }, { @@ -34693,7 +34671,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:06, 17.71blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 18.56blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" ] }, { @@ -34715,7 +34693,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.71blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 18.56blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" ] }, { @@ -34737,7 +34715,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.71blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 18.56blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" ] }, { @@ -34759,7 +34737,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.58blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.90blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" ] }, { @@ -34781,7 +34759,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.58blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.90blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" ] }, { @@ -34803,7 +34781,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.58blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.90blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" ] }, { @@ -34825,7 +34803,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.58blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.90blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" ] }, { @@ -34847,7 +34825,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.58blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.90blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" ] }, { @@ -34869,7 +34847,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 18.69blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.16blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" ] }, { @@ -34891,7 +34869,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 18.69blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.16blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" ] }, { @@ -34913,7 +34891,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 18.69blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.16blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" ] }, { @@ -34935,7 +34913,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.69blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 19.16blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" ] }, { @@ -34957,7 +34935,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.69blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 19.16blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" ] }, { @@ -34979,7 +34957,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.97blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 19.20blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" ] }, { @@ -35001,7 +34979,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.97blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 19.20blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" ] }, { @@ -35023,7 +35001,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.97blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 19.20blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" ] }, { @@ -35045,7 +35023,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 18.97blocks/s, ⧗=0, ▶=1, ✔=116, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 19.20blocks/s, ⧗=0, ▶=1, ✔=116, ✗=0, ∅=0]" ] }, { @@ -35067,7 +35045,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 18.97blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 19.20blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" ] }, { @@ -35089,7 +35067,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.80blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" ] }, { @@ -35111,7 +35089,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.80blocks/s, ⧗=0, ▶=1, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=1, ✔=117, ✗=0, ∅=0]" ] }, { @@ -35133,7 +35111,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.80blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" ] }, { @@ -35155,7 +35133,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.80blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" ] }, { @@ -35177,7 +35155,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.80blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.92blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" ] }, { @@ -35199,7 +35177,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.14blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.23blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" ] }, { @@ -35221,7 +35199,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.14blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.23blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" ] }, { @@ -35243,7 +35221,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.14blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.23blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" ] }, { @@ -35265,7 +35243,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.14blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.23blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" ] }, { @@ -35287,7 +35265,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.14blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.23blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" ] }, { @@ -35309,7 +35287,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.08blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" ] }, { @@ -35331,7 +35309,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.08blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" ] }, { @@ -35353,7 +35331,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:07<00:05, 18.08blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" ] }, { @@ -35375,7 +35353,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:06<00:05, 18.43blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.08blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" ] }, { @@ -35397,7 +35375,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.43blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.08blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" ] }, { @@ -35419,7 +35397,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 17.95blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 17.52blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" ] }, { @@ -35441,7 +35419,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 17.95blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 17.52blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" ] }, { @@ -35463,7 +35441,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 17.95blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 17.52blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" ] }, { @@ -35485,7 +35463,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.95blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.52blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" ] }, { @@ -35507,7 +35485,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.95blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.52blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" ] }, { @@ -35529,7 +35507,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.77blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" ] }, { @@ -35551,7 +35529,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.77blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" ] }, { @@ -35573,7 +35551,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.77blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" ] }, { @@ -35595,7 +35573,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.77blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" ] }, { @@ -35617,7 +35595,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.77blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" ] }, { @@ -35639,7 +35617,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 18.18blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" ] }, { @@ -35661,7 +35639,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 18.18blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" ] }, { @@ -35683,7 +35661,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 18.18blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" ] }, { @@ -35705,7 +35683,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:04, 18.18blocks/s, ⧗=0, ▶=1, ✔=128, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=1, ✔=128, ✗=0, ∅=0]" ] }, { @@ -35727,7 +35705,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:04, 18.18blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" ] }, { @@ -35749,7 +35727,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 18.32blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:05, 17.25blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" ] }, { @@ -35771,7 +35749,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 18.32blocks/s, ⧗=0, ▶=1, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:05, 17.25blocks/s, ⧗=0, ▶=1, ✔=129, ✗=0, ∅=0]" ] }, { @@ -35793,7 +35771,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 18.32blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:05, 17.25blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" ] }, { @@ -35815,7 +35793,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 18.32blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.25blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" ] }, { @@ -35837,7 +35815,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 18.32blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.25blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" ] }, { @@ -35859,7 +35837,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" ] }, { @@ -35881,7 +35859,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" ] }, { @@ -35903,7 +35881,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" ] }, { @@ -35925,7 +35903,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" ] }, { @@ -35947,7 +35925,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 17.69blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" ] }, { @@ -35969,7 +35947,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.33blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.23blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" ] }, { @@ -35991,7 +35969,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.73blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.23blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" ] }, { @@ -36013,7 +35991,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.73blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.23blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" ] }, { @@ -36035,7 +36013,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.73blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.23blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" ] }, { @@ -36057,7 +36035,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.73blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.23blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" ] }, { @@ -36079,7 +36057,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.71blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 17.52blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" ] }, { @@ -36101,7 +36079,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.71blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 17.52blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" ] }, { @@ -36123,7 +36101,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.71blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 17.52blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" ] }, { @@ -36145,7 +36123,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.71blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 17.52blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" ] }, { @@ -36167,7 +36145,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.71blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 17.52blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" ] }, { @@ -36189,7 +36167,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.67blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 17.20blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" ] }, { @@ -36211,7 +36189,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.67blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 17.20blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" ] }, { @@ -36233,7 +36211,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.67blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 17.20blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" ] }, { @@ -36255,7 +36233,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 18.67blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 17.20blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" ] }, { @@ -36277,7 +36255,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 18.67blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:08<00:04, 17.20blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" ] }, { @@ -36299,7 +36277,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.84blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" ] }, { @@ -36321,7 +36299,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.84blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" ] }, { @@ -36343,7 +36321,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.84blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" ] }, { @@ -36365,7 +36343,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.84blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" ] }, { @@ -36387,7 +36365,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:07<00:04, 18.80blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.84blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" ] }, { @@ -36409,7 +36387,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:07<00:04, 18.66blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.96blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" ] }, { @@ -36431,7 +36409,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:07<00:04, 18.66blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.96blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" ] }, { @@ -36453,7 +36431,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 18.66blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.96blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" ] }, { @@ -36475,7 +36453,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:03, 18.66blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 17.96blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" ] }, { @@ -36497,7 +36475,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:03, 18.66blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 17.96blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" ] }, { @@ -36519,7 +36497,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.80blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.37blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" ] }, { @@ -36541,7 +36519,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.80blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.37blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" ] }, { @@ -36563,7 +36541,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.80blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.37blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" ] }, { @@ -36585,7 +36563,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.80blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.37blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" ] }, { @@ -36607,7 +36585,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.80blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.37blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" ] }, { @@ -36629,7 +36607,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.94blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.43blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" ] }, { @@ -36651,7 +36629,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.94blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.43blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" ] }, { @@ -36673,7 +36651,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.94blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.43blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" ] }, { @@ -36695,7 +36673,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.94blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.43blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" ] }, { @@ -36717,7 +36695,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.94blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.43blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" ] }, { @@ -36739,7 +36717,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.26blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" ] }, { @@ -36761,7 +36739,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.26blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" ] }, { @@ -36783,7 +36761,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.26blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" ] }, { @@ -36805,7 +36783,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 18.26blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" ] }, { @@ -36827,7 +36805,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 18.26blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" ] }, { @@ -36849,7 +36827,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" ] }, { @@ -36871,7 +36849,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" ] }, { @@ -36893,7 +36871,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" ] }, { @@ -36915,7 +36893,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" ] }, { @@ -36937,7 +36915,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" ] }, { @@ -36959,7 +36937,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" ] }, { @@ -36981,7 +36959,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" ] }, { @@ -37003,7 +36981,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" ] }, { @@ -37025,7 +37003,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" ] }, { @@ -37047,7 +37025,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.87blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.88blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" ] }, { @@ -37069,7 +37047,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.08blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.66blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" ] }, { @@ -37091,7 +37069,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.08blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.66blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" ] }, { @@ -37113,7 +37091,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.08blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.66blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" ] }, { @@ -37135,7 +37113,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 17.08blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.66blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" ] }, { @@ -37157,7 +37135,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 17.08blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.66blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" ] }, { @@ -37179,7 +37157,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.43blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.85blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" ] }, { @@ -37201,7 +37179,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.43blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.85blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" ] }, { @@ -37223,7 +37201,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.43blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.85blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" ] }, { @@ -37245,7 +37223,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.43blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.85blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" ] }, { @@ -37267,7 +37245,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.43blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:09<00:03, 16.85blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" ] }, { @@ -37289,7 +37267,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:08<00:03, 16.55blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.69blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" ] }, { @@ -37311,7 +37289,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:08<00:03, 16.55blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.69blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" ] }, { @@ -37333,7 +37311,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:08<00:03, 16.55blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.69blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" ] }, { @@ -37355,7 +37333,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:08<00:03, 16.55blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.69blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" ] }, { @@ -37377,7 +37355,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.55blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.69blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" ] }, { @@ -37399,7 +37377,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.54blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.05blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" ] }, { @@ -37421,7 +37399,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.54blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.05blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" ] }, { @@ -37443,7 +37421,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.54blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.05blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" ] }, { @@ -37465,7 +37443,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.54blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.05blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" ] }, { @@ -37487,7 +37465,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.54blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.05blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" ] }, { @@ -37509,7 +37487,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.97blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.62blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" ] }, { @@ -37531,7 +37509,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.97blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.62blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" ] }, { @@ -37553,7 +37531,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.97blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.62blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" ] }, { @@ -37575,7 +37553,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.97blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.62blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" ] }, { @@ -37597,7 +37575,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.97blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.62blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" ] }, { @@ -37619,7 +37597,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.30blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 14.81blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" ] }, { @@ -37641,7 +37619,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.30blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 14.81blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" ] }, { @@ -37663,7 +37641,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.30blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 14.81blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" ] }, { @@ -37685,7 +37663,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.30blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 14.81blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" ] }, { @@ -37707,7 +37685,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.30blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 14.81blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" ] }, { @@ -37729,7 +37707,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.56blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.18blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" ] }, { @@ -37751,7 +37729,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.56blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.18blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" ] }, { @@ -37773,7 +37751,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.56blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.18blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" ] }, { @@ -37795,7 +37773,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.56blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.18blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" ] }, { @@ -37817,7 +37795,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.56blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.18blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" ] }, { @@ -37839,7 +37817,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.50blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" ] }, { @@ -37861,7 +37839,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.50blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" ] }, { @@ -37883,7 +37861,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.50blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" ] }, { @@ -37905,7 +37883,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.50blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" ] }, { @@ -37927,7 +37905,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.50blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.22blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" ] }, { @@ -37949,7 +37927,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 16.12blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.37blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" ] }, { @@ -37971,7 +37949,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 16.12blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.37blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" ] }, { @@ -37993,7 +37971,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 16.12blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.37blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" ] }, { @@ -38015,7 +37993,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 16.12blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.37blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" ] }, { @@ -38037,7 +38015,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 16.12blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:10<00:02, 15.37blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" ] }, { @@ -38059,7 +38037,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.30blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 15.05blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" ] }, { @@ -38081,7 +38059,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.30blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 15.05blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" ] }, { @@ -38103,7 +38081,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.30blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 15.05blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" ] }, { @@ -38125,7 +38103,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:09<00:02, 15.30blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.05blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" ] }, { @@ -38147,7 +38125,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:09<00:02, 15.30blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.05blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" ] }, { @@ -38169,7 +38147,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:09<00:02, 15.83blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.50blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" ] }, { @@ -38191,7 +38169,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:09<00:02, 15.83blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.50blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" ] }, { @@ -38213,7 +38191,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.83blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.50blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" ] }, { @@ -38235,7 +38213,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.83blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.50blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" ] }, { @@ -38257,7 +38235,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.83blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.50blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" ] }, { @@ -38279,7 +38257,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.06blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" ] }, { @@ -38301,7 +38279,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.06blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" ] }, { @@ -38323,7 +38301,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.06blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" ] }, { @@ -38345,7 +38323,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.06blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" ] }, { @@ -38367,7 +38345,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.06blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" ] }, { @@ -38389,7 +38367,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.05blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.09blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" ] }, { @@ -38411,7 +38389,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.05blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.09blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" ] }, { @@ -38433,7 +38411,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.05blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.09blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" ] }, { @@ -38455,7 +38433,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.05blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.09blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" ] }, { @@ -38477,7 +38455,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.05blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.09blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" ] }, { @@ -38499,7 +38477,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.59blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.46blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" ] }, { @@ -38521,7 +38499,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.59blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.46blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" ] }, { @@ -38543,7 +38521,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.59blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.46blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" ] }, { @@ -38565,7 +38543,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.59blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.46blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" ] }, { @@ -38587,7 +38565,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.59blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.46blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" ] }, { @@ -38609,7 +38587,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.62blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 17.02blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" ] }, { @@ -38631,7 +38609,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.62blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 17.02blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" ] }, { @@ -38653,7 +38631,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.62blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 17.02blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" ] }, { @@ -38675,7 +38653,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:02, 16.62blocks/s, ⧗=0, ▶=1, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.02blocks/s, ⧗=0, ▶=1, ✔=182, ✗=0, ∅=0]" ] }, { @@ -38697,7 +38675,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:02, 16.62blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.02blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" ] }, { @@ -38719,7 +38697,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 16.77blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.43blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" ] }, { @@ -38741,7 +38719,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 16.77blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.43blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" ] }, { @@ -38763,7 +38741,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 16.77blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.43blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" ] }, { @@ -38785,7 +38763,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 16.77blocks/s, ⧗=0, ▶=1, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.43blocks/s, ⧗=0, ▶=1, ✔=184, ✗=0, ∅=0]" ] }, { @@ -38807,7 +38785,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 16.77blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.43blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" ] }, { @@ -38829,7 +38807,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.18blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" ] }, { @@ -38851,7 +38829,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.18blocks/s, ⧗=0, ▶=1, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=1, ✔=185, ✗=0, ∅=0]" ] }, { @@ -38873,7 +38851,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.18blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" ] }, { @@ -38895,7 +38873,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.18blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" ] }, { @@ -38917,7 +38895,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.18blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" ] }, { @@ -38939,7 +38917,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 16.37blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.81blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" ] }, { @@ -38961,7 +38939,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 16.37blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.81blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" ] }, { @@ -38983,7 +38961,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 16.37blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.81blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" ] }, { @@ -39005,7 +38983,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 16.37blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 17.81blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" ] }, { @@ -39027,7 +39005,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 16.37blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:11<00:01, 17.81blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" ] }, { @@ -39049,7 +39027,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 16.22blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.75blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" ] }, { @@ -39071,7 +39049,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 16.22blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.75blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" ] }, { @@ -39093,7 +39071,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 16.22blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.75blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" ] }, { @@ -39115,7 +39093,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:10<00:01, 16.22blocks/s, ⧗=0, ▶=1, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.75blocks/s, ⧗=0, ▶=1, ✔=190, ✗=0, ∅=0]" ] }, { @@ -39137,7 +39115,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 16.22blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.75blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" ] }, { @@ -39159,7 +39137,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.85blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.91blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" ] }, { @@ -39181,7 +39159,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.85blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.91blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" ] }, { @@ -39203,7 +39181,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.85blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.91blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" ] }, { @@ -39225,7 +39203,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.85blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.91blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" ] }, { @@ -39247,7 +39225,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.85blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.91blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" ] }, { @@ -39269,7 +39247,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.04blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.94blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" ] }, { @@ -39291,7 +39269,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.04blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.94blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" ] }, { @@ -39313,7 +39291,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.04blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.94blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" ] }, { @@ -39335,7 +39313,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 17.04blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.94blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" ] }, { @@ -39357,7 +39335,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 17.04blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.94blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" ] }, { @@ -39489,7 +39467,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.00blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.64blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" ] }, { @@ -39511,7 +39489,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.00blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.64blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" ] }, { @@ -39533,7 +39511,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.00blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.64blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" ] }, { @@ -39555,7 +39533,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.00blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.64blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" ] }, { @@ -39577,7 +39555,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.00blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.64blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" ] }, { @@ -39599,7 +39577,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.34blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" ] }, { @@ -39621,7 +39599,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.34blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" ] }, { @@ -39643,7 +39621,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.34blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" ] }, { @@ -39665,7 +39643,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 16.34blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" ] }, { @@ -39687,7 +39665,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 16.34blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" ] }, { @@ -39709,7 +39687,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.68blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.08blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" ] }, { @@ -39731,7 +39709,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.68blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.08blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" ] }, { @@ -39753,7 +39731,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.68blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.08blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" ] }, { @@ -39775,7 +39753,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.68blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 17.08blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" ] }, { @@ -39797,7 +39775,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.68blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 17.08blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" ] }, { @@ -39819,7 +39797,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.09blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" ] }, { @@ -39841,7 +39819,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.09blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" ] }, { @@ -39863,7 +39841,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.09blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" ] }, { @@ -39885,7 +39863,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.09blocks/s, ⧗=0, ▶=1, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=1, ✔=204, ✗=0, ∅=0]" ] }, { @@ -39907,7 +39885,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.09blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" ] }, { @@ -39929,7 +39907,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.48blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" ] }, { @@ -39951,7 +39929,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=1, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.48blocks/s, ⧗=0, ▶=1, ✔=205, ✗=0, ∅=0]" ] }, { @@ -39973,7 +39951,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:12<00:00, 17.48blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" ] }, { @@ -39995,7 +39973,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.48blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" ] }, { @@ -40017,7 +39995,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:11<00:00, 17.02blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.48blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" ] }, { @@ -40039,7 +40017,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:11<00:00, 17.15blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.51blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" ] }, { @@ -40061,7 +40039,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:11<00:00, 17.15blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.51blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" ] }, { @@ -40083,7 +40061,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.15blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.51blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" ] }, { @@ -40105,7 +40083,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.15blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.51blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" ] }, { @@ -40127,7 +40105,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.15blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.51blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" ] }, { @@ -40149,7 +40127,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 16.79blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.77blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" ] }, { @@ -40171,7 +40149,7 @@ "output_type": "stream", "text": [ "\r", - 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"predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.36blocks/s, ⧗=0, ▶=1, ✔=215, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.70blocks/s, ⧗=0, ▶=1, ✔=215, ✗=0, ∅=0]" ] }, { @@ -40523,7 +40501,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 17.70blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -40545,7 +40523,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.70blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -40560,7 +40538,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.26blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.13blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -40614,7 +40592,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING:dacapo.experiments.trainers.gunpowder_trainer:Saving Snapshot. Iteration: 1000, Loss: 0.49502497911453247!\n" + "WARNING:dacapo.experiments.trainers.gunpowder_trainer:Saving Snapshot. Iteration: 1000, Loss: 0.486723393201828!\n" ] }, { @@ -40622,7 +40600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1001/2000 [06:35<2:57:00, 10.63s/it, loss=0.539]" + "training until 2000: 50%|█████ | 1001/2000 [06:27<2:41:49, 9.72s/it, loss=0.538]" ] }, { @@ -40630,7 +40608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1001/2000 [06:35<2:57:00, 10.63s/it, loss=0.495]" + "training until 2000: 50%|█████ | 1001/2000 [06:27<2:41:49, 9.72s/it, loss=0.487]" ] }, { @@ -40638,7 +40616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1002/2000 [06:35<2:05:22, 7.54s/it, loss=0.495]" + "training until 2000: 50%|█████ | 1002/2000 [06:27<1:54:48, 6.90s/it, loss=0.487]" ] }, { @@ -40646,7 +40624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1002/2000 [06:35<2:05:22, 7.54s/it, loss=0.469]" + "training until 2000: 50%|█████ | 1002/2000 [06:27<1:54:48, 6.90s/it, loss=0.577]" ] }, { @@ -40654,7 +40632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1003/2000 [06:35<1:29:16, 5.37s/it, loss=0.469]" + "training until 2000: 50%|█████ | 1003/2000 [06:27<1:21:52, 4.93s/it, loss=0.577]" ] }, { @@ -40662,7 +40640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1003/2000 [06:35<1:29:16, 5.37s/it, loss=0.533]" + "training until 2000: 50%|█████ | 1003/2000 [06:27<1:21:52, 4.93s/it, loss=0.552]" ] }, { @@ -40670,7 +40648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1004/2000 [06:36<1:04:02, 3.86s/it, loss=0.533]" + "training until 2000: 50%|█████ | 1004/2000 [06:28<58:54, 3.55s/it, loss=0.552] " ] }, { @@ -40678,7 +40656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1004/2000 [06:36<1:04:02, 3.86s/it, loss=0.467]" + "training until 2000: 50%|█████ | 1004/2000 [06:28<58:54, 3.55s/it, loss=0.471]" ] }, { @@ -40686,7 +40664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1005/2000 [06:36<46:27, 2.80s/it, loss=0.467] " + "training until 2000: 50%|█████ | 1005/2000 [06:28<42:55, 2.59s/it, loss=0.471]" ] }, { @@ -40694,7 +40672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1005/2000 [06:36<46:27, 2.80s/it, loss=0.494]" + "training until 2000: 50%|█████ | 1005/2000 [06:28<42:55, 2.59s/it, loss=0.628]" ] }, { @@ -40702,7 +40680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1006/2000 [06:36<34:09, 2.06s/it, loss=0.494]" + "training until 2000: 50%|█████ | 1006/2000 [06:28<31:36, 1.91s/it, loss=0.628]" ] }, { @@ -40710,7 +40688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1006/2000 [06:36<34:09, 2.06s/it, loss=0.505]" + "training until 2000: 50%|█████ | 1006/2000 [06:28<31:36, 1.91s/it, loss=0.562]" ] }, { @@ -40718,7 +40696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1007/2000 [06:37<25:30, 1.54s/it, loss=0.505]" + "training until 2000: 50%|█████ | 1007/2000 [06:29<23:44, 1.43s/it, loss=0.562]" ] }, { @@ -40726,7 +40704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1007/2000 [06:37<25:30, 1.54s/it, loss=0.562]" + "training until 2000: 50%|█████ | 1007/2000 [06:29<23:44, 1.43s/it, loss=0.528]" ] }, { @@ -40734,7 +40712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1008/2000 [06:37<19:23, 1.17s/it, loss=0.562]" + "training until 2000: 50%|█████ | 1008/2000 [06:29<18:13, 1.10s/it, loss=0.528]" ] }, { @@ -40742,7 +40720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1008/2000 [06:37<19:23, 1.17s/it, loss=0.514]" + "training until 2000: 50%|█████ | 1008/2000 [06:29<18:13, 1.10s/it, loss=0.453]" ] }, { @@ -40750,7 +40728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1009/2000 [06:37<15:11, 1.09it/s, loss=0.514]" + "training until 2000: 50%|█████ | 1009/2000 [06:29<14:22, 1.15it/s, loss=0.453]" ] }, { @@ -40758,7 +40736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1009/2000 [06:37<15:11, 1.09it/s, loss=0.497]" + "training until 2000: 50%|█████ | 1009/2000 [06:29<14:22, 1.15it/s, loss=0.475]" ] }, { @@ -40766,7 +40744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1010/2000 [06:38<12:13, 1.35it/s, loss=0.497]" + "training until 2000: 50%|█████ | 1010/2000 [06:30<11:39, 1.42it/s, loss=0.475]" ] }, { @@ -40774,7 +40752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 50%|█████ | 1010/2000 [06:38<12:13, 1.35it/s, loss=0.512]" + "training until 2000: 50%|█████ | 1010/2000 [06:30<11:39, 1.42it/s, loss=0.475]" ] }, { @@ -40782,7 +40760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1011/2000 [06:38<10:16, 1.60it/s, loss=0.512]" + "training until 2000: 51%|█████ | 1011/2000 [06:30<09:47, 1.68it/s, loss=0.475]" ] }, { @@ -40790,7 +40768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1011/2000 [06:38<10:16, 1.60it/s, loss=0.508]" + "training until 2000: 51%|█████ | 1011/2000 [06:30<09:47, 1.68it/s, loss=0.522]" ] }, { @@ -40798,7 +40776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1012/2000 [06:38<08:49, 1.87it/s, loss=0.508]" + "training until 2000: 51%|█████ | 1012/2000 [06:30<08:25, 1.95it/s, loss=0.522]" ] }, { @@ -40806,7 +40784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1012/2000 [06:38<08:49, 1.87it/s, loss=0.488]" + "training until 2000: 51%|█████ | 1012/2000 [06:30<08:25, 1.95it/s, loss=0.53] " ] }, { @@ -40814,7 +40792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1013/2000 [06:39<07:45, 2.12it/s, loss=0.488]" + "training until 2000: 51%|█████ | 1013/2000 [06:31<07:27, 2.21it/s, loss=0.53]" ] }, { @@ -40822,7 +40800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1013/2000 [06:39<07:45, 2.12it/s, loss=0.496]" + "training until 2000: 51%|█████ | 1013/2000 [06:31<07:27, 2.21it/s, loss=0.529]" ] }, { @@ -40830,7 +40808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1014/2000 [06:39<07:07, 2.31it/s, loss=0.496]" + "training until 2000: 51%|█████ | 1014/2000 [06:31<06:46, 2.43it/s, loss=0.529]" ] }, { @@ -40838,7 +40816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1014/2000 [06:39<07:07, 2.31it/s, loss=0.469]" + "training until 2000: 51%|█████ | 1014/2000 [06:31<06:46, 2.43it/s, loss=0.469]" ] }, { @@ -40846,7 +40824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1015/2000 [06:39<06:35, 2.49it/s, loss=0.469]" + "training until 2000: 51%|█████ | 1015/2000 [06:31<06:19, 2.60it/s, loss=0.469]" ] }, { @@ -40854,7 +40832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1015/2000 [06:39<06:35, 2.49it/s, loss=0.494]" + "training until 2000: 51%|█████ | 1015/2000 [06:31<06:19, 2.60it/s, loss=0.671]" ] }, { @@ -40862,7 +40840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1016/2000 [06:40<06:14, 2.63it/s, loss=0.494]" + "training until 2000: 51%|█████ | 1016/2000 [06:32<05:59, 2.74it/s, loss=0.671]" ] }, { @@ -40870,7 +40848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1016/2000 [06:40<06:14, 2.63it/s, loss=0.466]" + "training until 2000: 51%|█████ | 1016/2000 [06:32<05:59, 2.74it/s, loss=0.579]" ] }, { @@ -40878,7 +40856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1017/2000 [06:40<05:56, 2.76it/s, loss=0.466]" + "training until 2000: 51%|█████ | 1017/2000 [06:32<05:44, 2.86it/s, loss=0.579]" ] }, { @@ -40886,7 +40864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1017/2000 [06:40<05:56, 2.76it/s, loss=0.477]" + "training until 2000: 51%|█████ | 1017/2000 [06:32<05:44, 2.86it/s, loss=0.493]" ] }, { @@ -40894,7 +40872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1018/2000 [06:40<05:45, 2.85it/s, loss=0.477]" + "training until 2000: 51%|█████ | 1018/2000 [06:32<05:32, 2.95it/s, loss=0.493]" ] }, { @@ -40902,7 +40880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1018/2000 [06:40<05:45, 2.85it/s, loss=0.482]" + "training until 2000: 51%|█████ | 1018/2000 [06:32<05:32, 2.95it/s, loss=0.511]" ] }, { @@ -40910,7 +40888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1019/2000 [06:41<05:35, 2.92it/s, loss=0.482]" + "training until 2000: 51%|█████ | 1019/2000 [06:33<05:29, 2.98it/s, loss=0.511]" ] }, { @@ -40918,7 +40896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1019/2000 [06:41<05:35, 2.92it/s, loss=0.496]" + "training until 2000: 51%|█████ | 1019/2000 [06:33<05:29, 2.98it/s, loss=0.502]" ] }, { @@ -40926,7 +40904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1020/2000 [06:41<05:24, 3.02it/s, loss=0.496]" + "training until 2000: 51%|█████ | 1020/2000 [06:33<05:23, 3.03it/s, loss=0.502]" ] }, { @@ -40934,7 +40912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1020/2000 [06:41<05:24, 3.02it/s, loss=0.488]" + "training until 2000: 51%|█████ | 1020/2000 [06:33<05:23, 3.03it/s, loss=0.653]" ] }, { @@ -40942,7 +40920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1021/2000 [06:41<05:22, 3.04it/s, loss=0.488]" + "training until 2000: 51%|█████ | 1021/2000 [06:33<05:22, 3.04it/s, loss=0.653]" ] }, { @@ -40950,7 +40928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1021/2000 [06:41<05:22, 3.04it/s, loss=0.525]" + "training until 2000: 51%|█████ | 1021/2000 [06:33<05:22, 3.04it/s, loss=0.487]" ] }, { @@ -40958,7 +40936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1022/2000 [06:42<05:20, 3.06it/s, loss=0.525]" + "training until 2000: 51%|█████ | 1022/2000 [06:34<05:29, 2.97it/s, loss=0.487]" ] }, { @@ -40966,7 +40944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1022/2000 [06:42<05:20, 3.06it/s, loss=0.499]" + "training until 2000: 51%|█████ | 1022/2000 [06:34<05:29, 2.97it/s, loss=0.528]" ] }, { @@ -40974,7 +40952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1023/2000 [06:42<05:16, 3.08it/s, loss=0.499]" + "training until 2000: 51%|█████ | 1023/2000 [06:34<05:23, 3.02it/s, loss=0.528]" ] }, { @@ -40982,7 +40960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1023/2000 [06:42<05:16, 3.08it/s, loss=0.487]" + "training until 2000: 51%|█████ | 1023/2000 [06:34<05:23, 3.02it/s, loss=0.458]" ] }, { @@ -40990,7 +40968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1024/2000 [06:42<05:14, 3.10it/s, loss=0.487]" + "training until 2000: 51%|█████ | 1024/2000 [06:34<05:21, 3.03it/s, loss=0.458]" ] }, { @@ -40998,7 +40976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████ | 1024/2000 [06:42<05:14, 3.10it/s, loss=0.5] " + "training until 2000: 51%|█████ | 1024/2000 [06:34<05:21, 3.03it/s, loss=0.478]" ] }, { @@ -41006,7 +40984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1025/2000 [06:43<05:13, 3.11it/s, loss=0.5]" + "training until 2000: 51%|█████▏ | 1025/2000 [06:35<05:16, 3.08it/s, loss=0.478]" ] }, { @@ -41014,7 +40992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1025/2000 [06:43<05:13, 3.11it/s, loss=0.483]" + "training until 2000: 51%|█████▏ | 1025/2000 [06:35<05:16, 3.08it/s, loss=0.577]" ] }, { @@ -41022,7 +41000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1026/2000 [06:43<05:09, 3.15it/s, loss=0.483]" + "training until 2000: 51%|█████▏ | 1026/2000 [06:35<05:15, 3.08it/s, loss=0.577]" ] }, { @@ -41030,7 +41008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1026/2000 [06:43<05:09, 3.15it/s, loss=0.511]" + "training until 2000: 51%|█████▏ | 1026/2000 [06:35<05:15, 3.08it/s, loss=0.486]" ] }, { @@ -41038,7 +41016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1027/2000 [06:43<05:09, 3.15it/s, loss=0.511]" + "training until 2000: 51%|█████▏ | 1027/2000 [06:35<05:15, 3.08it/s, loss=0.486]" ] }, { @@ -41046,7 +41024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1027/2000 [06:43<05:09, 3.15it/s, loss=0.468]" + "training until 2000: 51%|█████▏ | 1027/2000 [06:35<05:15, 3.08it/s, loss=0.514]" ] }, { @@ -41054,7 +41032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1028/2000 [06:43<05:07, 3.16it/s, loss=0.468]" + "training until 2000: 51%|█████▏ | 1028/2000 [06:36<05:14, 3.09it/s, loss=0.514]" ] }, { @@ -41062,7 +41040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1028/2000 [06:43<05:07, 3.16it/s, loss=0.475]" + "training until 2000: 51%|█████▏ | 1028/2000 [06:36<05:14, 3.09it/s, loss=0.525]" ] }, { @@ -41070,7 +41048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1029/2000 [06:44<05:06, 3.16it/s, loss=0.475]" + "training until 2000: 51%|█████▏ | 1029/2000 [06:36<05:12, 3.10it/s, loss=0.525]" ] }, { @@ -41078,7 +41056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 51%|█████▏ | 1029/2000 [06:44<05:06, 3.16it/s, loss=0.491]" + "training until 2000: 51%|█████▏ | 1029/2000 [06:36<05:12, 3.10it/s, loss=0.496]" ] }, { @@ -41086,7 +41064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1030/2000 [06:44<05:09, 3.13it/s, loss=0.491]" + "training until 2000: 52%|█████▏ | 1030/2000 [06:36<05:13, 3.10it/s, loss=0.496]" ] }, { @@ -41094,7 +41072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1030/2000 [06:44<05:09, 3.13it/s, loss=0.475]" + "training until 2000: 52%|█████▏ | 1030/2000 [06:36<05:13, 3.10it/s, loss=0.507]" ] }, { @@ -41102,7 +41080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1031/2000 [06:44<05:09, 3.13it/s, loss=0.475]" + "training until 2000: 52%|█████▏ | 1031/2000 [06:36<05:10, 3.12it/s, loss=0.507]" ] }, { @@ -41110,7 +41088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1031/2000 [06:44<05:09, 3.13it/s, loss=0.503]" + "training until 2000: 52%|█████▏ | 1031/2000 [06:36<05:10, 3.12it/s, loss=0.474]" ] }, { @@ -41118,7 +41096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1032/2000 [06:45<05:13, 3.09it/s, loss=0.503]" + "training until 2000: 52%|█████▏ | 1032/2000 [06:37<05:14, 3.08it/s, loss=0.474]" ] }, { @@ -41126,7 +41104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1032/2000 [06:45<05:13, 3.09it/s, loss=0.489]" + "training until 2000: 52%|█████▏ | 1032/2000 [06:37<05:14, 3.08it/s, loss=0.487]" ] }, { @@ -41134,7 +41112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1033/2000 [06:45<05:13, 3.09it/s, loss=0.489]" + "training until 2000: 52%|█████▏ | 1033/2000 [06:37<05:12, 3.10it/s, loss=0.487]" ] }, { @@ -41142,7 +41120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1033/2000 [06:45<05:13, 3.09it/s, loss=0.477]" + "training until 2000: 52%|█████▏ | 1033/2000 [06:37<05:12, 3.10it/s, loss=0.46] " ] }, { @@ -41150,7 +41128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1034/2000 [06:45<05:17, 3.04it/s, loss=0.477]" + "training until 2000: 52%|█████▏ | 1034/2000 [06:37<05:11, 3.10it/s, loss=0.46]" ] }, { @@ -41158,7 +41136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1034/2000 [06:45<05:17, 3.04it/s, loss=0.482]" + "training until 2000: 52%|█████▏ | 1034/2000 [06:37<05:11, 3.10it/s, loss=0.555]" ] }, { @@ -41166,7 +41144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1035/2000 [06:46<05:16, 3.05it/s, loss=0.482]" + "training until 2000: 52%|█████▏ | 1035/2000 [06:38<05:11, 3.09it/s, loss=0.555]" ] }, { @@ -41174,7 +41152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1035/2000 [06:46<05:16, 3.05it/s, loss=0.474]" + "training until 2000: 52%|█████▏ | 1035/2000 [06:38<05:11, 3.09it/s, loss=0.491]" ] }, { @@ -41182,7 +41160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1036/2000 [06:46<05:13, 3.07it/s, loss=0.474]" + "training until 2000: 52%|█████▏ | 1036/2000 [06:38<05:09, 3.12it/s, loss=0.491]" ] }, { @@ -41190,7 +41168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1036/2000 [06:46<05:13, 3.07it/s, loss=0.473]" + "training until 2000: 52%|█████▏ | 1036/2000 [06:38<05:09, 3.12it/s, loss=0.512]" ] }, { @@ -41198,7 +41176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1037/2000 [06:46<05:10, 3.10it/s, loss=0.473]" + "training until 2000: 52%|█████▏ | 1037/2000 [06:38<05:10, 3.10it/s, loss=0.512]" ] }, { @@ -41206,7 +41184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1037/2000 [06:46<05:10, 3.10it/s, loss=0.495]" + "training until 2000: 52%|█████▏ | 1037/2000 [06:38<05:10, 3.10it/s, loss=0.536]" ] }, { @@ -41214,7 +41192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1038/2000 [06:47<05:09, 3.11it/s, loss=0.495]" + "training until 2000: 52%|█████▏ | 1038/2000 [06:39<05:12, 3.08it/s, loss=0.536]" ] }, { @@ -41222,7 +41200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1038/2000 [06:47<05:09, 3.11it/s, loss=0.481]" + "training until 2000: 52%|█████▏ | 1038/2000 [06:39<05:12, 3.08it/s, loss=0.521]" ] }, { @@ -41230,7 +41208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1039/2000 [06:47<05:14, 3.06it/s, loss=0.481]" + "training until 2000: 52%|█████▏ | 1039/2000 [06:39<06:22, 2.51it/s, loss=0.521]" ] }, { @@ -41238,7 +41216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1039/2000 [06:47<05:14, 3.06it/s, loss=0.508]" + "training until 2000: 52%|█████▏ | 1039/2000 [06:39<06:22, 2.51it/s, loss=0.523]" ] }, { @@ -41246,7 +41224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1040/2000 [06:47<05:15, 3.04it/s, loss=0.508]" + "training until 2000: 52%|█████▏ | 1040/2000 [06:40<06:01, 2.65it/s, loss=0.523]" ] }, { @@ -41254,7 +41232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1040/2000 [06:47<05:15, 3.04it/s, loss=0.491]" + "training until 2000: 52%|█████▏ | 1040/2000 [06:40<06:01, 2.65it/s, loss=0.575]" ] }, { @@ -41262,7 +41240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1041/2000 [06:48<05:16, 3.03it/s, loss=0.491]" + "training until 2000: 52%|█████▏ | 1041/2000 [06:40<05:51, 2.73it/s, loss=0.575]" ] }, { @@ -41270,7 +41248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1041/2000 [06:48<05:16, 3.03it/s, loss=0.527]" + "training until 2000: 52%|█████▏ | 1041/2000 [06:40<05:51, 2.73it/s, loss=0.456]" ] }, { @@ -41278,7 +41256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1042/2000 [06:48<05:14, 3.05it/s, loss=0.527]" + "training until 2000: 52%|█████▏ | 1042/2000 [06:40<05:38, 2.83it/s, loss=0.456]" ] }, { @@ -41286,7 +41264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1042/2000 [06:48<05:14, 3.05it/s, loss=0.499]" + "training until 2000: 52%|█████▏ | 1042/2000 [06:40<05:38, 2.83it/s, loss=0.481]" ] }, { @@ -41294,7 +41272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1043/2000 [06:48<05:09, 3.09it/s, loss=0.499]" + "training until 2000: 52%|█████▏ | 1043/2000 [06:41<05:27, 2.92it/s, loss=0.481]" ] }, { @@ -41302,7 +41280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1043/2000 [06:48<05:09, 3.09it/s, loss=0.514]" + "training until 2000: 52%|█████▏ | 1043/2000 [06:41<05:27, 2.92it/s, loss=0.487]" ] }, { @@ -41310,7 +41288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1044/2000 [06:49<05:08, 3.10it/s, loss=0.514]" + "training until 2000: 52%|█████▏ | 1044/2000 [06:41<05:19, 2.99it/s, loss=0.487]" ] }, { @@ -41318,7 +41296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1044/2000 [06:49<05:08, 3.10it/s, loss=0.512]" + "training until 2000: 52%|█████▏ | 1044/2000 [06:41<05:19, 2.99it/s, loss=0.478]" ] }, { @@ -41326,7 +41304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1045/2000 [06:49<05:09, 3.08it/s, loss=0.512]" + "training until 2000: 52%|█████▏ | 1045/2000 [06:41<05:13, 3.05it/s, loss=0.478]" ] }, { @@ -41334,7 +41312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1045/2000 [06:49<05:09, 3.08it/s, loss=0.473]" + "training until 2000: 52%|█████▏ | 1045/2000 [06:41<05:13, 3.05it/s, loss=0.517]" ] }, { @@ -41342,7 +41320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1046/2000 [06:49<05:12, 3.06it/s, loss=0.473]" + "training until 2000: 52%|█████▏ | 1046/2000 [06:42<05:08, 3.09it/s, loss=0.517]" ] }, { @@ -41350,7 +41328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1046/2000 [06:49<05:12, 3.06it/s, loss=0.483]" + "training until 2000: 52%|█████▏ | 1046/2000 [06:42<05:08, 3.09it/s, loss=0.509]" ] }, { @@ -41358,7 +41336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1047/2000 [06:50<05:11, 3.06it/s, loss=0.483]" + "training until 2000: 52%|█████▏ | 1047/2000 [06:42<05:09, 3.08it/s, loss=0.509]" ] }, { @@ -41366,7 +41344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1047/2000 [06:50<05:11, 3.06it/s, loss=0.474]" + "training until 2000: 52%|█████▏ | 1047/2000 [06:42<05:09, 3.08it/s, loss=0.492]" ] }, { @@ -41374,7 +41352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1048/2000 [06:50<06:19, 2.51it/s, loss=0.474]" + "training until 2000: 52%|█████▏ | 1048/2000 [06:42<05:05, 3.11it/s, loss=0.492]" ] }, { @@ -41382,7 +41360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1048/2000 [06:50<06:19, 2.51it/s, loss=0.476]" + "training until 2000: 52%|█████▏ | 1048/2000 [06:42<05:05, 3.11it/s, loss=0.502]" ] }, { @@ -41390,7 +41368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1049/2000 [06:51<05:59, 2.65it/s, loss=0.476]" + "training until 2000: 52%|█████▏ | 1049/2000 [06:43<05:04, 3.12it/s, loss=0.502]" ] }, { @@ -41398,7 +41376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▏ | 1049/2000 [06:51<05:59, 2.65it/s, loss=0.482]" + "training until 2000: 52%|█████▏ | 1049/2000 [06:43<05:04, 3.12it/s, loss=0.482]" ] }, { @@ -41406,7 +41384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▎ | 1050/2000 [06:51<05:44, 2.76it/s, loss=0.482]" + "training until 2000: 52%|█████▎ | 1050/2000 [06:43<05:07, 3.09it/s, loss=0.482]" ] }, { @@ -41414,7 +41392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 52%|█████▎ | 1050/2000 [06:51<05:44, 2.76it/s, loss=0.513]" + "training until 2000: 52%|█████▎ | 1050/2000 [06:43<05:07, 3.09it/s, loss=0.5] " ] }, { @@ -41422,7 +41400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1051/2000 [06:51<05:30, 2.87it/s, loss=0.513]" + "training until 2000: 53%|█████▎ | 1051/2000 [06:43<05:06, 3.09it/s, loss=0.5]" ] }, { @@ -41430,7 +41408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1051/2000 [06:51<05:30, 2.87it/s, loss=0.474]" + "training until 2000: 53%|█████▎ | 1051/2000 [06:43<05:06, 3.09it/s, loss=0.549]" ] }, { @@ -41438,7 +41416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1052/2000 [06:52<05:23, 2.93it/s, loss=0.474]" + "training until 2000: 53%|█████▎ | 1052/2000 [06:43<05:05, 3.10it/s, loss=0.549]" ] }, { @@ -41446,7 +41424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1052/2000 [06:52<05:23, 2.93it/s, loss=0.552]" + "training until 2000: 53%|█████▎ | 1052/2000 [06:44<05:05, 3.10it/s, loss=0.499]" ] }, { @@ -41454,7 +41432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1053/2000 [06:52<05:17, 2.98it/s, loss=0.552]" + "training until 2000: 53%|█████▎ | 1053/2000 [06:44<05:03, 3.12it/s, loss=0.499]" ] }, { @@ -41462,7 +41440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1053/2000 [06:52<05:17, 2.98it/s, loss=0.491]" + "training until 2000: 53%|█████▎ | 1053/2000 [06:44<05:03, 3.12it/s, loss=0.494]" ] }, { @@ -41470,7 +41448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1054/2000 [06:52<05:13, 3.02it/s, loss=0.491]" + "training until 2000: 53%|█████▎ | 1054/2000 [06:44<05:04, 3.11it/s, loss=0.494]" ] }, { @@ -41478,7 +41456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1054/2000 [06:52<05:13, 3.02it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1054/2000 [06:44<05:04, 3.11it/s, loss=0.507]" ] }, { @@ -41486,7 +41464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1055/2000 [06:52<05:10, 3.04it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1055/2000 [06:44<05:01, 3.14it/s, loss=0.507]" ] }, { @@ -41494,7 +41472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1055/2000 [06:52<05:10, 3.04it/s, loss=0.473]" + "training until 2000: 53%|█████▎ | 1055/2000 [06:44<05:01, 3.14it/s, loss=0.44] " ] }, { @@ -41502,7 +41480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1056/2000 [06:53<05:11, 3.03it/s, loss=0.473]" + "training until 2000: 53%|█████▎ | 1056/2000 [06:45<05:01, 3.13it/s, loss=0.44]" ] }, { @@ -41510,7 +41488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1056/2000 [06:53<05:11, 3.03it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1056/2000 [06:45<05:01, 3.13it/s, loss=0.503]" ] }, { @@ -41518,7 +41496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1057/2000 [06:53<05:09, 3.05it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1057/2000 [06:45<04:59, 3.14it/s, loss=0.503]" ] }, { @@ -41526,7 +41504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1057/2000 [06:53<05:09, 3.05it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1057/2000 [06:45<04:59, 3.14it/s, loss=0.502]" ] }, { @@ -41534,7 +41512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1058/2000 [06:53<05:04, 3.09it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1058/2000 [06:45<04:57, 3.17it/s, loss=0.502]" ] }, { @@ -41542,7 +41520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1058/2000 [06:53<05:04, 3.09it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1058/2000 [06:45<04:57, 3.17it/s, loss=0.511]" ] }, { @@ -41550,7 +41528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1059/2000 [06:54<05:06, 3.07it/s, loss=0.476]" + "training until 2000: 53%|█████▎ | 1059/2000 [06:46<04:55, 3.18it/s, loss=0.511]" ] }, { @@ -41558,7 +41536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1059/2000 [06:54<05:06, 3.07it/s, loss=0.518]" + "training until 2000: 53%|█████▎ | 1059/2000 [06:46<04:55, 3.18it/s, loss=0.465]" ] }, { @@ -41566,7 +41544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1060/2000 [06:54<05:06, 3.06it/s, loss=0.518]" + "training until 2000: 53%|█████▎ | 1060/2000 [06:46<04:57, 3.16it/s, loss=0.465]" ] }, { @@ -41574,7 +41552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1060/2000 [06:54<05:06, 3.06it/s, loss=0.46] " + "training until 2000: 53%|█████▎ | 1060/2000 [06:46<04:57, 3.16it/s, loss=0.492]" ] }, { @@ -41582,7 +41560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1061/2000 [06:54<05:04, 3.08it/s, loss=0.46]" + "training until 2000: 53%|█████▎ | 1061/2000 [06:46<04:54, 3.19it/s, loss=0.492]" ] }, { @@ -41590,7 +41568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1061/2000 [06:54<05:04, 3.08it/s, loss=0.558]" + "training until 2000: 53%|█████▎ | 1061/2000 [06:46<04:54, 3.19it/s, loss=0.547]" ] }, { @@ -41598,7 +41576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1062/2000 [06:55<05:07, 3.05it/s, loss=0.558]" + "training until 2000: 53%|█████▎ | 1062/2000 [06:47<04:56, 3.16it/s, loss=0.547]" ] }, { @@ -41606,7 +41584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1062/2000 [06:55<05:07, 3.05it/s, loss=0.467]" + "training until 2000: 53%|█████▎ | 1062/2000 [06:47<04:56, 3.16it/s, loss=0.452]" ] }, { @@ -41614,7 +41592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1063/2000 [06:55<05:10, 3.02it/s, loss=0.467]" + "training until 2000: 53%|█████▎ | 1063/2000 [06:47<05:02, 3.10it/s, loss=0.452]" ] }, { @@ -41622,7 +41600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1063/2000 [06:55<05:10, 3.02it/s, loss=0.45] " + "training until 2000: 53%|█████▎ | 1063/2000 [06:47<05:02, 3.10it/s, loss=0.46] " ] }, { @@ -41630,7 +41608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1064/2000 [06:55<05:08, 3.04it/s, loss=0.45]" + "training until 2000: 53%|█████▎ | 1064/2000 [06:47<05:00, 3.11it/s, loss=0.46]" ] }, { @@ -41638,7 +41616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1064/2000 [06:55<05:08, 3.04it/s, loss=0.457]" + "training until 2000: 53%|█████▎ | 1064/2000 [06:47<05:00, 3.11it/s, loss=0.479]" ] }, { @@ -41646,7 +41624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1065/2000 [06:56<05:06, 3.05it/s, loss=0.457]" + "training until 2000: 53%|█████▎ | 1065/2000 [06:48<05:01, 3.10it/s, loss=0.479]" ] }, { @@ -41654,7 +41632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1065/2000 [06:56<05:06, 3.05it/s, loss=0.478]" + "training until 2000: 53%|█████▎ | 1065/2000 [06:48<05:01, 3.10it/s, loss=0.5] " ] }, { @@ -41662,7 +41640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1066/2000 [06:56<05:04, 3.07it/s, loss=0.478]" + "training until 2000: 53%|█████▎ | 1066/2000 [06:48<05:02, 3.09it/s, loss=0.5]" ] }, { @@ -41670,7 +41648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1066/2000 [06:56<05:04, 3.07it/s, loss=0.486]" + "training until 2000: 53%|█████▎ | 1066/2000 [06:48<05:02, 3.09it/s, loss=0.567]" ] }, { @@ -41678,7 +41656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1067/2000 [06:56<05:02, 3.09it/s, loss=0.486]" + "training until 2000: 53%|█████▎ | 1067/2000 [06:48<04:56, 3.14it/s, loss=0.567]" ] }, { @@ -41686,7 +41664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1067/2000 [06:56<05:02, 3.09it/s, loss=0.491]" + "training until 2000: 53%|█████▎ | 1067/2000 [06:48<04:56, 3.14it/s, loss=0.491]" ] }, { @@ -41694,7 +41672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1068/2000 [06:57<05:01, 3.09it/s, loss=0.491]" + "training until 2000: 53%|█████▎ | 1068/2000 [06:49<04:52, 3.18it/s, loss=0.491]" ] }, { @@ -41702,7 +41680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1068/2000 [06:57<05:01, 3.09it/s, loss=0.522]" + "training until 2000: 53%|█████▎ | 1068/2000 [06:49<04:52, 3.18it/s, loss=0.481]" ] }, { @@ -41710,7 +41688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1069/2000 [06:57<04:58, 3.12it/s, loss=0.522]" + "training until 2000: 53%|█████▎ | 1069/2000 [06:49<04:55, 3.15it/s, loss=0.481]" ] }, { @@ -41718,7 +41696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 53%|█████▎ | 1069/2000 [06:57<04:58, 3.12it/s, loss=0.583]" + "training until 2000: 53%|█████▎ | 1069/2000 [06:49<04:55, 3.15it/s, loss=0.471]" ] }, { @@ -41726,7 +41704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1070/2000 [06:57<04:59, 3.11it/s, loss=0.583]" + "training until 2000: 54%|█████▎ | 1070/2000 [06:49<04:53, 3.17it/s, loss=0.471]" ] }, { @@ -41734,7 +41712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1070/2000 [06:57<04:59, 3.11it/s, loss=0.455]" + "training until 2000: 54%|█████▎ | 1070/2000 [06:49<04:53, 3.17it/s, loss=0.495]" ] }, { @@ -41742,7 +41720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1071/2000 [06:58<04:59, 3.10it/s, loss=0.455]" + "training until 2000: 54%|█████▎ | 1071/2000 [06:50<04:52, 3.17it/s, loss=0.495]" ] }, { @@ -41750,7 +41728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1071/2000 [06:58<04:59, 3.10it/s, loss=0.477]" + "training until 2000: 54%|█████▎ | 1071/2000 [06:50<04:52, 3.17it/s, loss=0.441]" ] }, { @@ -41758,7 +41736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1072/2000 [06:58<04:57, 3.12it/s, loss=0.477]" + "training until 2000: 54%|█████▎ | 1072/2000 [06:50<04:51, 3.19it/s, loss=0.441]" ] }, { @@ -41766,7 +41744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1072/2000 [06:58<04:57, 3.12it/s, loss=0.485]" + "training until 2000: 54%|█████▎ | 1072/2000 [06:50<04:51, 3.19it/s, loss=0.437]" ] }, { @@ -41774,7 +41752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1073/2000 [06:58<04:55, 3.14it/s, loss=0.485]" + "training until 2000: 54%|█████▎ | 1073/2000 [06:50<04:55, 3.14it/s, loss=0.437]" ] }, { @@ -41782,7 +41760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1073/2000 [06:58<04:55, 3.14it/s, loss=0.46] " + "training until 2000: 54%|█████▎ | 1073/2000 [06:50<04:55, 3.14it/s, loss=0.485]" ] }, { @@ -41790,7 +41768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1074/2000 [06:59<04:53, 3.15it/s, loss=0.46]" + "training until 2000: 54%|█████▎ | 1074/2000 [06:50<04:57, 3.11it/s, loss=0.485]" ] }, { @@ -41798,7 +41776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▎ | 1074/2000 [06:59<04:53, 3.15it/s, loss=0.664]" + "training until 2000: 54%|█████▎ | 1074/2000 [06:50<04:57, 3.11it/s, loss=0.543]" ] }, { @@ -41806,7 +41784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1075/2000 [06:59<04:51, 3.17it/s, loss=0.664]" + "training until 2000: 54%|█████▍ | 1075/2000 [06:51<04:57, 3.11it/s, loss=0.543]" ] }, { @@ -41814,7 +41792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1075/2000 [06:59<04:51, 3.17it/s, loss=0.501]" + "training until 2000: 54%|█████▍ | 1075/2000 [06:51<04:57, 3.11it/s, loss=0.462]" ] }, { @@ -41822,7 +41800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1076/2000 [06:59<04:51, 3.18it/s, loss=0.501]" + "training until 2000: 54%|█████▍ | 1076/2000 [06:51<04:56, 3.12it/s, loss=0.462]" ] }, { @@ -41830,7 +41808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1076/2000 [06:59<04:51, 3.18it/s, loss=0.531]" + "training until 2000: 54%|█████▍ | 1076/2000 [06:51<04:56, 3.12it/s, loss=0.574]" ] }, { @@ -41838,7 +41816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1077/2000 [07:00<04:51, 3.17it/s, loss=0.531]" + "training until 2000: 54%|█████▍ | 1077/2000 [06:51<04:52, 3.16it/s, loss=0.574]" ] }, { @@ -41846,7 +41824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1077/2000 [07:00<04:51, 3.17it/s, loss=0.497]" + "training until 2000: 54%|█████▍ | 1077/2000 [06:51<04:52, 3.16it/s, loss=0.482]" ] }, { @@ -41854,7 +41832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1078/2000 [07:00<04:53, 3.14it/s, loss=0.497]" + "training until 2000: 54%|█████▍ | 1078/2000 [06:52<04:53, 3.14it/s, loss=0.482]" ] }, { @@ -41862,7 +41840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1078/2000 [07:00<04:53, 3.14it/s, loss=0.495]" + "training until 2000: 54%|█████▍ | 1078/2000 [06:52<04:53, 3.14it/s, loss=0.495]" ] }, { @@ -41870,7 +41848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1079/2000 [07:00<04:52, 3.15it/s, loss=0.495]" + "training until 2000: 54%|█████▍ | 1079/2000 [06:52<04:51, 3.16it/s, loss=0.495]" ] }, { @@ -41878,7 +41856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1079/2000 [07:00<04:52, 3.15it/s, loss=0.476]" + "training until 2000: 54%|█████▍ | 1079/2000 [06:52<04:51, 3.16it/s, loss=0.445]" ] }, { @@ -41886,7 +41864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1080/2000 [07:01<04:53, 3.14it/s, loss=0.476]" + "training until 2000: 54%|█████▍ | 1080/2000 [06:52<04:50, 3.17it/s, loss=0.445]" ] }, { @@ -41894,7 +41872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1080/2000 [07:01<04:53, 3.14it/s, loss=0.545]" + "training until 2000: 54%|█████▍ | 1080/2000 [06:52<04:50, 3.17it/s, loss=0.457]" ] }, { @@ -41902,7 +41880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1081/2000 [07:01<04:58, 3.08it/s, loss=0.545]" + "training until 2000: 54%|█████▍ | 1081/2000 [06:53<04:49, 3.17it/s, loss=0.457]" ] }, { @@ -41910,7 +41888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1081/2000 [07:01<04:58, 3.08it/s, loss=0.472]" + "training until 2000: 54%|█████▍ | 1081/2000 [06:53<04:49, 3.17it/s, loss=0.641]" ] }, { @@ -41918,7 +41896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1082/2000 [07:01<04:57, 3.09it/s, loss=0.472]" + "training until 2000: 54%|█████▍ | 1082/2000 [06:53<04:51, 3.15it/s, loss=0.641]" ] }, { @@ -41926,7 +41904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1082/2000 [07:01<04:57, 3.09it/s, loss=0.526]" + "training until 2000: 54%|█████▍ | 1082/2000 [06:53<04:51, 3.15it/s, loss=0.545]" ] }, { @@ -41934,7 +41912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1083/2000 [07:02<04:53, 3.13it/s, loss=0.526]" + "training until 2000: 54%|█████▍ | 1083/2000 [06:53<04:49, 3.16it/s, loss=0.545]" ] }, { @@ -41942,7 +41920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1083/2000 [07:02<04:53, 3.13it/s, loss=0.49] " + "training until 2000: 54%|█████▍ | 1083/2000 [06:53<04:49, 3.16it/s, loss=0.507]" ] }, { @@ -41950,7 +41928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1084/2000 [07:02<04:53, 3.12it/s, loss=0.49]" + "training until 2000: 54%|█████▍ | 1084/2000 [06:54<04:49, 3.17it/s, loss=0.507]" ] }, { @@ -41958,7 +41936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1084/2000 [07:02<04:53, 3.12it/s, loss=0.504]" + "training until 2000: 54%|█████▍ | 1084/2000 [06:54<04:49, 3.17it/s, loss=0.494]" ] }, { @@ -41966,7 +41944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1085/2000 [07:02<04:53, 3.12it/s, loss=0.504]" + "training until 2000: 54%|█████▍ | 1085/2000 [06:54<04:49, 3.16it/s, loss=0.494]" ] }, { @@ -41974,7 +41952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1085/2000 [07:02<04:53, 3.12it/s, loss=0.511]" + "training until 2000: 54%|█████▍ | 1085/2000 [06:54<04:49, 3.16it/s, loss=0.61] " ] }, { @@ -41982,7 +41960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1086/2000 [07:02<04:52, 3.12it/s, loss=0.511]" + "training until 2000: 54%|█████▍ | 1086/2000 [06:54<04:48, 3.17it/s, loss=0.61]" ] }, { @@ -41990,7 +41968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1086/2000 [07:02<04:52, 3.12it/s, loss=0.52] " + "training until 2000: 54%|█████▍ | 1086/2000 [06:54<04:48, 3.17it/s, loss=0.443]" ] }, { @@ -41998,7 +41976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1087/2000 [07:03<04:55, 3.09it/s, loss=0.52]" + "training until 2000: 54%|█████▍ | 1087/2000 [06:55<04:47, 3.17it/s, loss=0.443]" ] }, { @@ -42006,7 +41984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1087/2000 [07:03<04:55, 3.09it/s, loss=0.485]" + "training until 2000: 54%|█████▍ | 1087/2000 [06:55<04:47, 3.17it/s, loss=0.457]" ] }, { @@ -42014,7 +41992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1088/2000 [07:03<04:56, 3.08it/s, loss=0.485]" + "training until 2000: 54%|█████▍ | 1088/2000 [06:55<04:46, 3.18it/s, loss=0.457]" ] }, { @@ -42022,7 +42000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1088/2000 [07:03<04:56, 3.08it/s, loss=0.489]" + "training until 2000: 54%|█████▍ | 1088/2000 [06:55<04:46, 3.18it/s, loss=0.442]" ] }, { @@ -42030,7 +42008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1089/2000 [07:03<04:55, 3.08it/s, loss=0.489]" + "training until 2000: 54%|█████▍ | 1089/2000 [06:55<04:48, 3.15it/s, loss=0.442]" ] }, { @@ -42038,7 +42016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 54%|█████▍ | 1089/2000 [07:03<04:55, 3.08it/s, loss=0.497]" + "training until 2000: 54%|█████▍ | 1089/2000 [06:55<04:48, 3.15it/s, loss=0.555]" ] }, { @@ -42046,7 +42024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1090/2000 [07:04<04:53, 3.10it/s, loss=0.497]" + "training until 2000: 55%|█████▍ | 1090/2000 [06:56<04:46, 3.18it/s, loss=0.555]" ] }, { @@ -42054,7 +42032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1090/2000 [07:04<04:53, 3.10it/s, loss=0.534]" + "training until 2000: 55%|█████▍ | 1090/2000 [06:56<04:46, 3.18it/s, loss=0.517]" ] }, { @@ -42062,7 +42040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1091/2000 [07:04<04:53, 3.10it/s, loss=0.534]" + "training until 2000: 55%|█████▍ | 1091/2000 [06:56<04:46, 3.17it/s, loss=0.517]" ] }, { @@ -42070,7 +42048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1091/2000 [07:04<04:53, 3.10it/s, loss=0.5] " + "training until 2000: 55%|█████▍ | 1091/2000 [06:56<04:46, 3.17it/s, loss=0.451]" ] }, { @@ -42078,7 +42056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1092/2000 [07:04<04:51, 3.11it/s, loss=0.5]" + "training until 2000: 55%|█████▍ | 1092/2000 [06:56<04:48, 3.15it/s, loss=0.451]" ] }, { @@ -42086,7 +42064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1092/2000 [07:04<04:51, 3.11it/s, loss=0.456]" + "training until 2000: 55%|█████▍ | 1092/2000 [06:56<04:48, 3.15it/s, loss=0.495]" ] }, { @@ -42094,7 +42072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1093/2000 [07:05<04:47, 3.15it/s, loss=0.456]" + "training until 2000: 55%|█████▍ | 1093/2000 [06:57<04:47, 3.16it/s, loss=0.495]" ] }, { @@ -42102,7 +42080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1093/2000 [07:05<04:47, 3.15it/s, loss=0.461]" + "training until 2000: 55%|█████▍ | 1093/2000 [06:57<04:47, 3.16it/s, loss=0.463]" ] }, { @@ -42110,7 +42088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1094/2000 [07:05<04:47, 3.15it/s, loss=0.461]" + "training until 2000: 55%|█████▍ | 1094/2000 [06:57<04:45, 3.18it/s, loss=0.463]" ] }, { @@ -42118,7 +42096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1094/2000 [07:05<04:47, 3.15it/s, loss=0.47] " + "training until 2000: 55%|█████▍ | 1094/2000 [06:57<04:45, 3.18it/s, loss=0.477]" ] }, { @@ -42126,7 +42104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1095/2000 [07:05<04:48, 3.13it/s, loss=0.47]" + "training until 2000: 55%|█████▍ | 1095/2000 [06:57<04:49, 3.13it/s, loss=0.477]" ] }, { @@ -42134,7 +42112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1095/2000 [07:05<04:48, 3.13it/s, loss=0.45]" + "training until 2000: 55%|█████▍ | 1095/2000 [06:57<04:49, 3.13it/s, loss=0.447]" ] }, { @@ -42142,7 +42120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1096/2000 [07:06<04:47, 3.14it/s, loss=0.45]" + "training until 2000: 55%|█████▍ | 1096/2000 [06:57<04:46, 3.15it/s, loss=0.447]" ] }, { @@ -42150,7 +42128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1096/2000 [07:06<04:47, 3.14it/s, loss=0.594]" + "training until 2000: 55%|█████▍ | 1096/2000 [06:57<04:46, 3.15it/s, loss=0.664]" ] }, { @@ -42158,7 +42136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1097/2000 [07:06<04:48, 3.13it/s, loss=0.594]" + "training until 2000: 55%|█████▍ | 1097/2000 [06:58<04:44, 3.18it/s, loss=0.664]" ] }, { @@ -42166,7 +42144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1097/2000 [07:06<04:48, 3.13it/s, loss=0.474]" + "training until 2000: 55%|█████▍ | 1097/2000 [06:58<04:44, 3.18it/s, loss=0.517]" ] }, { @@ -42174,7 +42152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1098/2000 [07:06<04:50, 3.11it/s, loss=0.474]" + "training until 2000: 55%|█████▍ | 1098/2000 [06:58<04:45, 3.16it/s, loss=0.517]" ] }, { @@ -42182,7 +42160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1098/2000 [07:06<04:50, 3.11it/s, loss=0.448]" + "training until 2000: 55%|█████▍ | 1098/2000 [06:58<04:45, 3.16it/s, loss=0.5] " ] }, { @@ -42190,7 +42168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1099/2000 [07:07<04:49, 3.11it/s, loss=0.448]" + "training until 2000: 55%|█████▍ | 1099/2000 [06:58<04:43, 3.18it/s, loss=0.5]" ] }, { @@ -42198,7 +42176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▍ | 1099/2000 [07:07<04:49, 3.11it/s, loss=0.5] " + "training until 2000: 55%|█████▍ | 1099/2000 [06:58<04:43, 3.18it/s, loss=0.529]" ] }, { @@ -42206,7 +42184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1100/2000 [07:07<04:50, 3.10it/s, loss=0.5]" + "training until 2000: 55%|█████▌ | 1100/2000 [06:59<04:43, 3.17it/s, loss=0.529]" ] }, { @@ -42214,7 +42192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1100/2000 [07:07<04:50, 3.10it/s, loss=0.48]" + "training until 2000: 55%|█████▌ | 1100/2000 [06:59<04:43, 3.17it/s, loss=0.462]" ] }, { @@ -42222,7 +42200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1101/2000 [07:07<04:49, 3.10it/s, loss=0.48]" + "training until 2000: 55%|█████▌ | 1101/2000 [06:59<05:51, 2.56it/s, loss=0.462]" ] }, { @@ -42230,7 +42208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1101/2000 [07:07<04:49, 3.10it/s, loss=0.47]" + "training until 2000: 55%|█████▌ | 1101/2000 [06:59<05:51, 2.56it/s, loss=0.526]" ] }, { @@ -42238,7 +42216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1102/2000 [07:08<04:47, 3.12it/s, loss=0.47]" + "training until 2000: 55%|█████▌ | 1102/2000 [07:00<05:30, 2.71it/s, loss=0.526]" ] }, { @@ -42246,7 +42224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1102/2000 [07:08<04:47, 3.12it/s, loss=0.486]" + "training until 2000: 55%|█████▌ | 1102/2000 [07:00<05:30, 2.71it/s, loss=0.44] " ] }, { @@ -42254,7 +42232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1103/2000 [07:08<04:47, 3.11it/s, loss=0.486]" + "training until 2000: 55%|█████▌ | 1103/2000 [07:00<05:19, 2.81it/s, loss=0.44]" ] }, { @@ -42262,7 +42240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1103/2000 [07:08<04:47, 3.11it/s, loss=0.497]" + "training until 2000: 55%|█████▌ | 1103/2000 [07:00<05:19, 2.81it/s, loss=0.592]" ] }, { @@ -42270,7 +42248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1104/2000 [07:08<04:46, 3.13it/s, loss=0.497]" + "training until 2000: 55%|█████▌ | 1104/2000 [07:00<05:11, 2.87it/s, loss=0.592]" ] }, { @@ -42278,7 +42256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1104/2000 [07:08<04:46, 3.13it/s, loss=0.512]" + "training until 2000: 55%|█████▌ | 1104/2000 [07:00<05:11, 2.87it/s, loss=0.606]" ] }, { @@ -42286,7 +42264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1105/2000 [07:09<04:46, 3.12it/s, loss=0.512]" + "training until 2000: 55%|█████▌ | 1105/2000 [07:01<05:03, 2.95it/s, loss=0.606]" ] }, { @@ -42294,7 +42272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1105/2000 [07:09<04:46, 3.12it/s, loss=0.476]" + "training until 2000: 55%|█████▌ | 1105/2000 [07:01<05:03, 2.95it/s, loss=0.495]" ] }, { @@ -42302,7 +42280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1106/2000 [07:09<04:50, 3.08it/s, loss=0.476]" + "training until 2000: 55%|█████▌ | 1106/2000 [07:01<04:59, 2.99it/s, loss=0.495]" ] }, { @@ -42310,7 +42288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1106/2000 [07:09<04:50, 3.08it/s, loss=0.479]" + "training until 2000: 55%|█████▌ | 1106/2000 [07:01<04:59, 2.99it/s, loss=0.524]" ] }, { @@ -42318,7 +42296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1107/2000 [07:09<04:47, 3.11it/s, loss=0.479]" + "training until 2000: 55%|█████▌ | 1107/2000 [07:01<04:55, 3.02it/s, loss=0.524]" ] }, { @@ -42326,7 +42304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1107/2000 [07:09<04:47, 3.11it/s, loss=0.458]" + "training until 2000: 55%|█████▌ | 1107/2000 [07:01<04:55, 3.02it/s, loss=0.444]" ] }, { @@ -42334,7 +42312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1108/2000 [07:10<04:45, 3.13it/s, loss=0.458]" + "training until 2000: 55%|█████▌ | 1108/2000 [07:02<04:47, 3.10it/s, loss=0.444]" ] }, { @@ -42342,7 +42320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1108/2000 [07:10<04:45, 3.13it/s, loss=0.524]" + "training until 2000: 55%|█████▌ | 1108/2000 [07:02<04:47, 3.10it/s, loss=0.47] " ] }, { @@ -42350,7 +42328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1109/2000 [07:10<04:46, 3.11it/s, loss=0.524]" + "training until 2000: 55%|█████▌ | 1109/2000 [07:02<04:45, 3.12it/s, loss=0.47]" ] }, { @@ -42358,7 +42336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 55%|█████▌ | 1109/2000 [07:10<04:46, 3.11it/s, loss=0.471]" + "training until 2000: 55%|█████▌ | 1109/2000 [07:02<04:45, 3.12it/s, loss=0.446]" ] }, { @@ -42366,7 +42344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1110/2000 [07:10<04:48, 3.09it/s, loss=0.471]" + "training until 2000: 56%|█████▌ | 1110/2000 [07:02<04:45, 3.12it/s, loss=0.446]" ] }, { @@ -42374,7 +42352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1110/2000 [07:10<04:48, 3.09it/s, loss=0.482]" + "training until 2000: 56%|█████▌ | 1110/2000 [07:02<04:45, 3.12it/s, loss=0.542]" ] }, { @@ -42382,7 +42360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1111/2000 [07:11<05:53, 2.52it/s, loss=0.482]" + "training until 2000: 56%|█████▌ | 1111/2000 [07:02<04:42, 3.14it/s, loss=0.542]" ] }, { @@ -42390,7 +42368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1111/2000 [07:11<05:53, 2.52it/s, loss=0.471]" + "training until 2000: 56%|█████▌ | 1111/2000 [07:02<04:42, 3.14it/s, loss=0.437]" ] }, { @@ -42398,7 +42376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1112/2000 [07:11<05:34, 2.65it/s, loss=0.471]" + "training until 2000: 56%|█████▌ | 1112/2000 [07:03<04:42, 3.14it/s, loss=0.437]" ] }, { @@ -42406,7 +42384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1112/2000 [07:11<05:34, 2.65it/s, loss=0.465]" + "training until 2000: 56%|█████▌ | 1112/2000 [07:03<04:42, 3.14it/s, loss=0.456]" ] }, { @@ -42414,7 +42392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1113/2000 [07:11<05:22, 2.75it/s, loss=0.465]" + "training until 2000: 56%|█████▌ | 1113/2000 [07:03<04:41, 3.15it/s, loss=0.456]" ] }, { @@ -42422,7 +42400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1113/2000 [07:11<05:22, 2.75it/s, loss=0.479]" + "training until 2000: 56%|█████▌ | 1113/2000 [07:03<04:41, 3.15it/s, loss=0.45] " ] }, { @@ -42430,7 +42408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1114/2000 [07:12<05:08, 2.87it/s, loss=0.479]" + "training until 2000: 56%|█████▌ | 1114/2000 [07:03<04:39, 3.17it/s, loss=0.45]" ] }, { @@ -42438,7 +42416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1114/2000 [07:12<05:08, 2.87it/s, loss=0.467]" + "training until 2000: 56%|█████▌ | 1114/2000 [07:03<04:39, 3.17it/s, loss=0.427]" ] }, { @@ -42446,7 +42424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1115/2000 [07:12<05:03, 2.92it/s, loss=0.467]" + "training until 2000: 56%|█████▌ | 1115/2000 [07:04<04:38, 3.18it/s, loss=0.427]" ] }, { @@ -42454,7 +42432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1115/2000 [07:12<05:03, 2.92it/s, loss=0.515]" + "training until 2000: 56%|█████▌ | 1115/2000 [07:04<04:38, 3.18it/s, loss=0.591]" ] }, { @@ -42462,7 +42440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1116/2000 [07:12<04:55, 2.99it/s, loss=0.515]" + "training until 2000: 56%|█████▌ | 1116/2000 [07:04<04:36, 3.20it/s, loss=0.591]" ] }, { @@ -42470,7 +42448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1116/2000 [07:12<04:55, 2.99it/s, loss=0.47] " + "training until 2000: 56%|█████▌ | 1116/2000 [07:04<04:36, 3.20it/s, loss=0.504]" ] }, { @@ -42478,7 +42456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1117/2000 [07:13<04:54, 3.00it/s, loss=0.47]" + "training until 2000: 56%|█████▌ | 1117/2000 [07:04<04:38, 3.17it/s, loss=0.504]" ] }, { @@ -42486,7 +42464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1117/2000 [07:13<04:54, 3.00it/s, loss=0.476]" + "training until 2000: 56%|█████▌ | 1117/2000 [07:04<04:38, 3.17it/s, loss=0.466]" ] }, { @@ -42494,7 +42472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1118/2000 [07:13<04:51, 3.03it/s, loss=0.476]" + "training until 2000: 56%|█████▌ | 1118/2000 [07:05<04:38, 3.17it/s, loss=0.466]" ] }, { @@ -42502,7 +42480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1118/2000 [07:13<04:51, 3.03it/s, loss=0.465]" + "training until 2000: 56%|█████▌ | 1118/2000 [07:05<04:38, 3.17it/s, loss=0.428]" ] }, { @@ -42510,7 +42488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1119/2000 [07:13<04:49, 3.04it/s, loss=0.465]" + "training until 2000: 56%|█████▌ | 1119/2000 [07:05<04:39, 3.16it/s, loss=0.428]" ] }, { @@ -42518,7 +42496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1119/2000 [07:13<04:49, 3.04it/s, loss=0.473]" + "training until 2000: 56%|█████▌ | 1119/2000 [07:05<04:39, 3.16it/s, loss=0.441]" ] }, { @@ -42526,7 +42504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1120/2000 [07:14<04:46, 3.08it/s, loss=0.473]" + "training until 2000: 56%|█████▌ | 1120/2000 [07:05<04:40, 3.14it/s, loss=0.441]" ] }, { @@ -42534,7 +42512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1120/2000 [07:14<04:46, 3.08it/s, loss=0.464]" + "training until 2000: 56%|█████▌ | 1120/2000 [07:05<04:40, 3.14it/s, loss=0.528]" ] }, { @@ -42542,7 +42520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1121/2000 [07:14<04:50, 3.03it/s, loss=0.464]" + "training until 2000: 56%|█████▌ | 1121/2000 [07:06<04:38, 3.15it/s, loss=0.528]" ] }, { @@ -42550,7 +42528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1121/2000 [07:14<04:50, 3.03it/s, loss=0.496]" + "training until 2000: 56%|█████▌ | 1121/2000 [07:06<04:38, 3.15it/s, loss=0.485]" ] }, { @@ -42558,7 +42536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1122/2000 [07:14<04:47, 3.06it/s, loss=0.496]" + "training until 2000: 56%|█████▌ | 1122/2000 [07:06<04:35, 3.19it/s, loss=0.485]" ] }, { @@ -42566,7 +42544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1122/2000 [07:14<04:47, 3.06it/s, loss=0.455]" + "training until 2000: 56%|█████▌ | 1122/2000 [07:06<04:35, 3.19it/s, loss=0.511]" ] }, { @@ -42574,7 +42552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1123/2000 [07:15<04:45, 3.08it/s, loss=0.455]" + "training until 2000: 56%|█████▌ | 1123/2000 [07:06<04:33, 3.21it/s, loss=0.511]" ] }, { @@ -42582,7 +42560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1123/2000 [07:15<04:45, 3.08it/s, loss=0.49] " + "training until 2000: 56%|█████▌ | 1123/2000 [07:06<04:33, 3.21it/s, loss=0.466]" ] }, { @@ -42590,7 +42568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1124/2000 [07:15<04:48, 3.04it/s, loss=0.49]" + "training until 2000: 56%|█████▌ | 1124/2000 [07:07<04:31, 3.23it/s, loss=0.466]" ] }, { @@ -42598,7 +42576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▌ | 1124/2000 [07:15<04:48, 3.04it/s, loss=0.486]" + "training until 2000: 56%|█████▌ | 1124/2000 [07:07<04:31, 3.23it/s, loss=0.491]" ] }, { @@ -42606,7 +42584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1125/2000 [07:15<04:50, 3.01it/s, loss=0.486]" + "training until 2000: 56%|█████▋ | 1125/2000 [07:07<04:33, 3.20it/s, loss=0.491]" ] }, { @@ -42614,7 +42592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1125/2000 [07:15<04:50, 3.01it/s, loss=0.475]" + "training until 2000: 56%|█████▋ | 1125/2000 [07:07<04:33, 3.20it/s, loss=0.51] " ] }, { @@ -42622,7 +42600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1126/2000 [07:16<04:46, 3.05it/s, loss=0.475]" + "training until 2000: 56%|█████▋ | 1126/2000 [07:07<04:32, 3.20it/s, loss=0.51]" ] }, { @@ -42630,7 +42608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1126/2000 [07:16<04:46, 3.05it/s, loss=0.493]" + "training until 2000: 56%|█████▋ | 1126/2000 [07:07<04:32, 3.20it/s, loss=0.428]" ] }, { @@ -42638,7 +42616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1127/2000 [07:16<04:45, 3.06it/s, loss=0.493]" + "training until 2000: 56%|█████▋ | 1127/2000 [07:07<04:32, 3.21it/s, loss=0.428]" ] }, { @@ -42646,7 +42624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1127/2000 [07:16<04:45, 3.06it/s, loss=0.52] " + "training until 2000: 56%|█████▋ | 1127/2000 [07:07<04:32, 3.21it/s, loss=0.455]" ] }, { @@ -42654,7 +42632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1128/2000 [07:16<04:43, 3.07it/s, loss=0.52]" + "training until 2000: 56%|█████▋ | 1128/2000 [07:08<04:30, 3.22it/s, loss=0.455]" ] }, { @@ -42662,7 +42640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1128/2000 [07:16<04:43, 3.07it/s, loss=0.469]" + "training until 2000: 56%|█████▋ | 1128/2000 [07:08<04:30, 3.22it/s, loss=0.474]" ] }, { @@ -42670,7 +42648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1129/2000 [07:17<04:43, 3.08it/s, loss=0.469]" + "training until 2000: 56%|█████▋ | 1129/2000 [07:08<04:30, 3.22it/s, loss=0.474]" ] }, { @@ -42678,7 +42656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1129/2000 [07:17<04:43, 3.08it/s, loss=0.46] " + "training until 2000: 56%|█████▋ | 1129/2000 [07:08<04:30, 3.22it/s, loss=0.519]" ] }, { @@ -42686,7 +42664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1130/2000 [07:17<04:43, 3.07it/s, loss=0.46]" + "training until 2000: 56%|█████▋ | 1130/2000 [07:08<04:38, 3.12it/s, loss=0.519]" ] }, { @@ -42694,7 +42672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 56%|█████▋ | 1130/2000 [07:17<04:43, 3.07it/s, loss=0.502]" + "training until 2000: 56%|█████▋ | 1130/2000 [07:08<04:38, 3.12it/s, loss=0.412]" ] }, { @@ -42702,7 +42680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1131/2000 [07:17<04:40, 3.09it/s, loss=0.502]" + "training until 2000: 57%|█████▋ | 1131/2000 [07:09<04:38, 3.12it/s, loss=0.412]" ] }, { @@ -42710,7 +42688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1131/2000 [07:17<04:40, 3.09it/s, loss=0.457]" + "training until 2000: 57%|█████▋ | 1131/2000 [07:09<04:38, 3.12it/s, loss=0.48] " ] }, { @@ -42718,7 +42696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1132/2000 [07:18<04:40, 3.09it/s, loss=0.457]" + "training until 2000: 57%|█████▋ | 1132/2000 [07:09<04:38, 3.11it/s, loss=0.48]" ] }, { @@ -42726,7 +42704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1132/2000 [07:18<04:40, 3.09it/s, loss=0.444]" + "training until 2000: 57%|█████▋ | 1132/2000 [07:09<04:38, 3.11it/s, loss=0.529]" ] }, { @@ -42734,7 +42712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1133/2000 [07:18<04:40, 3.10it/s, loss=0.444]" + "training until 2000: 57%|█████▋ | 1133/2000 [07:09<04:42, 3.06it/s, loss=0.529]" ] }, { @@ -42742,7 +42720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1133/2000 [07:18<04:40, 3.10it/s, loss=0.48] " + "training until 2000: 57%|█████▋ | 1133/2000 [07:09<04:42, 3.06it/s, loss=0.442]" ] }, { @@ -42750,7 +42728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1134/2000 [07:18<04:37, 3.12it/s, loss=0.48]" + "training until 2000: 57%|█████▋ | 1134/2000 [07:10<04:38, 3.11it/s, loss=0.442]" ] }, { @@ -42758,7 +42736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1134/2000 [07:18<04:37, 3.12it/s, loss=0.513]" + "training until 2000: 57%|█████▋ | 1134/2000 [07:10<04:38, 3.11it/s, loss=0.628]" ] }, { @@ -42766,7 +42744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1135/2000 [07:19<04:36, 3.12it/s, loss=0.513]" + "training until 2000: 57%|█████▋ | 1135/2000 [07:10<04:36, 3.13it/s, loss=0.628]" ] }, { @@ -42774,7 +42752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1135/2000 [07:19<04:36, 3.12it/s, loss=0.461]" + "training until 2000: 57%|█████▋ | 1135/2000 [07:10<04:36, 3.13it/s, loss=0.446]" ] }, { @@ -42782,7 +42760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1136/2000 [07:19<04:35, 3.14it/s, loss=0.461]" + "training until 2000: 57%|█████▋ | 1136/2000 [07:10<04:36, 3.13it/s, loss=0.446]" ] }, { @@ -42790,7 +42768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1136/2000 [07:19<04:35, 3.14it/s, loss=0.46] " + "training until 2000: 57%|█████▋ | 1136/2000 [07:10<04:36, 3.13it/s, loss=0.487]" ] }, { @@ -42798,7 +42776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1137/2000 [07:19<04:35, 3.13it/s, loss=0.46]" + "training until 2000: 57%|█████▋ | 1137/2000 [07:11<04:34, 3.14it/s, loss=0.487]" ] }, { @@ -42806,7 +42784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1137/2000 [07:19<04:35, 3.13it/s, loss=0.454]" + "training until 2000: 57%|█████▋ | 1137/2000 [07:11<04:34, 3.14it/s, loss=0.469]" ] }, { @@ -42814,7 +42792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1138/2000 [07:20<04:37, 3.10it/s, loss=0.454]" + "training until 2000: 57%|█████▋ | 1138/2000 [07:11<04:32, 3.16it/s, loss=0.469]" ] }, { @@ -42822,7 +42800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1138/2000 [07:20<04:37, 3.10it/s, loss=0.506]" + "training until 2000: 57%|█████▋ | 1138/2000 [07:11<04:32, 3.16it/s, loss=0.425]" ] }, { @@ -42830,7 +42808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1139/2000 [07:20<04:38, 3.09it/s, loss=0.506]" + "training until 2000: 57%|█████▋ | 1139/2000 [07:11<04:33, 3.14it/s, loss=0.425]" ] }, { @@ -42838,7 +42816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1139/2000 [07:20<04:38, 3.09it/s, loss=0.494]" + "training until 2000: 57%|█████▋ | 1139/2000 [07:11<04:33, 3.14it/s, loss=0.581]" ] }, { @@ -42846,7 +42824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1140/2000 [07:20<04:39, 3.07it/s, loss=0.494]" + "training until 2000: 57%|█████▋ | 1140/2000 [07:12<04:37, 3.10it/s, loss=0.581]" ] }, { @@ -42854,7 +42832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1140/2000 [07:20<04:39, 3.07it/s, loss=0.483]" + "training until 2000: 57%|█████▋ | 1140/2000 [07:12<04:37, 3.10it/s, loss=0.494]" ] }, { @@ -42862,7 +42840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1141/2000 [07:21<04:41, 3.06it/s, loss=0.483]" + "training until 2000: 57%|█████▋ | 1141/2000 [07:12<04:36, 3.10it/s, loss=0.494]" ] }, { @@ -42870,7 +42848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1141/2000 [07:21<04:41, 3.06it/s, loss=0.461]" + "training until 2000: 57%|█████▋ | 1141/2000 [07:12<04:36, 3.10it/s, loss=0.527]" ] }, { @@ -42878,7 +42856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1142/2000 [07:21<04:39, 3.07it/s, loss=0.461]" + "training until 2000: 57%|█████▋ | 1142/2000 [07:12<04:35, 3.11it/s, loss=0.527]" ] }, { @@ -42886,7 +42864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1142/2000 [07:21<04:39, 3.07it/s, loss=0.516]" + "training until 2000: 57%|█████▋ | 1142/2000 [07:12<04:35, 3.11it/s, loss=0.438]" ] }, { @@ -42894,7 +42872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1143/2000 [07:21<04:40, 3.05it/s, loss=0.516]" + "training until 2000: 57%|█████▋ | 1143/2000 [07:13<04:38, 3.08it/s, loss=0.438]" ] }, { @@ -42902,7 +42880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1143/2000 [07:21<04:40, 3.05it/s, loss=0.499]" + "training until 2000: 57%|█████▋ | 1143/2000 [07:13<04:38, 3.08it/s, loss=0.461]" ] }, { @@ -42910,7 +42888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1144/2000 [07:21<04:37, 3.08it/s, loss=0.499]" + "training until 2000: 57%|█████▋ | 1144/2000 [07:13<04:35, 3.10it/s, loss=0.461]" ] }, { @@ -42918,7 +42896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1144/2000 [07:21<04:37, 3.08it/s, loss=0.462]" + "training until 2000: 57%|█████▋ | 1144/2000 [07:13<04:35, 3.10it/s, loss=0.475]" ] }, { @@ -42926,7 +42904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1145/2000 [07:22<04:32, 3.13it/s, loss=0.462]" + "training until 2000: 57%|█████▋ | 1145/2000 [07:13<04:33, 3.12it/s, loss=0.475]" ] }, { @@ -42934,7 +42912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1145/2000 [07:22<04:32, 3.13it/s, loss=0.48] " + "training until 2000: 57%|█████▋ | 1145/2000 [07:13<04:33, 3.12it/s, loss=0.46] " ] }, { @@ -42942,7 +42920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1146/2000 [07:22<04:33, 3.13it/s, loss=0.48]" + "training until 2000: 57%|█████▋ | 1146/2000 [07:14<04:33, 3.13it/s, loss=0.46]" ] }, { @@ -42950,7 +42928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1146/2000 [07:22<04:33, 3.13it/s, loss=0.464]" + "training until 2000: 57%|█████▋ | 1146/2000 [07:14<04:33, 3.13it/s, loss=0.589]" ] }, { @@ -42958,7 +42936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1147/2000 [07:22<04:34, 3.11it/s, loss=0.464]" + "training until 2000: 57%|█████▋ | 1147/2000 [07:14<04:30, 3.15it/s, loss=0.589]" ] }, { @@ -42966,7 +42944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1147/2000 [07:22<04:34, 3.11it/s, loss=0.481]" + "training until 2000: 57%|█████▋ | 1147/2000 [07:14<04:30, 3.15it/s, loss=0.478]" ] }, { @@ -42974,7 +42952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1148/2000 [07:23<04:34, 3.11it/s, loss=0.481]" + "training until 2000: 57%|█████▋ | 1148/2000 [07:14<04:32, 3.13it/s, loss=0.478]" ] }, { @@ -42982,7 +42960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1148/2000 [07:23<04:34, 3.11it/s, loss=0.457]" + "training until 2000: 57%|█████▋ | 1148/2000 [07:14<04:32, 3.13it/s, loss=0.458]" ] }, { @@ -42990,7 +42968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1149/2000 [07:23<04:35, 3.09it/s, loss=0.457]" + "training until 2000: 57%|█████▋ | 1149/2000 [07:15<04:39, 3.05it/s, loss=0.458]" ] }, { @@ -42998,7 +42976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▋ | 1149/2000 [07:23<04:35, 3.09it/s, loss=0.465]" + "training until 2000: 57%|█████▋ | 1149/2000 [07:15<04:39, 3.05it/s, loss=0.478]" ] }, { @@ -43006,7 +42984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▊ | 1150/2000 [07:23<04:33, 3.11it/s, loss=0.465]" + "training until 2000: 57%|█████▊ | 1150/2000 [07:15<04:34, 3.10it/s, loss=0.478]" ] }, { @@ -43014,7 +42992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 57%|█████▊ | 1150/2000 [07:23<04:33, 3.11it/s, loss=0.469]" + "training until 2000: 57%|█████▊ | 1150/2000 [07:15<04:34, 3.10it/s, loss=0.479]" ] }, { @@ -43022,7 +43000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1151/2000 [07:24<04:31, 3.13it/s, loss=0.469]" + "training until 2000: 58%|█████▊ | 1151/2000 [07:15<04:33, 3.10it/s, loss=0.479]" ] }, { @@ -43030,7 +43008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1151/2000 [07:24<04:31, 3.13it/s, loss=0.476]" + "training until 2000: 58%|█████▊ | 1151/2000 [07:15<04:33, 3.10it/s, loss=0.537]" ] }, { @@ -43038,7 +43016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1152/2000 [07:24<04:29, 3.14it/s, loss=0.476]" + "training until 2000: 58%|█████▊ | 1152/2000 [07:16<04:31, 3.12it/s, loss=0.537]" ] }, { @@ -43046,7 +43024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1152/2000 [07:24<04:29, 3.14it/s, loss=0.471]" + "training until 2000: 58%|█████▊ | 1152/2000 [07:16<04:31, 3.12it/s, loss=0.448]" ] }, { @@ -43054,7 +43032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1153/2000 [07:24<04:33, 3.09it/s, loss=0.471]" + "training until 2000: 58%|█████▊ | 1153/2000 [07:16<04:31, 3.12it/s, loss=0.448]" ] }, { @@ -43062,7 +43040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1153/2000 [07:24<04:33, 3.09it/s, loss=0.474]" + "training until 2000: 58%|█████▊ | 1153/2000 [07:16<04:31, 3.12it/s, loss=0.45] " ] }, { @@ -43070,7 +43048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1154/2000 [07:25<04:33, 3.09it/s, loss=0.474]" + "training until 2000: 58%|█████▊ | 1154/2000 [07:16<04:31, 3.11it/s, loss=0.45]" ] }, { @@ -43078,7 +43056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1154/2000 [07:25<04:33, 3.09it/s, loss=0.504]" + "training until 2000: 58%|█████▊ | 1154/2000 [07:16<04:31, 3.11it/s, loss=0.417]" ] }, { @@ -43086,7 +43064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1155/2000 [07:25<04:30, 3.12it/s, loss=0.504]" + "training until 2000: 58%|█████▊ | 1155/2000 [07:16<04:33, 3.08it/s, loss=0.417]" ] }, { @@ -43094,7 +43072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1155/2000 [07:25<04:30, 3.12it/s, loss=0.482]" + "training until 2000: 58%|█████▊ | 1155/2000 [07:16<04:33, 3.08it/s, loss=0.426]" ] }, { @@ -43102,7 +43080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1156/2000 [07:25<04:32, 3.10it/s, loss=0.482]" + "training until 2000: 58%|█████▊ | 1156/2000 [07:17<04:33, 3.08it/s, loss=0.426]" ] }, { @@ -43110,7 +43088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1156/2000 [07:25<04:32, 3.10it/s, loss=0.54] " + "training until 2000: 58%|█████▊ | 1156/2000 [07:17<04:33, 3.08it/s, loss=0.511]" ] }, { @@ -43118,7 +43096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1157/2000 [07:26<04:33, 3.08it/s, loss=0.54]" + "training until 2000: 58%|█████▊ | 1157/2000 [07:17<04:36, 3.05it/s, loss=0.511]" ] }, { @@ -43126,7 +43104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1157/2000 [07:26<04:33, 3.08it/s, loss=0.467]" + "training until 2000: 58%|█████▊ | 1157/2000 [07:17<04:36, 3.05it/s, loss=0.463]" ] }, { @@ -43134,7 +43112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1158/2000 [07:26<04:32, 3.10it/s, loss=0.467]" + "training until 2000: 58%|█████▊ | 1158/2000 [07:17<04:33, 3.08it/s, loss=0.463]" ] }, { @@ -43142,7 +43120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1158/2000 [07:26<04:32, 3.10it/s, loss=0.448]" + "training until 2000: 58%|█████▊ | 1158/2000 [07:17<04:33, 3.08it/s, loss=0.513]" ] }, { @@ -43150,7 +43128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1159/2000 [07:26<04:29, 3.12it/s, loss=0.448]" + "training until 2000: 58%|█████▊ | 1159/2000 [07:18<04:34, 3.06it/s, loss=0.513]" ] }, { @@ -43158,7 +43136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1159/2000 [07:26<04:29, 3.12it/s, loss=0.445]" + "training until 2000: 58%|█████▊ | 1159/2000 [07:18<04:34, 3.06it/s, loss=0.435]" ] }, { @@ -43166,7 +43144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1160/2000 [07:27<04:28, 3.13it/s, loss=0.445]" + "training until 2000: 58%|█████▊ | 1160/2000 [07:18<04:36, 3.03it/s, loss=0.435]" ] }, { @@ -43174,7 +43152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1160/2000 [07:27<04:28, 3.13it/s, loss=0.447]" + "training until 2000: 58%|█████▊ | 1160/2000 [07:18<04:36, 3.03it/s, loss=0.424]" ] }, { @@ -43182,7 +43160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1161/2000 [07:27<04:29, 3.11it/s, loss=0.447]" + "training until 2000: 58%|█████▊ | 1161/2000 [07:18<04:33, 3.06it/s, loss=0.424]" ] }, { @@ -43190,7 +43168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1161/2000 [07:27<04:29, 3.11it/s, loss=0.499]" + "training until 2000: 58%|█████▊ | 1161/2000 [07:18<04:33, 3.06it/s, loss=0.641]" ] }, { @@ -43198,7 +43176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1162/2000 [07:27<04:29, 3.12it/s, loss=0.499]" + "training until 2000: 58%|█████▊ | 1162/2000 [07:19<04:39, 3.00it/s, loss=0.641]" ] }, { @@ -43206,7 +43184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1162/2000 [07:27<04:29, 3.12it/s, loss=0.449]" + "training until 2000: 58%|█████▊ | 1162/2000 [07:19<04:39, 3.00it/s, loss=0.446]" ] }, { @@ -43214,7 +43192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1163/2000 [07:28<04:26, 3.15it/s, loss=0.449]" + "training until 2000: 58%|█████▊ | 1163/2000 [07:19<05:40, 2.46it/s, loss=0.446]" ] }, { @@ -43222,7 +43200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1163/2000 [07:28<04:26, 3.15it/s, loss=0.457]" + "training until 2000: 58%|█████▊ | 1163/2000 [07:19<05:40, 2.46it/s, loss=0.474]" ] }, { @@ -43230,7 +43208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1164/2000 [07:28<04:23, 3.18it/s, loss=0.457]" + "training until 2000: 58%|█████▊ | 1164/2000 [07:20<05:19, 2.62it/s, loss=0.474]" ] }, { @@ -43238,7 +43216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1164/2000 [07:28<04:23, 3.18it/s, loss=0.454]" + "training until 2000: 58%|█████▊ | 1164/2000 [07:20<05:19, 2.62it/s, loss=0.495]" ] }, { @@ -43246,7 +43224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1165/2000 [07:28<04:23, 3.17it/s, loss=0.454]" + "training until 2000: 58%|█████▊ | 1165/2000 [07:20<05:03, 2.75it/s, loss=0.495]" ] }, { @@ -43254,7 +43232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1165/2000 [07:28<04:23, 3.17it/s, loss=0.468]" + "training until 2000: 58%|█████▊ | 1165/2000 [07:20<05:03, 2.75it/s, loss=0.44] " ] }, { @@ -43262,7 +43240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1166/2000 [07:29<04:24, 3.15it/s, loss=0.468]" + "training until 2000: 58%|█████▊ | 1166/2000 [07:20<04:54, 2.83it/s, loss=0.44]" ] }, { @@ -43270,7 +43248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1166/2000 [07:29<04:24, 3.15it/s, loss=0.465]" + "training until 2000: 58%|█████▊ | 1166/2000 [07:20<04:54, 2.83it/s, loss=0.495]" ] }, { @@ -43278,7 +43256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1167/2000 [07:29<04:26, 3.13it/s, loss=0.465]" + "training until 2000: 58%|█████▊ | 1167/2000 [07:21<04:48, 2.89it/s, loss=0.495]" ] }, { @@ -43286,7 +43264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1167/2000 [07:29<04:26, 3.13it/s, loss=0.451]" + "training until 2000: 58%|█████▊ | 1167/2000 [07:21<04:48, 2.89it/s, loss=0.51] " ] }, { @@ -43294,7 +43272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1168/2000 [07:29<04:25, 3.13it/s, loss=0.451]" + "training until 2000: 58%|█████▊ | 1168/2000 [07:21<04:41, 2.95it/s, loss=0.51]" ] }, { @@ -43302,7 +43280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1168/2000 [07:29<04:25, 3.13it/s, loss=0.502]" + "training until 2000: 58%|█████▊ | 1168/2000 [07:21<04:41, 2.95it/s, loss=0.463]" ] }, { @@ -43310,7 +43288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1169/2000 [07:29<04:27, 3.11it/s, loss=0.502]" + "training until 2000: 58%|█████▊ | 1169/2000 [07:21<04:39, 2.97it/s, loss=0.463]" ] }, { @@ -43318,7 +43296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1169/2000 [07:29<04:27, 3.11it/s, loss=0.503]" + "training until 2000: 58%|█████▊ | 1169/2000 [07:21<04:39, 2.97it/s, loss=0.53] " ] }, { @@ -43326,7 +43304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1170/2000 [07:30<04:26, 3.12it/s, loss=0.503]" + "training until 2000: 58%|█████▊ | 1170/2000 [07:22<04:36, 3.00it/s, loss=0.53]" ] }, { @@ -43334,7 +43312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 58%|█████▊ | 1170/2000 [07:30<04:26, 3.12it/s, loss=0.488]" + "training until 2000: 58%|█████▊ | 1170/2000 [07:22<04:36, 3.00it/s, loss=0.434]" ] }, { @@ -43342,7 +43320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1171/2000 [07:30<04:27, 3.10it/s, loss=0.488]" + "training until 2000: 59%|█████▊ | 1171/2000 [07:22<04:34, 3.02it/s, loss=0.434]" ] }, { @@ -43350,7 +43328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1171/2000 [07:30<04:27, 3.10it/s, loss=0.447]" + "training until 2000: 59%|█████▊ | 1171/2000 [07:22<04:34, 3.02it/s, loss=0.471]" ] }, { @@ -43358,7 +43336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1172/2000 [07:31<05:31, 2.50it/s, loss=0.447]" + "training until 2000: 59%|█████▊ | 1172/2000 [07:22<04:32, 3.03it/s, loss=0.471]" ] }, { @@ -43366,7 +43344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1172/2000 [07:31<05:31, 2.50it/s, loss=0.474]" + "training until 2000: 59%|█████▊ | 1172/2000 [07:22<04:32, 3.03it/s, loss=0.548]" ] }, { @@ -43374,7 +43352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1173/2000 [07:31<05:12, 2.65it/s, loss=0.474]" + "training until 2000: 59%|█████▊ | 1173/2000 [07:23<04:35, 3.00it/s, loss=0.548]" ] }, { @@ -43382,7 +43360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1173/2000 [07:31<05:12, 2.65it/s, loss=0.462]" + "training until 2000: 59%|█████▊ | 1173/2000 [07:23<04:35, 3.00it/s, loss=0.449]" ] }, { @@ -43390,7 +43368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1174/2000 [07:31<05:00, 2.75it/s, loss=0.462]" + "training until 2000: 59%|█████▊ | 1174/2000 [07:23<04:33, 3.02it/s, loss=0.449]" ] }, { @@ -43398,7 +43376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▊ | 1174/2000 [07:31<05:00, 2.75it/s, loss=0.474]" + "training until 2000: 59%|█████▊ | 1174/2000 [07:23<04:33, 3.02it/s, loss=0.429]" ] }, { @@ -43406,7 +43384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1175/2000 [07:32<04:50, 2.84it/s, loss=0.474]" + "training until 2000: 59%|█████▉ | 1175/2000 [07:23<04:30, 3.05it/s, loss=0.429]" ] }, { @@ -43414,7 +43392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1175/2000 [07:32<04:50, 2.84it/s, loss=0.458]" + "training until 2000: 59%|█████▉ | 1175/2000 [07:23<04:30, 3.05it/s, loss=0.429]" ] }, { @@ -43422,7 +43400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1176/2000 [07:32<04:42, 2.92it/s, loss=0.458]" + "training until 2000: 59%|█████▉ | 1176/2000 [07:24<04:28, 3.06it/s, loss=0.429]" ] }, { @@ -43430,7 +43408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1176/2000 [07:32<04:42, 2.92it/s, loss=0.446]" + "training until 2000: 59%|█████▉ | 1176/2000 [07:24<04:28, 3.06it/s, loss=0.46] " ] }, { @@ -43438,7 +43416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1177/2000 [07:32<04:37, 2.96it/s, loss=0.446]" + "training until 2000: 59%|█████▉ | 1177/2000 [07:24<04:30, 3.04it/s, loss=0.46]" ] }, { @@ -43446,7 +43424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1177/2000 [07:32<04:37, 2.96it/s, loss=0.488]" + "training until 2000: 59%|█████▉ | 1177/2000 [07:24<04:30, 3.04it/s, loss=0.462]" ] }, { @@ -43454,7 +43432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1178/2000 [07:33<04:35, 2.98it/s, loss=0.488]" + "training until 2000: 59%|█████▉ | 1178/2000 [07:24<04:27, 3.07it/s, loss=0.462]" ] }, { @@ -43462,7 +43440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1178/2000 [07:33<04:35, 2.98it/s, loss=0.48] " + "training until 2000: 59%|█████▉ | 1178/2000 [07:24<04:27, 3.07it/s, loss=0.48] " ] }, { @@ -43470,7 +43448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1179/2000 [07:33<04:29, 3.04it/s, loss=0.48]" + "training until 2000: 59%|█████▉ | 1179/2000 [07:25<04:29, 3.04it/s, loss=0.48]" ] }, { @@ -43478,7 +43456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1179/2000 [07:33<04:29, 3.04it/s, loss=0.451]" + "training until 2000: 59%|█████▉ | 1179/2000 [07:25<04:29, 3.04it/s, loss=0.469]" ] }, { @@ -43486,7 +43464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1180/2000 [07:33<04:28, 3.06it/s, loss=0.451]" + "training until 2000: 59%|█████▉ | 1180/2000 [07:25<04:24, 3.10it/s, loss=0.469]" ] }, { @@ -43494,7 +43472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1180/2000 [07:33<04:28, 3.06it/s, loss=0.449]" + "training until 2000: 59%|█████▉ | 1180/2000 [07:25<04:24, 3.10it/s, loss=0.482]" ] }, { @@ -43502,7 +43480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1181/2000 [07:34<04:27, 3.06it/s, loss=0.449]" + "training until 2000: 59%|█████▉ | 1181/2000 [07:25<04:22, 3.12it/s, loss=0.482]" ] }, { @@ -43510,7 +43488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1181/2000 [07:34<04:27, 3.06it/s, loss=0.468]" + "training until 2000: 59%|█████▉ | 1181/2000 [07:25<04:22, 3.12it/s, loss=0.426]" ] }, { @@ -43518,7 +43496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1182/2000 [07:34<04:26, 3.07it/s, loss=0.468]" + "training until 2000: 59%|█████▉ | 1182/2000 [07:26<04:23, 3.11it/s, loss=0.426]" ] }, { @@ -43526,7 +43504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1182/2000 [07:34<04:26, 3.07it/s, loss=0.452]" + "training until 2000: 59%|█████▉ | 1182/2000 [07:26<04:23, 3.11it/s, loss=0.488]" ] }, { @@ -43534,7 +43512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1183/2000 [07:34<04:23, 3.10it/s, loss=0.452]" + "training until 2000: 59%|█████▉ | 1183/2000 [07:26<04:23, 3.10it/s, loss=0.488]" ] }, { @@ -43542,7 +43520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1183/2000 [07:34<04:23, 3.10it/s, loss=0.642]" + "training until 2000: 59%|█████▉ | 1183/2000 [07:26<04:23, 3.10it/s, loss=0.448]" ] }, { @@ -43550,7 +43528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1184/2000 [07:35<04:22, 3.11it/s, loss=0.642]" + "training until 2000: 59%|█████▉ | 1184/2000 [07:26<04:22, 3.10it/s, loss=0.448]" ] }, { @@ -43558,7 +43536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1184/2000 [07:35<04:22, 3.11it/s, loss=0.527]" + "training until 2000: 59%|█████▉ | 1184/2000 [07:26<04:22, 3.10it/s, loss=0.579]" ] }, { @@ -43566,7 +43544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1185/2000 [07:35<04:20, 3.13it/s, loss=0.527]" + "training until 2000: 59%|█████▉ | 1185/2000 [07:27<04:19, 3.14it/s, loss=0.579]" ] }, { @@ -43574,7 +43552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1185/2000 [07:35<04:20, 3.13it/s, loss=0.463]" + "training until 2000: 59%|█████▉ | 1185/2000 [07:27<04:19, 3.14it/s, loss=0.43] " ] }, { @@ -43582,7 +43560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1186/2000 [07:35<04:17, 3.16it/s, loss=0.463]" + "training until 2000: 59%|█████▉ | 1186/2000 [07:27<04:22, 3.11it/s, loss=0.43]" ] }, { @@ -43590,7 +43568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1186/2000 [07:35<04:17, 3.16it/s, loss=0.469]" + "training until 2000: 59%|█████▉ | 1186/2000 [07:27<04:22, 3.11it/s, loss=0.483]" ] }, { @@ -43598,7 +43576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1187/2000 [07:36<04:15, 3.18it/s, loss=0.469]" + "training until 2000: 59%|█████▉ | 1187/2000 [07:27<04:24, 3.07it/s, loss=0.483]" ] }, { @@ -43606,7 +43584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1187/2000 [07:36<04:15, 3.18it/s, loss=0.487]" + "training until 2000: 59%|█████▉ | 1187/2000 [07:27<04:24, 3.07it/s, loss=0.494]" ] }, { @@ -43614,7 +43592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1188/2000 [07:36<04:16, 3.16it/s, loss=0.487]" + "training until 2000: 59%|█████▉ | 1188/2000 [07:28<04:22, 3.10it/s, loss=0.494]" ] }, { @@ -43622,7 +43600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1188/2000 [07:36<04:16, 3.16it/s, loss=0.444]" + "training until 2000: 59%|█████▉ | 1188/2000 [07:28<04:22, 3.10it/s, loss=0.452]" ] }, { @@ -43630,7 +43608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1189/2000 [07:36<04:17, 3.15it/s, loss=0.444]" + "training until 2000: 59%|█████▉ | 1189/2000 [07:28<04:18, 3.14it/s, loss=0.452]" ] }, { @@ -43638,7 +43616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 59%|█████▉ | 1189/2000 [07:36<04:17, 3.15it/s, loss=0.464]" + "training until 2000: 59%|█████▉ | 1189/2000 [07:28<04:18, 3.14it/s, loss=0.45] " ] }, { @@ -43646,7 +43624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1190/2000 [07:36<04:20, 3.11it/s, loss=0.464]" + "training until 2000: 60%|█████▉ | 1190/2000 [07:28<04:16, 3.16it/s, loss=0.45]" ] }, { @@ -43654,7 +43632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1190/2000 [07:36<04:20, 3.11it/s, loss=0.441]" + "training until 2000: 60%|█████▉ | 1190/2000 [07:28<04:16, 3.16it/s, loss=0.662]" ] }, { @@ -43662,7 +43640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1191/2000 [07:37<04:21, 3.10it/s, loss=0.441]" + "training until 2000: 60%|█████▉ | 1191/2000 [07:28<04:14, 3.18it/s, loss=0.662]" ] }, { @@ -43670,7 +43648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1191/2000 [07:37<04:21, 3.10it/s, loss=0.474]" + "training until 2000: 60%|█████▉ | 1191/2000 [07:28<04:14, 3.18it/s, loss=0.502]" ] }, { @@ -43678,7 +43656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1192/2000 [07:37<04:22, 3.08it/s, loss=0.474]" + "training until 2000: 60%|█████▉ | 1192/2000 [07:29<04:16, 3.15it/s, loss=0.502]" ] }, { @@ -43686,7 +43664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1192/2000 [07:37<04:22, 3.08it/s, loss=0.457]" + "training until 2000: 60%|█████▉ | 1192/2000 [07:29<04:16, 3.15it/s, loss=0.441]" ] }, { @@ -43694,7 +43672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1193/2000 [07:37<04:20, 3.10it/s, loss=0.457]" + "training until 2000: 60%|█████▉ | 1193/2000 [07:29<04:19, 3.11it/s, loss=0.441]" ] }, { @@ -43702,7 +43680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1193/2000 [07:37<04:20, 3.10it/s, loss=0.484]" + "training until 2000: 60%|█████▉ | 1193/2000 [07:29<04:19, 3.11it/s, loss=0.452]" ] }, { @@ -43710,7 +43688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1194/2000 [07:38<04:22, 3.07it/s, loss=0.484]" + "training until 2000: 60%|█████▉ | 1194/2000 [07:29<04:18, 3.11it/s, loss=0.452]" ] }, { @@ -43718,7 +43696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1194/2000 [07:38<04:22, 3.07it/s, loss=0.472]" + "training until 2000: 60%|█████▉ | 1194/2000 [07:29<04:18, 3.11it/s, loss=0.451]" ] }, { @@ -43726,7 +43704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1195/2000 [07:38<04:23, 3.06it/s, loss=0.472]" + "training until 2000: 60%|█████▉ | 1195/2000 [07:30<04:19, 3.10it/s, loss=0.451]" ] }, { @@ -43734,7 +43712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1195/2000 [07:38<04:23, 3.06it/s, loss=0.456]" + "training until 2000: 60%|█████▉ | 1195/2000 [07:30<04:19, 3.10it/s, loss=0.502]" ] }, { @@ -43742,7 +43720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1196/2000 [07:38<04:18, 3.11it/s, loss=0.456]" + "training until 2000: 60%|█████▉ | 1196/2000 [07:30<04:17, 3.12it/s, loss=0.502]" ] }, { @@ -43750,7 +43728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1196/2000 [07:38<04:18, 3.11it/s, loss=0.455]" + "training until 2000: 60%|█████▉ | 1196/2000 [07:30<04:17, 3.12it/s, loss=0.573]" ] }, { @@ -43758,7 +43736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1197/2000 [07:39<04:18, 3.10it/s, loss=0.455]" + "training until 2000: 60%|█████▉ | 1197/2000 [07:30<04:17, 3.12it/s, loss=0.573]" ] }, { @@ -43766,7 +43744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1197/2000 [07:39<04:18, 3.10it/s, loss=0.486]" + "training until 2000: 60%|█████▉ | 1197/2000 [07:30<04:17, 3.12it/s, loss=0.435]" ] }, { @@ -43774,7 +43752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1198/2000 [07:39<04:19, 3.09it/s, loss=0.486]" + "training until 2000: 60%|█████▉ | 1198/2000 [07:31<04:18, 3.11it/s, loss=0.435]" ] }, { @@ -43782,7 +43760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1198/2000 [07:39<04:19, 3.09it/s, loss=0.474]" + "training until 2000: 60%|█████▉ | 1198/2000 [07:31<04:18, 3.11it/s, loss=0.423]" ] }, { @@ -43790,7 +43768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1199/2000 [07:39<04:18, 3.10it/s, loss=0.474]" + "training until 2000: 60%|█████▉ | 1199/2000 [07:31<04:15, 3.13it/s, loss=0.423]" ] }, { @@ -43798,7 +43776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|█████▉ | 1199/2000 [07:39<04:18, 3.10it/s, loss=0.455]" + "training until 2000: 60%|█████▉ | 1199/2000 [07:31<04:15, 3.13it/s, loss=0.425]" ] }, { @@ -43806,7 +43784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1200/2000 [07:40<04:18, 3.09it/s, loss=0.455]" + "training until 2000: 60%|██████ | 1200/2000 [07:31<04:20, 3.08it/s, loss=0.425]" ] }, { @@ -43814,7 +43792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1200/2000 [07:40<04:18, 3.09it/s, loss=0.455]" + "training until 2000: 60%|██████ | 1200/2000 [07:31<04:20, 3.08it/s, loss=0.478]" ] }, { @@ -43822,7 +43800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1201/2000 [07:40<04:16, 3.11it/s, loss=0.455]" + "training until 2000: 60%|██████ | 1201/2000 [07:32<04:18, 3.09it/s, loss=0.478]" ] }, { @@ -43830,7 +43808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1201/2000 [07:40<04:16, 3.11it/s, loss=0.473]" + "training until 2000: 60%|██████ | 1201/2000 [07:32<04:18, 3.09it/s, loss=0.524]" ] }, { @@ -43838,7 +43816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1202/2000 [07:40<04:15, 3.12it/s, loss=0.473]" + "training until 2000: 60%|██████ | 1202/2000 [07:32<04:18, 3.09it/s, loss=0.524]" ] }, { @@ -43846,7 +43824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1202/2000 [07:40<04:15, 3.12it/s, loss=0.463]" + "training until 2000: 60%|██████ | 1202/2000 [07:32<04:18, 3.09it/s, loss=0.425]" ] }, { @@ -43854,7 +43832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1203/2000 [07:41<04:12, 3.15it/s, loss=0.463]" + "training until 2000: 60%|██████ | 1203/2000 [07:32<04:16, 3.10it/s, loss=0.425]" ] }, { @@ -43862,7 +43840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1203/2000 [07:41<04:12, 3.15it/s, loss=0.444]" + "training until 2000: 60%|██████ | 1203/2000 [07:32<04:16, 3.10it/s, loss=0.434]" ] }, { @@ -43870,7 +43848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1204/2000 [07:41<04:12, 3.15it/s, loss=0.444]" + "training until 2000: 60%|██████ | 1204/2000 [07:33<04:14, 3.13it/s, loss=0.434]" ] }, { @@ -43878,7 +43856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1204/2000 [07:41<04:12, 3.15it/s, loss=0.468]" + "training until 2000: 60%|██████ | 1204/2000 [07:33<04:14, 3.13it/s, loss=0.585]" ] }, { @@ -43886,7 +43864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1205/2000 [07:41<04:14, 3.13it/s, loss=0.468]" + "training until 2000: 60%|██████ | 1205/2000 [07:33<04:15, 3.12it/s, loss=0.585]" ] }, { @@ -43894,7 +43872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1205/2000 [07:41<04:14, 3.13it/s, loss=0.454]" + "training until 2000: 60%|██████ | 1205/2000 [07:33<04:15, 3.12it/s, loss=0.444]" ] }, { @@ -43902,7 +43880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1206/2000 [07:42<04:14, 3.12it/s, loss=0.454]" + "training until 2000: 60%|██████ | 1206/2000 [07:33<04:14, 3.13it/s, loss=0.444]" ] }, { @@ -43910,7 +43888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1206/2000 [07:42<04:14, 3.12it/s, loss=0.445]" + "training until 2000: 60%|██████ | 1206/2000 [07:33<04:14, 3.13it/s, loss=0.597]" ] }, { @@ -43918,7 +43896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1207/2000 [07:42<04:13, 3.13it/s, loss=0.445]" + "training until 2000: 60%|██████ | 1207/2000 [07:34<04:13, 3.12it/s, loss=0.597]" ] }, { @@ -43926,7 +43904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1207/2000 [07:42<04:13, 3.13it/s, loss=0.473]" + "training until 2000: 60%|██████ | 1207/2000 [07:34<04:13, 3.12it/s, loss=0.434]" ] }, { @@ -43934,7 +43912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1208/2000 [07:42<04:13, 3.12it/s, loss=0.473]" + "training until 2000: 60%|██████ | 1208/2000 [07:34<04:15, 3.10it/s, loss=0.434]" ] }, { @@ -43942,7 +43920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1208/2000 [07:42<04:13, 3.12it/s, loss=0.457]" + "training until 2000: 60%|██████ | 1208/2000 [07:34<04:15, 3.10it/s, loss=0.47] " ] }, { @@ -43950,7 +43928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1209/2000 [07:43<04:15, 3.09it/s, loss=0.457]" + "training until 2000: 60%|██████ | 1209/2000 [07:34<04:14, 3.11it/s, loss=0.47]" ] }, { @@ -43958,7 +43936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1209/2000 [07:43<04:15, 3.09it/s, loss=0.438]" + "training until 2000: 60%|██████ | 1209/2000 [07:34<04:14, 3.11it/s, loss=0.46]" ] }, { @@ -43966,7 +43944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1210/2000 [07:43<04:14, 3.11it/s, loss=0.438]" + "training until 2000: 60%|██████ | 1210/2000 [07:35<04:16, 3.09it/s, loss=0.46]" ] }, { @@ -43974,7 +43952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 60%|██████ | 1210/2000 [07:43<04:14, 3.11it/s, loss=0.446]" + "training until 2000: 60%|██████ | 1210/2000 [07:35<04:16, 3.09it/s, loss=0.488]" ] }, { @@ -43982,7 +43960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1211/2000 [07:43<04:16, 3.07it/s, loss=0.446]" + "training until 2000: 61%|██████ | 1211/2000 [07:35<04:16, 3.08it/s, loss=0.488]" ] }, { @@ -43990,7 +43968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1211/2000 [07:43<04:16, 3.07it/s, loss=0.458]" + "training until 2000: 61%|██████ | 1211/2000 [07:35<04:16, 3.08it/s, loss=0.576]" ] }, { @@ -43998,7 +43976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1212/2000 [07:44<04:15, 3.08it/s, loss=0.458]" + "training until 2000: 61%|██████ | 1212/2000 [07:35<04:14, 3.09it/s, loss=0.576]" ] }, { @@ -44006,7 +43984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1212/2000 [07:44<04:15, 3.08it/s, loss=0.442]" + "training until 2000: 61%|██████ | 1212/2000 [07:35<04:14, 3.09it/s, loss=0.459]" ] }, { @@ -44014,7 +43992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1213/2000 [07:44<04:13, 3.11it/s, loss=0.442]" + "training until 2000: 61%|██████ | 1213/2000 [07:36<04:12, 3.12it/s, loss=0.459]" ] }, { @@ -44022,7 +44000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1213/2000 [07:44<04:13, 3.11it/s, loss=0.439]" + "training until 2000: 61%|██████ | 1213/2000 [07:36<04:12, 3.12it/s, loss=0.458]" ] }, { @@ -44030,7 +44008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1214/2000 [07:44<04:13, 3.10it/s, loss=0.439]" + "training until 2000: 61%|██████ | 1214/2000 [07:36<04:12, 3.12it/s, loss=0.458]" ] }, { @@ -44038,7 +44016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1214/2000 [07:44<04:13, 3.10it/s, loss=0.449]" + "training until 2000: 61%|██████ | 1214/2000 [07:36<04:12, 3.12it/s, loss=0.532]" ] }, { @@ -44046,7 +44024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1215/2000 [07:45<04:11, 3.12it/s, loss=0.449]" + "training until 2000: 61%|██████ | 1215/2000 [07:36<04:13, 3.10it/s, loss=0.532]" ] }, { @@ -44054,7 +44032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1215/2000 [07:45<04:11, 3.12it/s, loss=0.46] " + "training until 2000: 61%|██████ | 1215/2000 [07:36<04:13, 3.10it/s, loss=0.522]" ] }, { @@ -44062,7 +44040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1216/2000 [07:45<04:12, 3.11it/s, loss=0.46]" + "training until 2000: 61%|██████ | 1216/2000 [07:36<04:10, 3.12it/s, loss=0.522]" ] }, { @@ -44070,7 +44048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1216/2000 [07:45<04:12, 3.11it/s, loss=0.444]" + "training until 2000: 61%|██████ | 1216/2000 [07:36<04:10, 3.12it/s, loss=0.497]" ] }, { @@ -44078,7 +44056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1217/2000 [07:45<04:13, 3.09it/s, loss=0.444]" + "training until 2000: 61%|██████ | 1217/2000 [07:37<04:08, 3.15it/s, loss=0.497]" ] }, { @@ -44086,7 +44064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1217/2000 [07:45<04:13, 3.09it/s, loss=0.449]" + "training until 2000: 61%|██████ | 1217/2000 [07:37<04:08, 3.15it/s, loss=0.428]" ] }, { @@ -44094,7 +44072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1218/2000 [07:46<04:11, 3.10it/s, loss=0.449]" + "training until 2000: 61%|██████ | 1218/2000 [07:37<04:11, 3.11it/s, loss=0.428]" ] }, { @@ -44102,7 +44080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1218/2000 [07:46<04:11, 3.10it/s, loss=0.468]" + "training until 2000: 61%|██████ | 1218/2000 [07:37<04:11, 3.11it/s, loss=0.508]" ] }, { @@ -44110,7 +44088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1219/2000 [07:46<04:12, 3.09it/s, loss=0.468]" + "training until 2000: 61%|██████ | 1219/2000 [07:37<04:11, 3.10it/s, loss=0.508]" ] }, { @@ -44118,7 +44096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1219/2000 [07:46<04:12, 3.09it/s, loss=0.489]" + "training until 2000: 61%|██████ | 1219/2000 [07:37<04:11, 3.10it/s, loss=0.505]" ] }, { @@ -44126,7 +44104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1220/2000 [07:46<04:15, 3.05it/s, loss=0.489]" + "training until 2000: 61%|██████ | 1220/2000 [07:38<04:11, 3.10it/s, loss=0.505]" ] }, { @@ -44134,7 +44112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1220/2000 [07:46<04:15, 3.05it/s, loss=0.462]" + "training until 2000: 61%|██████ | 1220/2000 [07:38<04:11, 3.10it/s, loss=0.511]" ] }, { @@ -44142,7 +44120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1221/2000 [07:47<04:14, 3.06it/s, loss=0.462]" + "training until 2000: 61%|██████ | 1221/2000 [07:38<04:13, 3.07it/s, loss=0.511]" ] }, { @@ -44150,7 +44128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1221/2000 [07:47<04:14, 3.06it/s, loss=0.442]" + "training until 2000: 61%|██████ | 1221/2000 [07:38<04:13, 3.07it/s, loss=0.469]" ] }, { @@ -44158,7 +44136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1222/2000 [07:47<04:12, 3.08it/s, loss=0.442]" + "training until 2000: 61%|██████ | 1222/2000 [07:38<04:15, 3.05it/s, loss=0.469]" ] }, { @@ -44166,7 +44144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1222/2000 [07:47<04:12, 3.08it/s, loss=0.456]" + "training until 2000: 61%|██████ | 1222/2000 [07:38<04:15, 3.05it/s, loss=0.47] " ] }, { @@ -44174,7 +44152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1223/2000 [07:47<04:10, 3.10it/s, loss=0.456]" + "training until 2000: 61%|██████ | 1223/2000 [07:39<04:11, 3.09it/s, loss=0.47]" ] }, { @@ -44182,7 +44160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1223/2000 [07:47<04:10, 3.10it/s, loss=0.478]" + "training until 2000: 61%|██████ | 1223/2000 [07:39<04:11, 3.09it/s, loss=0.611]" ] }, { @@ -44190,7 +44168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1224/2000 [07:47<04:08, 3.12it/s, loss=0.478]" + "training until 2000: 61%|██████ | 1224/2000 [07:39<04:13, 3.06it/s, loss=0.611]" ] }, { @@ -44198,7 +44176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████ | 1224/2000 [07:47<04:08, 3.12it/s, loss=0.509]" + "training until 2000: 61%|██████ | 1224/2000 [07:39<04:13, 3.06it/s, loss=0.439]" ] }, { @@ -44206,7 +44184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1225/2000 [07:48<04:10, 3.09it/s, loss=0.509]" + "training until 2000: 61%|██████▏ | 1225/2000 [07:39<04:11, 3.08it/s, loss=0.439]" ] }, { @@ -44214,7 +44192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1225/2000 [07:48<04:10, 3.09it/s, loss=0.516]" + "training until 2000: 61%|██████▏ | 1225/2000 [07:39<04:11, 3.08it/s, loss=0.574]" ] }, { @@ -44222,7 +44200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1226/2000 [07:48<04:11, 3.08it/s, loss=0.516]" + "training until 2000: 61%|██████▏ | 1226/2000 [07:40<04:11, 3.08it/s, loss=0.574]" ] }, { @@ -44230,7 +44208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1226/2000 [07:48<04:11, 3.08it/s, loss=0.44] " + "training until 2000: 61%|██████▏ | 1226/2000 [07:40<04:11, 3.08it/s, loss=0.44] " ] }, { @@ -44238,7 +44216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1227/2000 [07:48<04:08, 3.11it/s, loss=0.44]" + "training until 2000: 61%|██████▏ | 1227/2000 [07:40<04:12, 3.06it/s, loss=0.44]" ] }, { @@ -44246,7 +44224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1227/2000 [07:48<04:08, 3.11it/s, loss=0.489]" + "training until 2000: 61%|██████▏ | 1227/2000 [07:40<04:12, 3.06it/s, loss=0.466]" ] }, { @@ -44254,7 +44232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1228/2000 [07:49<04:10, 3.09it/s, loss=0.489]" + "training until 2000: 61%|██████▏ | 1228/2000 [07:41<05:12, 2.47it/s, loss=0.466]" ] }, { @@ -44262,7 +44240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1228/2000 [07:49<04:10, 3.09it/s, loss=0.463]" + "training until 2000: 61%|██████▏ | 1228/2000 [07:41<05:12, 2.47it/s, loss=0.444]" ] }, { @@ -44270,7 +44248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1229/2000 [07:49<04:08, 3.10it/s, loss=0.463]" + "training until 2000: 61%|██████▏ | 1229/2000 [07:41<04:54, 2.62it/s, loss=0.444]" ] }, { @@ -44278,7 +44256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 61%|██████▏ | 1229/2000 [07:49<04:08, 3.10it/s, loss=0.454]" + "training until 2000: 61%|██████▏ | 1229/2000 [07:41<04:54, 2.62it/s, loss=0.416]" ] }, { @@ -44286,7 +44264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1230/2000 [07:49<04:05, 3.14it/s, loss=0.454]" + "training until 2000: 62%|██████▏ | 1230/2000 [07:41<04:37, 2.77it/s, loss=0.416]" ] }, { @@ -44294,7 +44272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1230/2000 [07:49<04:05, 3.14it/s, loss=0.431]" + "training until 2000: 62%|██████▏ | 1230/2000 [07:41<04:37, 2.77it/s, loss=0.477]" ] }, { @@ -44302,7 +44280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1231/2000 [07:50<04:05, 3.14it/s, loss=0.431]" + "training until 2000: 62%|██████▏ | 1231/2000 [07:42<04:30, 2.85it/s, loss=0.477]" ] }, { @@ -44310,7 +44288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1231/2000 [07:50<04:05, 3.14it/s, loss=0.563]" + "training until 2000: 62%|██████▏ | 1231/2000 [07:42<04:30, 2.85it/s, loss=0.462]" ] }, { @@ -44318,7 +44296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1232/2000 [07:50<04:05, 3.13it/s, loss=0.563]" + "training until 2000: 62%|██████▏ | 1232/2000 [07:42<04:20, 2.95it/s, loss=0.462]" ] }, { @@ -44326,7 +44304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1232/2000 [07:50<04:05, 3.13it/s, loss=0.444]" + "training until 2000: 62%|██████▏ | 1232/2000 [07:42<04:20, 2.95it/s, loss=0.422]" ] }, { @@ -44334,7 +44312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1233/2000 [07:50<04:08, 3.08it/s, loss=0.444]" + "training until 2000: 62%|██████▏ | 1233/2000 [07:42<04:19, 2.95it/s, loss=0.422]" ] }, { @@ -44342,7 +44320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1233/2000 [07:50<04:08, 3.08it/s, loss=0.446]" + "training until 2000: 62%|██████▏ | 1233/2000 [07:42<04:19, 2.95it/s, loss=0.501]" ] }, { @@ -44350,7 +44328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1234/2000 [07:51<04:04, 3.13it/s, loss=0.446]" + "training until 2000: 62%|██████▏ | 1234/2000 [07:43<04:15, 3.00it/s, loss=0.501]" ] }, { @@ -44358,7 +44336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1234/2000 [07:51<04:04, 3.13it/s, loss=0.459]" + "training until 2000: 62%|██████▏ | 1234/2000 [07:43<04:15, 3.00it/s, loss=0.503]" ] }, { @@ -44366,7 +44344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1235/2000 [07:51<04:04, 3.13it/s, loss=0.459]" + "training until 2000: 62%|██████▏ | 1235/2000 [07:43<04:09, 3.06it/s, loss=0.503]" ] }, { @@ -44374,7 +44352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1235/2000 [07:51<04:04, 3.13it/s, loss=0.468]" + "training until 2000: 62%|██████▏ | 1235/2000 [07:43<04:09, 3.06it/s, loss=0.434]" ] }, { @@ -44382,7 +44360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1236/2000 [07:52<04:59, 2.55it/s, loss=0.468]" + "training until 2000: 62%|██████▏ | 1236/2000 [07:43<04:07, 3.08it/s, loss=0.434]" ] }, { @@ -44390,7 +44368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1236/2000 [07:52<04:59, 2.55it/s, loss=0.44] " + "training until 2000: 62%|██████▏ | 1236/2000 [07:43<04:07, 3.08it/s, loss=0.45] " ] }, { @@ -44398,7 +44376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1237/2000 [07:52<04:43, 2.69it/s, loss=0.44]" + "training until 2000: 62%|██████▏ | 1237/2000 [07:44<04:06, 3.09it/s, loss=0.45]" ] }, { @@ -44406,7 +44384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1237/2000 [07:52<04:43, 2.69it/s, loss=0.437]" + "training until 2000: 62%|██████▏ | 1237/2000 [07:44<04:06, 3.09it/s, loss=0.438]" ] }, { @@ -44414,7 +44392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1238/2000 [07:52<04:28, 2.84it/s, loss=0.437]" + "training until 2000: 62%|██████▏ | 1238/2000 [07:44<04:04, 3.11it/s, loss=0.438]" ] }, { @@ -44422,7 +44400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1238/2000 [07:52<04:28, 2.84it/s, loss=0.441]" + "training until 2000: 62%|██████▏ | 1238/2000 [07:44<04:04, 3.11it/s, loss=0.457]" ] }, { @@ -44430,7 +44408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1239/2000 [07:53<04:20, 2.92it/s, loss=0.441]" + "training until 2000: 62%|██████▏ | 1239/2000 [07:44<04:04, 3.12it/s, loss=0.457]" ] }, { @@ -44438,7 +44416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1239/2000 [07:53<04:20, 2.92it/s, loss=0.456]" + "training until 2000: 62%|██████▏ | 1239/2000 [07:44<04:04, 3.12it/s, loss=0.487]" ] }, { @@ -44446,7 +44424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1240/2000 [07:53<04:15, 2.97it/s, loss=0.456]" + "training until 2000: 62%|██████▏ | 1240/2000 [07:44<04:00, 3.16it/s, loss=0.487]" ] }, { @@ -44454,7 +44432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1240/2000 [07:53<04:15, 2.97it/s, loss=0.472]" + "training until 2000: 62%|██████▏ | 1240/2000 [07:44<04:00, 3.16it/s, loss=0.459]" ] }, { @@ -44462,7 +44440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1241/2000 [07:53<04:13, 2.99it/s, loss=0.472]" + "training until 2000: 62%|██████▏ | 1241/2000 [07:45<04:00, 3.16it/s, loss=0.459]" ] }, { @@ -44470,7 +44448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1241/2000 [07:53<04:13, 2.99it/s, loss=0.451]" + "training until 2000: 62%|██████▏ | 1241/2000 [07:45<04:00, 3.16it/s, loss=0.468]" ] }, { @@ -44478,7 +44456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1242/2000 [07:53<04:08, 3.05it/s, loss=0.451]" + "training until 2000: 62%|██████▏ | 1242/2000 [07:45<04:01, 3.14it/s, loss=0.468]" ] }, { @@ -44486,7 +44464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1242/2000 [07:53<04:08, 3.05it/s, loss=0.497]" + "training until 2000: 62%|██████▏ | 1242/2000 [07:45<04:01, 3.14it/s, loss=0.427]" ] }, { @@ -44494,7 +44472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1243/2000 [07:54<04:04, 3.10it/s, loss=0.497]" + "training until 2000: 62%|██████▏ | 1243/2000 [07:45<04:03, 3.11it/s, loss=0.427]" ] }, { @@ -44502,7 +44480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1243/2000 [07:54<04:04, 3.10it/s, loss=0.433]" + "training until 2000: 62%|██████▏ | 1243/2000 [07:45<04:03, 3.11it/s, loss=0.425]" ] }, { @@ -44510,7 +44488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1244/2000 [07:54<04:02, 3.12it/s, loss=0.433]" + "training until 2000: 62%|██████▏ | 1244/2000 [07:46<04:04, 3.10it/s, loss=0.425]" ] }, { @@ -44518,7 +44496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1244/2000 [07:54<04:02, 3.12it/s, loss=0.536]" + "training until 2000: 62%|██████▏ | 1244/2000 [07:46<04:04, 3.10it/s, loss=0.471]" ] }, { @@ -44526,7 +44504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1245/2000 [07:54<04:01, 3.12it/s, loss=0.536]" + "training until 2000: 62%|██████▏ | 1245/2000 [07:46<04:02, 3.11it/s, loss=0.471]" ] }, { @@ -44534,7 +44512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1245/2000 [07:54<04:01, 3.12it/s, loss=0.484]" + "training until 2000: 62%|██████▏ | 1245/2000 [07:46<04:02, 3.11it/s, loss=0.423]" ] }, { @@ -44542,7 +44520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1246/2000 [07:55<04:03, 3.10it/s, loss=0.484]" + "training until 2000: 62%|██████▏ | 1246/2000 [07:46<04:02, 3.11it/s, loss=0.423]" ] }, { @@ -44550,7 +44528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1246/2000 [07:55<04:03, 3.10it/s, loss=0.448]" + "training until 2000: 62%|██████▏ | 1246/2000 [07:46<04:02, 3.11it/s, loss=0.484]" ] }, { @@ -44558,7 +44536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1247/2000 [07:55<04:03, 3.09it/s, loss=0.448]" + "training until 2000: 62%|██████▏ | 1247/2000 [07:47<04:00, 3.13it/s, loss=0.484]" ] }, { @@ -44566,7 +44544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1247/2000 [07:55<04:03, 3.09it/s, loss=0.455]" + "training until 2000: 62%|██████▏ | 1247/2000 [07:47<04:00, 3.13it/s, loss=0.402]" ] }, { @@ -44574,7 +44552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1248/2000 [07:55<04:02, 3.11it/s, loss=0.455]" + "training until 2000: 62%|██████▏ | 1248/2000 [07:47<03:58, 3.15it/s, loss=0.402]" ] }, { @@ -44582,7 +44560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1248/2000 [07:55<04:02, 3.11it/s, loss=0.431]" + "training until 2000: 62%|██████▏ | 1248/2000 [07:47<03:58, 3.15it/s, loss=0.464]" ] }, { @@ -44590,7 +44568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1249/2000 [07:56<04:00, 3.12it/s, loss=0.431]" + "training until 2000: 62%|██████▏ | 1249/2000 [07:47<04:00, 3.12it/s, loss=0.464]" ] }, { @@ -44598,7 +44576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▏ | 1249/2000 [07:56<04:00, 3.12it/s, loss=0.462]" + "training until 2000: 62%|██████▏ | 1249/2000 [07:47<04:00, 3.12it/s, loss=0.436]" ] }, { @@ -44606,7 +44584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▎ | 1250/2000 [07:56<04:00, 3.12it/s, loss=0.462]" + "training until 2000: 62%|██████▎ | 1250/2000 [07:48<03:59, 3.14it/s, loss=0.436]" ] }, { @@ -44614,7 +44592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 62%|██████▎ | 1250/2000 [07:56<04:00, 3.12it/s, loss=0.452]" + "training until 2000: 62%|██████▎ | 1250/2000 [07:48<03:59, 3.14it/s, loss=0.496]" ] }, { @@ -44622,7 +44600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1251/2000 [07:56<03:58, 3.15it/s, loss=0.452]" + "training until 2000: 63%|██████▎ | 1251/2000 [07:48<03:57, 3.16it/s, loss=0.496]" ] }, { @@ -44630,7 +44608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1251/2000 [07:56<03:58, 3.15it/s, loss=0.443]" + "training until 2000: 63%|██████▎ | 1251/2000 [07:48<03:57, 3.16it/s, loss=0.421]" ] }, { @@ -44638,7 +44616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1252/2000 [07:57<03:57, 3.14it/s, loss=0.443]" + "training until 2000: 63%|██████▎ | 1252/2000 [07:48<03:56, 3.16it/s, loss=0.421]" ] }, { @@ -44646,7 +44624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1252/2000 [07:57<03:57, 3.14it/s, loss=0.444]" + "training until 2000: 63%|██████▎ | 1252/2000 [07:48<03:56, 3.16it/s, loss=0.45] " ] }, { @@ -44654,7 +44632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1253/2000 [07:57<03:59, 3.11it/s, loss=0.444]" + "training until 2000: 63%|██████▎ | 1253/2000 [07:49<03:55, 3.17it/s, loss=0.45]" ] }, { @@ -44662,7 +44640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1253/2000 [07:57<03:59, 3.11it/s, loss=0.542]" + "training until 2000: 63%|██████▎ | 1253/2000 [07:49<03:55, 3.17it/s, loss=0.412]" ] }, { @@ -44670,7 +44648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1254/2000 [07:57<03:58, 3.13it/s, loss=0.542]" + "training until 2000: 63%|██████▎ | 1254/2000 [07:49<03:55, 3.17it/s, loss=0.412]" ] }, { @@ -44678,7 +44656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1254/2000 [07:57<03:58, 3.13it/s, loss=0.442]" + "training until 2000: 63%|██████▎ | 1254/2000 [07:49<03:55, 3.17it/s, loss=0.443]" ] }, { @@ -44686,7 +44664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1255/2000 [07:58<03:58, 3.12it/s, loss=0.442]" + "training until 2000: 63%|██████▎ | 1255/2000 [07:49<03:56, 3.15it/s, loss=0.443]" ] }, { @@ -44694,7 +44672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1255/2000 [07:58<03:58, 3.12it/s, loss=0.447]" + "training until 2000: 63%|██████▎ | 1255/2000 [07:49<03:56, 3.15it/s, loss=0.429]" ] }, { @@ -44702,7 +44680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1256/2000 [07:58<04:01, 3.08it/s, loss=0.447]" + "training until 2000: 63%|██████▎ | 1256/2000 [07:50<03:58, 3.12it/s, loss=0.429]" ] }, { @@ -44710,7 +44688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1256/2000 [07:58<04:01, 3.08it/s, loss=0.504]" + "training until 2000: 63%|██████▎ | 1256/2000 [07:50<03:58, 3.12it/s, loss=0.445]" ] }, { @@ -44718,7 +44696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1257/2000 [07:58<04:00, 3.09it/s, loss=0.504]" + "training until 2000: 63%|██████▎ | 1257/2000 [07:50<04:00, 3.10it/s, loss=0.445]" ] }, { @@ -44726,7 +44704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1257/2000 [07:58<04:00, 3.09it/s, loss=0.446]" + "training until 2000: 63%|██████▎ | 1257/2000 [07:50<04:00, 3.10it/s, loss=0.452]" ] }, { @@ -44734,7 +44712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1258/2000 [07:59<04:03, 3.04it/s, loss=0.446]" + "training until 2000: 63%|██████▎ | 1258/2000 [07:50<03:58, 3.11it/s, loss=0.452]" ] }, { @@ -44742,7 +44720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1258/2000 [07:59<04:03, 3.04it/s, loss=0.436]" + "training until 2000: 63%|██████▎ | 1258/2000 [07:50<03:58, 3.11it/s, loss=0.451]" ] }, { @@ -44750,7 +44728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1259/2000 [07:59<04:01, 3.07it/s, loss=0.436]" + "training until 2000: 63%|██████▎ | 1259/2000 [07:51<03:59, 3.10it/s, loss=0.451]" ] }, { @@ -44758,7 +44736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1259/2000 [07:59<04:01, 3.07it/s, loss=0.426]" + "training until 2000: 63%|██████▎ | 1259/2000 [07:51<03:59, 3.10it/s, loss=0.532]" ] }, { @@ -44766,7 +44744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1260/2000 [07:59<04:00, 3.08it/s, loss=0.426]" + "training until 2000: 63%|██████▎ | 1260/2000 [07:51<03:58, 3.10it/s, loss=0.532]" ] }, { @@ -44774,7 +44752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1260/2000 [07:59<04:00, 3.08it/s, loss=0.469]" + "training until 2000: 63%|██████▎ | 1260/2000 [07:51<03:58, 3.10it/s, loss=0.49] " ] }, { @@ -44782,7 +44760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1261/2000 [08:00<04:01, 3.06it/s, loss=0.469]" + "training until 2000: 63%|██████▎ | 1261/2000 [07:51<03:57, 3.11it/s, loss=0.49]" ] }, { @@ -44790,7 +44768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1261/2000 [08:00<04:01, 3.06it/s, loss=0.595]" + "training until 2000: 63%|██████▎ | 1261/2000 [07:51<03:57, 3.11it/s, loss=0.445]" ] }, { @@ -44798,7 +44776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1262/2000 [08:00<04:02, 3.05it/s, loss=0.595]" + "training until 2000: 63%|██████▎ | 1262/2000 [07:52<03:57, 3.11it/s, loss=0.445]" ] }, { @@ -44806,7 +44784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1262/2000 [08:00<04:02, 3.05it/s, loss=0.454]" + "training until 2000: 63%|██████▎ | 1262/2000 [07:52<03:57, 3.11it/s, loss=0.618]" ] }, { @@ -44814,7 +44792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1263/2000 [08:00<04:01, 3.05it/s, loss=0.454]" + "training until 2000: 63%|██████▎ | 1263/2000 [07:52<03:56, 3.11it/s, loss=0.618]" ] }, { @@ -44822,7 +44800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1263/2000 [08:00<04:01, 3.05it/s, loss=0.457]" + "training until 2000: 63%|██████▎ | 1263/2000 [07:52<03:56, 3.11it/s, loss=0.445]" ] }, { @@ -44830,7 +44808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1264/2000 [08:01<03:58, 3.09it/s, loss=0.457]" + "training until 2000: 63%|██████▎ | 1264/2000 [07:52<03:54, 3.13it/s, loss=0.445]" ] }, { @@ -44838,7 +44816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1264/2000 [08:01<03:58, 3.09it/s, loss=0.449]" + "training until 2000: 63%|██████▎ | 1264/2000 [07:52<03:54, 3.13it/s, loss=0.442]" ] }, { @@ -44846,7 +44824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1265/2000 [08:01<03:56, 3.11it/s, loss=0.449]" + "training until 2000: 63%|██████▎ | 1265/2000 [07:52<03:55, 3.12it/s, loss=0.442]" ] }, { @@ -44854,7 +44832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1265/2000 [08:01<03:56, 3.11it/s, loss=0.444]" + "training until 2000: 63%|██████▎ | 1265/2000 [07:52<03:55, 3.12it/s, loss=0.479]" ] }, { @@ -44862,7 +44840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1266/2000 [08:01<03:58, 3.07it/s, loss=0.444]" + "training until 2000: 63%|██████▎ | 1266/2000 [07:53<03:55, 3.11it/s, loss=0.479]" ] }, { @@ -44870,7 +44848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1266/2000 [08:01<03:58, 3.07it/s, loss=0.552]" + "training until 2000: 63%|██████▎ | 1266/2000 [07:53<03:55, 3.11it/s, loss=0.507]" ] }, { @@ -44878,7 +44856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1267/2000 [08:02<03:58, 3.07it/s, loss=0.552]" + "training until 2000: 63%|██████▎ | 1267/2000 [07:53<03:54, 3.12it/s, loss=0.507]" ] }, { @@ -44886,7 +44864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1267/2000 [08:02<03:58, 3.07it/s, loss=0.452]" + "training until 2000: 63%|██████▎ | 1267/2000 [07:53<03:54, 3.12it/s, loss=0.489]" ] }, { @@ -44894,7 +44872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1268/2000 [08:02<03:57, 3.08it/s, loss=0.452]" + "training until 2000: 63%|██████▎ | 1268/2000 [07:53<03:53, 3.13it/s, loss=0.489]" ] }, { @@ -44902,7 +44880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1268/2000 [08:02<03:57, 3.08it/s, loss=0.47] " + "training until 2000: 63%|██████▎ | 1268/2000 [07:53<03:53, 3.13it/s, loss=0.444]" ] }, { @@ -44910,7 +44888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1269/2000 [08:02<03:58, 3.06it/s, loss=0.47]" + "training until 2000: 63%|██████▎ | 1269/2000 [07:54<03:55, 3.10it/s, loss=0.444]" ] }, { @@ -44918,7 +44896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 63%|██████▎ | 1269/2000 [08:02<03:58, 3.06it/s, loss=0.474]" + "training until 2000: 63%|██████▎ | 1269/2000 [07:54<03:55, 3.10it/s, loss=0.418]" ] }, { @@ -44926,7 +44904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1270/2000 [08:03<03:59, 3.05it/s, loss=0.474]" + "training until 2000: 64%|██████▎ | 1270/2000 [07:54<03:52, 3.14it/s, loss=0.418]" ] }, { @@ -44934,7 +44912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1270/2000 [08:03<03:59, 3.05it/s, loss=0.45] " + "training until 2000: 64%|██████▎ | 1270/2000 [07:54<03:52, 3.14it/s, loss=0.479]" ] }, { @@ -44942,7 +44920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1271/2000 [08:03<03:55, 3.10it/s, loss=0.45]" + "training until 2000: 64%|██████▎ | 1271/2000 [07:54<03:52, 3.14it/s, loss=0.479]" ] }, { @@ -44950,7 +44928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1271/2000 [08:03<03:55, 3.10it/s, loss=0.446]" + "training until 2000: 64%|██████▎ | 1271/2000 [07:54<03:52, 3.14it/s, loss=0.42] " ] }, { @@ -44958,7 +44936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1272/2000 [08:03<03:54, 3.11it/s, loss=0.446]" + "training until 2000: 64%|██████▎ | 1272/2000 [07:55<03:52, 3.13it/s, loss=0.42]" ] }, { @@ -44966,7 +44944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1272/2000 [08:03<03:54, 3.11it/s, loss=0.448]" + "training until 2000: 64%|██████▎ | 1272/2000 [07:55<03:52, 3.13it/s, loss=0.433]" ] }, { @@ -44974,7 +44952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1273/2000 [08:03<03:56, 3.08it/s, loss=0.448]" + "training until 2000: 64%|██████▎ | 1273/2000 [07:55<03:50, 3.15it/s, loss=0.433]" ] }, { @@ -44982,7 +44960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1273/2000 [08:03<03:56, 3.08it/s, loss=0.547]" + "training until 2000: 64%|██████▎ | 1273/2000 [07:55<03:50, 3.15it/s, loss=0.58] " ] }, { @@ -44990,7 +44968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1274/2000 [08:04<03:52, 3.12it/s, loss=0.547]" + "training until 2000: 64%|██████▎ | 1274/2000 [07:55<03:51, 3.14it/s, loss=0.58]" ] }, { @@ -44998,7 +44976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▎ | 1274/2000 [08:04<03:52, 3.12it/s, loss=0.467]" + "training until 2000: 64%|██████▎ | 1274/2000 [07:55<03:51, 3.14it/s, loss=0.64]" ] }, { @@ -45006,7 +44984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1275/2000 [08:04<03:55, 3.08it/s, loss=0.467]" + "training until 2000: 64%|██████▍ | 1275/2000 [07:56<03:49, 3.16it/s, loss=0.64]" ] }, { @@ -45014,7 +44992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1275/2000 [08:04<03:55, 3.08it/s, loss=0.632]" + "training until 2000: 64%|██████▍ | 1275/2000 [07:56<03:49, 3.16it/s, loss=0.551]" ] }, { @@ -45022,7 +45000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1276/2000 [08:04<03:57, 3.04it/s, loss=0.632]" + "training until 2000: 64%|██████▍ | 1276/2000 [07:56<03:48, 3.17it/s, loss=0.551]" ] }, { @@ -45030,7 +45008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1276/2000 [08:04<03:57, 3.04it/s, loss=0.435]" + "training until 2000: 64%|██████▍ | 1276/2000 [07:56<03:48, 3.17it/s, loss=0.439]" ] }, { @@ -45038,7 +45016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1277/2000 [08:05<03:52, 3.11it/s, loss=0.435]" + "training until 2000: 64%|██████▍ | 1277/2000 [07:56<03:49, 3.15it/s, loss=0.439]" ] }, { @@ -45046,7 +45024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1277/2000 [08:05<03:52, 3.11it/s, loss=0.467]" + "training until 2000: 64%|██████▍ | 1277/2000 [07:56<03:49, 3.15it/s, loss=0.636]" ] }, { @@ -45054,7 +45032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1278/2000 [08:05<03:53, 3.10it/s, loss=0.467]" + "training until 2000: 64%|██████▍ | 1278/2000 [07:57<03:50, 3.13it/s, loss=0.636]" ] }, { @@ -45062,7 +45040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1278/2000 [08:05<03:53, 3.10it/s, loss=0.448]" + "training until 2000: 64%|██████▍ | 1278/2000 [07:57<03:50, 3.13it/s, loss=0.432]" ] }, { @@ -45070,7 +45048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1279/2000 [08:05<03:52, 3.10it/s, loss=0.448]" + "training until 2000: 64%|██████▍ | 1279/2000 [07:57<03:50, 3.12it/s, loss=0.432]" ] }, { @@ -45078,7 +45056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1279/2000 [08:05<03:52, 3.10it/s, loss=0.587]" + "training until 2000: 64%|██████▍ | 1279/2000 [07:57<03:50, 3.12it/s, loss=0.437]" ] }, { @@ -45086,7 +45064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1280/2000 [08:06<03:53, 3.09it/s, loss=0.587]" + "training until 2000: 64%|██████▍ | 1280/2000 [07:57<03:48, 3.15it/s, loss=0.437]" ] }, { @@ -45094,7 +45072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1280/2000 [08:06<03:53, 3.09it/s, loss=0.431]" + "training until 2000: 64%|██████▍ | 1280/2000 [07:57<03:48, 3.15it/s, loss=0.424]" ] }, { @@ -45102,7 +45080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1281/2000 [08:06<03:54, 3.07it/s, loss=0.431]" + "training until 2000: 64%|██████▍ | 1281/2000 [07:58<03:47, 3.17it/s, loss=0.424]" ] }, { @@ -45110,7 +45088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1281/2000 [08:06<03:54, 3.07it/s, loss=0.447]" + "training until 2000: 64%|██████▍ | 1281/2000 [07:58<03:47, 3.17it/s, loss=0.424]" ] }, { @@ -45118,7 +45096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1282/2000 [08:06<03:50, 3.11it/s, loss=0.447]" + "training until 2000: 64%|██████▍ | 1282/2000 [07:58<03:48, 3.14it/s, loss=0.424]" ] }, { @@ -45126,7 +45104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1282/2000 [08:06<03:50, 3.11it/s, loss=0.453]" + "training until 2000: 64%|██████▍ | 1282/2000 [07:58<03:48, 3.14it/s, loss=0.441]" ] }, { @@ -45134,7 +45112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1283/2000 [08:07<03:49, 3.12it/s, loss=0.453]" + "training until 2000: 64%|██████▍ | 1283/2000 [07:58<03:44, 3.19it/s, loss=0.441]" ] }, { @@ -45142,7 +45120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1283/2000 [08:07<03:49, 3.12it/s, loss=0.467]" + "training until 2000: 64%|██████▍ | 1283/2000 [07:58<03:44, 3.19it/s, loss=0.451]" ] }, { @@ -45150,7 +45128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1284/2000 [08:07<03:49, 3.12it/s, loss=0.467]" + "training until 2000: 64%|██████▍ | 1284/2000 [07:59<03:44, 3.19it/s, loss=0.451]" ] }, { @@ -45158,7 +45136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1284/2000 [08:07<03:49, 3.12it/s, loss=0.481]" + "training until 2000: 64%|██████▍ | 1284/2000 [07:59<03:44, 3.19it/s, loss=0.432]" ] }, { @@ -45166,7 +45144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1285/2000 [08:07<03:48, 3.13it/s, loss=0.481]" + "training until 2000: 64%|██████▍ | 1285/2000 [07:59<03:43, 3.19it/s, loss=0.432]" ] }, { @@ -45174,7 +45152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1285/2000 [08:07<03:48, 3.13it/s, loss=0.453]" + "training until 2000: 64%|██████▍ | 1285/2000 [07:59<03:43, 3.19it/s, loss=0.5] " ] }, { @@ -45182,7 +45160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1286/2000 [08:08<03:54, 3.05it/s, loss=0.453]" + "training until 2000: 64%|██████▍ | 1286/2000 [07:59<03:43, 3.20it/s, loss=0.5]" ] }, { @@ -45190,7 +45168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1286/2000 [08:08<03:54, 3.05it/s, loss=0.439]" + "training until 2000: 64%|██████▍ | 1286/2000 [07:59<03:43, 3.20it/s, loss=0.464]" ] }, { @@ -45198,7 +45176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1287/2000 [08:08<03:53, 3.06it/s, loss=0.439]" + "training until 2000: 64%|██████▍ | 1287/2000 [07:59<03:43, 3.19it/s, loss=0.464]" ] }, { @@ -45206,7 +45184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1287/2000 [08:08<03:53, 3.06it/s, loss=0.438]" + "training until 2000: 64%|██████▍ | 1287/2000 [07:59<03:43, 3.19it/s, loss=0.494]" ] }, { @@ -45214,7 +45192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1288/2000 [08:08<03:54, 3.03it/s, loss=0.438]" + "training until 2000: 64%|██████▍ | 1288/2000 [08:00<03:45, 3.16it/s, loss=0.494]" ] }, { @@ -45222,7 +45200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1288/2000 [08:08<03:54, 3.03it/s, loss=0.449]" + "training until 2000: 64%|██████▍ | 1288/2000 [08:00<03:45, 3.16it/s, loss=0.455]" ] }, { @@ -45230,7 +45208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1289/2000 [08:09<03:51, 3.06it/s, loss=0.449]" + "training until 2000: 64%|██████▍ | 1289/2000 [08:00<03:46, 3.15it/s, loss=0.455]" ] }, { @@ -45238,7 +45216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1289/2000 [08:09<03:51, 3.06it/s, loss=0.443]" + "training until 2000: 64%|██████▍ | 1289/2000 [08:00<03:46, 3.15it/s, loss=0.513]" ] }, { @@ -45246,7 +45224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1290/2000 [08:09<03:52, 3.05it/s, loss=0.443]" + "training until 2000: 64%|██████▍ | 1290/2000 [08:00<03:42, 3.18it/s, loss=0.513]" ] }, { @@ -45254,7 +45232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 64%|██████▍ | 1290/2000 [08:09<03:52, 3.05it/s, loss=0.438]" + "training until 2000: 64%|██████▍ | 1290/2000 [08:00<03:42, 3.18it/s, loss=0.483]" ] }, { @@ -45262,7 +45240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1291/2000 [08:09<03:50, 3.07it/s, loss=0.438]" + "training until 2000: 65%|██████▍ | 1291/2000 [08:01<03:43, 3.17it/s, loss=0.483]" ] }, { @@ -45270,7 +45248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1291/2000 [08:09<03:50, 3.07it/s, loss=0.474]" + "training until 2000: 65%|██████▍ | 1291/2000 [08:01<03:43, 3.17it/s, loss=0.415]" ] }, { @@ -45278,7 +45256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1292/2000 [08:10<03:49, 3.09it/s, loss=0.474]" + "training until 2000: 65%|██████▍ | 1292/2000 [08:01<03:41, 3.20it/s, loss=0.415]" ] }, { @@ -45286,7 +45264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1292/2000 [08:10<03:49, 3.09it/s, loss=0.439]" + "training until 2000: 65%|██████▍ | 1292/2000 [08:01<03:41, 3.20it/s, loss=0.421]" ] }, { @@ -45294,7 +45272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1293/2000 [08:10<03:47, 3.10it/s, loss=0.439]" + "training until 2000: 65%|██████▍ | 1293/2000 [08:02<04:37, 2.55it/s, loss=0.421]" ] }, { @@ -45302,7 +45280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1293/2000 [08:10<03:47, 3.10it/s, loss=0.429]" + "training until 2000: 65%|██████▍ | 1293/2000 [08:02<04:37, 2.55it/s, loss=0.472]" ] }, { @@ -45310,7 +45288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1294/2000 [08:10<03:46, 3.11it/s, loss=0.429]" + "training until 2000: 65%|██████▍ | 1294/2000 [08:02<04:25, 2.66it/s, loss=0.472]" ] }, { @@ -45318,7 +45296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1294/2000 [08:10<03:46, 3.11it/s, loss=0.444]" + "training until 2000: 65%|██████▍ | 1294/2000 [08:02<04:25, 2.66it/s, loss=0.427]" ] }, { @@ -45326,7 +45304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1295/2000 [08:11<03:46, 3.11it/s, loss=0.444]" + "training until 2000: 65%|██████▍ | 1295/2000 [08:02<04:14, 2.77it/s, loss=0.427]" ] }, { @@ -45334,7 +45312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1295/2000 [08:11<03:46, 3.11it/s, loss=0.493]" + "training until 2000: 65%|██████▍ | 1295/2000 [08:02<04:14, 2.77it/s, loss=0.488]" ] }, { @@ -45342,7 +45320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1296/2000 [08:11<03:45, 3.12it/s, loss=0.493]" + "training until 2000: 65%|██████▍ | 1296/2000 [08:03<04:06, 2.86it/s, loss=0.488]" ] }, { @@ -45350,7 +45328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1296/2000 [08:11<03:45, 3.12it/s, loss=0.461]" + "training until 2000: 65%|██████▍ | 1296/2000 [08:03<04:06, 2.86it/s, loss=0.465]" ] }, { @@ -45358,7 +45336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1297/2000 [08:11<03:48, 3.07it/s, loss=0.461]" + "training until 2000: 65%|██████▍ | 1297/2000 [08:03<03:58, 2.95it/s, loss=0.465]" ] }, { @@ -45366,7 +45344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1297/2000 [08:11<03:48, 3.07it/s, loss=0.444]" + "training until 2000: 65%|██████▍ | 1297/2000 [08:03<03:58, 2.95it/s, loss=0.476]" ] }, { @@ -45374,7 +45352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1298/2000 [08:12<03:49, 3.05it/s, loss=0.444]" + "training until 2000: 65%|██████▍ | 1298/2000 [08:03<03:53, 3.01it/s, loss=0.476]" ] }, { @@ -45382,7 +45360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1298/2000 [08:12<03:49, 3.05it/s, loss=0.451]" + "training until 2000: 65%|██████▍ | 1298/2000 [08:03<03:53, 3.01it/s, loss=0.431]" ] }, { @@ -45390,7 +45368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1299/2000 [08:12<03:56, 2.96it/s, loss=0.451]" + "training until 2000: 65%|██████▍ | 1299/2000 [08:04<03:47, 3.09it/s, loss=0.431]" ] }, { @@ -45398,7 +45376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▍ | 1299/2000 [08:12<03:56, 2.96it/s, loss=0.46] " + "training until 2000: 65%|██████▍ | 1299/2000 [08:04<03:47, 3.09it/s, loss=0.511]" ] }, { @@ -45406,7 +45384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1300/2000 [08:12<03:53, 2.99it/s, loss=0.46]" + "training until 2000: 65%|██████▌ | 1300/2000 [08:04<03:45, 3.11it/s, loss=0.511]" ] }, { @@ -45414,7 +45392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1300/2000 [08:12<03:53, 2.99it/s, loss=0.447]" + "training until 2000: 65%|██████▌ | 1300/2000 [08:04<03:45, 3.11it/s, loss=0.413]" ] }, { @@ -45422,7 +45400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1301/2000 [08:13<04:45, 2.45it/s, loss=0.447]" + "training until 2000: 65%|██████▌ | 1301/2000 [08:04<03:45, 3.11it/s, loss=0.413]" ] }, { @@ -45430,7 +45408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1301/2000 [08:13<04:45, 2.45it/s, loss=0.438]" + "training until 2000: 65%|██████▌ | 1301/2000 [08:04<03:45, 3.11it/s, loss=0.424]" ] }, { @@ -45438,7 +45416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1302/2000 [08:13<04:28, 2.60it/s, loss=0.438]" + "training until 2000: 65%|██████▌ | 1302/2000 [08:04<03:45, 3.10it/s, loss=0.424]" ] }, { @@ -45446,7 +45424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1302/2000 [08:13<04:28, 2.60it/s, loss=0.438]" + "training until 2000: 65%|██████▌ | 1302/2000 [08:04<03:45, 3.10it/s, loss=0.419]" ] }, { @@ -45454,7 +45432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1303/2000 [08:14<04:14, 2.74it/s, loss=0.438]" + "training until 2000: 65%|██████▌ | 1303/2000 [08:05<03:44, 3.10it/s, loss=0.419]" ] }, { @@ -45462,7 +45440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1303/2000 [08:14<04:14, 2.74it/s, loss=0.428]" + "training until 2000: 65%|██████▌ | 1303/2000 [08:05<03:44, 3.10it/s, loss=0.436]" ] }, { @@ -45470,7 +45448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1304/2000 [08:14<04:06, 2.82it/s, loss=0.428]" + "training until 2000: 65%|██████▌ | 1304/2000 [08:05<03:44, 3.10it/s, loss=0.436]" ] }, { @@ -45478,7 +45456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1304/2000 [08:14<04:06, 2.82it/s, loss=0.47] " + "training until 2000: 65%|██████▌ | 1304/2000 [08:05<03:44, 3.10it/s, loss=0.428]" ] }, { @@ -45486,7 +45464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1305/2000 [08:14<04:00, 2.89it/s, loss=0.47]" + "training until 2000: 65%|██████▌ | 1305/2000 [08:05<03:44, 3.10it/s, loss=0.428]" ] }, { @@ -45494,7 +45472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1305/2000 [08:14<04:00, 2.89it/s, loss=0.46]" + "training until 2000: 65%|██████▌ | 1305/2000 [08:05<03:44, 3.10it/s, loss=0.438]" ] }, { @@ -45502,7 +45480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1306/2000 [08:14<03:55, 2.95it/s, loss=0.46]" + "training until 2000: 65%|██████▌ | 1306/2000 [08:06<03:46, 3.07it/s, loss=0.438]" ] }, { @@ -45510,7 +45488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1306/2000 [08:14<03:55, 2.95it/s, loss=0.443]" + "training until 2000: 65%|██████▌ | 1306/2000 [08:06<03:46, 3.07it/s, loss=0.459]" ] }, { @@ -45518,7 +45496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1307/2000 [08:15<03:52, 2.98it/s, loss=0.443]" + "training until 2000: 65%|██████▌ | 1307/2000 [08:06<03:44, 3.08it/s, loss=0.459]" ] }, { @@ -45526,7 +45504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1307/2000 [08:15<03:52, 2.98it/s, loss=0.452]" + "training until 2000: 65%|██████▌ | 1307/2000 [08:06<03:44, 3.08it/s, loss=0.415]" ] }, { @@ -45534,7 +45512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1308/2000 [08:15<03:49, 3.02it/s, loss=0.452]" + "training until 2000: 65%|██████▌ | 1308/2000 [08:06<03:45, 3.07it/s, loss=0.415]" ] }, { @@ -45542,7 +45520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1308/2000 [08:15<03:49, 3.02it/s, loss=0.427]" + "training until 2000: 65%|██████▌ | 1308/2000 [08:06<03:45, 3.07it/s, loss=0.423]" ] }, { @@ -45550,7 +45528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1309/2000 [08:15<03:46, 3.06it/s, loss=0.427]" + "training until 2000: 65%|██████▌ | 1309/2000 [08:07<03:43, 3.09it/s, loss=0.423]" ] }, { @@ -45558,7 +45536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 65%|██████▌ | 1309/2000 [08:15<03:46, 3.06it/s, loss=0.433]" + "training until 2000: 65%|██████▌ | 1309/2000 [08:07<03:43, 3.09it/s, loss=0.465]" ] }, { @@ -45566,7 +45544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1310/2000 [08:16<03:45, 3.06it/s, loss=0.433]" + "training until 2000: 66%|██████▌ | 1310/2000 [08:07<03:45, 3.06it/s, loss=0.465]" ] }, { @@ -45574,7 +45552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1310/2000 [08:16<03:45, 3.06it/s, loss=0.45] " + "training until 2000: 66%|██████▌ | 1310/2000 [08:07<03:45, 3.06it/s, loss=0.445]" ] }, { @@ -45582,7 +45560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1311/2000 [08:16<03:45, 3.05it/s, loss=0.45]" + "training until 2000: 66%|██████▌ | 1311/2000 [08:07<03:43, 3.08it/s, loss=0.445]" ] }, { @@ -45590,7 +45568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1311/2000 [08:16<03:45, 3.05it/s, loss=0.445]" + "training until 2000: 66%|██████▌ | 1311/2000 [08:07<03:43, 3.08it/s, loss=0.461]" ] }, { @@ -45598,7 +45576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1312/2000 [08:16<03:50, 2.99it/s, loss=0.445]" + "training until 2000: 66%|██████▌ | 1312/2000 [08:08<03:44, 3.06it/s, loss=0.461]" ] }, { @@ -45606,7 +45584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1312/2000 [08:16<03:50, 2.99it/s, loss=0.463]" + "training until 2000: 66%|██████▌ | 1312/2000 [08:08<03:44, 3.06it/s, loss=0.408]" ] }, { @@ -45614,7 +45592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1313/2000 [08:17<03:51, 2.96it/s, loss=0.463]" + "training until 2000: 66%|██████▌ | 1313/2000 [08:08<03:46, 3.03it/s, loss=0.408]" ] }, { @@ -45622,7 +45600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1313/2000 [08:17<03:51, 2.96it/s, loss=0.441]" + "training until 2000: 66%|██████▌ | 1313/2000 [08:08<03:46, 3.03it/s, loss=0.652]" ] }, { @@ -45630,7 +45608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1314/2000 [08:17<03:46, 3.02it/s, loss=0.441]" + "training until 2000: 66%|██████▌ | 1314/2000 [08:08<03:44, 3.05it/s, loss=0.652]" ] }, { @@ -45638,7 +45616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1314/2000 [08:17<03:46, 3.02it/s, loss=0.441]" + "training until 2000: 66%|██████▌ | 1314/2000 [08:08<03:44, 3.05it/s, loss=0.405]" ] }, { @@ -45646,7 +45624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1315/2000 [08:17<03:44, 3.05it/s, loss=0.441]" + "training until 2000: 66%|██████▌ | 1315/2000 [08:09<03:42, 3.08it/s, loss=0.405]" ] }, { @@ -45654,7 +45632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1315/2000 [08:17<03:44, 3.05it/s, loss=0.427]" + "training until 2000: 66%|██████▌ | 1315/2000 [08:09<03:42, 3.08it/s, loss=0.409]" ] }, { @@ -45662,7 +45640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1316/2000 [08:18<03:42, 3.07it/s, loss=0.427]" + "training until 2000: 66%|██████▌ | 1316/2000 [08:09<03:42, 3.07it/s, loss=0.409]" ] }, { @@ -45670,7 +45648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1316/2000 [08:18<03:42, 3.07it/s, loss=0.509]" + "training until 2000: 66%|██████▌ | 1316/2000 [08:09<03:42, 3.07it/s, loss=0.494]" ] }, { @@ -45678,7 +45656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1317/2000 [08:18<03:40, 3.10it/s, loss=0.509]" + "training until 2000: 66%|██████▌ | 1317/2000 [08:09<03:42, 3.07it/s, loss=0.494]" ] }, { @@ -45686,7 +45664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1317/2000 [08:18<03:40, 3.10it/s, loss=0.435]" + "training until 2000: 66%|██████▌ | 1317/2000 [08:09<03:42, 3.07it/s, loss=0.436]" ] }, { @@ -45694,7 +45672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1318/2000 [08:18<03:41, 3.08it/s, loss=0.435]" + "training until 2000: 66%|██████▌ | 1318/2000 [08:10<03:42, 3.06it/s, loss=0.436]" ] }, { @@ -45702,7 +45680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1318/2000 [08:18<03:41, 3.08it/s, loss=0.445]" + "training until 2000: 66%|██████▌ | 1318/2000 [08:10<03:42, 3.06it/s, loss=0.482]" ] }, { @@ -45710,7 +45688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1319/2000 [08:19<03:38, 3.11it/s, loss=0.445]" + "training until 2000: 66%|██████▌ | 1319/2000 [08:10<03:46, 3.00it/s, loss=0.482]" ] }, { @@ -45718,7 +45696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1319/2000 [08:19<03:38, 3.11it/s, loss=0.418]" + "training until 2000: 66%|██████▌ | 1319/2000 [08:10<03:46, 3.00it/s, loss=0.418]" ] }, { @@ -45726,7 +45704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1320/2000 [08:19<03:38, 3.12it/s, loss=0.418]" + "training until 2000: 66%|██████▌ | 1320/2000 [08:10<03:42, 3.05it/s, loss=0.418]" ] }, { @@ -45734,7 +45712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1320/2000 [08:19<03:38, 3.12it/s, loss=0.431]" + "training until 2000: 66%|██████▌ | 1320/2000 [08:10<03:42, 3.05it/s, loss=0.438]" ] }, { @@ -45742,7 +45720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1321/2000 [08:19<03:39, 3.10it/s, loss=0.431]" + "training until 2000: 66%|██████▌ | 1321/2000 [08:11<03:40, 3.08it/s, loss=0.438]" ] }, { @@ -45750,7 +45728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1321/2000 [08:19<03:39, 3.10it/s, loss=0.489]" + "training until 2000: 66%|██████▌ | 1321/2000 [08:11<03:40, 3.08it/s, loss=0.454]" ] }, { @@ -45758,7 +45736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1322/2000 [08:20<03:38, 3.11it/s, loss=0.489]" + "training until 2000: 66%|██████▌ | 1322/2000 [08:11<03:39, 3.08it/s, loss=0.454]" ] }, { @@ -45766,7 +45744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1322/2000 [08:20<03:38, 3.11it/s, loss=0.452]" + "training until 2000: 66%|██████▌ | 1322/2000 [08:11<03:39, 3.08it/s, loss=0.407]" ] }, { @@ -45774,7 +45752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1323/2000 [08:20<03:37, 3.11it/s, loss=0.452]" + "training until 2000: 66%|██████▌ | 1323/2000 [08:11<03:40, 3.07it/s, loss=0.407]" ] }, { @@ -45782,7 +45760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1323/2000 [08:20<03:37, 3.11it/s, loss=0.524]" + "training until 2000: 66%|██████▌ | 1323/2000 [08:11<03:40, 3.07it/s, loss=0.499]" ] }, { @@ -45790,7 +45768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1324/2000 [08:20<03:36, 3.12it/s, loss=0.524]" + "training until 2000: 66%|██████▌ | 1324/2000 [08:12<03:37, 3.11it/s, loss=0.499]" ] }, { @@ -45798,7 +45776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▌ | 1324/2000 [08:20<03:36, 3.12it/s, loss=0.479]" + "training until 2000: 66%|██████▌ | 1324/2000 [08:12<03:37, 3.11it/s, loss=0.499]" ] }, { @@ -45806,7 +45784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1325/2000 [08:21<03:36, 3.12it/s, loss=0.479]" + "training until 2000: 66%|██████▋ | 1325/2000 [08:12<03:36, 3.12it/s, loss=0.499]" ] }, { @@ -45814,7 +45792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1325/2000 [08:21<03:36, 3.12it/s, loss=0.438]" + "training until 2000: 66%|██████▋ | 1325/2000 [08:12<03:36, 3.12it/s, loss=0.406]" ] }, { @@ -45822,7 +45800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1326/2000 [08:21<03:35, 3.12it/s, loss=0.438]" + "training until 2000: 66%|██████▋ | 1326/2000 [08:12<03:37, 3.11it/s, loss=0.406]" ] }, { @@ -45830,7 +45808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1326/2000 [08:21<03:35, 3.12it/s, loss=0.631]" + "training until 2000: 66%|██████▋ | 1326/2000 [08:12<03:37, 3.11it/s, loss=0.439]" ] }, { @@ -45838,7 +45816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1327/2000 [08:21<03:38, 3.08it/s, loss=0.631]" + "training until 2000: 66%|██████▋ | 1327/2000 [08:13<03:37, 3.09it/s, loss=0.439]" ] }, { @@ -45846,7 +45824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1327/2000 [08:21<03:38, 3.08it/s, loss=0.521]" + "training until 2000: 66%|██████▋ | 1327/2000 [08:13<03:37, 3.09it/s, loss=0.407]" ] }, { @@ -45854,7 +45832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1328/2000 [08:22<03:34, 3.13it/s, loss=0.521]" + "training until 2000: 66%|██████▋ | 1328/2000 [08:13<03:36, 3.10it/s, loss=0.407]" ] }, { @@ -45862,7 +45840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1328/2000 [08:22<03:34, 3.13it/s, loss=0.421]" + "training until 2000: 66%|██████▋ | 1328/2000 [08:13<03:36, 3.10it/s, loss=0.58] " ] }, { @@ -45870,7 +45848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1329/2000 [08:22<03:35, 3.12it/s, loss=0.421]" + "training until 2000: 66%|██████▋ | 1329/2000 [08:13<03:34, 3.13it/s, loss=0.58]" ] }, { @@ -45878,7 +45856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1329/2000 [08:22<03:35, 3.12it/s, loss=0.438]" + "training until 2000: 66%|██████▋ | 1329/2000 [08:13<03:34, 3.13it/s, loss=0.459]" ] }, { @@ -45886,7 +45864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1330/2000 [08:22<03:32, 3.15it/s, loss=0.438]" + "training until 2000: 66%|██████▋ | 1330/2000 [08:14<03:33, 3.13it/s, loss=0.459]" ] }, { @@ -45894,7 +45872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 66%|██████▋ | 1330/2000 [08:22<03:32, 3.15it/s, loss=0.449]" + "training until 2000: 66%|██████▋ | 1330/2000 [08:14<03:33, 3.13it/s, loss=0.469]" ] }, { @@ -45902,7 +45880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1331/2000 [08:23<03:32, 3.15it/s, loss=0.449]" + "training until 2000: 67%|██████▋ | 1331/2000 [08:14<03:31, 3.16it/s, loss=0.469]" ] }, { @@ -45910,7 +45888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1331/2000 [08:23<03:32, 3.15it/s, loss=0.431]" + "training until 2000: 67%|██████▋ | 1331/2000 [08:14<03:31, 3.16it/s, loss=0.415]" ] }, { @@ -45918,7 +45896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1332/2000 [08:23<03:31, 3.17it/s, loss=0.431]" + "training until 2000: 67%|██████▋ | 1332/2000 [08:14<03:32, 3.14it/s, loss=0.415]" ] }, { @@ -45926,7 +45904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1332/2000 [08:23<03:31, 3.17it/s, loss=0.434]" + "training until 2000: 67%|██████▋ | 1332/2000 [08:14<03:32, 3.14it/s, loss=0.623]" ] }, { @@ -45934,7 +45912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1333/2000 [08:23<03:34, 3.11it/s, loss=0.434]" + "training until 2000: 67%|██████▋ | 1333/2000 [08:15<03:34, 3.11it/s, loss=0.623]" ] }, { @@ -45942,7 +45920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1333/2000 [08:23<03:34, 3.11it/s, loss=0.469]" + "training until 2000: 67%|██████▋ | 1333/2000 [08:15<03:34, 3.11it/s, loss=0.409]" ] }, { @@ -45950,7 +45928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1334/2000 [08:24<03:37, 3.07it/s, loss=0.469]" + "training until 2000: 67%|██████▋ | 1334/2000 [08:15<03:33, 3.12it/s, loss=0.409]" ] }, { @@ -45958,7 +45936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1334/2000 [08:24<03:37, 3.07it/s, loss=0.449]" + "training until 2000: 67%|██████▋ | 1334/2000 [08:15<03:33, 3.12it/s, loss=0.572]" ] }, { @@ -45966,7 +45944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1335/2000 [08:24<03:36, 3.08it/s, loss=0.449]" + "training until 2000: 67%|██████▋ | 1335/2000 [08:15<03:34, 3.11it/s, loss=0.572]" ] }, { @@ -45974,7 +45952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1335/2000 [08:24<03:36, 3.08it/s, loss=0.435]" + "training until 2000: 67%|██████▋ | 1335/2000 [08:15<03:34, 3.11it/s, loss=0.397]" ] }, { @@ -45982,7 +45960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1336/2000 [08:24<03:36, 3.07it/s, loss=0.435]" + "training until 2000: 67%|██████▋ | 1336/2000 [08:15<03:32, 3.12it/s, loss=0.397]" ] }, { @@ -45990,7 +45968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1336/2000 [08:24<03:36, 3.07it/s, loss=0.474]" + "training until 2000: 67%|██████▋ | 1336/2000 [08:15<03:32, 3.12it/s, loss=0.412]" ] }, { @@ -45998,7 +45976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1337/2000 [08:25<03:34, 3.10it/s, loss=0.474]" + "training until 2000: 67%|██████▋ | 1337/2000 [08:16<03:30, 3.15it/s, loss=0.412]" ] }, { @@ -46006,7 +45984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1337/2000 [08:25<03:34, 3.10it/s, loss=0.458]" + "training until 2000: 67%|██████▋ | 1337/2000 [08:16<03:30, 3.15it/s, loss=0.477]" ] }, { @@ -46014,7 +45992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1338/2000 [08:25<03:34, 3.08it/s, loss=0.458]" + "training until 2000: 67%|██████▋ | 1338/2000 [08:16<03:32, 3.12it/s, loss=0.477]" ] }, { @@ -46022,7 +46000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1338/2000 [08:25<03:34, 3.08it/s, loss=0.456]" + "training until 2000: 67%|██████▋ | 1338/2000 [08:16<03:32, 3.12it/s, loss=0.459]" ] }, { @@ -46030,7 +46008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1339/2000 [08:25<03:32, 3.10it/s, loss=0.456]" + "training until 2000: 67%|██████▋ | 1339/2000 [08:16<03:30, 3.13it/s, loss=0.459]" ] }, { @@ -46038,7 +46016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1339/2000 [08:25<03:32, 3.10it/s, loss=0.493]" + "training until 2000: 67%|██████▋ | 1339/2000 [08:16<03:30, 3.13it/s, loss=0.438]" ] }, { @@ -46046,7 +46024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1340/2000 [08:25<03:32, 3.10it/s, loss=0.493]" + "training until 2000: 67%|██████▋ | 1340/2000 [08:17<03:32, 3.11it/s, loss=0.438]" ] }, { @@ -46054,7 +46032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1340/2000 [08:25<03:32, 3.10it/s, loss=0.427]" + "training until 2000: 67%|██████▋ | 1340/2000 [08:17<03:32, 3.11it/s, loss=0.401]" ] }, { @@ -46062,7 +46040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1341/2000 [08:26<03:33, 3.09it/s, loss=0.427]" + "training until 2000: 67%|██████▋ | 1341/2000 [08:17<03:31, 3.12it/s, loss=0.401]" ] }, { @@ -46070,7 +46048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1341/2000 [08:26<03:33, 3.09it/s, loss=0.49] " + "training until 2000: 67%|██████▋ | 1341/2000 [08:17<03:31, 3.12it/s, loss=0.464]" ] }, { @@ -46078,7 +46056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1342/2000 [08:26<03:33, 3.08it/s, loss=0.49]" + "training until 2000: 67%|██████▋ | 1342/2000 [08:17<03:31, 3.11it/s, loss=0.464]" ] }, { @@ -46086,7 +46064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1342/2000 [08:26<03:33, 3.08it/s, loss=0.422]" + "training until 2000: 67%|██████▋ | 1342/2000 [08:17<03:31, 3.11it/s, loss=0.422]" ] }, { @@ -46094,7 +46072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1343/2000 [08:26<03:31, 3.11it/s, loss=0.422]" + "training until 2000: 67%|██████▋ | 1343/2000 [08:18<03:31, 3.11it/s, loss=0.422]" ] }, { @@ -46102,7 +46080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1343/2000 [08:26<03:31, 3.11it/s, loss=0.468]" + "training until 2000: 67%|██████▋ | 1343/2000 [08:18<03:31, 3.11it/s, loss=0.614]" ] }, { @@ -46110,7 +46088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1344/2000 [08:27<03:33, 3.08it/s, loss=0.468]" + "training until 2000: 67%|██████▋ | 1344/2000 [08:18<03:30, 3.11it/s, loss=0.614]" ] }, { @@ -46118,7 +46096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1344/2000 [08:27<03:33, 3.08it/s, loss=0.458]" + "training until 2000: 67%|██████▋ | 1344/2000 [08:18<03:30, 3.11it/s, loss=0.408]" ] }, { @@ -46126,7 +46104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1345/2000 [08:27<03:32, 3.08it/s, loss=0.458]" + "training until 2000: 67%|██████▋ | 1345/2000 [08:18<03:29, 3.12it/s, loss=0.408]" ] }, { @@ -46134,7 +46112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1345/2000 [08:27<03:32, 3.08it/s, loss=0.438]" + "training until 2000: 67%|██████▋ | 1345/2000 [08:18<03:29, 3.12it/s, loss=0.537]" ] }, { @@ -46142,7 +46120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1346/2000 [08:27<03:29, 3.12it/s, loss=0.438]" + "training until 2000: 67%|██████▋ | 1346/2000 [08:19<03:28, 3.14it/s, loss=0.537]" ] }, { @@ -46150,7 +46128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1346/2000 [08:27<03:29, 3.12it/s, loss=0.427]" + "training until 2000: 67%|██████▋ | 1346/2000 [08:19<03:28, 3.14it/s, loss=0.535]" ] }, { @@ -46158,7 +46136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1347/2000 [08:28<03:31, 3.09it/s, loss=0.427]" + "training until 2000: 67%|██████▋ | 1347/2000 [08:19<03:29, 3.11it/s, loss=0.535]" ] }, { @@ -46166,7 +46144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1347/2000 [08:28<03:31, 3.09it/s, loss=0.45] " + "training until 2000: 67%|██████▋ | 1347/2000 [08:19<03:29, 3.11it/s, loss=0.475]" ] }, { @@ -46174,7 +46152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1348/2000 [08:28<03:31, 3.09it/s, loss=0.45]" + "training until 2000: 67%|██████▋ | 1348/2000 [08:19<03:27, 3.14it/s, loss=0.475]" ] }, { @@ -46182,7 +46160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1348/2000 [08:28<03:31, 3.09it/s, loss=0.431]" + "training until 2000: 67%|██████▋ | 1348/2000 [08:19<03:27, 3.14it/s, loss=0.43] " ] }, { @@ -46190,7 +46168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1349/2000 [08:28<03:29, 3.11it/s, loss=0.431]" + "training until 2000: 67%|██████▋ | 1349/2000 [08:20<03:27, 3.13it/s, loss=0.43]" ] }, { @@ -46198,7 +46176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 67%|██████▋ | 1349/2000 [08:28<03:29, 3.11it/s, loss=0.442]" + "training until 2000: 67%|██████▋ | 1349/2000 [08:20<03:27, 3.13it/s, loss=0.425]" ] }, { @@ -46206,7 +46184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1350/2000 [08:29<03:31, 3.07it/s, loss=0.442]" + "training until 2000: 68%|██████▊ | 1350/2000 [08:20<03:29, 3.11it/s, loss=0.425]" ] }, { @@ -46214,7 +46192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1350/2000 [08:29<03:31, 3.07it/s, loss=0.436]" + "training until 2000: 68%|██████▊ | 1350/2000 [08:20<03:29, 3.11it/s, loss=0.45] " ] }, { @@ -46222,7 +46200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1351/2000 [08:29<03:30, 3.08it/s, loss=0.436]" + "training until 2000: 68%|██████▊ | 1351/2000 [08:20<03:27, 3.13it/s, loss=0.45]" ] }, { @@ -46230,7 +46208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1351/2000 [08:29<03:30, 3.08it/s, loss=0.433]" + "training until 2000: 68%|██████▊ | 1351/2000 [08:20<03:27, 3.13it/s, loss=0.487]" ] }, { @@ -46238,7 +46216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1352/2000 [08:29<03:28, 3.11it/s, loss=0.433]" + "training until 2000: 68%|██████▊ | 1352/2000 [08:21<03:24, 3.17it/s, loss=0.487]" ] }, { @@ -46246,7 +46224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1352/2000 [08:29<03:28, 3.11it/s, loss=0.434]" + "training until 2000: 68%|██████▊ | 1352/2000 [08:21<03:24, 3.17it/s, loss=0.549]" ] }, { @@ -46254,7 +46232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1353/2000 [08:30<03:25, 3.16it/s, loss=0.434]" + "training until 2000: 68%|██████▊ | 1353/2000 [08:21<03:23, 3.19it/s, loss=0.549]" ] }, { @@ -46262,7 +46240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1353/2000 [08:30<03:25, 3.16it/s, loss=0.459]" + "training until 2000: 68%|██████▊ | 1353/2000 [08:21<03:23, 3.19it/s, loss=0.494]" ] }, { @@ -46270,7 +46248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1354/2000 [08:30<03:24, 3.16it/s, loss=0.459]" + "training until 2000: 68%|██████▊ | 1354/2000 [08:21<03:23, 3.18it/s, loss=0.494]" ] }, { @@ -46278,7 +46256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1354/2000 [08:30<03:24, 3.16it/s, loss=0.439]" + "training until 2000: 68%|██████▊ | 1354/2000 [08:21<03:23, 3.18it/s, loss=0.45] " ] }, { @@ -46286,7 +46264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1355/2000 [08:30<03:27, 3.11it/s, loss=0.439]" + "training until 2000: 68%|██████▊ | 1355/2000 [08:22<03:23, 3.18it/s, loss=0.45]" ] }, { @@ -46294,7 +46272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1355/2000 [08:30<03:27, 3.11it/s, loss=0.451]" + "training until 2000: 68%|██████▊ | 1355/2000 [08:22<03:23, 3.18it/s, loss=0.437]" ] }, { @@ -46302,7 +46280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1356/2000 [08:31<03:26, 3.12it/s, loss=0.451]" + "training until 2000: 68%|██████▊ | 1356/2000 [08:22<03:23, 3.17it/s, loss=0.437]" ] }, { @@ -46310,7 +46288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1356/2000 [08:31<03:26, 3.12it/s, loss=0.446]" + "training until 2000: 68%|██████▊ | 1356/2000 [08:22<03:23, 3.17it/s, loss=0.505]" ] }, { @@ -46318,7 +46296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1357/2000 [08:31<03:25, 3.14it/s, loss=0.446]" + "training until 2000: 68%|██████▊ | 1357/2000 [08:22<03:23, 3.16it/s, loss=0.505]" ] }, { @@ -46326,7 +46304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1357/2000 [08:31<03:25, 3.14it/s, loss=0.45] " + "training until 2000: 68%|██████▊ | 1357/2000 [08:22<03:23, 3.16it/s, loss=0.462]" ] }, { @@ -46334,7 +46312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1358/2000 [08:31<03:25, 3.13it/s, loss=0.45]" + "training until 2000: 68%|██████▊ | 1358/2000 [08:23<03:23, 3.15it/s, loss=0.462]" ] }, { @@ -46342,7 +46320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1358/2000 [08:31<03:25, 3.13it/s, loss=0.445]" + "training until 2000: 68%|██████▊ | 1358/2000 [08:23<03:23, 3.15it/s, loss=0.43] " ] }, { @@ -46350,7 +46328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1359/2000 [08:32<03:23, 3.15it/s, loss=0.445]" + "training until 2000: 68%|██████▊ | 1359/2000 [08:23<04:12, 2.54it/s, loss=0.43]" ] }, { @@ -46358,7 +46336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1359/2000 [08:32<03:23, 3.15it/s, loss=0.428]" + "training until 2000: 68%|██████▊ | 1359/2000 [08:23<04:12, 2.54it/s, loss=0.432]" ] }, { @@ -46366,7 +46344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1360/2000 [08:32<03:23, 3.14it/s, loss=0.428]" + "training until 2000: 68%|██████▊ | 1360/2000 [08:23<03:56, 2.71it/s, loss=0.432]" ] }, { @@ -46374,7 +46352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1360/2000 [08:32<03:23, 3.14it/s, loss=0.435]" + "training until 2000: 68%|██████▊ | 1360/2000 [08:23<03:56, 2.71it/s, loss=0.431]" ] }, { @@ -46382,7 +46360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1361/2000 [08:32<03:23, 3.15it/s, loss=0.435]" + "training until 2000: 68%|██████▊ | 1361/2000 [08:24<03:44, 2.85it/s, loss=0.431]" ] }, { @@ -46390,7 +46368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1361/2000 [08:32<03:23, 3.15it/s, loss=0.442]" + "training until 2000: 68%|██████▊ | 1361/2000 [08:24<03:44, 2.85it/s, loss=0.41] " ] }, { @@ -46398,7 +46376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1362/2000 [08:33<03:23, 3.14it/s, loss=0.442]" + "training until 2000: 68%|██████▊ | 1362/2000 [08:24<03:43, 2.85it/s, loss=0.41]" ] }, { @@ -46406,7 +46384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1362/2000 [08:33<03:23, 3.14it/s, loss=0.44] " + "training until 2000: 68%|██████▊ | 1362/2000 [08:24<03:43, 2.85it/s, loss=0.446]" ] }, { @@ -46414,7 +46392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1363/2000 [08:33<03:28, 3.05it/s, loss=0.44]" + "training until 2000: 68%|██████▊ | 1363/2000 [08:24<03:39, 2.90it/s, loss=0.446]" ] }, { @@ -46422,7 +46400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1363/2000 [08:33<03:28, 3.05it/s, loss=0.45]" + "training until 2000: 68%|██████▊ | 1363/2000 [08:24<03:39, 2.90it/s, loss=0.5] " ] }, { @@ -46430,7 +46408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1364/2000 [08:33<03:26, 3.07it/s, loss=0.45]" + "training until 2000: 68%|██████▊ | 1364/2000 [08:25<03:34, 2.96it/s, loss=0.5]" ] }, { @@ -46438,7 +46416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1364/2000 [08:33<03:26, 3.07it/s, loss=0.45]" + "training until 2000: 68%|██████▊ | 1364/2000 [08:25<03:34, 2.96it/s, loss=0.414]" ] }, { @@ -46446,7 +46424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1365/2000 [08:34<03:27, 3.07it/s, loss=0.45]" + "training until 2000: 68%|██████▊ | 1365/2000 [08:25<03:31, 3.00it/s, loss=0.414]" ] }, { @@ -46454,7 +46432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1365/2000 [08:34<03:27, 3.07it/s, loss=0.437]" + "training until 2000: 68%|██████▊ | 1365/2000 [08:25<03:31, 3.00it/s, loss=0.514]" ] }, { @@ -46462,7 +46440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1366/2000 [08:34<04:16, 2.47it/s, loss=0.437]" + "training until 2000: 68%|██████▊ | 1366/2000 [08:25<03:31, 3.00it/s, loss=0.514]" ] }, { @@ -46470,7 +46448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1366/2000 [08:34<04:16, 2.47it/s, loss=0.419]" + "training until 2000: 68%|██████▊ | 1366/2000 [08:25<03:31, 3.00it/s, loss=0.43] " ] }, { @@ -46478,7 +46456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1367/2000 [08:34<04:00, 2.63it/s, loss=0.419]" + "training until 2000: 68%|██████▊ | 1367/2000 [08:26<03:27, 3.06it/s, loss=0.43]" ] }, { @@ -46486,7 +46464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1367/2000 [08:34<04:00, 2.63it/s, loss=0.42] " + "training until 2000: 68%|██████▊ | 1367/2000 [08:26<03:27, 3.06it/s, loss=0.425]" ] }, { @@ -46494,7 +46472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1368/2000 [08:35<03:51, 2.72it/s, loss=0.42]" + "training until 2000: 68%|██████▊ | 1368/2000 [08:26<03:24, 3.09it/s, loss=0.425]" ] }, { @@ -46502,7 +46480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1368/2000 [08:35<03:51, 2.72it/s, loss=0.473]" + "training until 2000: 68%|██████▊ | 1368/2000 [08:26<03:24, 3.09it/s, loss=0.456]" ] }, { @@ -46510,7 +46488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1369/2000 [08:35<03:44, 2.81it/s, loss=0.473]" + "training until 2000: 68%|██████▊ | 1369/2000 [08:26<03:23, 3.10it/s, loss=0.456]" ] }, { @@ -46518,7 +46496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1369/2000 [08:35<03:44, 2.81it/s, loss=0.454]" + "training until 2000: 68%|██████▊ | 1369/2000 [08:26<03:23, 3.10it/s, loss=0.463]" ] }, { @@ -46526,7 +46504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1370/2000 [08:35<03:40, 2.86it/s, loss=0.454]" + "training until 2000: 68%|██████▊ | 1370/2000 [08:27<03:19, 3.15it/s, loss=0.463]" ] }, { @@ -46534,7 +46512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 68%|██████▊ | 1370/2000 [08:35<03:40, 2.86it/s, loss=0.443]" + "training until 2000: 68%|██████▊ | 1370/2000 [08:27<03:19, 3.15it/s, loss=0.444]" ] }, { @@ -46542,7 +46520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1371/2000 [08:36<03:38, 2.88it/s, loss=0.443]" + "training until 2000: 69%|██████▊ | 1371/2000 [08:27<03:20, 3.14it/s, loss=0.444]" ] }, { @@ -46550,7 +46528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1371/2000 [08:36<03:38, 2.88it/s, loss=0.44] " + "training until 2000: 69%|██████▊ | 1371/2000 [08:27<03:20, 3.14it/s, loss=0.416]" ] }, { @@ -46558,7 +46536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1372/2000 [08:36<03:32, 2.96it/s, loss=0.44]" + "training until 2000: 69%|██████▊ | 1372/2000 [08:27<03:22, 3.10it/s, loss=0.416]" ] }, { @@ -46566,7 +46544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1372/2000 [08:36<03:32, 2.96it/s, loss=0.483]" + "training until 2000: 69%|██████▊ | 1372/2000 [08:27<03:22, 3.10it/s, loss=0.407]" ] }, { @@ -46574,7 +46552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1373/2000 [08:36<03:29, 2.99it/s, loss=0.483]" + "training until 2000: 69%|██████▊ | 1373/2000 [08:28<03:18, 3.15it/s, loss=0.407]" ] }, { @@ -46582,7 +46560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1373/2000 [08:36<03:29, 2.99it/s, loss=0.425]" + "training until 2000: 69%|██████▊ | 1373/2000 [08:28<03:18, 3.15it/s, loss=0.391]" ] }, { @@ -46590,7 +46568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1374/2000 [08:37<03:26, 3.03it/s, loss=0.425]" + "training until 2000: 69%|██████▊ | 1374/2000 [08:28<03:16, 3.18it/s, loss=0.391]" ] }, { @@ -46598,7 +46576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▊ | 1374/2000 [08:37<03:26, 3.03it/s, loss=0.42] " + "training until 2000: 69%|██████▊ | 1374/2000 [08:28<03:16, 3.18it/s, loss=0.411]" ] }, { @@ -46606,7 +46584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1375/2000 [08:37<03:21, 3.10it/s, loss=0.42]" + "training until 2000: 69%|██████▉ | 1375/2000 [08:28<03:18, 3.14it/s, loss=0.411]" ] }, { @@ -46614,7 +46592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1375/2000 [08:37<03:21, 3.10it/s, loss=0.443]" + "training until 2000: 69%|██████▉ | 1375/2000 [08:28<03:18, 3.14it/s, loss=0.399]" ] }, { @@ -46622,7 +46600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1376/2000 [08:37<03:22, 3.08it/s, loss=0.443]" + "training until 2000: 69%|██████▉ | 1376/2000 [08:29<03:23, 3.06it/s, loss=0.399]" ] }, { @@ -46630,7 +46608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1376/2000 [08:37<03:22, 3.08it/s, loss=0.567]" + "training until 2000: 69%|██████▉ | 1376/2000 [08:29<03:23, 3.06it/s, loss=0.436]" ] }, { @@ -46638,7 +46616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1377/2000 [08:38<03:22, 3.08it/s, loss=0.567]" + "training until 2000: 69%|██████▉ | 1377/2000 [08:29<03:24, 3.05it/s, loss=0.436]" ] }, { @@ -46646,7 +46624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1377/2000 [08:38<03:22, 3.08it/s, loss=0.421]" + "training until 2000: 69%|██████▉ | 1377/2000 [08:29<03:24, 3.05it/s, loss=0.481]" ] }, { @@ -46654,7 +46632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1378/2000 [08:38<03:22, 3.07it/s, loss=0.421]" + "training until 2000: 69%|██████▉ | 1378/2000 [08:29<03:27, 3.00it/s, loss=0.481]" ] }, { @@ -46662,7 +46640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1378/2000 [08:38<03:22, 3.07it/s, loss=0.441]" + "training until 2000: 69%|██████▉ | 1378/2000 [08:29<03:27, 3.00it/s, loss=0.402]" ] }, { @@ -46670,7 +46648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1379/2000 [08:38<03:22, 3.06it/s, loss=0.441]" + "training until 2000: 69%|██████▉ | 1379/2000 [08:30<03:26, 3.01it/s, loss=0.402]" ] }, { @@ -46678,7 +46656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1379/2000 [08:38<03:22, 3.06it/s, loss=0.433]" + "training until 2000: 69%|██████▉ | 1379/2000 [08:30<03:26, 3.01it/s, loss=0.404]" ] }, { @@ -46686,7 +46664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1380/2000 [08:39<03:19, 3.11it/s, loss=0.433]" + "training until 2000: 69%|██████▉ | 1380/2000 [08:30<03:25, 3.01it/s, loss=0.404]" ] }, { @@ -46694,7 +46672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1380/2000 [08:39<03:19, 3.11it/s, loss=0.463]" + "training until 2000: 69%|██████▉ | 1380/2000 [08:30<03:25, 3.01it/s, loss=0.482]" ] }, { @@ -46702,7 +46680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1381/2000 [08:39<03:20, 3.08it/s, loss=0.463]" + "training until 2000: 69%|██████▉ | 1381/2000 [08:30<03:23, 3.04it/s, loss=0.482]" ] }, { @@ -46710,7 +46688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1381/2000 [08:39<03:20, 3.08it/s, loss=0.415]" + "training until 2000: 69%|██████▉ | 1381/2000 [08:30<03:23, 3.04it/s, loss=0.408]" ] }, { @@ -46718,7 +46696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1382/2000 [08:39<03:17, 3.13it/s, loss=0.415]" + "training until 2000: 69%|██████▉ | 1382/2000 [08:31<03:21, 3.07it/s, loss=0.408]" ] }, { @@ -46726,7 +46704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1382/2000 [08:39<03:17, 3.13it/s, loss=0.441]" + "training until 2000: 69%|██████▉ | 1382/2000 [08:31<03:21, 3.07it/s, loss=0.433]" ] }, { @@ -46734,7 +46712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1383/2000 [08:40<03:17, 3.12it/s, loss=0.441]" + "training until 2000: 69%|██████▉ | 1383/2000 [08:31<03:21, 3.06it/s, loss=0.433]" ] }, { @@ -46742,7 +46720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1383/2000 [08:40<03:17, 3.12it/s, loss=0.589]" + "training until 2000: 69%|██████▉ | 1383/2000 [08:31<03:21, 3.06it/s, loss=0.482]" ] }, { @@ -46750,7 +46728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1384/2000 [08:40<03:18, 3.11it/s, loss=0.589]" + "training until 2000: 69%|██████▉ | 1384/2000 [08:31<03:17, 3.12it/s, loss=0.482]" ] }, { @@ -46758,7 +46736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1384/2000 [08:40<03:18, 3.11it/s, loss=0.448]" + "training until 2000: 69%|██████▉ | 1384/2000 [08:31<03:17, 3.12it/s, loss=0.407]" ] }, { @@ -46766,7 +46744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1385/2000 [08:40<03:20, 3.07it/s, loss=0.448]" + "training until 2000: 69%|██████▉ | 1385/2000 [08:31<03:19, 3.09it/s, loss=0.407]" ] }, { @@ -46774,7 +46752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1385/2000 [08:40<03:20, 3.07it/s, loss=0.419]" + "training until 2000: 69%|██████▉ | 1385/2000 [08:31<03:19, 3.09it/s, loss=0.472]" ] }, { @@ -46782,7 +46760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1386/2000 [08:41<03:17, 3.11it/s, loss=0.419]" + "training until 2000: 69%|██████▉ | 1386/2000 [08:32<03:17, 3.10it/s, loss=0.472]" ] }, { @@ -46790,7 +46768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1386/2000 [08:41<03:17, 3.11it/s, loss=0.425]" + "training until 2000: 69%|██████▉ | 1386/2000 [08:32<03:17, 3.10it/s, loss=0.407]" ] }, { @@ -46798,7 +46776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1387/2000 [08:41<03:18, 3.09it/s, loss=0.425]" + "training until 2000: 69%|██████▉ | 1387/2000 [08:32<03:16, 3.11it/s, loss=0.407]" ] }, { @@ -46806,7 +46784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1387/2000 [08:41<03:18, 3.09it/s, loss=0.43] " + "training until 2000: 69%|██████▉ | 1387/2000 [08:32<03:16, 3.11it/s, loss=0.402]" ] }, { @@ -46814,7 +46792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1388/2000 [08:41<03:15, 3.12it/s, loss=0.43]" + "training until 2000: 69%|██████▉ | 1388/2000 [08:32<03:17, 3.10it/s, loss=0.402]" ] }, { @@ -46822,7 +46800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1388/2000 [08:41<03:15, 3.12it/s, loss=0.434]" + "training until 2000: 69%|██████▉ | 1388/2000 [08:32<03:17, 3.10it/s, loss=0.523]" ] }, { @@ -46830,7 +46808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1389/2000 [08:42<03:14, 3.13it/s, loss=0.434]" + "training until 2000: 69%|██████▉ | 1389/2000 [08:33<03:15, 3.12it/s, loss=0.523]" ] }, { @@ -46838,7 +46816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 69%|██████▉ | 1389/2000 [08:42<03:14, 3.13it/s, loss=0.418]" + "training until 2000: 69%|██████▉ | 1389/2000 [08:33<03:15, 3.12it/s, loss=0.424]" ] }, { @@ -46846,7 +46824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1390/2000 [08:42<03:12, 3.16it/s, loss=0.418]" + "training until 2000: 70%|██████▉ | 1390/2000 [08:33<03:17, 3.10it/s, loss=0.424]" ] }, { @@ -46854,7 +46832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1390/2000 [08:42<03:12, 3.16it/s, loss=0.43] " + "training until 2000: 70%|██████▉ | 1390/2000 [08:33<03:17, 3.10it/s, loss=0.393]" ] }, { @@ -46862,7 +46840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1391/2000 [08:42<03:13, 3.14it/s, loss=0.43]" + "training until 2000: 70%|██████▉ | 1391/2000 [08:33<03:14, 3.12it/s, loss=0.393]" ] }, { @@ -46870,7 +46848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1391/2000 [08:42<03:13, 3.14it/s, loss=0.42]" + "training until 2000: 70%|██████▉ | 1391/2000 [08:33<03:14, 3.12it/s, loss=0.398]" ] }, { @@ -46878,7 +46856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1392/2000 [08:43<03:11, 3.18it/s, loss=0.42]" + "training until 2000: 70%|██████▉ | 1392/2000 [08:34<03:13, 3.15it/s, loss=0.398]" ] }, { @@ -46886,7 +46864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1392/2000 [08:43<03:11, 3.18it/s, loss=0.429]" + "training until 2000: 70%|██████▉ | 1392/2000 [08:34<03:13, 3.15it/s, loss=0.394]" ] }, { @@ -46894,7 +46872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1393/2000 [08:43<03:11, 3.18it/s, loss=0.429]" + "training until 2000: 70%|██████▉ | 1393/2000 [08:34<03:14, 3.13it/s, loss=0.394]" ] }, { @@ -46902,7 +46880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1393/2000 [08:43<03:11, 3.18it/s, loss=0.492]" + "training until 2000: 70%|██████▉ | 1393/2000 [08:34<03:14, 3.13it/s, loss=0.531]" ] }, { @@ -46910,7 +46888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1394/2000 [08:43<03:10, 3.18it/s, loss=0.492]" + "training until 2000: 70%|██████▉ | 1394/2000 [08:34<03:12, 3.15it/s, loss=0.531]" ] }, { @@ -46918,7 +46896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1394/2000 [08:43<03:10, 3.18it/s, loss=0.431]" + "training until 2000: 70%|██████▉ | 1394/2000 [08:34<03:12, 3.15it/s, loss=0.399]" ] }, { @@ -46926,7 +46904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1395/2000 [08:43<03:12, 3.15it/s, loss=0.431]" + "training until 2000: 70%|██████▉ | 1395/2000 [08:35<03:09, 3.19it/s, loss=0.399]" ] }, { @@ -46934,7 +46912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1395/2000 [08:43<03:12, 3.15it/s, loss=0.436]" + "training until 2000: 70%|██████▉ | 1395/2000 [08:35<03:09, 3.19it/s, loss=0.48] " ] }, { @@ -46942,7 +46920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1396/2000 [08:44<03:11, 3.16it/s, loss=0.436]" + "training until 2000: 70%|██████▉ | 1396/2000 [08:35<03:08, 3.20it/s, loss=0.48]" ] }, { @@ -46950,7 +46928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1396/2000 [08:44<03:11, 3.16it/s, loss=0.419]" + "training until 2000: 70%|██████▉ | 1396/2000 [08:35<03:08, 3.20it/s, loss=0.411]" ] }, { @@ -46958,7 +46936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1397/2000 [08:44<03:11, 3.15it/s, loss=0.419]" + "training until 2000: 70%|██████▉ | 1397/2000 [08:35<03:10, 3.17it/s, loss=0.411]" ] }, { @@ -46966,7 +46944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1397/2000 [08:44<03:11, 3.15it/s, loss=0.441]" + "training until 2000: 70%|██████▉ | 1397/2000 [08:35<03:10, 3.17it/s, loss=0.421]" ] }, { @@ -46974,7 +46952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1398/2000 [08:44<03:09, 3.18it/s, loss=0.441]" + "training until 2000: 70%|██████▉ | 1398/2000 [08:36<03:12, 3.13it/s, loss=0.421]" ] }, { @@ -46982,7 +46960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1398/2000 [08:44<03:09, 3.18it/s, loss=0.451]" + "training until 2000: 70%|██████▉ | 1398/2000 [08:36<03:12, 3.13it/s, loss=0.434]" ] }, { @@ -46990,7 +46968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1399/2000 [08:45<03:08, 3.18it/s, loss=0.451]" + "training until 2000: 70%|██████▉ | 1399/2000 [08:36<03:11, 3.14it/s, loss=0.434]" ] }, { @@ -46998,7 +46976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|██████▉ | 1399/2000 [08:45<03:08, 3.18it/s, loss=0.451]" + "training until 2000: 70%|██████▉ | 1399/2000 [08:36<03:11, 3.14it/s, loss=0.422]" ] }, { @@ -47006,7 +46984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1400/2000 [08:45<03:09, 3.17it/s, loss=0.451]" + "training until 2000: 70%|███████ | 1400/2000 [08:36<03:14, 3.08it/s, loss=0.422]" ] }, { @@ -47014,7 +46992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1400/2000 [08:45<03:09, 3.17it/s, loss=0.428]" + "training until 2000: 70%|███████ | 1400/2000 [08:36<03:14, 3.08it/s, loss=0.445]" ] }, { @@ -47022,7 +47000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1401/2000 [08:45<03:11, 3.12it/s, loss=0.428]" + "training until 2000: 70%|███████ | 1401/2000 [08:37<03:14, 3.08it/s, loss=0.445]" ] }, { @@ -47030,7 +47008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1401/2000 [08:45<03:11, 3.12it/s, loss=0.446]" + "training until 2000: 70%|███████ | 1401/2000 [08:37<03:14, 3.08it/s, loss=0.423]" ] }, { @@ -47038,7 +47016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1402/2000 [08:46<03:11, 3.12it/s, loss=0.446]" + "training until 2000: 70%|███████ | 1402/2000 [08:37<03:13, 3.10it/s, loss=0.423]" ] }, { @@ -47046,7 +47024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1402/2000 [08:46<03:11, 3.12it/s, loss=0.436]" + "training until 2000: 70%|███████ | 1402/2000 [08:37<03:13, 3.10it/s, loss=0.419]" ] }, { @@ -47054,7 +47032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1403/2000 [08:46<03:10, 3.14it/s, loss=0.436]" + "training until 2000: 70%|███████ | 1403/2000 [08:37<03:11, 3.12it/s, loss=0.419]" ] }, { @@ -47062,7 +47040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1403/2000 [08:46<03:10, 3.14it/s, loss=0.434]" + "training until 2000: 70%|███████ | 1403/2000 [08:37<03:11, 3.12it/s, loss=0.441]" ] }, { @@ -47070,7 +47048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1404/2000 [08:46<03:08, 3.17it/s, loss=0.434]" + "training until 2000: 70%|███████ | 1404/2000 [08:38<03:11, 3.12it/s, loss=0.441]" ] }, { @@ -47078,7 +47056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1404/2000 [08:46<03:08, 3.17it/s, loss=0.441]" + "training until 2000: 70%|███████ | 1404/2000 [08:38<03:11, 3.12it/s, loss=0.458]" ] }, { @@ -47086,7 +47064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1405/2000 [08:47<03:06, 3.19it/s, loss=0.441]" + "training until 2000: 70%|███████ | 1405/2000 [08:38<03:09, 3.14it/s, loss=0.458]" ] }, { @@ -47094,7 +47072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1405/2000 [08:47<03:06, 3.19it/s, loss=0.419]" + "training until 2000: 70%|███████ | 1405/2000 [08:38<03:09, 3.14it/s, loss=0.439]" ] }, { @@ -47102,7 +47080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1406/2000 [08:47<03:09, 3.14it/s, loss=0.419]" + "training until 2000: 70%|███████ | 1406/2000 [08:38<03:08, 3.15it/s, loss=0.439]" ] }, { @@ -47110,7 +47088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1406/2000 [08:47<03:09, 3.14it/s, loss=0.413]" + "training until 2000: 70%|███████ | 1406/2000 [08:38<03:08, 3.15it/s, loss=0.49] " ] }, { @@ -47118,7 +47096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1407/2000 [08:47<03:09, 3.13it/s, loss=0.413]" + "training until 2000: 70%|███████ | 1407/2000 [08:39<03:09, 3.13it/s, loss=0.49]" ] }, { @@ -47126,7 +47104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1407/2000 [08:47<03:09, 3.13it/s, loss=0.422]" + "training until 2000: 70%|███████ | 1407/2000 [08:39<03:09, 3.13it/s, loss=0.418]" ] }, { @@ -47134,7 +47112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1408/2000 [08:48<03:08, 3.14it/s, loss=0.422]" + "training until 2000: 70%|███████ | 1408/2000 [08:39<03:08, 3.14it/s, loss=0.418]" ] }, { @@ -47142,7 +47120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1408/2000 [08:48<03:08, 3.14it/s, loss=0.452]" + "training until 2000: 70%|███████ | 1408/2000 [08:39<03:08, 3.14it/s, loss=0.459]" ] }, { @@ -47150,7 +47128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1409/2000 [08:48<03:06, 3.17it/s, loss=0.452]" + "training until 2000: 70%|███████ | 1409/2000 [08:39<03:07, 3.15it/s, loss=0.459]" ] }, { @@ -47158,7 +47136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1409/2000 [08:48<03:06, 3.17it/s, loss=0.426]" + "training until 2000: 70%|███████ | 1409/2000 [08:39<03:07, 3.15it/s, loss=0.452]" ] }, { @@ -47166,7 +47144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1410/2000 [08:48<03:08, 3.13it/s, loss=0.426]" + "training until 2000: 70%|███████ | 1410/2000 [08:39<03:09, 3.12it/s, loss=0.452]" ] }, { @@ -47174,7 +47152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 70%|███████ | 1410/2000 [08:48<03:08, 3.13it/s, loss=0.421]" + "training until 2000: 70%|███████ | 1410/2000 [08:39<03:09, 3.12it/s, loss=0.426]" ] }, { @@ -47182,7 +47160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1411/2000 [08:49<03:06, 3.16it/s, loss=0.421]" + "training until 2000: 71%|███████ | 1411/2000 [08:40<03:08, 3.13it/s, loss=0.426]" ] }, { @@ -47190,7 +47168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1411/2000 [08:49<03:06, 3.16it/s, loss=0.453]" + "training until 2000: 71%|███████ | 1411/2000 [08:40<03:08, 3.13it/s, loss=0.433]" ] }, { @@ -47198,7 +47176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1412/2000 [08:49<03:06, 3.16it/s, loss=0.453]" + "training until 2000: 71%|███████ | 1412/2000 [08:40<03:07, 3.14it/s, loss=0.433]" ] }, { @@ -47206,7 +47184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1412/2000 [08:49<03:06, 3.16it/s, loss=0.442]" + "training until 2000: 71%|███████ | 1412/2000 [08:40<03:07, 3.14it/s, loss=0.405]" ] }, { @@ -47214,7 +47192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1413/2000 [08:49<03:08, 3.11it/s, loss=0.442]" + "training until 2000: 71%|███████ | 1413/2000 [08:40<03:07, 3.13it/s, loss=0.405]" ] }, { @@ -47222,7 +47200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1413/2000 [08:49<03:08, 3.11it/s, loss=0.439]" + "training until 2000: 71%|███████ | 1413/2000 [08:40<03:07, 3.13it/s, loss=0.407]" ] }, { @@ -47230,7 +47208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1414/2000 [08:49<03:07, 3.12it/s, loss=0.439]" + "training until 2000: 71%|███████ | 1414/2000 [08:41<03:06, 3.14it/s, loss=0.407]" ] }, { @@ -47238,7 +47216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1414/2000 [08:49<03:07, 3.12it/s, loss=0.43] " + "training until 2000: 71%|███████ | 1414/2000 [08:41<03:06, 3.14it/s, loss=0.51] " ] }, { @@ -47246,7 +47224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1415/2000 [08:50<03:07, 3.12it/s, loss=0.43]" + "training until 2000: 71%|███████ | 1415/2000 [08:41<03:05, 3.15it/s, loss=0.51]" ] }, { @@ -47254,7 +47232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1415/2000 [08:50<03:07, 3.12it/s, loss=0.43]" + "training until 2000: 71%|███████ | 1415/2000 [08:41<03:05, 3.15it/s, loss=0.514]" ] }, { @@ -47262,7 +47240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1416/2000 [08:50<03:09, 3.08it/s, loss=0.43]" + "training until 2000: 71%|███████ | 1416/2000 [08:41<03:07, 3.12it/s, loss=0.514]" ] }, { @@ -47270,7 +47248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1416/2000 [08:50<03:09, 3.08it/s, loss=0.418]" + "training until 2000: 71%|███████ | 1416/2000 [08:41<03:07, 3.12it/s, loss=0.465]" ] }, { @@ -47278,7 +47256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1417/2000 [08:50<03:07, 3.11it/s, loss=0.418]" + "training until 2000: 71%|███████ | 1417/2000 [08:42<03:06, 3.12it/s, loss=0.465]" ] }, { @@ -47286,7 +47264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1417/2000 [08:50<03:07, 3.11it/s, loss=0.415]" + "training until 2000: 71%|███████ | 1417/2000 [08:42<03:06, 3.12it/s, loss=0.633]" ] }, { @@ -47294,7 +47272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1418/2000 [08:51<03:12, 3.02it/s, loss=0.415]" + "training until 2000: 71%|███████ | 1418/2000 [08:42<03:04, 3.15it/s, loss=0.633]" ] }, { @@ -47302,7 +47280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1418/2000 [08:51<03:12, 3.02it/s, loss=0.438]" + "training until 2000: 71%|███████ | 1418/2000 [08:42<03:04, 3.15it/s, loss=0.586]" ] }, { @@ -47310,7 +47288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1419/2000 [08:51<03:10, 3.05it/s, loss=0.438]" + "training until 2000: 71%|███████ | 1419/2000 [08:42<03:05, 3.13it/s, loss=0.586]" ] }, { @@ -47318,7 +47296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1419/2000 [08:51<03:10, 3.05it/s, loss=0.435]" + "training until 2000: 71%|███████ | 1419/2000 [08:42<03:05, 3.13it/s, loss=0.434]" ] }, { @@ -47326,7 +47304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1420/2000 [08:51<03:11, 3.03it/s, loss=0.435]" + "training until 2000: 71%|███████ | 1420/2000 [08:43<03:06, 3.11it/s, loss=0.434]" ] }, { @@ -47334,7 +47312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1420/2000 [08:51<03:11, 3.03it/s, loss=0.423]" + "training until 2000: 71%|███████ | 1420/2000 [08:43<03:06, 3.11it/s, loss=0.478]" ] }, { @@ -47342,7 +47320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1421/2000 [08:52<03:10, 3.03it/s, loss=0.423]" + "training until 2000: 71%|███████ | 1421/2000 [08:43<03:07, 3.09it/s, loss=0.478]" ] }, { @@ -47350,7 +47328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1421/2000 [08:52<03:10, 3.03it/s, loss=0.449]" + "training until 2000: 71%|███████ | 1421/2000 [08:43<03:07, 3.09it/s, loss=0.399]" ] }, { @@ -47358,7 +47336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1422/2000 [08:52<03:12, 3.01it/s, loss=0.449]" + "training until 2000: 71%|███████ | 1422/2000 [08:43<03:06, 3.11it/s, loss=0.399]" ] }, { @@ -47366,7 +47344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1422/2000 [08:52<03:12, 3.01it/s, loss=0.444]" + "training until 2000: 71%|███████ | 1422/2000 [08:43<03:06, 3.11it/s, loss=0.479]" ] }, { @@ -47374,7 +47352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1423/2000 [08:52<03:10, 3.03it/s, loss=0.444]" + "training until 2000: 71%|███████ | 1423/2000 [08:44<03:09, 3.05it/s, loss=0.479]" ] }, { @@ -47382,7 +47360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1423/2000 [08:52<03:10, 3.03it/s, loss=0.493]" + "training until 2000: 71%|███████ | 1423/2000 [08:44<03:09, 3.05it/s, loss=0.523]" ] }, { @@ -47390,7 +47368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1424/2000 [08:53<03:08, 3.05it/s, loss=0.493]" + "training until 2000: 71%|███████ | 1424/2000 [08:44<03:47, 2.53it/s, loss=0.523]" ] }, { @@ -47398,7 +47376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████ | 1424/2000 [08:53<03:08, 3.05it/s, loss=0.477]" + "training until 2000: 71%|███████ | 1424/2000 [08:44<03:47, 2.53it/s, loss=0.581]" ] }, { @@ -47406,7 +47384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1425/2000 [08:53<03:08, 3.05it/s, loss=0.477]" + "training until 2000: 71%|███████▏ | 1425/2000 [08:45<03:34, 2.68it/s, loss=0.581]" ] }, { @@ -47414,7 +47392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1425/2000 [08:53<03:08, 3.05it/s, loss=0.446]" + "training until 2000: 71%|███████▏ | 1425/2000 [08:45<03:34, 2.68it/s, loss=0.461]" ] }, { @@ -47422,7 +47400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1426/2000 [08:53<03:08, 3.04it/s, loss=0.446]" + "training until 2000: 71%|███████▏ | 1426/2000 [08:45<03:23, 2.82it/s, loss=0.461]" ] }, { @@ -47430,7 +47408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1426/2000 [08:53<03:08, 3.04it/s, loss=0.424]" + "training until 2000: 71%|███████▏ | 1426/2000 [08:45<03:23, 2.82it/s, loss=0.456]" ] }, { @@ -47438,7 +47416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1427/2000 [08:54<03:06, 3.08it/s, loss=0.424]" + "training until 2000: 71%|███████▏ | 1427/2000 [08:45<03:15, 2.92it/s, loss=0.456]" ] }, { @@ -47446,7 +47424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1427/2000 [08:54<03:06, 3.08it/s, loss=0.42] " + "training until 2000: 71%|███████▏ | 1427/2000 [08:45<03:15, 2.92it/s, loss=0.442]" ] }, { @@ -47454,7 +47432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1428/2000 [08:54<03:06, 3.06it/s, loss=0.42]" + "training until 2000: 71%|███████▏ | 1428/2000 [08:45<03:13, 2.96it/s, loss=0.442]" ] }, { @@ -47462,7 +47440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1428/2000 [08:54<03:06, 3.06it/s, loss=0.419]" + "training until 2000: 71%|███████▏ | 1428/2000 [08:45<03:13, 2.96it/s, loss=0.432]" ] }, { @@ -47470,7 +47448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1429/2000 [08:54<03:05, 3.07it/s, loss=0.419]" + "training until 2000: 71%|███████▏ | 1429/2000 [08:46<03:09, 3.01it/s, loss=0.432]" ] }, { @@ -47478,7 +47456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 71%|███████▏ | 1429/2000 [08:54<03:05, 3.07it/s, loss=0.419]" + "training until 2000: 71%|███████▏ | 1429/2000 [08:46<03:09, 3.01it/s, loss=0.429]" ] }, { @@ -47486,7 +47464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1430/2000 [08:55<03:06, 3.05it/s, loss=0.419]" + "training until 2000: 72%|███████▏ | 1430/2000 [08:46<03:07, 3.03it/s, loss=0.429]" ] }, { @@ -47494,7 +47472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1430/2000 [08:55<03:06, 3.05it/s, loss=0.418]" + "training until 2000: 72%|███████▏ | 1430/2000 [08:46<03:07, 3.03it/s, loss=0.459]" ] }, { @@ -47502,7 +47480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1431/2000 [08:55<03:08, 3.01it/s, loss=0.418]" + "training until 2000: 72%|███████▏ | 1431/2000 [08:46<03:05, 3.07it/s, loss=0.459]" ] }, { @@ -47510,7 +47488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1431/2000 [08:55<03:08, 3.01it/s, loss=0.417]" + "training until 2000: 72%|███████▏ | 1431/2000 [08:46<03:05, 3.07it/s, loss=0.409]" ] }, { @@ -47518,7 +47496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1432/2000 [08:56<03:58, 2.38it/s, loss=0.417]" + "training until 2000: 72%|███████▏ | 1432/2000 [08:47<03:03, 3.10it/s, loss=0.409]" ] }, { @@ -47526,7 +47504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1432/2000 [08:56<03:58, 2.38it/s, loss=0.433]" + "training until 2000: 72%|███████▏ | 1432/2000 [08:47<03:03, 3.10it/s, loss=0.418]" ] }, { @@ -47534,7 +47512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1433/2000 [08:56<03:43, 2.54it/s, loss=0.433]" + "training until 2000: 72%|███████▏ | 1433/2000 [08:47<03:02, 3.10it/s, loss=0.418]" ] }, { @@ -47542,7 +47520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1433/2000 [08:56<03:43, 2.54it/s, loss=0.429]" + "training until 2000: 72%|███████▏ | 1433/2000 [08:47<03:02, 3.10it/s, loss=0.409]" ] }, { @@ -47550,7 +47528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1434/2000 [08:56<03:32, 2.66it/s, loss=0.429]" + "training until 2000: 72%|███████▏ | 1434/2000 [08:47<03:02, 3.11it/s, loss=0.409]" ] }, { @@ -47558,7 +47536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1434/2000 [08:56<03:32, 2.66it/s, loss=0.428]" + "training until 2000: 72%|███████▏ | 1434/2000 [08:47<03:02, 3.11it/s, loss=0.396]" ] }, { @@ -47566,7 +47544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1435/2000 [08:57<03:24, 2.76it/s, loss=0.428]" + "training until 2000: 72%|███████▏ | 1435/2000 [08:48<03:01, 3.11it/s, loss=0.396]" ] }, { @@ -47574,7 +47552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1435/2000 [08:57<03:24, 2.76it/s, loss=0.442]" + "training until 2000: 72%|███████▏ | 1435/2000 [08:48<03:01, 3.11it/s, loss=0.621]" ] }, { @@ -47582,7 +47560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1436/2000 [08:57<03:18, 2.85it/s, loss=0.442]" + "training until 2000: 72%|███████▏ | 1436/2000 [08:48<02:59, 3.15it/s, loss=0.621]" ] }, { @@ -47590,7 +47568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1436/2000 [08:57<03:18, 2.85it/s, loss=0.413]" + "training until 2000: 72%|███████▏ | 1436/2000 [08:48<02:59, 3.15it/s, loss=0.454]" ] }, { @@ -47598,7 +47576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1437/2000 [08:57<03:16, 2.87it/s, loss=0.413]" + "training until 2000: 72%|███████▏ | 1437/2000 [08:48<02:57, 3.17it/s, loss=0.454]" ] }, { @@ -47606,7 +47584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1437/2000 [08:57<03:16, 2.87it/s, loss=0.446]" + "training until 2000: 72%|███████▏ | 1437/2000 [08:48<02:57, 3.17it/s, loss=0.544]" ] }, { @@ -47614,7 +47592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1438/2000 [08:58<03:11, 2.94it/s, loss=0.446]" + "training until 2000: 72%|███████▏ | 1438/2000 [08:49<02:56, 3.18it/s, loss=0.544]" ] }, { @@ -47622,7 +47600,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1438/2000 [08:58<03:11, 2.94it/s, loss=0.473]" + "training until 2000: 72%|███████▏ | 1438/2000 [08:49<02:56, 3.18it/s, loss=0.393]" ] }, { @@ -47630,7 +47608,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1439/2000 [08:58<03:12, 2.92it/s, loss=0.473]" + "training until 2000: 72%|███████▏ | 1439/2000 [08:49<02:58, 3.15it/s, loss=0.393]" ] }, { @@ -47638,7 +47616,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1439/2000 [08:58<03:12, 2.92it/s, loss=0.405]" + "training until 2000: 72%|███████▏ | 1439/2000 [08:49<02:58, 3.15it/s, loss=0.416]" ] }, { @@ -47646,7 +47624,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1440/2000 [08:58<03:07, 2.99it/s, loss=0.405]" + "training until 2000: 72%|███████▏ | 1440/2000 [08:49<02:55, 3.18it/s, loss=0.416]" ] }, { @@ -47654,7 +47632,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1440/2000 [08:58<03:07, 2.99it/s, loss=0.446]" + "training until 2000: 72%|███████▏ | 1440/2000 [08:49<02:55, 3.18it/s, loss=0.405]" ] }, { @@ -47662,7 +47640,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1441/2000 [08:59<03:05, 3.02it/s, loss=0.446]" + "training until 2000: 72%|███████▏ | 1441/2000 [08:50<02:55, 3.19it/s, loss=0.405]" ] }, { @@ -47670,7 +47648,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1441/2000 [08:59<03:05, 3.02it/s, loss=0.442]" + "training until 2000: 72%|███████▏ | 1441/2000 [08:50<02:55, 3.19it/s, loss=0.39] " ] }, { @@ -47678,7 +47656,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1442/2000 [08:59<03:05, 3.00it/s, loss=0.442]" + "training until 2000: 72%|███████▏ | 1442/2000 [08:50<02:56, 3.16it/s, loss=0.39]" ] }, { @@ -47686,7 +47664,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1442/2000 [08:59<03:05, 3.00it/s, loss=0.518]" + "training until 2000: 72%|███████▏ | 1442/2000 [08:50<02:56, 3.16it/s, loss=0.406]" ] }, { @@ -47694,7 +47672,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1443/2000 [08:59<03:06, 2.99it/s, loss=0.518]" + "training until 2000: 72%|███████▏ | 1443/2000 [08:50<02:56, 3.15it/s, loss=0.406]" ] }, { @@ -47702,7 +47680,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1443/2000 [08:59<03:06, 2.99it/s, loss=0.426]" + "training until 2000: 72%|███████▏ | 1443/2000 [08:50<02:56, 3.15it/s, loss=0.421]" ] }, { @@ -47710,7 +47688,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1444/2000 [09:00<03:04, 3.01it/s, loss=0.426]" + "training until 2000: 72%|███████▏ | 1444/2000 [08:51<02:58, 3.11it/s, loss=0.421]" ] }, { @@ -47718,7 +47696,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1444/2000 [09:00<03:04, 3.01it/s, loss=0.444]" + "training until 2000: 72%|███████▏ | 1444/2000 [08:51<02:58, 3.11it/s, loss=0.42] " ] }, { @@ -47726,7 +47704,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1445/2000 [09:00<03:02, 3.04it/s, loss=0.444]" + "training until 2000: 72%|███████▏ | 1445/2000 [08:51<02:57, 3.12it/s, loss=0.42]" ] }, { @@ -47734,7 +47712,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1445/2000 [09:00<03:02, 3.04it/s, loss=0.48] " + "training until 2000: 72%|███████▏ | 1445/2000 [08:51<02:57, 3.12it/s, loss=0.66]" ] }, { @@ -47742,7 +47720,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1446/2000 [09:00<03:04, 3.01it/s, loss=0.48]" + "training until 2000: 72%|███████▏ | 1446/2000 [08:51<02:58, 3.10it/s, loss=0.66]" ] }, { @@ -47750,7 +47728,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1446/2000 [09:00<03:04, 3.01it/s, loss=0.429]" + "training until 2000: 72%|███████▏ | 1446/2000 [08:51<02:58, 3.10it/s, loss=0.409]" ] }, { @@ -47758,7 +47736,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1447/2000 [09:01<03:01, 3.04it/s, loss=0.429]" + "training until 2000: 72%|███████▏ | 1447/2000 [08:52<02:58, 3.10it/s, loss=0.409]" ] }, { @@ -47766,7 +47744,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1447/2000 [09:01<03:01, 3.04it/s, loss=0.425]" + "training until 2000: 72%|███████▏ | 1447/2000 [08:52<02:58, 3.10it/s, loss=0.395]" ] }, { @@ -47774,7 +47752,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1448/2000 [09:01<03:01, 3.04it/s, loss=0.425]" + "training until 2000: 72%|███████▏ | 1448/2000 [08:52<02:59, 3.08it/s, loss=0.395]" ] }, { @@ -47782,7 +47760,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1448/2000 [09:01<03:01, 3.04it/s, loss=0.423]" + "training until 2000: 72%|███████▏ | 1448/2000 [08:52<02:59, 3.08it/s, loss=0.423]" ] }, { @@ -47790,7 +47768,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1449/2000 [09:01<03:04, 2.99it/s, loss=0.423]" + "training until 2000: 72%|███████▏ | 1449/2000 [08:52<02:56, 3.12it/s, loss=0.423]" ] }, { @@ -47798,7 +47776,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▏ | 1449/2000 [09:01<03:04, 2.99it/s, loss=0.427]" + "training until 2000: 72%|███████▏ | 1449/2000 [08:52<02:56, 3.12it/s, loss=0.411]" ] }, { @@ -47806,7 +47784,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▎ | 1450/2000 [09:02<03:03, 3.00it/s, loss=0.427]" + "training until 2000: 72%|███████▎ | 1450/2000 [08:52<02:55, 3.14it/s, loss=0.411]" ] }, { @@ -47814,7 +47792,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 72%|███████▎ | 1450/2000 [09:02<03:03, 3.00it/s, loss=0.472]" + "training until 2000: 72%|███████▎ | 1450/2000 [08:52<02:55, 3.14it/s, loss=0.436]" ] }, { @@ -47822,7 +47800,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1451/2000 [09:02<02:58, 3.07it/s, loss=0.472]" + "training until 2000: 73%|███████▎ | 1451/2000 [08:53<02:54, 3.14it/s, loss=0.436]" ] }, { @@ -47830,7 +47808,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1451/2000 [09:02<02:58, 3.07it/s, loss=0.419]" + "training until 2000: 73%|███████▎ | 1451/2000 [08:53<02:54, 3.14it/s, loss=0.42] " ] }, { @@ -47838,7 +47816,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1452/2000 [09:02<02:56, 3.10it/s, loss=0.419]" + "training until 2000: 73%|███████▎ | 1452/2000 [08:53<02:54, 3.14it/s, loss=0.42]" ] }, { @@ -47846,7 +47824,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1452/2000 [09:02<02:56, 3.10it/s, loss=0.464]" + "training until 2000: 73%|███████▎ | 1452/2000 [08:53<02:54, 3.14it/s, loss=0.486]" ] }, { @@ -47854,7 +47832,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1453/2000 [09:03<02:54, 3.14it/s, loss=0.464]" + "training until 2000: 73%|███████▎ | 1453/2000 [08:53<02:54, 3.13it/s, loss=0.486]" ] }, { @@ -47862,7 +47840,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1453/2000 [09:03<02:54, 3.14it/s, loss=0.435]" + "training until 2000: 73%|███████▎ | 1453/2000 [08:53<02:54, 3.13it/s, loss=0.431]" ] }, { @@ -47870,7 +47848,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1454/2000 [09:03<02:58, 3.06it/s, loss=0.435]" + "training until 2000: 73%|███████▎ | 1454/2000 [08:54<02:55, 3.11it/s, loss=0.431]" ] }, { @@ -47878,7 +47856,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1454/2000 [09:03<02:58, 3.06it/s, loss=0.409]" + "training until 2000: 73%|███████▎ | 1454/2000 [08:54<02:55, 3.11it/s, loss=0.431]" ] }, { @@ -47886,7 +47864,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1455/2000 [09:03<02:58, 3.05it/s, loss=0.409]" + "training until 2000: 73%|███████▎ | 1455/2000 [08:54<02:55, 3.10it/s, loss=0.431]" ] }, { @@ -47894,7 +47872,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1455/2000 [09:03<02:58, 3.05it/s, loss=0.463]" + "training until 2000: 73%|███████▎ | 1455/2000 [08:54<02:55, 3.10it/s, loss=0.429]" ] }, { @@ -47902,7 +47880,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1456/2000 [09:04<02:56, 3.09it/s, loss=0.463]" + "training until 2000: 73%|███████▎ | 1456/2000 [08:54<02:54, 3.12it/s, loss=0.429]" ] }, { @@ -47910,7 +47888,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1456/2000 [09:04<02:56, 3.09it/s, loss=0.418]" + "training until 2000: 73%|███████▎ | 1456/2000 [08:54<02:54, 3.12it/s, loss=0.398]" ] }, { @@ -47918,7 +47896,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1457/2000 [09:04<02:54, 3.11it/s, loss=0.418]" + "training until 2000: 73%|███████▎ | 1457/2000 [08:55<02:54, 3.11it/s, loss=0.398]" ] }, { @@ -47926,7 +47904,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1457/2000 [09:04<02:54, 3.11it/s, loss=0.45] " + "training until 2000: 73%|███████▎ | 1457/2000 [08:55<02:54, 3.11it/s, loss=0.506]" ] }, { @@ -47934,7 +47912,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1458/2000 [09:04<02:53, 3.12it/s, loss=0.45]" + "training until 2000: 73%|███████▎ | 1458/2000 [08:55<02:53, 3.12it/s, loss=0.506]" ] }, { @@ -47942,7 +47920,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1458/2000 [09:04<02:53, 3.12it/s, loss=0.427]" + "training until 2000: 73%|███████▎ | 1458/2000 [08:55<02:53, 3.12it/s, loss=0.428]" ] }, { @@ -47950,7 +47928,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1459/2000 [09:05<02:52, 3.14it/s, loss=0.427]" + "training until 2000: 73%|███████▎ | 1459/2000 [08:55<02:52, 3.14it/s, loss=0.428]" ] }, { @@ -47958,7 +47936,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1459/2000 [09:05<02:52, 3.14it/s, loss=0.413]" + "training until 2000: 73%|███████▎ | 1459/2000 [08:55<02:52, 3.14it/s, loss=0.42] " ] }, { @@ -47966,7 +47944,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1460/2000 [09:05<02:50, 3.17it/s, loss=0.413]" + "training until 2000: 73%|███████▎ | 1460/2000 [08:56<02:54, 3.09it/s, loss=0.42]" ] }, { @@ -47974,7 +47952,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1460/2000 [09:05<02:50, 3.17it/s, loss=0.414]" + "training until 2000: 73%|███████▎ | 1460/2000 [08:56<02:54, 3.09it/s, loss=0.395]" ] }, { @@ -47982,7 +47960,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1461/2000 [09:05<02:51, 3.15it/s, loss=0.414]" + "training until 2000: 73%|███████▎ | 1461/2000 [08:56<02:53, 3.10it/s, loss=0.395]" ] }, { @@ -47990,7 +47968,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1461/2000 [09:05<02:51, 3.15it/s, loss=0.397]" + "training until 2000: 73%|███████▎ | 1461/2000 [08:56<02:53, 3.10it/s, loss=0.409]" ] }, { @@ -47998,7 +47976,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1462/2000 [09:06<02:52, 3.12it/s, loss=0.397]" + "training until 2000: 73%|███████▎ | 1462/2000 [08:56<02:51, 3.14it/s, loss=0.409]" ] }, { @@ -48006,7 +47984,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1462/2000 [09:06<02:52, 3.12it/s, loss=0.425]" + "training until 2000: 73%|███████▎ | 1462/2000 [08:56<02:51, 3.14it/s, loss=0.565]" ] }, { @@ -48014,7 +47992,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1463/2000 [09:06<02:50, 3.14it/s, loss=0.425]" + "training until 2000: 73%|███████▎ | 1463/2000 [08:57<02:51, 3.14it/s, loss=0.565]" ] }, { @@ -48022,7 +48000,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1463/2000 [09:06<02:50, 3.14it/s, loss=0.416]" + "training until 2000: 73%|███████▎ | 1463/2000 [08:57<02:51, 3.14it/s, loss=0.426]" ] }, { @@ -48030,7 +48008,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1464/2000 [09:06<02:48, 3.18it/s, loss=0.416]" + "training until 2000: 73%|███████▎ | 1464/2000 [08:57<02:50, 3.15it/s, loss=0.426]" ] }, { @@ -48038,7 +48016,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1464/2000 [09:06<02:48, 3.18it/s, loss=0.422]" + "training until 2000: 73%|███████▎ | 1464/2000 [08:57<02:50, 3.15it/s, loss=0.411]" ] }, { @@ -48046,7 +48024,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1465/2000 [09:06<02:49, 3.17it/s, loss=0.422]" + "training until 2000: 73%|███████▎ | 1465/2000 [08:57<02:49, 3.16it/s, loss=0.411]" ] }, { @@ -48054,7 +48032,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1465/2000 [09:06<02:49, 3.17it/s, loss=0.442]" + "training until 2000: 73%|███████▎ | 1465/2000 [08:57<02:49, 3.16it/s, loss=0.433]" ] }, { @@ -48062,7 +48040,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1466/2000 [09:07<02:50, 3.13it/s, loss=0.442]" + "training until 2000: 73%|███████▎ | 1466/2000 [08:58<02:48, 3.17it/s, loss=0.433]" ] }, { @@ -48070,7 +48048,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1466/2000 [09:07<02:50, 3.13it/s, loss=0.423]" + "training until 2000: 73%|███████▎ | 1466/2000 [08:58<02:48, 3.17it/s, loss=0.455]" ] }, { @@ -48078,7 +48056,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1467/2000 [09:07<02:50, 3.13it/s, loss=0.423]" + "training until 2000: 73%|███████▎ | 1467/2000 [08:58<02:49, 3.15it/s, loss=0.455]" ] }, { @@ -48086,7 +48064,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1467/2000 [09:07<02:50, 3.13it/s, loss=0.404]" + "training until 2000: 73%|███████▎ | 1467/2000 [08:58<02:49, 3.15it/s, loss=0.409]" ] }, { @@ -48094,7 +48072,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1468/2000 [09:07<02:50, 3.13it/s, loss=0.404]" + "training until 2000: 73%|███████▎ | 1468/2000 [08:58<02:47, 3.17it/s, loss=0.409]" ] }, { @@ -48102,7 +48080,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1468/2000 [09:07<02:50, 3.13it/s, loss=0.422]" + "training until 2000: 73%|███████▎ | 1468/2000 [08:58<02:47, 3.17it/s, loss=0.435]" ] }, { @@ -48110,7 +48088,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1469/2000 [09:08<02:50, 3.11it/s, loss=0.422]" + "training until 2000: 73%|███████▎ | 1469/2000 [08:59<02:47, 3.17it/s, loss=0.435]" ] }, { @@ -48118,7 +48096,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 73%|███████▎ | 1469/2000 [09:08<02:50, 3.11it/s, loss=0.442]" + "training until 2000: 73%|███████▎ | 1469/2000 [08:59<02:47, 3.17it/s, loss=0.431]" ] }, { @@ -48126,7 +48104,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1470/2000 [09:08<02:51, 3.09it/s, loss=0.442]" + "training until 2000: 74%|███████▎ | 1470/2000 [08:59<02:46, 3.18it/s, loss=0.431]" ] }, { @@ -48134,7 +48112,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1470/2000 [09:08<02:51, 3.09it/s, loss=0.452]" + "training until 2000: 74%|███████▎ | 1470/2000 [08:59<02:46, 3.18it/s, loss=0.452]" ] }, { @@ -48142,7 +48120,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1471/2000 [09:08<02:53, 3.06it/s, loss=0.452]" + "training until 2000: 74%|███████▎ | 1471/2000 [08:59<02:45, 3.20it/s, loss=0.452]" ] }, { @@ -48150,7 +48128,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1471/2000 [09:08<02:53, 3.06it/s, loss=0.42] " + "training until 2000: 74%|███████▎ | 1471/2000 [08:59<02:45, 3.20it/s, loss=0.44] " ] }, { @@ -48158,7 +48136,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1472/2000 [09:09<02:54, 3.02it/s, loss=0.42]" + "training until 2000: 74%|███████▎ | 1472/2000 [08:59<02:45, 3.18it/s, loss=0.44]" ] }, { @@ -48166,7 +48144,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1472/2000 [09:09<02:54, 3.02it/s, loss=0.47]" + "training until 2000: 74%|███████▎ | 1472/2000 [08:59<02:45, 3.18it/s, loss=0.392]" ] }, { @@ -48174,7 +48152,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1473/2000 [09:09<02:52, 3.05it/s, loss=0.47]" + "training until 2000: 74%|███████▎ | 1473/2000 [09:00<02:48, 3.12it/s, loss=0.392]" ] }, { @@ -48182,7 +48160,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1473/2000 [09:09<02:52, 3.05it/s, loss=0.411]" + "training until 2000: 74%|███████▎ | 1473/2000 [09:00<02:48, 3.12it/s, loss=0.429]" ] }, { @@ -48190,7 +48168,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1474/2000 [09:09<02:52, 3.06it/s, loss=0.411]" + "training until 2000: 74%|███████▎ | 1474/2000 [09:00<02:47, 3.13it/s, loss=0.429]" ] }, { @@ -48198,7 +48176,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▎ | 1474/2000 [09:09<02:52, 3.06it/s, loss=0.456]" + "training until 2000: 74%|███████▎ | 1474/2000 [09:00<02:47, 3.13it/s, loss=0.396]" ] }, { @@ -48206,7 +48184,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1475/2000 [09:10<02:51, 3.05it/s, loss=0.456]" + "training until 2000: 74%|███████▍ | 1475/2000 [09:00<02:45, 3.17it/s, loss=0.396]" ] }, { @@ -48214,7 +48192,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1475/2000 [09:10<02:51, 3.05it/s, loss=0.425]" + "training until 2000: 74%|███████▍ | 1475/2000 [09:00<02:45, 3.17it/s, loss=0.497]" ] }, { @@ -48222,7 +48200,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1476/2000 [09:10<02:52, 3.04it/s, loss=0.425]" + "training until 2000: 74%|███████▍ | 1476/2000 [09:01<02:46, 3.15it/s, loss=0.497]" ] }, { @@ -48230,7 +48208,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1476/2000 [09:10<02:52, 3.04it/s, loss=0.442]" + "training until 2000: 74%|███████▍ | 1476/2000 [09:01<02:46, 3.15it/s, loss=0.393]" ] }, { @@ -48238,7 +48216,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1477/2000 [09:10<02:49, 3.09it/s, loss=0.442]" + "training until 2000: 74%|███████▍ | 1477/2000 [09:01<02:46, 3.14it/s, loss=0.393]" ] }, { @@ -48246,7 +48224,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1477/2000 [09:10<02:49, 3.09it/s, loss=0.419]" + "training until 2000: 74%|███████▍ | 1477/2000 [09:01<02:46, 3.14it/s, loss=0.435]" ] }, { @@ -48254,7 +48232,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1478/2000 [09:11<02:50, 3.06it/s, loss=0.419]" + "training until 2000: 74%|███████▍ | 1478/2000 [09:01<02:45, 3.16it/s, loss=0.435]" ] }, { @@ -48262,7 +48240,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1478/2000 [09:11<02:50, 3.06it/s, loss=0.445]" + "training until 2000: 74%|███████▍ | 1478/2000 [09:01<02:45, 3.16it/s, loss=0.469]" ] }, { @@ -48270,7 +48248,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1479/2000 [09:11<02:50, 3.05it/s, loss=0.445]" + "training until 2000: 74%|███████▍ | 1479/2000 [09:02<02:44, 3.17it/s, loss=0.469]" ] }, { @@ -48278,7 +48256,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1479/2000 [09:11<02:50, 3.05it/s, loss=0.402]" + "training until 2000: 74%|███████▍ | 1479/2000 [09:02<02:44, 3.17it/s, loss=0.436]" ] }, { @@ -48286,7 +48264,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1480/2000 [09:11<02:50, 3.06it/s, loss=0.402]" + "training until 2000: 74%|███████▍ | 1480/2000 [09:02<02:43, 3.17it/s, loss=0.436]" ] }, { @@ -48294,7 +48272,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1480/2000 [09:11<02:50, 3.06it/s, loss=0.41] " + "training until 2000: 74%|███████▍ | 1480/2000 [09:02<02:43, 3.17it/s, loss=0.433]" ] }, { @@ -48302,7 +48280,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1481/2000 [09:12<02:50, 3.04it/s, loss=0.41]" + "training until 2000: 74%|███████▍ | 1481/2000 [09:02<02:44, 3.15it/s, loss=0.433]" ] }, { @@ -48310,7 +48288,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1481/2000 [09:12<02:50, 3.04it/s, loss=0.435]" + "training until 2000: 74%|███████▍ | 1481/2000 [09:02<02:44, 3.15it/s, loss=0.427]" ] }, { @@ -48318,7 +48296,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1482/2000 [09:12<02:51, 3.02it/s, loss=0.435]" + "training until 2000: 74%|███████▍ | 1482/2000 [09:03<02:42, 3.19it/s, loss=0.427]" ] }, { @@ -48326,7 +48304,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1482/2000 [09:12<02:51, 3.02it/s, loss=0.523]" + "training until 2000: 74%|███████▍ | 1482/2000 [09:03<02:42, 3.19it/s, loss=0.435]" ] }, { @@ -48334,7 +48312,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1483/2000 [09:12<02:46, 3.10it/s, loss=0.523]" + "training until 2000: 74%|███████▍ | 1483/2000 [09:03<02:42, 3.18it/s, loss=0.435]" ] }, { @@ -48342,7 +48320,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1483/2000 [09:12<02:46, 3.10it/s, loss=0.415]" + "training until 2000: 74%|███████▍ | 1483/2000 [09:03<02:42, 3.18it/s, loss=0.462]" ] }, { @@ -48350,7 +48328,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1484/2000 [09:13<02:48, 3.06it/s, loss=0.415]" + "training until 2000: 74%|███████▍ | 1484/2000 [09:03<02:43, 3.16it/s, loss=0.462]" ] }, { @@ -48358,7 +48336,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1484/2000 [09:13<02:48, 3.06it/s, loss=0.409]" + "training until 2000: 74%|███████▍ | 1484/2000 [09:03<02:43, 3.16it/s, loss=0.452]" ] }, { @@ -48366,7 +48344,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1485/2000 [09:13<02:45, 3.10it/s, loss=0.409]" + "training until 2000: 74%|███████▍ | 1485/2000 [09:04<02:43, 3.16it/s, loss=0.452]" ] }, { @@ -48374,7 +48352,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1485/2000 [09:13<02:45, 3.10it/s, loss=0.422]" + "training until 2000: 74%|███████▍ | 1485/2000 [09:04<02:43, 3.16it/s, loss=0.524]" ] }, { @@ -48382,7 +48360,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1486/2000 [09:13<02:43, 3.14it/s, loss=0.422]" + "training until 2000: 74%|███████▍ | 1486/2000 [09:04<02:43, 3.14it/s, loss=0.524]" ] }, { @@ -48390,7 +48368,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1486/2000 [09:13<02:43, 3.14it/s, loss=0.405]" + "training until 2000: 74%|███████▍ | 1486/2000 [09:04<02:43, 3.14it/s, loss=0.381]" ] }, { @@ -48398,7 +48376,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1487/2000 [09:14<02:45, 3.11it/s, loss=0.405]" + "training until 2000: 74%|███████▍ | 1487/2000 [09:04<02:44, 3.11it/s, loss=0.381]" ] }, { @@ -48406,7 +48384,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1487/2000 [09:14<02:45, 3.11it/s, loss=0.403]" + "training until 2000: 74%|███████▍ | 1487/2000 [09:04<02:44, 3.11it/s, loss=0.4] " ] }, { @@ -48414,7 +48392,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1488/2000 [09:14<02:46, 3.08it/s, loss=0.403]" + "training until 2000: 74%|███████▍ | 1488/2000 [09:05<03:21, 2.54it/s, loss=0.4]" ] }, { @@ -48422,7 +48400,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1488/2000 [09:14<02:46, 3.08it/s, loss=0.407]" + "training until 2000: 74%|███████▍ | 1488/2000 [09:05<03:21, 2.54it/s, loss=0.541]" ] }, { @@ -48430,7 +48408,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1489/2000 [09:14<02:46, 3.07it/s, loss=0.407]" + "training until 2000: 74%|███████▍ | 1489/2000 [09:05<03:09, 2.70it/s, loss=0.541]" ] }, { @@ -48438,7 +48416,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1489/2000 [09:14<02:46, 3.07it/s, loss=0.419]" + "training until 2000: 74%|███████▍ | 1489/2000 [09:05<03:09, 2.70it/s, loss=0.455]" ] }, { @@ -48446,7 +48424,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1490/2000 [09:15<02:45, 3.09it/s, loss=0.419]" + "training until 2000: 74%|███████▍ | 1490/2000 [09:05<02:59, 2.84it/s, loss=0.455]" ] }, { @@ -48454,7 +48432,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 74%|███████▍ | 1490/2000 [09:15<02:45, 3.09it/s, loss=0.408]" + "training until 2000: 74%|███████▍ | 1490/2000 [09:05<02:59, 2.84it/s, loss=0.418]" ] }, { @@ -48462,7 +48440,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1491/2000 [09:15<02:45, 3.07it/s, loss=0.408]" + "training until 2000: 75%|███████▍ | 1491/2000 [09:06<02:54, 2.92it/s, loss=0.418]" ] }, { @@ -48470,7 +48448,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1491/2000 [09:15<02:45, 3.07it/s, loss=0.423]" + "training until 2000: 75%|███████▍ | 1491/2000 [09:06<02:54, 2.92it/s, loss=0.456]" ] }, { @@ -48478,7 +48456,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1492/2000 [09:15<02:45, 3.08it/s, loss=0.423]" + "training until 2000: 75%|███████▍ | 1492/2000 [09:06<02:49, 2.99it/s, loss=0.456]" ] }, { @@ -48486,7 +48464,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1492/2000 [09:15<02:45, 3.08it/s, loss=0.458]" + "training until 2000: 75%|███████▍ | 1492/2000 [09:06<02:49, 2.99it/s, loss=0.405]" ] }, { @@ -48494,7 +48472,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1493/2000 [09:16<02:46, 3.05it/s, loss=0.458]" + "training until 2000: 75%|███████▍ | 1493/2000 [09:06<02:46, 3.05it/s, loss=0.405]" ] }, { @@ -48502,7 +48480,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1493/2000 [09:16<02:46, 3.05it/s, loss=0.402]" + "training until 2000: 75%|███████▍ | 1493/2000 [09:06<02:46, 3.05it/s, loss=0.46] " ] }, { @@ -48510,7 +48488,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1494/2000 [09:16<02:44, 3.08it/s, loss=0.402]" + "training until 2000: 75%|███████▍ | 1494/2000 [09:07<02:44, 3.08it/s, loss=0.46]" ] }, { @@ -48518,7 +48496,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1494/2000 [09:16<02:44, 3.08it/s, loss=0.41] " + "training until 2000: 75%|███████▍ | 1494/2000 [09:07<02:44, 3.08it/s, loss=0.424]" ] }, { @@ -48526,7 +48504,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1495/2000 [09:16<03:24, 2.47it/s, loss=0.41]" + "training until 2000: 75%|███████▍ | 1495/2000 [09:07<02:43, 3.09it/s, loss=0.424]" ] }, { @@ -48534,7 +48512,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1495/2000 [09:16<03:24, 2.47it/s, loss=0.396]" + "training until 2000: 75%|███████▍ | 1495/2000 [09:07<02:43, 3.09it/s, loss=0.419]" ] }, { @@ -48542,7 +48520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1496/2000 [09:17<03:12, 2.62it/s, loss=0.396]" + "training until 2000: 75%|███████▍ | 1496/2000 [09:07<02:42, 3.10it/s, loss=0.419]" ] }, { @@ -48550,7 +48528,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1496/2000 [09:17<03:12, 2.62it/s, loss=0.451]" + "training until 2000: 75%|███████▍ | 1496/2000 [09:07<02:42, 3.10it/s, loss=0.406]" ] }, { @@ -48558,7 +48536,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1497/2000 [09:17<03:04, 2.72it/s, loss=0.451]" + "training until 2000: 75%|███████▍ | 1497/2000 [09:08<02:41, 3.11it/s, loss=0.406]" ] }, { @@ -48566,7 +48544,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1497/2000 [09:17<03:04, 2.72it/s, loss=0.42] " + "training until 2000: 75%|███████▍ | 1497/2000 [09:08<02:41, 3.11it/s, loss=0.41] " ] }, { @@ -48574,7 +48552,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1498/2000 [09:17<02:57, 2.84it/s, loss=0.42]" + "training until 2000: 75%|███████▍ | 1498/2000 [09:08<02:39, 3.15it/s, loss=0.41]" ] }, { @@ -48582,7 +48560,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1498/2000 [09:17<02:57, 2.84it/s, loss=0.398]" + "training until 2000: 75%|███████▍ | 1498/2000 [09:08<02:39, 3.15it/s, loss=0.424]" ] }, { @@ -48590,7 +48568,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1499/2000 [09:18<02:53, 2.88it/s, loss=0.398]" + "training until 2000: 75%|███████▍ | 1499/2000 [09:08<02:38, 3.16it/s, loss=0.424]" ] }, { @@ -48598,7 +48576,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▍ | 1499/2000 [09:18<02:53, 2.88it/s, loss=0.52] " + "training until 2000: 75%|███████▍ | 1499/2000 [09:08<02:38, 3.16it/s, loss=0.568]" ] }, { @@ -48606,7 +48584,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1500/2000 [09:18<02:51, 2.92it/s, loss=0.52]" + "training until 2000: 75%|███████▌ | 1500/2000 [09:09<02:39, 3.13it/s, loss=0.568]" ] }, { @@ -48614,7 +48592,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1500/2000 [09:18<02:51, 2.92it/s, loss=0.419]" + "training until 2000: 75%|███████▌ | 1500/2000 [09:09<02:39, 3.13it/s, loss=0.487]" ] }, { @@ -48702,7 +48680,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:10, 20.53blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:10, 20.72blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" ] }, { @@ -48724,7 +48702,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:20, 10.42blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:20, 10.51blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" ] }, { @@ -48746,7 +48724,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 20.50blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 20.69blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" ] }, { @@ -48768,7 +48746,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:15, 13.70blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:15, 13.69blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" ] }, { @@ -48790,7 +48768,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.42blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.41blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" ] }, { @@ -48812,7 +48790,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.42blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.41blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" ] }, { @@ -48834,7 +48812,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.42blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 20.41blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" ] }, { @@ -48856,7 +48834,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.42blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.41blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" ] }, { @@ -48878,7 +48856,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.42blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:10, 20.41blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" ] }, { @@ -48900,7 +48878,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.42blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.41blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" ] }, { @@ -48922,7 +48900,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.42blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:10, 20.41blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -48944,7 +48922,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 19.15blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -48966,7 +48944,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 19.15blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" ] }, { @@ -48988,7 +48966,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 19.15blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" ] }, { @@ -49010,7 +48988,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 19.15blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" ] }, { @@ -49032,7 +49010,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 19.15blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:10, 19.83blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" ] }, { @@ -49054,7 +49032,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.23blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.74blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" ] }, { @@ -49076,7 +49054,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.23blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.74blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" ] }, { @@ -49098,7 +49076,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.23blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.74blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" ] }, { @@ -49120,7 +49098,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.23blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.74blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" ] }, { @@ -49142,7 +49120,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.23blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.74blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" ] }, { @@ -49164,7 +49142,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 19.08blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" ] }, { @@ -49186,7 +49164,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 19.08blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" ] }, { @@ -49208,7 +49186,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 19.08blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" ] }, { @@ -49230,7 +49208,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.08blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" ] }, { @@ -49252,7 +49230,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 19.08blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" ] }, { @@ -49274,7 +49252,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:11, 17.38blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 19.20blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" ] }, { @@ -49296,7 +49274,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:11, 17.38blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 19.20blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" ] }, { @@ -49318,7 +49296,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:11, 17.38blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 19.20blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" ] }, { @@ -49340,7 +49318,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:11, 17.38blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.20blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" ] }, { @@ -49362,7 +49340,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:11, 17.38blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 19.20blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" ] }, { @@ -49494,7 +49472,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.17blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:12, 16.61blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" ] }, { @@ -49516,7 +49494,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.17blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:12, 16.61blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" ] }, { @@ -49538,7 +49516,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.17blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:12, 16.61blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" ] }, { @@ -49560,7 +49538,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.17blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 16.61blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" ] }, { @@ -49582,7 +49560,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:01<00:11, 17.17blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 16.61blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" ] }, { @@ -49604,7 +49582,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:00<00:11, 17.05blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" ] }, { @@ -49626,7 +49604,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.05blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" ] }, { @@ -49648,7 +49626,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.05blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" ] }, { @@ -49670,7 +49648,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.05blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" ] }, { @@ -49692,7 +49670,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.14blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.05blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" ] }, { @@ -49714,7 +49692,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.66blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.74blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" ] }, { @@ -49736,7 +49714,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.66blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.74blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" ] }, { @@ -49758,7 +49736,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.66blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.74blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" ] }, { @@ -49780,7 +49758,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 17.66blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:10, 17.74blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" ] }, { @@ -49802,7 +49780,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 17.66blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:10, 17.74blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" ] }, { @@ -49824,7 +49802,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.68blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.86blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" ] }, { @@ -49846,7 +49824,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.68blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.86blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" ] }, { @@ -49868,7 +49846,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.68blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.86blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" ] }, { @@ -49890,7 +49868,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.68blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.86blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" ] }, { @@ -49912,7 +49890,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.68blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.86blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" ] }, { @@ -49934,7 +49912,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.97blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.63blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" ] }, { @@ -49956,7 +49934,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.97blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.63blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" ] }, { @@ -49978,7 +49956,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.97blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.63blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" ] }, { @@ -50000,7 +49978,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:11, 16.97blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.63blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" ] }, { @@ -50022,7 +50000,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:11, 16.97blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.63blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" ] }, { @@ -50044,7 +50022,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 17.31blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" ] }, { @@ -50066,7 +50044,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 17.31blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" ] }, { @@ -50088,7 +50066,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 17.31blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" ] }, { @@ -50110,7 +50088,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 17.31blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" ] }, { @@ -50132,7 +50110,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 17.31blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" ] }, { @@ -50154,7 +50132,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.16blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.98blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" ] }, { @@ -50176,7 +50154,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.16blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.98blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" ] }, { @@ -50198,7 +50176,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.16blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.98blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" ] }, { @@ -50220,7 +50198,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.16blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.98blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" ] }, { @@ -50242,7 +50220,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.16blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.98blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" ] }, { @@ -50264,7 +50242,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.51blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.21blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" ] }, { @@ -50286,7 +50264,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.51blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.21blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" ] }, { @@ -50308,7 +50286,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.51blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.21blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" ] }, { @@ -50330,7 +50308,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 17.51blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.21blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" ] }, { @@ -50352,7 +50330,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 17.51blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.21blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" ] }, { @@ -50374,7 +50352,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 17.83blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.61blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" ] }, { @@ -50396,7 +50374,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 17.83blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.61blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" ] }, { @@ -50418,7 +50396,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 17.83blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.61blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" ] }, { @@ -50440,7 +50418,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 17.83blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.61blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" ] }, { @@ -50462,7 +50440,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 17.83blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.61blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" ] }, { @@ -50484,7 +50462,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 18.01blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.62blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" ] }, { @@ -50506,7 +50484,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 18.01blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.62blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" ] }, { @@ -50528,7 +50506,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 18.01blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.62blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" ] }, { @@ -50550,7 +50528,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 18.01blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.62blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" ] }, { @@ -50572,7 +50550,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:02<00:10, 18.01blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.62blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" ] }, { @@ -50594,7 +50572,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.50blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.95blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" ] }, { @@ -50616,7 +50594,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.50blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.95blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" ] }, { @@ -50638,7 +50616,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.50blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.95blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" ] }, { @@ -50660,7 +50638,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.50blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.95blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" ] }, { @@ -50682,7 +50660,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.50blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.95blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" ] }, { @@ -50704,7 +50682,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.63blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.89blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" ] }, { @@ -50726,7 +50704,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.63blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.89blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" ] }, { @@ -50748,7 +50726,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.63blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.89blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" ] }, { @@ -50770,7 +50748,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.63blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.89blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" ] }, { @@ -50792,7 +50770,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.63blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.89blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" ] }, { @@ -50814,7 +50792,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.36blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.37blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" ] }, { @@ -50836,7 +50814,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.36blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.37blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" ] }, { @@ -50858,7 +50836,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.36blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.37blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" ] }, { @@ -50880,7 +50858,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.36blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.37blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" ] }, { @@ -50902,7 +50880,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.36blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.37blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" ] }, { @@ -50924,7 +50902,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.16blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" ] }, { @@ -50946,7 +50924,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.16blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" ] }, { @@ -50968,7 +50946,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.16blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" ] }, { @@ -50990,7 +50968,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=1, ✔=43, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.16blocks/s, ⧗=0, ▶=1, ✔=43, ✗=0, ∅=0]" ] }, { @@ -51012,7 +50990,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.27blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|█▉ | 43/216 [00:02<00:09, 18.16blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" ] }, { @@ -51034,7 +51012,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:10, 16.81blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.08blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" ] }, { @@ -51056,7 +51034,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:10, 16.81blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.08blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" ] }, { @@ -51078,7 +51056,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:10, 16.81blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.08blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" ] }, { @@ -51100,7 +51078,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:10, 16.81blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 18.08blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" ] }, { @@ -51122,7 +51100,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:10, 16.81blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 18.08blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" ] }, { @@ -51144,7 +51122,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.49blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" ] }, { @@ -51166,7 +51144,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.49blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" ] }, { @@ -51188,7 +51166,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.49blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" ] }, { @@ -51210,7 +51188,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.49blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" ] }, { @@ -51232,7 +51210,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.49blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" ] }, { @@ -51254,7 +51232,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 16.89blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.60blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" ] }, { @@ -51276,7 +51254,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 16.89blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.60blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" ] }, { @@ -51298,7 +51276,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 16.89blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.60blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" ] }, { @@ -51320,7 +51298,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 16.89blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.60blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" ] }, { @@ -51342,7 +51320,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 16.89blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.60blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" ] }, { @@ -51364,7 +51342,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.48blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.47blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" ] }, { @@ -51386,7 +51364,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.48blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.47blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" ] }, { @@ -51408,7 +51386,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.48blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.47blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" ] }, { @@ -51430,7 +51408,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.48blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.47blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" ] }, { @@ -51452,7 +51430,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.48blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.47blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" ] }, { @@ -51474,7 +51452,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.38blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" ] }, { @@ -51496,7 +51474,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.38blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" ] }, { @@ -51518,7 +51496,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:03<00:10, 15.38blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" ] }, { @@ -51540,7 +51518,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.38blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:02<00:10, 15.53blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" ] }, { @@ -51562,7 +51540,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.38blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.53blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" ] }, { @@ -51584,7 +51562,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 14.92blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.68blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" ] }, { @@ -51606,7 +51584,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 14.92blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.68blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" ] }, { @@ -51628,7 +51606,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 14.92blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.68blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" ] }, { @@ -51650,7 +51628,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 14.92blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 15.68blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" ] }, { @@ -51672,7 +51650,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 14.92blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 15.68blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" ] }, { @@ -51694,7 +51672,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.39blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" ] }, { @@ -51716,7 +51694,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.39blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" ] }, { @@ -51738,7 +51716,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:09, 16.39blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" ] }, { @@ -51760,7 +51738,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:09, 16.39blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" ] }, { @@ -51782,7 +51760,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:09, 16.39blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" ] }, { @@ -51804,7 +51782,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.17blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.79blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" ] }, { @@ -51826,7 +51804,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.17blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.79blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" ] }, { @@ -51848,7 +51826,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.17blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 15.79blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" ] }, { @@ -51870,7 +51848,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 27%|██▋ | 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[00:03<00:10, 15.46blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" ] }, { @@ -51936,7 +51914,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.48blocks/s, ⧗=0, ▶=1, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.46blocks/s, ⧗=0, ▶=1, ✔=60, ✗=0, ∅=0]" ] }, { @@ -51958,7 +51936,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.48blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.46blocks/s, ⧗=0, ▶=0, ✔=61, ✗=0, ∅=0]" ] }, { @@ -51980,7 +51958,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:10, 15.48blocks/s, ⧗=0, ▶=1, ✔=61, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:10, 15.46blocks/s, ⧗=0, ▶=1, ✔=61, ✗=0, ∅=0]" ] }, { @@ -52002,7 +51980,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:10, 15.48blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:10, 15.46blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" ] }, { @@ -52024,7 +52002,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.43blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.73blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" ] }, { @@ -52046,7 +52024,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.43blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.73blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" ] }, { @@ -52068,7 +52046,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.43blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 15.73blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" ] }, { @@ -52090,7 +52068,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.43blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.73blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" ] }, { @@ -52112,7 +52090,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.43blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 15.73blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" ] }, { @@ -52134,7 +52112,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 30%|██▉ | 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[00:03<00:10, 14.82blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" ] }, { @@ -52200,7 +52178,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:10, 15.06blocks/s, ⧗=0, ▶=1, ✔=65, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:10, 14.82blocks/s, ⧗=0, ▶=1, ✔=65, ✗=0, ∅=0]" ] }, { @@ -52222,7 +52200,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:10, 15.06blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 30%|███ | 65/216 [00:03<00:10, 14.82blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" ] }, { @@ -52244,7 +52222,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 15.64blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:10, 14.54blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" ] }, { @@ -52266,7 +52244,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 15.64blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:10, 14.54blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" ] }, { @@ -52288,7 +52266,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 15.64blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:10, 14.54blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" ] }, { @@ -52310,7 +52288,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:09, 15.64blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:10, 14.54blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" ] }, { @@ -52332,7 +52310,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:04<00:09, 15.64blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:10, 14.54blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" ] }, { @@ -52354,7 +52332,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:10, 14.34blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" ] }, { @@ -52376,7 +52354,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:10, 14.34blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" ] }, { @@ -52398,7 +52376,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:10, 14.34blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" ] }, { @@ -52420,7 +52398,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:10, 14.34blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" ] }, { @@ -52442,7 +52420,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:10, 14.34blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" ] }, { @@ -52464,7 +52442,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.41blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 14.74blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" ] }, { @@ -52486,7 +52464,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.41blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 14.74blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" ] }, { @@ -52508,7 +52486,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 16.41blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 14.74blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" ] }, { @@ -52530,7 +52508,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.41blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:09, 14.74blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" ] }, { @@ -52552,7 +52530,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.41blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:09, 14.74blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" ] }, { @@ -52574,7 +52552,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.96blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:09, 15.39blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" ] }, { @@ -52596,7 +52574,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.96blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:09, 15.39blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" ] }, { @@ -52618,7 +52596,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.96blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:09, 15.39blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" ] }, { @@ -52640,7 +52618,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 16.96blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:09, 15.39blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" ] }, { @@ -52662,7 +52640,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 16.96blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:09, 15.39blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" ] }, { @@ -52684,7 +52662,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.53blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 16.21blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" ] }, { @@ -52706,7 +52684,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.53blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 16.21blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" ] }, { @@ -52728,7 +52706,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.53blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 16.21blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" ] }, { @@ -52750,7 +52728,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.53blocks/s, ⧗=0, ▶=1, ✔=75, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 16.21blocks/s, ⧗=0, ▶=1, ✔=75, ✗=0, ∅=0]" ] }, { @@ -52772,7 +52750,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 17.53blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▍ | 75/216 [00:04<00:08, 16.21blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" ] }, { @@ -52794,7 +52772,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 17.73blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 16.30blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" ] }, { @@ -52816,7 +52794,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 17.73blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 16.30blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" ] }, { @@ -52838,7 +52816,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 17.73blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 16.30blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" ] }, { @@ -52860,7 +52838,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.73blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:08, 16.30blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" ] }, { @@ -52882,7 +52860,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.73blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:08, 16.30blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" ] }, { @@ -52904,7 +52882,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.87blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:08, 17.17blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" ] }, { @@ -52926,7 +52904,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.87blocks/s, ⧗=0, ▶=1, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:08, 17.17blocks/s, ⧗=0, ▶=1, ✔=78, ✗=0, ∅=0]" ] }, { @@ -52948,7 +52926,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.87blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:08, 17.17blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" ] }, { @@ -52970,7 +52948,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.87blocks/s, ⧗=0, ▶=1, ✔=79, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.17blocks/s, ⧗=0, ▶=1, ✔=79, ✗=0, ∅=0]" ] }, { @@ -52992,7 +52970,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.87blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.17blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" ] }, { @@ -53014,7 +52992,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 18.15blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.69blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" ] }, { @@ -53036,7 +53014,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 18.15blocks/s, ⧗=0, ▶=1, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.69blocks/s, ⧗=0, ▶=1, ✔=80, ✗=0, ∅=0]" ] }, { @@ -53058,7 +53036,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 18.15blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.69blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" ] }, { @@ -53080,7 +53058,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 18.15blocks/s, ⧗=0, ▶=1, ✔=81, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 17.69blocks/s, ⧗=0, ▶=1, ✔=81, ✗=0, ∅=0]" ] }, { @@ -53102,7 +53080,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 18.15blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 17.69blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" ] }, { @@ -53124,7 +53102,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.81blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.50blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" ] }, { @@ -53146,7 +53124,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.81blocks/s, ⧗=0, ▶=1, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.50blocks/s, ⧗=0, ▶=1, ✔=82, ✗=0, ∅=0]" ] }, { @@ -53168,7 +53146,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.81blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.50blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" ] }, { @@ -53190,7 +53168,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.81blocks/s, ⧗=0, ▶=1, ✔=83, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.50blocks/s, ⧗=0, ▶=1, ✔=83, ✗=0, ∅=0]" ] }, { @@ -53212,7 +53190,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.81blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.50blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" ] }, { @@ -53234,7 +53212,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.83blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" ] }, { @@ -53256,7 +53234,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=1, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.83blocks/s, ⧗=0, ▶=1, ✔=84, ✗=0, ∅=0]" ] }, { @@ -53278,7 +53256,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.83blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" ] }, { @@ -53300,7 +53278,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.78blocks/s, ⧗=0, ▶=1, ✔=85, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.83blocks/s, ⧗=0, ▶=1, ✔=85, ✗=0, ∅=0]" ] }, { @@ -53322,7 +53300,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:05<00:07, 17.78blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:05<00:07, 17.83blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" ] }, { @@ -53344,7 +53322,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.60blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" ] }, { @@ -53366,7 +53344,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=1, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.60blocks/s, ⧗=0, ▶=1, ✔=86, ✗=0, ∅=0]" ] }, { @@ -53388,7 +53366,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.60blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" ] }, { @@ -53410,7 +53388,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=1, ✔=87, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.60blocks/s, ⧗=0, ▶=1, ✔=87, ✗=0, ∅=0]" ] }, { @@ -53432,7 +53410,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.48blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.60blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" ] }, { @@ -53454,7 +53432,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.78blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.92blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" ] }, { @@ -53476,7 +53454,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.78blocks/s, ⧗=0, ▶=1, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.92blocks/s, ⧗=0, ▶=1, ✔=88, ✗=0, ∅=0]" ] }, { @@ -53498,7 +53476,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.78blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.92blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" ] }, { @@ -53520,7 +53498,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.78blocks/s, ⧗=0, ▶=1, ✔=89, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.92blocks/s, ⧗=0, ▶=1, ✔=89, ✗=0, ∅=0]" ] }, { @@ -53542,7 +53520,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.78blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.92blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" ] }, { @@ -53564,7 +53542,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.82blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.13blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" ] }, { @@ -53586,7 +53564,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.82blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.13blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" ] }, { @@ -53608,7 +53586,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.82blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 17.13blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" ] }, { @@ -53630,7 +53608,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.82blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.13blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" ] }, { @@ -53652,7 +53630,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.82blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.13blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" ] }, { @@ -53674,7 +53652,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.32blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 16.17blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" ] }, { @@ -53696,7 +53674,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.32blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 16.17blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" ] }, { @@ -53718,7 +53696,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.32blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 16.17blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" ] }, { @@ -53740,7 +53718,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.32blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 16.17blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" ] }, { @@ -53762,7 +53740,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.32blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 16.17blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" ] }, { @@ -53784,7 +53762,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.43blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:08, 15.25blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" ] }, { @@ -53806,7 +53784,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.43blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:08, 15.25blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" ] }, { @@ -53828,7 +53806,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.43blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:08, 15.25blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" ] }, { @@ -53850,7 +53828,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.43blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 15.25blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" ] }, { @@ -53872,7 +53850,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.43blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 15.25blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" ] }, { @@ -53894,7 +53872,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.60blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 15.58blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" ] }, { @@ -53916,7 +53894,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.60blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 15.58blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" ] }, { @@ -53938,7 +53916,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.60blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:07, 15.58blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" ] }, { @@ -53960,7 +53938,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.60blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:07, 15.58blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" ] }, { @@ -53982,7 +53960,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.60blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:07, 15.58blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" ] }, { @@ -54004,7 +53982,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.80blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:07, 15.34blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" ] }, { @@ -54026,7 +54004,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.80blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:07, 15.34blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" ] }, { @@ -54048,7 +54026,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.80blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:07, 15.34blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" ] }, { @@ -54070,7 +54048,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.80blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:07, 15.34blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" ] }, { @@ -54092,7 +54070,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.80blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:07, 15.34blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" ] }, { @@ -54114,7 +54092,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.99blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:07, 15.32blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" ] }, { @@ -54136,7 +54114,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.99blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:07, 15.32blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" ] }, { @@ -54158,7 +54136,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 16.99blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:07, 15.32blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" ] }, { @@ -54180,7 +54158,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.99blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:07, 15.32blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" ] }, { @@ -54202,7 +54180,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.99blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:06<00:07, 15.32blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" ] }, { @@ -54224,7 +54202,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.15blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:06<00:07, 16.15blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" ] }, { @@ -54246,7 +54224,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.15blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:06<00:07, 16.15blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" ] }, { @@ -54268,7 +54246,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:06<00:06, 17.15blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:06<00:07, 16.15blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" ] }, { @@ -54290,7 +54268,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 17.15blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 16.15blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" ] }, { @@ -54312,7 +54290,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 17.15blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 16.15blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" ] }, { @@ -54334,7 +54312,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.17blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.03blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" ] }, { @@ -54356,7 +54334,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.17blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.03blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" ] }, { @@ -54378,7 +54356,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.17blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 17.03blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" ] }, { @@ -54400,7 +54378,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.17blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.03blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" ] }, { @@ -54422,7 +54400,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.17blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 17.03blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" ] }, { @@ -54444,7 +54422,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.44blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.21blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" ] }, { @@ -54466,7 +54444,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.44blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.21blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" ] }, { @@ -54488,7 +54466,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.44blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 17.21blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" ] }, { @@ -54510,7 +54488,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.44blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.21blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" ] }, { @@ -54532,7 +54510,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.44blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 17.21blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" ] }, { @@ -54554,7 +54532,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.92blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.69blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" ] }, { @@ -54576,7 +54554,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.92blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.69blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" ] }, { @@ -54598,7 +54576,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.92blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 17.69blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" ] }, { @@ -54620,7 +54598,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 17.92blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:06, 17.69blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" ] }, { @@ -54642,7 +54620,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 17.92blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:06, 17.69blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" ] }, { @@ -54664,7 +54642,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.92blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.70blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" ] }, { @@ -54686,7 +54664,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.92blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.70blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" ] }, { @@ -54708,7 +54686,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.88blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 17.70blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" ] }, { @@ -54730,7 +54708,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.88blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 17.70blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" ] }, { @@ -54752,7 +54730,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.88blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 17.70blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" ] }, { @@ -54774,7 +54752,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.88blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.09blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" ] }, { @@ -54796,7 +54774,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.88blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.09blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" ] }, { @@ -54818,7 +54796,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.02blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 18.09blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" ] }, { @@ -54840,7 +54818,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.02blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 18.09blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" ] }, { @@ -54862,7 +54840,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.02blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 18.09blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" ] }, { @@ -54884,7 +54862,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 19.02blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.09blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" ] }, { @@ -54906,7 +54884,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 19.02blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" ] }, { @@ -54928,7 +54906,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.32blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" ] }, { @@ -54950,7 +54928,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.32blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" ] }, { @@ -54972,7 +54950,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.32blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" ] }, { @@ -54994,7 +54972,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 18.32blocks/s, ⧗=0, ▶=1, ✔=116, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 17.32blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" ] }, { @@ -55016,7 +54994,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 18.32blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 17.32blocks/s, ⧗=0, ▶=1, ✔=116, ✗=0, ∅=0]" ] }, { @@ -55038,7 +55016,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.27blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▎ | 116/216 [00:06<00:05, 17.32blocks/s, ⧗=0, ▶=0, ✔=117, ✗=0, ∅=0]" ] }, { @@ -55060,7 +55038,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.27blocks/s, ⧗=0, ▶=1, ✔=117, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 17.32blocks/s, ⧗=0, ▶=1, ✔=117, ✗=0, ∅=0]" ] }, { @@ -55082,7 +55060,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 18.27blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 54%|█████▍ | 117/216 [00:06<00:05, 17.32blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" ] }, { @@ -55104,7 +55082,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.27blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 17.24blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" ] }, { @@ -55126,7 +55104,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.27blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 17.24blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" ] }, { @@ -55148,7 +55126,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:07<00:05, 17.24blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" ] }, { @@ -55170,7 +55148,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:07<00:05, 17.24blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" ] }, { @@ -55192,7 +55170,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:07<00:05, 17.24blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" ] }, { @@ -55214,7 +55192,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:07<00:05, 16.48blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" ] }, { @@ -55236,7 +55214,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.48blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:07<00:05, 16.48blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" ] }, { @@ -55258,7 +55236,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.74blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:07<00:05, 16.48blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" ] }, { @@ -55280,7 +55258,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.74blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:07<00:05, 16.48blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" ] }, { @@ -55302,7 +55280,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:07<00:05, 18.74blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:07<00:05, 16.48blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" ] }, { @@ -55324,7 +55302,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.74blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 16.63blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" ] }, { @@ -55346,7 +55324,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.74blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 16.63blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" ] }, { @@ -55368,7 +55346,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.23blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 16.63blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" ] }, { @@ -55390,7 +55368,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.23blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 16.63blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" ] }, { @@ -55412,7 +55390,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.23blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 16.63blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" ] }, { @@ -55434,7 +55412,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 18.23blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" ] }, { @@ -55456,7 +55434,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 18.23blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" ] }, { @@ -55478,7 +55456,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.91blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" ] }, { @@ -55500,7 +55478,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.91blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" ] }, { @@ -55522,7 +55500,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.91blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" ] }, { @@ -55544,7 +55522,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.91blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 16.49blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" ] }, { @@ -55566,7 +55544,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.91blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 16.49blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" ] }, { @@ -55588,7 +55566,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 17.57blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 16.49blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" ] }, { @@ -55610,7 +55588,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 17.57blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 16.49blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" ] }, { @@ -55632,7 +55610,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 17.57blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 16.49blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" ] }, { @@ -55654,7 +55632,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 17.57blocks/s, ⧗=0, ▶=1, ✔=128, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 16.46blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" ] }, { @@ -55676,7 +55654,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 17.57blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 16.46blocks/s, ⧗=0, ▶=1, ✔=128, ✗=0, ∅=0]" ] }, { @@ -55698,7 +55676,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 17.64blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 59%|█████▉ | 128/216 [00:07<00:05, 16.46blocks/s, ⧗=0, ▶=0, ✔=129, ✗=0, ∅=0]" ] }, { @@ -55720,7 +55698,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 17.64blocks/s, ⧗=0, ▶=1, ✔=129, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:05, 16.46blocks/s, ⧗=0, ▶=1, ✔=129, ✗=0, ∅=0]" ] }, { @@ -55742,7 +55720,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:04, 17.64blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|█████▉ | 129/216 [00:07<00:05, 16.46blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" ] }, { @@ -55764,7 +55742,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.64blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:05, 16.91blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" ] }, { @@ -55786,7 +55764,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.64blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:05, 16.91blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" ] }, { @@ -55808,7 +55786,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:05, 16.98blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:05, 16.91blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" ] }, { @@ -55830,7 +55808,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:05, 16.98blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:05, 16.91blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" ] }, { @@ -55852,7 +55830,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:05, 16.98blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:05, 16.91blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" ] }, { @@ -55874,7 +55852,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 16.98blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" ] }, { @@ -55896,7 +55874,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 16.98blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" ] }, { @@ -55918,7 +55896,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 17.66blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:05, 16.70blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" ] }, { @@ -55940,7 +55918,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 17.66blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 16.70blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" ] }, { @@ -55962,7 +55940,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 17.66blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 16.70blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" ] }, { @@ -55984,7 +55962,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 17.66blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 17.44blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" ] }, { @@ -56006,7 +55984,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 17.66blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 17.44blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" ] }, { @@ -56028,7 +56006,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 17.44blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" ] }, { @@ -56050,7 +56028,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 17.44blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" ] }, { @@ -56072,7 +56050,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:08<00:04, 17.44blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" ] }, { @@ -56094,7 +56072,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:08<00:04, 17.64blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" ] }, { @@ -56116,7 +56094,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 17.87blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:08<00:04, 17.64blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" ] }, { @@ -56138,7 +56116,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 17.41blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:08<00:04, 17.64blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" ] }, { @@ -56160,7 +56138,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 17.41blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:08<00:04, 17.64blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" ] }, { @@ -56182,7 +56160,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 17.41blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:08<00:04, 17.64blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" ] }, { @@ -56204,7 +56182,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 17.41blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" ] }, { @@ -56226,7 +56204,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:08<00:04, 17.41blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" ] }, { @@ -56248,7 +56226,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.62blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" ] }, { @@ -56270,7 +56248,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.62blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" ] }, { @@ -56292,7 +56270,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.62blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 17.75blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" ] }, { @@ -56314,7 +56292,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.62blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.91blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" ] }, { @@ -56336,7 +56314,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.62blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.91blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" ] }, { @@ -56358,7 +56336,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 18.06blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.91blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" ] }, { @@ -56380,7 +56358,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 18.06blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.91blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" ] }, { @@ -56402,7 +56380,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 18.06blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.91blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" ] }, { @@ -56424,7 +56402,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 18.06blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 18.02blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" ] }, { @@ -56446,7 +56424,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 18.06blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 18.02blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" ] }, { @@ -56468,7 +56446,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.35blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 18.02blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" ] }, { @@ -56490,7 +56468,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.35blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 18.02blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" ] }, { @@ -56512,7 +56490,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.35blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 18.02blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" ] }, { @@ -56534,7 +56512,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.35blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" ] }, { @@ -56556,7 +56534,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.35blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" ] }, { @@ -56578,7 +56556,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.27blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" ] }, { @@ -56600,7 +56578,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.27blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" ] }, { @@ -56622,7 +56600,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.27blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.08blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" ] }, { @@ -56644,7 +56622,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.27blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:04, 17.05blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" ] }, { @@ -56666,7 +56644,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.27blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:04, 17.05blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" ] }, { @@ -56688,7 +56666,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.93blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:04, 17.05blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" ] }, { @@ -56710,7 +56688,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.93blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:04, 17.05blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" ] }, { @@ -56732,7 +56710,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 17.93blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:04, 17.05blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" ] }, { @@ -56754,7 +56732,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.93blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:04, 15.83blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" ] }, { @@ -56776,7 +56754,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.93blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:04, 15.83blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" ] }, { @@ -56798,7 +56776,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.90blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:04, 15.83blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" ] }, { @@ -56820,7 +56798,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.90blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:04, 15.83blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" ] }, { @@ -56842,7 +56820,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.90blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:04, 15.83blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" ] }, { @@ -56864,7 +56842,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.90blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:04, 16.38blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" ] }, { @@ -56886,7 +56864,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.90blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:04, 16.38blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" ] }, { @@ -56908,7 +56886,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.74blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:04, 16.38blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" ] }, { @@ -56930,7 +56908,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.74blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 16.38blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" ] }, { @@ -56952,7 +56930,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.74blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 16.38blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" ] }, { @@ -56974,7 +56952,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.74blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.83blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" ] }, { @@ -56996,7 +56974,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.74blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.83blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" ] }, { @@ -57018,7 +56996,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.24blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:09<00:03, 16.83blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" ] }, { @@ -57040,7 +57018,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.24blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:09<00:03, 16.83blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" ] }, { @@ -57062,7 +57040,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.24blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:09<00:03, 16.83blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" ] }, { @@ -57084,7 +57062,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 17.24blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:09<00:03, 16.56blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" ] }, { @@ -57106,7 +57084,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 17.24blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:09<00:03, 16.56blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" ] }, { @@ -57128,7 +57106,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 17.33blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:09<00:03, 16.56blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" ] }, { @@ -57150,7 +57128,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 17.33blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:09<00:03, 16.56blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" ] }, { @@ -57172,7 +57150,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 17.33blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:09<00:03, 16.56blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" ] }, { @@ -57194,7 +57172,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 17.33blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:09<00:03, 16.65blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" ] }, { @@ -57216,7 +57194,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:09<00:03, 17.33blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:09<00:03, 16.65blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" ] }, { @@ -57238,7 +57216,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.48blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:09<00:03, 16.65blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" ] }, { @@ -57260,7 +57238,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.48blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.65blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" ] }, { @@ -57282,7 +57260,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.48blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.65blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" ] }, { @@ -57304,7 +57282,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.48blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.35blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" ] }, { @@ -57326,7 +57304,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.48blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.35blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" ] }, { @@ -57348,7 +57326,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.15blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.35blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" ] }, { @@ -57370,7 +57348,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.15blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 16.35blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" ] }, { @@ -57392,7 +57370,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.15blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 16.35blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" ] }, { @@ -57414,7 +57392,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.15blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.26blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" ] }, { @@ -57436,7 +57414,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.15blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.26blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" ] }, { @@ -57458,7 +57436,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.92blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.26blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" ] }, { @@ -57480,7 +57458,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.92blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 15.26blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" ] }, { @@ -57502,7 +57480,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.92blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 15.26blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" ] }, { @@ -57524,7 +57502,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.92blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.35blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" ] }, { @@ -57546,7 +57524,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.92blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.35blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" ] }, { @@ -57568,7 +57546,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.27blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.35blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" ] }, { @@ -57590,7 +57568,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.27blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.35blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" ] }, { @@ -57612,7 +57590,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.27blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.35blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" ] }, { @@ -57634,7 +57612,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.27blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 16.05blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" ] }, { @@ -57656,7 +57634,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.27blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 16.05blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" ] }, { @@ -57678,7 +57656,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 14.80blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 16.05blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" ] }, { @@ -57700,7 +57678,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 14.80blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 16.05blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" ] }, { @@ -57722,7 +57700,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 14.80blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 16.05blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" ] }, { @@ -57744,7 +57722,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 14.80blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.51blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" ] }, { @@ -57766,7 +57744,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 14.80blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.51blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" ] }, { @@ -57788,7 +57766,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.20blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.51blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" ] }, { @@ -57810,7 +57788,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.20blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.51blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" ] }, { @@ -57832,7 +57810,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.20blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.51blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" ] }, { @@ -57854,7 +57832,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.20blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.57blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" ] }, { @@ -57876,7 +57854,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.20blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.57blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" ] }, { @@ -57898,7 +57876,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 15.78blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:10<00:03, 15.57blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" ] }, { @@ -57920,7 +57898,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 15.78blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:10<00:03, 15.57blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" ] }, { @@ -57942,7 +57920,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:02, 15.78blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:10<00:03, 15.57blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" ] }, { @@ -57964,7 +57942,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.78blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:10<00:02, 16.10blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" ] }, { @@ -57986,7 +57964,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.78blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:10<00:02, 16.10blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" ] }, { @@ -58008,7 +57986,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.57blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:10<00:02, 16.10blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" ] }, { @@ -58030,7 +58008,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.57blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 16.10blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" ] }, { @@ -58052,7 +58030,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 16.10blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" ] }, { @@ -58074,7 +58052,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.82blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" ] }, { @@ -58096,7 +58074,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.57blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.82blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" ] }, { @@ -58118,7 +58096,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.68blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.82blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" ] }, { @@ -58140,7 +58118,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.68blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.82blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" ] }, { @@ -58162,7 +58140,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.68blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.82blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" ] }, { @@ -58184,7 +58162,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.68blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.27blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" ] }, { @@ -58206,7 +58184,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.68blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.27blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" ] }, { @@ -58228,7 +58206,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.18blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.27blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" ] }, { @@ -58250,7 +58228,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.18blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.27blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" ] }, { @@ -58272,7 +58250,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.18blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.27blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" ] }, { @@ -58294,7 +58272,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.18blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.77blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" ] }, { @@ -58316,7 +58294,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.18blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.77blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" ] }, { @@ -58338,7 +58316,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.77blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" ] }, { @@ -58360,7 +58338,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.77blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" ] }, { @@ -58382,7 +58360,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.77blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" ] }, { @@ -58404,7 +58382,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" ] }, { @@ -58426,7 +58404,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.01blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" ] }, { @@ -58448,7 +58426,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 15.77blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" ] }, { @@ -58470,7 +58448,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 15.77blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" ] }, { @@ -58492,7 +58470,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 15.77blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.45blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" ] }, { @@ -58514,7 +58492,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 15.77blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.79blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" ] }, { @@ -58536,7 +58514,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 15.77blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.79blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" ] }, { @@ -58558,7 +58536,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.51blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.79blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" ] }, { @@ -58580,7 +58558,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.51blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.79blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" ] }, { @@ -58602,7 +58580,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.51blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.79blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" ] }, { @@ -58624,7 +58602,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:02, 16.51blocks/s, ⧗=0, ▶=1, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" ] }, { @@ -58646,7 +58624,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:02, 16.51blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=1, ✔=182, ✗=0, ∅=0]" ] }, { @@ -58668,7 +58646,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=0, ✔=183, ✗=0, ∅=0]" ] }, { @@ -58690,7 +58668,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" ] }, { @@ -58712,7 +58690,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:02, 16.25blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.27blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" ] }, { @@ -58734,7 +58712,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 16.25blocks/s, ⧗=0, ▶=1, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.10blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" ] }, { @@ -58756,7 +58734,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 16.25blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.10blocks/s, ⧗=0, ▶=1, ✔=184, ✗=0, ∅=0]" ] }, { @@ -58778,7 +58756,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.00blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.10blocks/s, ⧗=0, ▶=0, ✔=185, ✗=0, ∅=0]" ] }, { @@ -58800,7 +58778,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.00blocks/s, ⧗=0, ▶=1, ✔=185, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.10blocks/s, ⧗=0, ▶=1, ✔=185, ✗=0, ∅=0]" ] }, { @@ -58822,7 +58800,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:10<00:01, 17.00blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 185/216 [00:11<00:01, 17.10blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" ] }, { @@ -58844,7 +58822,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.00blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" ] }, { @@ -58866,7 +58844,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.00blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" ] }, { @@ -58888,7 +58866,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.65blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" ] }, { @@ -58910,7 +58888,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.65blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" ] }, { @@ -58932,7 +58910,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.65blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:11<00:01, 16.43blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" ] }, { @@ -58954,7 +58932,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 17.65blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:11<00:01, 16.25blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" ] }, { @@ -58976,7 +58954,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:11<00:01, 17.65blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:11<00:01, 16.25blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" ] }, { @@ -58998,7 +58976,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.49blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:11<00:01, 16.25blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" ] }, { @@ -59020,7 +58998,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.49blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 16.25blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" ] }, { @@ -59042,7 +59020,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.49blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 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[ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.56blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 16.34blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" ] }, { @@ -59130,7 +59108,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.56blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.34blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" ] }, { @@ -59152,7 +59130,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.56blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 16.34blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" ] }, { @@ -59174,7 +59152,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.56blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.45blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" ] }, { @@ -59196,7 +59174,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.56blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.45blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" ] }, { @@ -59218,7 +59196,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.66blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 16.45blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" ] }, { @@ -59240,7 +59218,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.66blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.45blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" ] }, { @@ -59262,7 +59240,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.66blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 16.45blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" ] }, { @@ -59284,7 +59262,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 17.66blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.63blocks/s, ⧗=0, ▶=0, ✔=194, ✗=0, ∅=0]" ] }, { @@ -59306,7 +59284,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 17.66blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.63blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" ] }, { @@ -59328,7 +59306,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 17.02blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.63blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" ] }, { @@ -59350,7 +59328,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 17.02blocks/s, ⧗=0, ▶=1, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 16.63blocks/s, ⧗=0, ▶=1, ✔=195, ✗=0, ∅=0]" ] }, { @@ -59372,7 +59350,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 17.02blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 16.63blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" ] }, { @@ -59394,7 +59372,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 17.02blocks/s, ⧗=0, ▶=1, ✔=196, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.37blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" ] }, { @@ -59416,7 +59394,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 17.02blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.37blocks/s, ⧗=0, ▶=1, ✔=196, ✗=0, ∅=0]" ] }, { @@ -59438,7 +59416,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.96blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.37blocks/s, ⧗=0, ▶=0, ✔=197, ✗=0, ∅=0]" ] }, { @@ -59460,7 +59438,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.96blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.37blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" ] }, { @@ -59482,7 +59460,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.96blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.37blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" ] }, { @@ -59504,7 +59482,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.96blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.71blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" ] }, { @@ -59526,7 +59504,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.96blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.71blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" ] }, { @@ -59548,7 +59526,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.70blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.71blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" ] }, { @@ -59570,7 +59548,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.70blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.71blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" ] }, { @@ -59592,7 +59570,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.70blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.71blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" ] }, { @@ -59614,7 +59592,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 16.70blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.38blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" ] }, { @@ -59636,7 +59614,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 16.70blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.38blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" ] }, { @@ -59658,7 +59636,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.81blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.38blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" ] }, { @@ -59680,7 +59658,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.81blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.38blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" ] }, { @@ -59702,7 +59680,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.81blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.38blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" ] }, { @@ -59724,7 +59702,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.81blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 17.40blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" ] }, { @@ -59746,7 +59724,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.81blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 17.40blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" ] }, { @@ -59768,7 +59746,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.64blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:12<00:00, 17.40blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" ] }, { @@ -59790,7 +59768,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.64blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:12<00:00, 17.40blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" ] }, { @@ -59812,7 +59790,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.64blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 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17.21blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:12<00:00, 17.19blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" ] }, { @@ -59944,7 +59922,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.21blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.60blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" ] }, { @@ -59966,7 +59944,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.21blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.60blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" ] }, { @@ -59988,7 +59966,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.28blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.60blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" ] }, { @@ -60010,7 +59988,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.28blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.60blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" ] }, { @@ -60032,7 +60010,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.28blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.60blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" ] }, { @@ -60054,7 +60032,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.28blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.13blocks/s, ⧗=0, ▶=0, ✔=208, ✗=0, ∅=0]" ] }, { @@ -60076,7 +60054,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.28blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.13blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" ] }, { @@ -60098,7 +60076,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.50blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 17.13blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" ] }, { @@ -60120,7 +60098,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.50blocks/s, ⧗=0, ▶=1, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.13blocks/s, ⧗=0, ▶=1, ✔=209, ✗=0, ∅=0]" ] }, { @@ -60142,7 +60120,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.50blocks/s, ⧗=0, ▶=0, ✔=210, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 17.13blocks/s, ⧗=0, ▶=0, ✔=210, ✗=0, ∅=0]" ] }, { @@ -60164,7 +60142,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.50blocks/s, ⧗=0, ▶=1, ✔=210, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.36blocks/s, ⧗=0, ▶=0, ✔=210, ✗=0, ∅=0]" ] }, { @@ -60186,7 +60164,7 @@ "output_type": "stream", "text": 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"text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 16.89blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ▶: 100%|██████████| 216/216 [00:12<00:00, 18.27blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -60504,12 +60482,34 @@ "\u001b[A" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.12blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 18.27blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[A" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "predict_/home/runner/dacapo/example_run/validation.zarr_1500/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 16.90blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -60564,7 +60564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1501/2000 [09:55<1:34:11, 11.33s/it, loss=0.419]" + "training until 2000: 75%|███████▌ | 1501/2000 [09:39<1:18:39, 9.46s/it, loss=0.487]" ] }, { @@ -60572,7 +60572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1501/2000 [09:55<1:34:11, 11.33s/it, loss=0.412]" + "training until 2000: 75%|███████▌ | 1501/2000 [09:39<1:18:39, 9.46s/it, loss=0.445]" ] }, { @@ -60580,7 +60580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1502/2000 [09:55<1:06:37, 8.03s/it, loss=0.412]" + "training until 2000: 75%|███████▌ | 1502/2000 [09:40<55:44, 6.72s/it, loss=0.445] " ] }, { @@ -60588,7 +60588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1502/2000 [09:55<1:06:37, 8.03s/it, loss=0.409]" + "training until 2000: 75%|███████▌ | 1502/2000 [09:40<55:44, 6.72s/it, loss=0.412]" ] }, { @@ -60596,7 +60596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1503/2000 [09:56<47:20, 5.71s/it, loss=0.409] " + "training until 2000: 75%|███████▌ | 1503/2000 [09:40<39:43, 4.80s/it, loss=0.412]" ] }, { @@ -60604,7 +60604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1503/2000 [09:56<47:20, 5.71s/it, loss=0.415]" + "training until 2000: 75%|███████▌ | 1503/2000 [09:40<39:43, 4.80s/it, loss=0.388]" ] }, { @@ -60612,7 +60612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1504/2000 [09:56<33:54, 4.10s/it, loss=0.415]" + "training until 2000: 75%|███████▌ | 1504/2000 [09:40<28:32, 3.45s/it, loss=0.388]" ] }, { @@ -60620,7 +60620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1504/2000 [09:56<33:54, 4.10s/it, loss=0.404]" + "training until 2000: 75%|███████▌ | 1504/2000 [09:40<28:32, 3.45s/it, loss=0.442]" ] }, { @@ -60628,7 +60628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1505/2000 [09:56<24:29, 2.97s/it, loss=0.404]" + "training until 2000: 75%|███████▌ | 1505/2000 [09:41<20:46, 2.52s/it, loss=0.442]" ] }, { @@ -60636,7 +60636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1505/2000 [09:56<24:29, 2.97s/it, loss=0.439]" + "training until 2000: 75%|███████▌ | 1505/2000 [09:41<20:46, 2.52s/it, loss=0.417]" ] }, { @@ -60644,7 +60644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1506/2000 [09:57<17:53, 2.17s/it, loss=0.439]" + "training until 2000: 75%|███████▌ | 1506/2000 [09:41<15:17, 1.86s/it, loss=0.417]" ] }, { @@ -60652,7 +60652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1506/2000 [09:57<17:53, 2.17s/it, loss=0.461]" + "training until 2000: 75%|███████▌ | 1506/2000 [09:41<15:17, 1.86s/it, loss=0.432]" ] }, { @@ -60660,7 +60660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1507/2000 [09:57<13:16, 1.62s/it, loss=0.461]" + "training until 2000: 75%|███████▌ | 1507/2000 [09:41<11:26, 1.39s/it, loss=0.432]" ] }, { @@ -60668,7 +60668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1507/2000 [09:57<13:16, 1.62s/it, loss=0.444]" + "training until 2000: 75%|███████▌ | 1507/2000 [09:41<11:26, 1.39s/it, loss=0.426]" ] }, { @@ -60676,7 +60676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1508/2000 [09:57<10:05, 1.23s/it, loss=0.444]" + "training until 2000: 75%|███████▌ | 1508/2000 [09:42<08:46, 1.07s/it, loss=0.426]" ] }, { @@ -60684,7 +60684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1508/2000 [09:57<10:05, 1.23s/it, loss=0.406]" + "training until 2000: 75%|███████▌ | 1508/2000 [09:42<08:46, 1.07s/it, loss=0.414]" ] }, { @@ -60692,7 +60692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1509/2000 [09:58<07:50, 1.04it/s, loss=0.406]" + "training until 2000: 75%|███████▌ | 1509/2000 [09:42<06:53, 1.19it/s, loss=0.414]" ] }, { @@ -60700,7 +60700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 75%|███████▌ | 1509/2000 [09:58<07:50, 1.04it/s, loss=0.437]" + "training until 2000: 75%|███████▌ | 1509/2000 [09:42<06:53, 1.19it/s, loss=0.451]" ] }, { @@ -60708,7 +60708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1510/2000 [09:58<06:14, 1.31it/s, loss=0.437]" + "training until 2000: 76%|███████▌ | 1510/2000 [09:42<05:36, 1.46it/s, loss=0.451]" ] }, { @@ -60716,7 +60716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1510/2000 [09:58<06:14, 1.31it/s, loss=0.399]" + "training until 2000: 76%|███████▌ | 1510/2000 [09:42<05:36, 1.46it/s, loss=0.402]" ] }, { @@ -60724,7 +60724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1511/2000 [09:58<05:09, 1.58it/s, loss=0.399]" + "training until 2000: 76%|███████▌ | 1511/2000 [09:43<04:41, 1.74it/s, loss=0.402]" ] }, { @@ -60732,7 +60732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1511/2000 [09:58<05:09, 1.58it/s, loss=0.421]" + "training until 2000: 76%|███████▌ | 1511/2000 [09:43<04:41, 1.74it/s, loss=0.558]" ] }, { @@ -60740,7 +60740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1512/2000 [09:59<04:23, 1.85it/s, loss=0.421]" + "training until 2000: 76%|███████▌ | 1512/2000 [09:43<04:02, 2.01it/s, loss=0.558]" ] }, { @@ -60748,7 +60748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 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[09:59<03:27, 2.34it/s, loss=0.467]" + "training until 2000: 76%|███████▌ | 1514/2000 [09:44<03:16, 2.47it/s, loss=0.407]" ] }, { @@ -60788,7 +60788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1515/2000 [10:00<03:11, 2.54it/s, loss=0.467]" + "training until 2000: 76%|███████▌ | 1515/2000 [09:44<03:04, 2.64it/s, loss=0.407]" ] }, { @@ -60796,7 +60796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1515/2000 [10:00<03:11, 2.54it/s, loss=0.419]" + "training until 2000: 76%|███████▌ | 1515/2000 [09:44<03:04, 2.64it/s, loss=0.444]" ] }, { @@ -60804,7 +60804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1516/2000 [10:00<03:00, 2.68it/s, loss=0.419]" + "training until 2000: 76%|███████▌ | 1516/2000 [09:44<02:55, 2.76it/s, loss=0.444]" ] }, { @@ -60812,7 +60812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1516/2000 [10:00<03:00, 2.68it/s, loss=0.42] " + "training until 2000: 76%|███████▌ | 1516/2000 [09:44<02:55, 2.76it/s, loss=0.382]" ] }, { @@ -60820,7 +60820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1517/2000 [10:00<02:52, 2.80it/s, loss=0.42]" + "training until 2000: 76%|███████▌ | 1517/2000 [09:44<02:49, 2.86it/s, loss=0.382]" ] }, { @@ -60828,7 +60828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1517/2000 [10:00<02:52, 2.80it/s, loss=0.418]" + "training until 2000: 76%|███████▌ | 1517/2000 [09:44<02:49, 2.86it/s, loss=0.395]" ] }, { @@ -60836,7 +60836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1518/2000 [10:01<02:49, 2.85it/s, loss=0.418]" + "training until 2000: 76%|███████▌ | 1518/2000 [09:45<02:43, 2.94it/s, loss=0.395]" ] }, { @@ -60844,7 +60844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1518/2000 [10:01<02:49, 2.85it/s, loss=0.401]" + "training until 2000: 76%|███████▌ | 1518/2000 [09:45<02:43, 2.94it/s, loss=0.382]" ] }, { @@ -60852,7 +60852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1519/2000 [10:01<02:44, 2.93it/s, loss=0.401]" + "training until 2000: 76%|███████▌ | 1519/2000 [09:45<02:39, 3.01it/s, loss=0.382]" ] }, { @@ -60860,7 +60860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1519/2000 [10:01<02:44, 2.93it/s, loss=0.409]" + "training until 2000: 76%|███████▌ | 1519/2000 [09:45<02:39, 3.01it/s, loss=0.422]" ] }, { @@ -60868,7 +60868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1520/2000 [10:01<02:40, 3.00it/s, loss=0.409]" + "training until 2000: 76%|███████▌ | 1520/2000 [09:45<02:36, 3.06it/s, loss=0.422]" ] }, { @@ -60876,7 +60876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1520/2000 [10:01<02:40, 3.00it/s, loss=0.422]" + "training until 2000: 76%|███████▌ | 1520/2000 [09:45<02:36, 3.06it/s, loss=0.384]" ] }, { @@ -60884,7 +60884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1521/2000 [10:02<02:38, 3.01it/s, loss=0.422]" + "training until 2000: 76%|███████▌ | 1521/2000 [09:46<02:35, 3.09it/s, loss=0.384]" ] }, { @@ -60892,7 +60892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1521/2000 [10:02<02:38, 3.01it/s, loss=0.431]" + "training until 2000: 76%|███████▌ | 1521/2000 [09:46<02:35, 3.09it/s, loss=0.467]" ] }, { @@ -60900,7 +60900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1522/2000 [10:02<02:36, 3.06it/s, loss=0.431]" + "training until 2000: 76%|███████▌ | 1522/2000 [09:46<02:35, 3.07it/s, loss=0.467]" ] }, { @@ -60908,7 +60908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1522/2000 [10:02<02:36, 3.06it/s, loss=0.425]" + "training until 2000: 76%|███████▌ | 1522/2000 [09:46<02:35, 3.07it/s, loss=0.447]" ] }, { @@ -60916,7 +60916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1523/2000 [10:02<02:34, 3.08it/s, loss=0.425]" + "training until 2000: 76%|███████▌ | 1523/2000 [09:46<02:33, 3.10it/s, loss=0.447]" ] }, { @@ -60924,7 +60924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1523/2000 [10:02<02:34, 3.08it/s, loss=0.525]" + "training until 2000: 76%|███████▌ | 1523/2000 [09:46<02:33, 3.10it/s, loss=0.443]" ] }, { @@ -60932,7 +60932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1524/2000 [10:03<02:37, 3.03it/s, loss=0.525]" + "training until 2000: 76%|███████▌ | 1524/2000 [09:47<02:33, 3.11it/s, loss=0.443]" ] }, { @@ -60940,7 +60940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▌ | 1524/2000 [10:03<02:37, 3.03it/s, loss=0.425]" + "training until 2000: 76%|███████▌ | 1524/2000 [09:47<02:33, 3.11it/s, loss=0.396]" ] }, { @@ -60948,7 +60948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1525/2000 [10:03<02:36, 3.03it/s, loss=0.425]" + "training until 2000: 76%|███████▋ | 1525/2000 [09:47<02:32, 3.12it/s, loss=0.396]" ] }, { @@ -60956,7 +60956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1525/2000 [10:03<02:36, 3.03it/s, loss=0.398]" + "training until 2000: 76%|███████▋ | 1525/2000 [09:47<02:32, 3.12it/s, loss=0.402]" ] }, { @@ -60964,7 +60964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1526/2000 [10:03<02:37, 3.02it/s, loss=0.398]" + "training until 2000: 76%|███████▋ | 1526/2000 [09:47<02:30, 3.15it/s, loss=0.402]" ] }, { @@ -60972,7 +60972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1526/2000 [10:03<02:37, 3.02it/s, loss=0.402]" + "training until 2000: 76%|███████▋ | 1526/2000 [09:47<02:30, 3.15it/s, loss=0.546]" ] }, { @@ -60980,7 +60980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1527/2000 [10:04<02:35, 3.03it/s, loss=0.402]" + "training until 2000: 76%|███████▋ | 1527/2000 [09:48<02:31, 3.13it/s, loss=0.546]" ] }, { @@ -60988,7 +60988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1527/2000 [10:04<02:35, 3.03it/s, loss=0.425]" + "training until 2000: 76%|███████▋ | 1527/2000 [09:48<02:31, 3.13it/s, loss=0.407]" ] }, { @@ -60996,7 +60996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1528/2000 [10:04<02:36, 3.01it/s, loss=0.425]" + "training until 2000: 76%|███████▋ | 1528/2000 [09:48<02:31, 3.12it/s, loss=0.407]" ] }, { @@ -61004,7 +61004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1528/2000 [10:04<02:36, 3.01it/s, loss=0.392]" + "training until 2000: 76%|███████▋ | 1528/2000 [09:48<02:31, 3.12it/s, loss=0.398]" ] }, { @@ -61012,7 +61012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1529/2000 [10:04<02:35, 3.03it/s, loss=0.392]" + "training until 2000: 76%|███████▋ | 1529/2000 [09:48<02:30, 3.13it/s, loss=0.398]" ] }, { @@ -61020,7 +61020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1529/2000 [10:04<02:35, 3.03it/s, loss=0.4] " + "training until 2000: 76%|███████▋ | 1529/2000 [09:48<02:30, 3.13it/s, loss=0.453]" ] }, { @@ -61028,7 +61028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1530/2000 [10:05<02:39, 2.95it/s, loss=0.4]" + "training until 2000: 76%|███████▋ | 1530/2000 [09:49<02:29, 3.14it/s, loss=0.453]" ] }, { @@ -61036,7 +61036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 76%|███████▋ | 1530/2000 [10:05<02:39, 2.95it/s, loss=0.4]" + "training until 2000: 76%|███████▋ | 1530/2000 [09:49<02:29, 3.14it/s, loss=0.48] " ] }, { @@ -61044,7 +61044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1531/2000 [10:05<02:39, 2.95it/s, loss=0.4]" + "training until 2000: 77%|███████▋ | 1531/2000 [09:49<02:28, 3.15it/s, loss=0.48]" ] }, { @@ -61052,7 +61052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1531/2000 [10:05<02:39, 2.95it/s, loss=0.401]" + "training until 2000: 77%|███████▋ | 1531/2000 [09:49<02:28, 3.15it/s, loss=0.416]" ] }, { @@ -61060,7 +61060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1532/2000 [10:05<02:37, 2.98it/s, loss=0.401]" + "training until 2000: 77%|███████▋ | 1532/2000 [09:49<02:28, 3.15it/s, loss=0.416]" ] }, { @@ -61068,7 +61068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1532/2000 [10:05<02:37, 2.98it/s, loss=0.465]" + "training until 2000: 77%|███████▋ | 1532/2000 [09:49<02:28, 3.15it/s, loss=0.388]" ] }, { @@ -61076,7 +61076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1533/2000 [10:06<02:34, 3.03it/s, loss=0.465]" + "training until 2000: 77%|███████▋ | 1533/2000 [09:50<02:29, 3.12it/s, loss=0.388]" ] }, { @@ -61084,7 +61084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1533/2000 [10:06<02:34, 3.03it/s, loss=0.412]" + "training until 2000: 77%|███████▋ | 1533/2000 [09:50<02:29, 3.12it/s, loss=0.465]" ] }, { @@ -61092,7 +61092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1534/2000 [10:06<02:33, 3.03it/s, loss=0.412]" + "training until 2000: 77%|███████▋ | 1534/2000 [09:50<02:29, 3.11it/s, loss=0.465]" ] }, { @@ -61100,7 +61100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1534/2000 [10:06<02:33, 3.03it/s, loss=0.435]" + "training until 2000: 77%|███████▋ | 1534/2000 [09:50<02:29, 3.11it/s, loss=0.418]" ] }, { @@ -61108,7 +61108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1535/2000 [10:06<02:31, 3.07it/s, loss=0.435]" + "training until 2000: 77%|███████▋ | 1535/2000 [09:50<02:29, 3.12it/s, loss=0.418]" ] }, { @@ -61116,7 +61116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1535/2000 [10:06<02:31, 3.07it/s, loss=0.441]" + "training until 2000: 77%|███████▋ | 1535/2000 [09:50<02:29, 3.12it/s, loss=0.436]" ] }, { @@ -61124,7 +61124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1536/2000 [10:06<02:30, 3.08it/s, loss=0.441]" + "training until 2000: 77%|███████▋ | 1536/2000 [09:51<02:28, 3.13it/s, loss=0.436]" ] }, { @@ -61132,7 +61132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1536/2000 [10:06<02:30, 3.08it/s, loss=0.415]" + "training until 2000: 77%|███████▋ | 1536/2000 [09:51<02:28, 3.13it/s, loss=0.527]" ] }, { @@ -61140,7 +61140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1537/2000 [10:07<02:30, 3.08it/s, loss=0.415]" + "training until 2000: 77%|███████▋ | 1537/2000 [09:51<02:28, 3.11it/s, loss=0.527]" ] }, { @@ -61148,7 +61148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1537/2000 [10:07<02:30, 3.08it/s, loss=0.421]" + "training until 2000: 77%|███████▋ | 1537/2000 [09:51<02:28, 3.11it/s, loss=0.459]" ] }, { @@ -61156,7 +61156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1538/2000 [10:07<02:30, 3.06it/s, loss=0.421]" + "training until 2000: 77%|███████▋ | 1538/2000 [09:51<02:31, 3.06it/s, loss=0.459]" ] }, { @@ -61164,7 +61164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1538/2000 [10:07<02:30, 3.06it/s, loss=0.438]" + "training until 2000: 77%|███████▋ | 1538/2000 [09:51<02:31, 3.06it/s, loss=0.422]" ] }, { @@ -61172,7 +61172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1539/2000 [10:07<02:31, 3.04it/s, loss=0.438]" + "training until 2000: 77%|███████▋ | 1539/2000 [09:52<02:30, 3.07it/s, loss=0.422]" ] }, { @@ -61180,7 +61180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1539/2000 [10:07<02:31, 3.04it/s, loss=0.42] " + "training until 2000: 77%|███████▋ | 1539/2000 [09:52<02:30, 3.07it/s, loss=0.402]" ] }, { @@ -61188,7 +61188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1540/2000 [10:08<02:30, 3.05it/s, loss=0.42]" + "training until 2000: 77%|███████▋ | 1540/2000 [09:52<02:29, 3.07it/s, loss=0.402]" ] }, { @@ -61196,7 +61196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1540/2000 [10:08<02:30, 3.05it/s, loss=0.413]" + "training until 2000: 77%|███████▋ | 1540/2000 [09:52<02:29, 3.07it/s, loss=0.431]" ] }, { @@ -61204,7 +61204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1541/2000 [10:08<02:28, 3.09it/s, loss=0.413]" + "training until 2000: 77%|███████▋ | 1541/2000 [09:52<02:27, 3.12it/s, loss=0.431]" ] }, { @@ -61212,7 +61212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1541/2000 [10:08<02:28, 3.09it/s, loss=0.49] " + "training until 2000: 77%|███████▋ | 1541/2000 [09:52<02:27, 3.12it/s, loss=0.429]" ] }, { @@ -61220,7 +61220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1542/2000 [10:08<02:27, 3.11it/s, loss=0.49]" + "training until 2000: 77%|███████▋ | 1542/2000 [09:52<02:26, 3.14it/s, loss=0.429]" ] }, { @@ -61228,7 +61228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1542/2000 [10:08<02:27, 3.11it/s, loss=0.439]" + "training until 2000: 77%|███████▋ | 1542/2000 [09:52<02:26, 3.14it/s, loss=0.392]" ] }, { @@ -61236,7 +61236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1543/2000 [10:09<02:28, 3.09it/s, loss=0.439]" + "training until 2000: 77%|███████▋ | 1543/2000 [09:53<02:24, 3.16it/s, loss=0.392]" ] }, { @@ -61244,7 +61244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1543/2000 [10:09<02:28, 3.09it/s, loss=0.418]" + "training until 2000: 77%|███████▋ | 1543/2000 [09:53<02:24, 3.16it/s, loss=0.421]" ] }, { @@ -61252,7 +61252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1544/2000 [10:09<02:26, 3.10it/s, loss=0.418]" + "training until 2000: 77%|███████▋ | 1544/2000 [09:53<02:25, 3.13it/s, loss=0.421]" ] }, { @@ -61260,7 +61260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1544/2000 [10:09<02:26, 3.10it/s, loss=0.399]" + "training until 2000: 77%|███████▋ | 1544/2000 [09:53<02:25, 3.13it/s, loss=0.425]" ] }, { @@ -61268,7 +61268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1545/2000 [10:09<02:25, 3.12it/s, loss=0.399]" + "training until 2000: 77%|███████▋ | 1545/2000 [09:53<02:25, 3.12it/s, loss=0.425]" ] }, { @@ -61276,7 +61276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1545/2000 [10:09<02:25, 3.12it/s, loss=0.396]" + "training until 2000: 77%|███████▋ | 1545/2000 [09:53<02:25, 3.12it/s, loss=0.395]" ] }, { @@ -61284,7 +61284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1546/2000 [10:10<02:24, 3.14it/s, loss=0.396]" + "training until 2000: 77%|███████▋ | 1546/2000 [09:54<02:24, 3.14it/s, loss=0.395]" ] }, { @@ -61292,7 +61292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1546/2000 [10:10<02:24, 3.14it/s, loss=0.39] " + "training until 2000: 77%|███████▋ | 1546/2000 [09:54<02:24, 3.14it/s, loss=0.45] " ] }, { @@ -61300,7 +61300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1547/2000 [10:10<02:24, 3.14it/s, loss=0.39]" + "training until 2000: 77%|███████▋ | 1547/2000 [09:54<02:23, 3.16it/s, loss=0.45]" ] }, { @@ -61308,7 +61308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1547/2000 [10:10<02:24, 3.14it/s, loss=0.421]" + "training until 2000: 77%|███████▋ | 1547/2000 [09:54<02:23, 3.16it/s, loss=0.387]" ] }, { @@ -61316,7 +61316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1548/2000 [10:10<02:21, 3.18it/s, loss=0.421]" + "training until 2000: 77%|███████▋ | 1548/2000 [09:54<02:22, 3.17it/s, loss=0.387]" ] }, { @@ -61324,7 +61324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1548/2000 [10:10<02:21, 3.18it/s, loss=0.468]" + "training until 2000: 77%|███████▋ | 1548/2000 [09:54<02:22, 3.17it/s, loss=0.45] " ] }, { @@ -61332,7 +61332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1549/2000 [10:11<02:21, 3.18it/s, loss=0.468]" + "training until 2000: 77%|███████▋ | 1549/2000 [09:55<02:21, 3.19it/s, loss=0.45]" ] }, { @@ -61340,7 +61340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 77%|███████▋ | 1549/2000 [10:11<02:21, 3.18it/s, loss=0.413]" + "training until 2000: 77%|███████▋ | 1549/2000 [09:55<02:21, 3.19it/s, loss=0.601]" ] }, { @@ -61348,7 +61348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1550/2000 [10:11<02:23, 3.13it/s, loss=0.413]" + "training until 2000: 78%|███████▊ | 1550/2000 [09:55<02:21, 3.17it/s, loss=0.601]" ] }, { @@ -61356,7 +61356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1550/2000 [10:11<02:23, 3.13it/s, loss=0.411]" + "training until 2000: 78%|███████▊ | 1550/2000 [09:55<02:21, 3.17it/s, loss=0.389]" ] }, { @@ -61364,7 +61364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1551/2000 [10:11<02:22, 3.15it/s, loss=0.411]" + "training until 2000: 78%|███████▊ | 1551/2000 [09:55<02:23, 3.13it/s, loss=0.389]" ] }, { @@ -61372,7 +61372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1551/2000 [10:11<02:22, 3.15it/s, loss=0.435]" + "training until 2000: 78%|███████▊ | 1551/2000 [09:55<02:23, 3.13it/s, loss=0.41] " ] }, { @@ -61380,7 +61380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1552/2000 [10:12<02:23, 3.12it/s, loss=0.435]" + "training until 2000: 78%|███████▊ | 1552/2000 [09:56<02:23, 3.13it/s, loss=0.41]" ] }, { @@ -61388,7 +61388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1552/2000 [10:12<02:23, 3.12it/s, loss=0.398]" + "training until 2000: 78%|███████▊ | 1552/2000 [09:56<02:23, 3.13it/s, loss=0.407]" ] }, { @@ -61396,7 +61396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1553/2000 [10:12<02:21, 3.16it/s, loss=0.398]" + "training until 2000: 78%|███████▊ | 1553/2000 [09:56<02:23, 3.11it/s, loss=0.407]" ] }, { @@ -61404,7 +61404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1553/2000 [10:12<02:21, 3.16it/s, loss=0.414]" + "training until 2000: 78%|███████▊ | 1553/2000 [09:56<02:23, 3.11it/s, loss=0.41] " ] }, { @@ -61412,7 +61412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1554/2000 [10:12<02:20, 3.16it/s, loss=0.414]" + "training until 2000: 78%|███████▊ | 1554/2000 [09:57<02:56, 2.52it/s, loss=0.41]" ] }, { @@ -61420,7 +61420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1554/2000 [10:12<02:20, 3.16it/s, loss=0.41] " + "training until 2000: 78%|███████▊ | 1554/2000 [09:57<02:56, 2.52it/s, loss=0.396]" ] }, { @@ -61428,7 +61428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1555/2000 [10:13<02:21, 3.15it/s, loss=0.41]" + "training until 2000: 78%|███████▊ | 1555/2000 [09:57<02:47, 2.66it/s, loss=0.396]" ] }, { @@ -61436,7 +61436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1555/2000 [10:13<02:21, 3.15it/s, loss=0.482]" + "training until 2000: 78%|███████▊ | 1555/2000 [09:57<02:47, 2.66it/s, loss=0.385]" ] }, { @@ -61444,7 +61444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1556/2000 [10:13<02:21, 3.13it/s, loss=0.482]" + "training until 2000: 78%|███████▊ | 1556/2000 [09:57<02:39, 2.79it/s, loss=0.385]" ] }, { @@ -61452,7 +61452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1556/2000 [10:13<02:21, 3.13it/s, loss=0.395]" + "training until 2000: 78%|███████▊ | 1556/2000 [09:57<02:39, 2.79it/s, loss=0.383]" ] }, { @@ -61460,7 +61460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1557/2000 [10:13<02:23, 3.09it/s, loss=0.395]" + "training until 2000: 78%|███████▊ | 1557/2000 [09:58<02:33, 2.88it/s, loss=0.383]" ] }, { @@ -61468,7 +61468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1557/2000 [10:13<02:23, 3.09it/s, loss=0.4] " + "training until 2000: 78%|███████▊ | 1557/2000 [09:58<02:33, 2.88it/s, loss=0.382]" ] }, { @@ -61476,7 +61476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1558/2000 [10:14<02:22, 3.09it/s, loss=0.4]" + "training until 2000: 78%|███████▊ | 1558/2000 [09:58<02:29, 2.95it/s, loss=0.382]" ] }, { @@ -61484,7 +61484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1558/2000 [10:14<02:22, 3.09it/s, loss=0.463]" + "training until 2000: 78%|███████▊ | 1558/2000 [09:58<02:29, 2.95it/s, loss=0.393]" ] }, { @@ -61492,7 +61492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1559/2000 [10:14<02:23, 3.08it/s, loss=0.463]" + "training until 2000: 78%|███████▊ | 1559/2000 [09:58<02:27, 2.99it/s, loss=0.393]" ] }, { @@ -61500,7 +61500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1559/2000 [10:14<02:23, 3.08it/s, loss=0.412]" + "training until 2000: 78%|███████▊ | 1559/2000 [09:58<02:27, 2.99it/s, loss=0.46] " ] }, { @@ -61508,7 +61508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1560/2000 [10:14<02:22, 3.09it/s, loss=0.412]" + "training until 2000: 78%|███████▊ | 1560/2000 [09:58<02:24, 3.05it/s, loss=0.46]" ] }, { @@ -61516,7 +61516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1560/2000 [10:14<02:22, 3.09it/s, loss=0.392]" + "training until 2000: 78%|███████▊ | 1560/2000 [09:58<02:24, 3.05it/s, loss=0.397]" ] }, { @@ -61524,7 +61524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1561/2000 [10:15<02:22, 3.09it/s, loss=0.392]" + "training until 2000: 78%|███████▊ | 1561/2000 [09:59<02:23, 3.06it/s, loss=0.397]" ] }, { @@ -61532,7 +61532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1561/2000 [10:15<02:22, 3.09it/s, loss=0.435]" + "training until 2000: 78%|███████▊ | 1561/2000 [09:59<02:23, 3.06it/s, loss=0.444]" ] }, { @@ -61540,7 +61540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1562/2000 [10:15<02:54, 2.51it/s, loss=0.435]" + "training until 2000: 78%|███████▊ | 1562/2000 [09:59<02:23, 3.05it/s, loss=0.444]" ] }, { @@ -61548,7 +61548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1562/2000 [10:15<02:54, 2.51it/s, loss=0.425]" + "training until 2000: 78%|███████▊ | 1562/2000 [09:59<02:23, 3.05it/s, loss=0.434]" ] }, { @@ -61556,7 +61556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1563/2000 [10:15<02:43, 2.67it/s, loss=0.425]" + "training until 2000: 78%|███████▊ | 1563/2000 [09:59<02:20, 3.12it/s, loss=0.434]" ] }, { @@ -61564,7 +61564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1563/2000 [10:15<02:43, 2.67it/s, loss=0.418]" + "training until 2000: 78%|███████▊ | 1563/2000 [09:59<02:20, 3.12it/s, loss=0.412]" ] }, { @@ -61572,7 +61572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1564/2000 [10:16<02:38, 2.74it/s, loss=0.418]" + "training until 2000: 78%|███████▊ | 1564/2000 [10:00<02:18, 3.14it/s, loss=0.412]" ] }, { @@ -61580,7 +61580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1564/2000 [10:16<02:38, 2.74it/s, loss=0.389]" + "training until 2000: 78%|███████▊ | 1564/2000 [10:00<02:18, 3.14it/s, loss=0.38] " ] }, { @@ -61588,7 +61588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1565/2000 [10:16<02:33, 2.83it/s, loss=0.389]" + "training until 2000: 78%|███████▊ | 1565/2000 [10:00<02:18, 3.14it/s, loss=0.38]" ] }, { @@ -61596,7 +61596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1565/2000 [10:16<02:33, 2.83it/s, loss=0.403]" + "training until 2000: 78%|███████▊ | 1565/2000 [10:00<02:18, 3.14it/s, loss=0.403]" ] }, { @@ -61604,7 +61604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1566/2000 [10:16<02:29, 2.91it/s, loss=0.403]" + "training until 2000: 78%|███████▊ | 1566/2000 [10:00<02:17, 3.16it/s, loss=0.403]" ] }, { @@ -61612,7 +61612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1566/2000 [10:16<02:29, 2.91it/s, loss=0.472]" + "training until 2000: 78%|███████▊ | 1566/2000 [10:00<02:17, 3.16it/s, loss=0.436]" ] }, { @@ -61620,7 +61620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1567/2000 [10:17<02:25, 2.98it/s, loss=0.472]" + "training until 2000: 78%|███████▊ | 1567/2000 [10:01<02:17, 3.15it/s, loss=0.436]" ] }, { @@ -61628,7 +61628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1567/2000 [10:17<02:25, 2.98it/s, loss=0.41] " + "training until 2000: 78%|███████▊ | 1567/2000 [10:01<02:17, 3.15it/s, loss=0.425]" ] }, { @@ -61636,7 +61636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1568/2000 [10:17<02:23, 3.01it/s, loss=0.41]" + "training until 2000: 78%|███████▊ | 1568/2000 [10:01<02:16, 3.17it/s, loss=0.425]" ] }, { @@ -61644,7 +61644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1568/2000 [10:17<02:23, 3.01it/s, loss=0.496]" + "training until 2000: 78%|███████▊ | 1568/2000 [10:01<02:16, 3.17it/s, loss=0.428]" ] }, { @@ -61652,7 +61652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1569/2000 [10:17<02:21, 3.05it/s, loss=0.496]" + "training until 2000: 78%|███████▊ | 1569/2000 [10:01<02:15, 3.18it/s, loss=0.428]" ] }, { @@ -61660,7 +61660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1569/2000 [10:17<02:21, 3.05it/s, loss=0.501]" + "training until 2000: 78%|███████▊ | 1569/2000 [10:01<02:15, 3.18it/s, loss=0.382]" ] }, { @@ -61668,7 +61668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1570/2000 [10:18<02:19, 3.08it/s, loss=0.501]" + "training until 2000: 78%|███████▊ | 1570/2000 [10:02<02:14, 3.21it/s, loss=0.382]" ] }, { @@ -61676,7 +61676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 78%|███████▊ | 1570/2000 [10:18<02:19, 3.08it/s, loss=0.401]" + "training until 2000: 78%|███████▊ | 1570/2000 [10:02<02:14, 3.21it/s, loss=0.399]" ] }, { @@ -61684,7 +61684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1571/2000 [10:18<02:18, 3.10it/s, loss=0.401]" + "training until 2000: 79%|███████▊ | 1571/2000 [10:02<02:14, 3.18it/s, loss=0.399]" ] }, { @@ -61692,7 +61692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1571/2000 [10:18<02:18, 3.10it/s, loss=0.398]" + "training until 2000: 79%|███████▊ | 1571/2000 [10:02<02:14, 3.18it/s, loss=0.417]" ] }, { @@ -61700,7 +61700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1572/2000 [10:18<02:17, 3.11it/s, loss=0.398]" + "training until 2000: 79%|███████▊ | 1572/2000 [10:02<02:14, 3.19it/s, loss=0.417]" ] }, { @@ -61708,7 +61708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1572/2000 [10:18<02:17, 3.11it/s, loss=0.466]" + "training until 2000: 79%|███████▊ | 1572/2000 [10:02<02:14, 3.19it/s, loss=0.438]" ] }, { @@ -61716,7 +61716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1573/2000 [10:19<02:16, 3.12it/s, loss=0.466]" + "training until 2000: 79%|███████▊ | 1573/2000 [10:03<02:14, 3.18it/s, loss=0.438]" ] }, { @@ -61724,7 +61724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1573/2000 [10:19<02:16, 3.12it/s, loss=0.399]" + "training until 2000: 79%|███████▊ | 1573/2000 [10:03<02:14, 3.18it/s, loss=0.4] " ] }, { @@ -61732,7 +61732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1574/2000 [10:19<02:17, 3.10it/s, loss=0.399]" + "training until 2000: 79%|███████▊ | 1574/2000 [10:03<02:15, 3.14it/s, loss=0.4]" ] }, { @@ -61740,7 +61740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▊ | 1574/2000 [10:19<02:17, 3.10it/s, loss=0.425]" + "training until 2000: 79%|███████▊ | 1574/2000 [10:03<02:15, 3.14it/s, loss=0.387]" ] }, { @@ -61748,7 +61748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1575/2000 [10:19<02:17, 3.09it/s, loss=0.425]" + "training until 2000: 79%|███████▉ | 1575/2000 [10:03<02:16, 3.10it/s, loss=0.387]" ] }, { @@ -61756,7 +61756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1575/2000 [10:19<02:17, 3.09it/s, loss=0.401]" + "training until 2000: 79%|███████▉ | 1575/2000 [10:03<02:16, 3.10it/s, loss=0.397]" ] }, { @@ -61764,7 +61764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1576/2000 [10:20<02:17, 3.08it/s, loss=0.401]" + "training until 2000: 79%|███████▉ | 1576/2000 [10:04<02:14, 3.16it/s, loss=0.397]" ] }, { @@ -61772,7 +61772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1576/2000 [10:20<02:17, 3.08it/s, loss=0.464]" + "training until 2000: 79%|███████▉ | 1576/2000 [10:04<02:14, 3.16it/s, loss=0.573]" ] }, { @@ -61780,7 +61780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1577/2000 [10:20<02:18, 3.06it/s, loss=0.464]" + "training until 2000: 79%|███████▉ | 1577/2000 [10:04<02:15, 3.13it/s, loss=0.573]" ] }, { @@ -61788,7 +61788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1577/2000 [10:20<02:18, 3.06it/s, loss=0.512]" + "training until 2000: 79%|███████▉ | 1577/2000 [10:04<02:15, 3.13it/s, loss=0.463]" ] }, { @@ -61796,7 +61796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1578/2000 [10:20<02:16, 3.08it/s, loss=0.512]" + "training until 2000: 79%|███████▉ | 1578/2000 [10:04<02:14, 3.13it/s, loss=0.463]" ] }, { @@ -61804,7 +61804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1578/2000 [10:20<02:16, 3.08it/s, loss=0.398]" + "training until 2000: 79%|███████▉ | 1578/2000 [10:04<02:14, 3.13it/s, loss=0.412]" ] }, { @@ -61812,7 +61812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1579/2000 [10:21<02:15, 3.11it/s, loss=0.398]" + "training until 2000: 79%|███████▉ | 1579/2000 [10:05<02:13, 3.15it/s, loss=0.412]" ] }, { @@ -61820,7 +61820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1579/2000 [10:21<02:15, 3.11it/s, loss=0.414]" + "training until 2000: 79%|███████▉ | 1579/2000 [10:05<02:13, 3.15it/s, loss=0.43] " ] }, { @@ -61828,7 +61828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1580/2000 [10:21<02:14, 3.12it/s, loss=0.414]" + "training until 2000: 79%|███████▉ | 1580/2000 [10:05<02:12, 3.16it/s, loss=0.43]" ] }, { @@ -61836,7 +61836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1580/2000 [10:21<02:14, 3.12it/s, loss=0.409]" + "training until 2000: 79%|███████▉ | 1580/2000 [10:05<02:12, 3.16it/s, loss=0.401]" ] }, { @@ -61844,7 +61844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1581/2000 [10:21<02:14, 3.11it/s, loss=0.409]" + "training until 2000: 79%|███████▉ | 1581/2000 [10:05<02:12, 3.15it/s, loss=0.401]" ] }, { @@ -61852,7 +61852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1581/2000 [10:21<02:14, 3.11it/s, loss=0.474]" + "training until 2000: 79%|███████▉ | 1581/2000 [10:05<02:12, 3.15it/s, loss=0.401]" ] }, { @@ -61860,7 +61860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1582/2000 [10:22<02:15, 3.08it/s, loss=0.474]" + "training until 2000: 79%|███████▉ | 1582/2000 [10:05<02:10, 3.19it/s, loss=0.401]" ] }, { @@ -61868,7 +61868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1582/2000 [10:22<02:15, 3.08it/s, loss=0.418]" + "training until 2000: 79%|███████▉ | 1582/2000 [10:05<02:10, 3.19it/s, loss=0.591]" ] }, { @@ -61876,7 +61876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1583/2000 [10:22<02:13, 3.12it/s, loss=0.418]" + "training until 2000: 79%|███████▉ | 1583/2000 [10:06<02:10, 3.20it/s, loss=0.591]" ] }, { @@ -61884,7 +61884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1583/2000 [10:22<02:13, 3.12it/s, loss=0.409]" + "training until 2000: 79%|███████▉ | 1583/2000 [10:06<02:10, 3.20it/s, loss=0.4] " ] }, { @@ -61892,7 +61892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1584/2000 [10:22<02:13, 3.11it/s, loss=0.409]" + "training until 2000: 79%|███████▉ | 1584/2000 [10:06<02:10, 3.19it/s, loss=0.4]" ] }, { @@ -61900,7 +61900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1584/2000 [10:22<02:13, 3.11it/s, loss=0.449]" + "training until 2000: 79%|███████▉ | 1584/2000 [10:06<02:10, 3.19it/s, loss=0.466]" ] }, { @@ -61908,7 +61908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1585/2000 [10:22<02:13, 3.12it/s, loss=0.449]" + "training until 2000: 79%|███████▉ | 1585/2000 [10:06<02:10, 3.17it/s, loss=0.466]" ] }, { @@ -61916,7 +61916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1585/2000 [10:22<02:13, 3.12it/s, loss=0.405]" + "training until 2000: 79%|███████▉ | 1585/2000 [10:06<02:10, 3.17it/s, loss=0.407]" ] }, { @@ -61924,7 +61924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1586/2000 [10:23<02:11, 3.15it/s, loss=0.405]" + "training until 2000: 79%|███████▉ | 1586/2000 [10:07<02:09, 3.19it/s, loss=0.407]" ] }, { @@ -61932,7 +61932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1586/2000 [10:23<02:11, 3.15it/s, loss=0.402]" + "training until 2000: 79%|███████▉ | 1586/2000 [10:07<02:09, 3.19it/s, loss=0.409]" ] }, { @@ -61940,7 +61940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1587/2000 [10:23<02:11, 3.14it/s, loss=0.402]" + "training until 2000: 79%|███████▉ | 1587/2000 [10:07<02:09, 3.18it/s, loss=0.409]" ] }, { @@ -61948,7 +61948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1587/2000 [10:23<02:11, 3.14it/s, loss=0.442]" + "training until 2000: 79%|███████▉ | 1587/2000 [10:07<02:09, 3.18it/s, loss=0.39] " ] }, { @@ -61956,7 +61956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1588/2000 [10:23<02:12, 3.12it/s, loss=0.442]" + "training until 2000: 79%|███████▉ | 1588/2000 [10:07<02:08, 3.20it/s, loss=0.39]" ] }, { @@ -61964,7 +61964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1588/2000 [10:23<02:12, 3.12it/s, loss=0.402]" + "training until 2000: 79%|███████▉ | 1588/2000 [10:07<02:08, 3.20it/s, loss=0.506]" ] }, { @@ -61972,7 +61972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1589/2000 [10:24<02:11, 3.13it/s, loss=0.402]" + "training until 2000: 79%|███████▉ | 1589/2000 [10:08<02:08, 3.19it/s, loss=0.506]" ] }, { @@ -61980,7 +61980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 79%|███████▉ | 1589/2000 [10:24<02:11, 3.13it/s, loss=0.51] " + "training until 2000: 79%|███████▉ | 1589/2000 [10:08<02:08, 3.19it/s, loss=0.438]" ] }, { @@ -61988,7 +61988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1590/2000 [10:24<02:10, 3.15it/s, loss=0.51]" + "training until 2000: 80%|███████▉ | 1590/2000 [10:08<02:08, 3.18it/s, loss=0.438]" ] }, { @@ -61996,7 +61996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1590/2000 [10:24<02:10, 3.15it/s, loss=0.422]" + "training until 2000: 80%|███████▉ | 1590/2000 [10:08<02:08, 3.18it/s, loss=0.442]" ] }, { @@ -62004,7 +62004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1591/2000 [10:24<02:09, 3.16it/s, loss=0.422]" + "training until 2000: 80%|███████▉ | 1591/2000 [10:08<02:08, 3.19it/s, loss=0.442]" ] }, { @@ -62012,7 +62012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1591/2000 [10:24<02:09, 3.16it/s, loss=0.418]" + "training until 2000: 80%|███████▉ | 1591/2000 [10:08<02:08, 3.19it/s, loss=0.39] " ] }, { @@ -62020,7 +62020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1592/2000 [10:25<02:10, 3.14it/s, loss=0.418]" + "training until 2000: 80%|███████▉ | 1592/2000 [10:09<02:07, 3.19it/s, loss=0.39]" ] }, { @@ -62028,7 +62028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1592/2000 [10:25<02:10, 3.14it/s, loss=0.393]" + "training until 2000: 80%|███████▉ | 1592/2000 [10:09<02:07, 3.19it/s, loss=0.374]" ] }, { @@ -62036,7 +62036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1593/2000 [10:25<02:08, 3.16it/s, loss=0.393]" + "training until 2000: 80%|███████▉ | 1593/2000 [10:09<02:08, 3.18it/s, loss=0.374]" ] }, { @@ -62044,7 +62044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1593/2000 [10:25<02:08, 3.16it/s, loss=0.429]" + "training until 2000: 80%|███████▉ | 1593/2000 [10:09<02:08, 3.18it/s, loss=0.402]" ] }, { @@ -62052,7 +62052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1594/2000 [10:25<02:08, 3.15it/s, loss=0.429]" + "training until 2000: 80%|███████▉ | 1594/2000 [10:09<02:10, 3.11it/s, loss=0.402]" ] }, { @@ -62060,7 +62060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1594/2000 [10:25<02:08, 3.15it/s, loss=0.403]" + "training until 2000: 80%|███████▉ | 1594/2000 [10:09<02:10, 3.11it/s, loss=0.384]" ] }, { @@ -62068,7 +62068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1595/2000 [10:26<02:08, 3.15it/s, loss=0.403]" + "training until 2000: 80%|███████▉ | 1595/2000 [10:10<02:10, 3.11it/s, loss=0.384]" ] }, { @@ -62076,7 +62076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1595/2000 [10:26<02:08, 3.15it/s, loss=0.43] " + "training until 2000: 80%|███████▉ | 1595/2000 [10:10<02:10, 3.11it/s, loss=0.407]" ] }, { @@ -62084,7 +62084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1596/2000 [10:26<02:08, 3.14it/s, loss=0.43]" + "training until 2000: 80%|███████▉ | 1596/2000 [10:10<02:09, 3.12it/s, loss=0.407]" ] }, { @@ -62092,7 +62092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1596/2000 [10:26<02:08, 3.14it/s, loss=0.408]" + "training until 2000: 80%|███████▉ | 1596/2000 [10:10<02:09, 3.12it/s, loss=0.406]" ] }, { @@ -62100,7 +62100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1597/2000 [10:26<02:09, 3.11it/s, loss=0.408]" + "training until 2000: 80%|███████▉ | 1597/2000 [10:10<02:07, 3.17it/s, loss=0.406]" ] }, { @@ -62108,7 +62108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1597/2000 [10:26<02:09, 3.11it/s, loss=0.448]" + "training until 2000: 80%|███████▉ | 1597/2000 [10:10<02:07, 3.17it/s, loss=0.472]" ] }, { @@ -62116,7 +62116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1598/2000 [10:27<02:08, 3.13it/s, loss=0.448]" + "training until 2000: 80%|███████▉ | 1598/2000 [10:11<02:08, 3.14it/s, loss=0.472]" ] }, { @@ -62124,7 +62124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1598/2000 [10:27<02:08, 3.13it/s, loss=0.449]" + "training until 2000: 80%|███████▉ | 1598/2000 [10:11<02:08, 3.14it/s, loss=0.407]" ] }, { @@ -62132,7 +62132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1599/2000 [10:27<02:07, 3.13it/s, loss=0.449]" + "training until 2000: 80%|███████▉ | 1599/2000 [10:11<02:08, 3.12it/s, loss=0.407]" ] }, { @@ -62140,7 +62140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|███████▉ | 1599/2000 [10:27<02:07, 3.13it/s, loss=0.403]" + "training until 2000: 80%|███████▉ | 1599/2000 [10:11<02:08, 3.12it/s, loss=0.51] " ] }, { @@ -62148,7 +62148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1600/2000 [10:27<02:09, 3.09it/s, loss=0.403]" + "training until 2000: 80%|████████ | 1600/2000 [10:11<02:08, 3.11it/s, loss=0.51]" ] }, { @@ -62156,7 +62156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1600/2000 [10:27<02:09, 3.09it/s, loss=0.396]" + "training until 2000: 80%|████████ | 1600/2000 [10:11<02:08, 3.11it/s, loss=0.403]" ] }, { @@ -62164,7 +62164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1601/2000 [10:28<02:11, 3.04it/s, loss=0.396]" + "training until 2000: 80%|████████ | 1601/2000 [10:11<02:08, 3.12it/s, loss=0.403]" ] }, { @@ -62172,7 +62172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1601/2000 [10:28<02:11, 3.04it/s, loss=0.448]" + "training until 2000: 80%|████████ | 1601/2000 [10:11<02:08, 3.12it/s, loss=0.384]" ] }, { @@ -62180,7 +62180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1602/2000 [10:28<02:10, 3.04it/s, loss=0.448]" + "training until 2000: 80%|████████ | 1602/2000 [10:12<02:07, 3.12it/s, loss=0.384]" ] }, { @@ -62188,7 +62188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1602/2000 [10:28<02:10, 3.04it/s, loss=0.404]" + "training until 2000: 80%|████████ | 1602/2000 [10:12<02:07, 3.12it/s, loss=0.39] " ] }, { @@ -62196,7 +62196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1603/2000 [10:28<02:09, 3.06it/s, loss=0.404]" + "training until 2000: 80%|████████ | 1603/2000 [10:12<02:07, 3.11it/s, loss=0.39]" ] }, { @@ -62204,7 +62204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1603/2000 [10:28<02:09, 3.06it/s, loss=0.401]" + "training until 2000: 80%|████████ | 1603/2000 [10:12<02:07, 3.11it/s, loss=0.385]" ] }, { @@ -62212,7 +62212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1604/2000 [10:29<02:07, 3.10it/s, loss=0.401]" + "training until 2000: 80%|████████ | 1604/2000 [10:12<02:06, 3.12it/s, loss=0.385]" ] }, { @@ -62220,7 +62220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1604/2000 [10:29<02:07, 3.10it/s, loss=0.4] " + "training until 2000: 80%|████████ | 1604/2000 [10:12<02:06, 3.12it/s, loss=0.398]" ] }, { @@ -62228,7 +62228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1605/2000 [10:29<02:07, 3.09it/s, loss=0.4]" + "training until 2000: 80%|████████ | 1605/2000 [10:13<02:06, 3.12it/s, loss=0.398]" ] }, { @@ -62236,7 +62236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1605/2000 [10:29<02:07, 3.09it/s, loss=0.421]" + "training until 2000: 80%|████████ | 1605/2000 [10:13<02:06, 3.12it/s, loss=0.418]" ] }, { @@ -62244,7 +62244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1606/2000 [10:29<02:06, 3.12it/s, loss=0.421]" + "training until 2000: 80%|████████ | 1606/2000 [10:13<02:06, 3.13it/s, loss=0.418]" ] }, { @@ -62252,7 +62252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1606/2000 [10:29<02:06, 3.12it/s, loss=0.42] " + "training until 2000: 80%|████████ | 1606/2000 [10:13<02:06, 3.13it/s, loss=0.391]" ] }, { @@ -62260,7 +62260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1607/2000 [10:30<02:08, 3.07it/s, loss=0.42]" + "training until 2000: 80%|████████ | 1607/2000 [10:13<02:07, 3.08it/s, loss=0.391]" ] }, { @@ -62268,7 +62268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1607/2000 [10:30<02:08, 3.07it/s, loss=0.412]" + "training until 2000: 80%|████████ | 1607/2000 [10:13<02:07, 3.08it/s, loss=0.374]" ] }, { @@ -62276,7 +62276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1608/2000 [10:30<02:06, 3.11it/s, loss=0.412]" + "training until 2000: 80%|████████ | 1608/2000 [10:14<02:07, 3.07it/s, loss=0.374]" ] }, { @@ -62284,7 +62284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1608/2000 [10:30<02:06, 3.11it/s, loss=0.438]" + "training until 2000: 80%|████████ | 1608/2000 [10:14<02:07, 3.07it/s, loss=0.397]" ] }, { @@ -62292,7 +62292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1609/2000 [10:30<02:04, 3.14it/s, loss=0.438]" + "training until 2000: 80%|████████ | 1609/2000 [10:14<02:06, 3.09it/s, loss=0.397]" ] }, { @@ -62300,7 +62300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1609/2000 [10:30<02:04, 3.14it/s, loss=0.419]" + "training until 2000: 80%|████████ | 1609/2000 [10:14<02:06, 3.09it/s, loss=0.389]" ] }, { @@ -62308,7 +62308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1610/2000 [10:31<02:04, 3.13it/s, loss=0.419]" + "training until 2000: 80%|████████ | 1610/2000 [10:14<02:08, 3.04it/s, loss=0.389]" ] }, { @@ -62316,7 +62316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 80%|████████ | 1610/2000 [10:31<02:04, 3.13it/s, loss=0.417]" + "training until 2000: 80%|████████ | 1610/2000 [10:14<02:08, 3.04it/s, loss=0.52] " ] }, { @@ -62324,7 +62324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1611/2000 [10:31<02:05, 3.10it/s, loss=0.417]" + "training until 2000: 81%|████████ | 1611/2000 [10:15<02:07, 3.06it/s, loss=0.52]" ] }, { @@ -62332,7 +62332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1611/2000 [10:31<02:05, 3.10it/s, loss=0.543]" + "training until 2000: 81%|████████ | 1611/2000 [10:15<02:07, 3.06it/s, loss=0.408]" ] }, { @@ -62340,7 +62340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1612/2000 [10:31<02:04, 3.13it/s, loss=0.543]" + "training until 2000: 81%|████████ | 1612/2000 [10:15<02:05, 3.08it/s, loss=0.408]" ] }, { @@ -62348,7 +62348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1612/2000 [10:31<02:04, 3.13it/s, loss=0.42] " + "training until 2000: 81%|████████ | 1612/2000 [10:15<02:05, 3.08it/s, loss=0.384]" ] }, { @@ -62356,7 +62356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1613/2000 [10:31<02:03, 3.13it/s, loss=0.42]" + "training until 2000: 81%|████████ | 1613/2000 [10:15<02:04, 3.11it/s, loss=0.384]" ] }, { @@ -62364,7 +62364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1613/2000 [10:31<02:03, 3.13it/s, loss=0.394]" + "training until 2000: 81%|████████ | 1613/2000 [10:15<02:04, 3.11it/s, loss=0.433]" ] }, { @@ -62372,7 +62372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1614/2000 [10:32<02:03, 3.13it/s, loss=0.394]" + "training until 2000: 81%|████████ | 1614/2000 [10:16<02:04, 3.10it/s, loss=0.433]" ] }, { @@ -62380,7 +62380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1614/2000 [10:32<02:03, 3.13it/s, loss=0.418]" + "training until 2000: 81%|████████ | 1614/2000 [10:16<02:04, 3.10it/s, loss=0.403]" ] }, { @@ -62388,7 +62388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1615/2000 [10:32<02:03, 3.13it/s, loss=0.418]" + "training until 2000: 81%|████████ | 1615/2000 [10:16<02:05, 3.07it/s, loss=0.403]" ] }, { @@ -62396,7 +62396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1615/2000 [10:32<02:03, 3.13it/s, loss=0.427]" + "training until 2000: 81%|████████ | 1615/2000 [10:16<02:05, 3.07it/s, loss=0.436]" ] }, { @@ -62404,7 +62404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1616/2000 [10:32<02:03, 3.11it/s, loss=0.427]" + "training until 2000: 81%|████████ | 1616/2000 [10:16<02:05, 3.07it/s, loss=0.436]" ] }, { @@ -62412,7 +62412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1616/2000 [10:32<02:03, 3.11it/s, loss=0.383]" + "training until 2000: 81%|████████ | 1616/2000 [10:16<02:05, 3.07it/s, loss=0.395]" ] }, { @@ -62420,7 +62420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1617/2000 [10:33<02:02, 3.14it/s, loss=0.383]" + "training until 2000: 81%|████████ | 1617/2000 [10:17<02:05, 3.06it/s, loss=0.395]" ] }, { @@ -62428,7 +62428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1617/2000 [10:33<02:02, 3.14it/s, loss=0.394]" + "training until 2000: 81%|████████ | 1617/2000 [10:17<02:05, 3.06it/s, loss=0.44] " ] }, { @@ -62436,7 +62436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1618/2000 [10:33<02:03, 3.10it/s, loss=0.394]" + "training until 2000: 81%|████████ | 1618/2000 [10:17<02:04, 3.07it/s, loss=0.44]" ] }, { @@ -62444,7 +62444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1618/2000 [10:33<02:03, 3.10it/s, loss=0.465]" + "training until 2000: 81%|████████ | 1618/2000 [10:17<02:04, 3.07it/s, loss=0.422]" ] }, { @@ -62452,7 +62452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1619/2000 [10:33<02:03, 3.10it/s, loss=0.465]" + "training until 2000: 81%|████████ | 1619/2000 [10:17<02:03, 3.09it/s, loss=0.422]" ] }, { @@ -62460,7 +62460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1619/2000 [10:33<02:03, 3.10it/s, loss=0.444]" + "training until 2000: 81%|████████ | 1619/2000 [10:17<02:03, 3.09it/s, loss=0.427]" ] }, { @@ -62468,7 +62468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1620/2000 [10:34<02:02, 3.11it/s, loss=0.444]" + "training until 2000: 81%|████████ | 1620/2000 [10:18<02:32, 2.49it/s, loss=0.427]" ] }, { @@ -62476,7 +62476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1620/2000 [10:34<02:02, 3.11it/s, loss=0.385]" + "training until 2000: 81%|████████ | 1620/2000 [10:18<02:32, 2.49it/s, loss=0.43] " ] }, { @@ -62484,7 +62484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1621/2000 [10:34<02:00, 3.13it/s, loss=0.385]" + "training until 2000: 81%|████████ | 1621/2000 [10:18<02:23, 2.63it/s, loss=0.43]" ] }, { @@ -62492,7 +62492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1621/2000 [10:34<02:00, 3.13it/s, loss=0.383]" + "training until 2000: 81%|████████ | 1621/2000 [10:18<02:23, 2.63it/s, loss=0.413]" ] }, { @@ -62500,7 +62500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1622/2000 [10:34<02:01, 3.12it/s, loss=0.383]" + "training until 2000: 81%|████████ | 1622/2000 [10:19<02:17, 2.75it/s, loss=0.413]" ] }, { @@ -62508,7 +62508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1622/2000 [10:34<02:01, 3.12it/s, loss=0.397]" + "training until 2000: 81%|████████ | 1622/2000 [10:19<02:17, 2.75it/s, loss=0.41] " ] }, { @@ -62516,7 +62516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1623/2000 [10:35<01:59, 3.15it/s, loss=0.397]" + "training until 2000: 81%|████████ | 1623/2000 [10:19<02:11, 2.86it/s, loss=0.41]" ] }, { @@ -62524,7 +62524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1623/2000 [10:35<01:59, 3.15it/s, loss=0.389]" + "training until 2000: 81%|████████ | 1623/2000 [10:19<02:11, 2.86it/s, loss=0.46]" ] }, { @@ -62532,7 +62532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1624/2000 [10:35<01:59, 3.13it/s, loss=0.389]" + "training until 2000: 81%|████████ | 1624/2000 [10:19<02:07, 2.95it/s, loss=0.46]" ] }, { @@ -62540,7 +62540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████ | 1624/2000 [10:35<01:59, 3.13it/s, loss=0.398]" + "training until 2000: 81%|████████ | 1624/2000 [10:19<02:07, 2.95it/s, loss=0.401]" ] }, { @@ -62548,7 +62548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1625/2000 [10:35<02:00, 3.12it/s, loss=0.398]" + "training until 2000: 81%|████████▏ | 1625/2000 [10:19<02:04, 3.01it/s, loss=0.401]" ] }, { @@ -62556,7 +62556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1625/2000 [10:35<02:00, 3.12it/s, loss=0.489]" + "training until 2000: 81%|████████▏ | 1625/2000 [10:19<02:04, 3.01it/s, loss=0.392]" ] }, { @@ -62564,7 +62564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1626/2000 [10:36<01:59, 3.13it/s, loss=0.489]" + "training until 2000: 81%|████████▏ | 1626/2000 [10:20<02:02, 3.05it/s, loss=0.392]" ] }, { @@ -62572,7 +62572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1626/2000 [10:36<01:59, 3.13it/s, loss=0.405]" + "training until 2000: 81%|████████▏ | 1626/2000 [10:20<02:02, 3.05it/s, loss=0.381]" ] }, { @@ -62580,7 +62580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1627/2000 [10:36<01:58, 3.15it/s, loss=0.405]" + "training until 2000: 81%|████████▏ | 1627/2000 [10:20<02:02, 3.04it/s, loss=0.381]" ] }, { @@ -62588,7 +62588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1627/2000 [10:36<01:58, 3.15it/s, loss=0.399]" + "training until 2000: 81%|████████▏ | 1627/2000 [10:20<02:02, 3.04it/s, loss=0.377]" ] }, { @@ -62596,7 +62596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1628/2000 [10:37<02:26, 2.54it/s, loss=0.399]" + "training until 2000: 81%|████████▏ | 1628/2000 [10:20<02:01, 3.06it/s, loss=0.377]" ] }, { @@ -62604,7 +62604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1628/2000 [10:37<02:26, 2.54it/s, loss=0.397]" + "training until 2000: 81%|████████▏ | 1628/2000 [10:20<02:01, 3.06it/s, loss=0.447]" ] }, { @@ -62612,7 +62612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1629/2000 [10:37<02:18, 2.69it/s, loss=0.397]" + "training until 2000: 81%|████████▏ | 1629/2000 [10:21<02:00, 3.07it/s, loss=0.447]" ] }, { @@ -62620,7 +62620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 81%|████████▏ | 1629/2000 [10:37<02:18, 2.69it/s, loss=0.399]" + "training until 2000: 81%|████████▏ | 1629/2000 [10:21<02:00, 3.07it/s, loss=0.425]" ] }, { @@ -62628,7 +62628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1630/2000 [10:37<02:13, 2.78it/s, loss=0.399]" + "training until 2000: 82%|████████▏ | 1630/2000 [10:21<01:59, 3.10it/s, loss=0.425]" ] }, { @@ -62636,7 +62636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1630/2000 [10:37<02:13, 2.78it/s, loss=0.429]" + "training until 2000: 82%|████████▏ | 1630/2000 [10:21<01:59, 3.10it/s, loss=0.372]" ] }, { @@ -62644,7 +62644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1631/2000 [10:37<02:08, 2.88it/s, loss=0.429]" + "training until 2000: 82%|████████▏ | 1631/2000 [10:21<01:59, 3.09it/s, loss=0.372]" ] }, { @@ -62652,7 +62652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1631/2000 [10:37<02:08, 2.88it/s, loss=0.389]" + "training until 2000: 82%|████████▏ | 1631/2000 [10:21<01:59, 3.09it/s, loss=0.373]" ] }, { @@ -62660,7 +62660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1632/2000 [10:38<02:04, 2.95it/s, loss=0.389]" + "training until 2000: 82%|████████▏ | 1632/2000 [10:22<02:01, 3.04it/s, loss=0.373]" ] }, { @@ -62668,7 +62668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1632/2000 [10:38<02:04, 2.95it/s, loss=0.438]" + "training until 2000: 82%|████████▏ | 1632/2000 [10:22<02:01, 3.04it/s, loss=0.379]" ] }, { @@ -62676,7 +62676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1633/2000 [10:38<02:03, 2.98it/s, loss=0.438]" + "training until 2000: 82%|████████▏ | 1633/2000 [10:22<02:00, 3.05it/s, loss=0.379]" ] }, { @@ -62684,7 +62684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1633/2000 [10:38<02:03, 2.98it/s, loss=0.391]" + "training until 2000: 82%|████████▏ | 1633/2000 [10:22<02:00, 3.05it/s, loss=0.424]" ] }, { @@ -62692,7 +62692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1634/2000 [10:38<02:00, 3.05it/s, loss=0.391]" + "training until 2000: 82%|████████▏ | 1634/2000 [10:22<02:00, 3.04it/s, loss=0.424]" ] }, { @@ -62700,7 +62700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1634/2000 [10:38<02:00, 3.05it/s, loss=0.405]" + "training until 2000: 82%|████████▏ | 1634/2000 [10:22<02:00, 3.04it/s, loss=0.391]" ] }, { @@ -62708,7 +62708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1635/2000 [10:39<01:58, 3.07it/s, loss=0.405]" + "training until 2000: 82%|████████▏ | 1635/2000 [10:23<01:59, 3.06it/s, loss=0.391]" ] }, { @@ -62716,7 +62716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1635/2000 [10:39<01:58, 3.07it/s, loss=0.478]" + "training until 2000: 82%|████████▏ | 1635/2000 [10:23<01:59, 3.06it/s, loss=0.552]" ] }, { @@ -62724,7 +62724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1636/2000 [10:39<01:56, 3.11it/s, loss=0.478]" + "training until 2000: 82%|████████▏ | 1636/2000 [10:23<01:58, 3.06it/s, loss=0.552]" ] }, { @@ -62732,7 +62732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1636/2000 [10:39<01:56, 3.11it/s, loss=0.393]" + "training until 2000: 82%|████████▏ | 1636/2000 [10:23<01:58, 3.06it/s, loss=0.619]" ] }, { @@ -62740,7 +62740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1637/2000 [10:39<01:58, 3.06it/s, loss=0.393]" + "training until 2000: 82%|████████▏ | 1637/2000 [10:23<01:58, 3.06it/s, loss=0.619]" ] }, { @@ -62748,7 +62748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1637/2000 [10:39<01:58, 3.06it/s, loss=0.409]" + "training until 2000: 82%|████████▏ | 1637/2000 [10:23<01:58, 3.06it/s, loss=0.449]" ] }, { @@ -62756,7 +62756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1638/2000 [10:40<01:57, 3.08it/s, loss=0.409]" + "training until 2000: 82%|████████▏ | 1638/2000 [10:24<02:00, 3.00it/s, loss=0.449]" ] }, { @@ -62764,7 +62764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1638/2000 [10:40<01:57, 3.08it/s, loss=0.404]" + "training until 2000: 82%|████████▏ | 1638/2000 [10:24<02:00, 3.00it/s, loss=0.57] " ] }, { @@ -62772,7 +62772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1639/2000 [10:40<01:58, 3.05it/s, loss=0.404]" + "training until 2000: 82%|████████▏ | 1639/2000 [10:24<01:59, 3.02it/s, loss=0.57]" ] }, { @@ -62780,7 +62780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1639/2000 [10:40<01:58, 3.05it/s, loss=0.39] " + "training until 2000: 82%|████████▏ | 1639/2000 [10:24<01:59, 3.02it/s, loss=0.395]" ] }, { @@ -62788,7 +62788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1640/2000 [10:40<01:57, 3.07it/s, loss=0.39]" + "training until 2000: 82%|████████▏ | 1640/2000 [10:24<01:58, 3.05it/s, loss=0.395]" ] }, { @@ -62796,7 +62796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1640/2000 [10:40<01:57, 3.07it/s, loss=0.502]" + "training until 2000: 82%|████████▏ | 1640/2000 [10:24<01:58, 3.05it/s, loss=0.405]" ] }, { @@ -62804,7 +62804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1641/2000 [10:41<01:55, 3.11it/s, loss=0.502]" + "training until 2000: 82%|████████▏ | 1641/2000 [10:25<01:58, 3.02it/s, loss=0.405]" ] }, { @@ -62812,7 +62812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1641/2000 [10:41<01:55, 3.11it/s, loss=0.422]" + "training until 2000: 82%|████████▏ | 1641/2000 [10:25<01:58, 3.02it/s, loss=0.405]" ] }, { @@ -62820,7 +62820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1642/2000 [10:41<01:54, 3.11it/s, loss=0.422]" + "training until 2000: 82%|████████▏ | 1642/2000 [10:25<01:57, 3.05it/s, loss=0.405]" ] }, { @@ -62828,7 +62828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1642/2000 [10:41<01:54, 3.11it/s, loss=0.421]" + "training until 2000: 82%|████████▏ | 1642/2000 [10:25<01:57, 3.05it/s, loss=0.457]" ] }, { @@ -62836,7 +62836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1643/2000 [10:41<01:54, 3.12it/s, loss=0.421]" + "training until 2000: 82%|████████▏ | 1643/2000 [10:25<01:59, 3.00it/s, loss=0.457]" ] }, { @@ -62844,7 +62844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1643/2000 [10:41<01:54, 3.12it/s, loss=0.408]" + "training until 2000: 82%|████████▏ | 1643/2000 [10:25<01:59, 3.00it/s, loss=0.453]" ] }, { @@ -62852,7 +62852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1644/2000 [10:42<01:54, 3.11it/s, loss=0.408]" + "training until 2000: 82%|████████▏ | 1644/2000 [10:26<01:57, 3.03it/s, loss=0.453]" ] }, { @@ -62860,7 +62860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1644/2000 [10:42<01:54, 3.11it/s, loss=0.387]" + "training until 2000: 82%|████████▏ | 1644/2000 [10:26<01:57, 3.03it/s, loss=0.447]" ] }, { @@ -62868,7 +62868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1645/2000 [10:42<01:55, 3.07it/s, loss=0.387]" + "training until 2000: 82%|████████▏ | 1645/2000 [10:26<01:55, 3.07it/s, loss=0.447]" ] }, { @@ -62876,7 +62876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1645/2000 [10:42<01:55, 3.07it/s, loss=0.393]" + "training until 2000: 82%|████████▏ | 1645/2000 [10:26<01:55, 3.07it/s, loss=0.376]" ] }, { @@ -62884,7 +62884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1646/2000 [10:42<01:53, 3.11it/s, loss=0.393]" + "training until 2000: 82%|████████▏ | 1646/2000 [10:26<01:54, 3.08it/s, loss=0.376]" ] }, { @@ -62892,7 +62892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1646/2000 [10:42<01:53, 3.11it/s, loss=0.45] " + "training until 2000: 82%|████████▏ | 1646/2000 [10:26<01:54, 3.08it/s, loss=0.407]" ] }, { @@ -62900,7 +62900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1647/2000 [10:43<01:53, 3.10it/s, loss=0.45]" + "training until 2000: 82%|████████▏ | 1647/2000 [10:27<01:55, 3.07it/s, loss=0.407]" ] }, { @@ -62908,7 +62908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1647/2000 [10:43<01:53, 3.10it/s, loss=0.404]" + "training until 2000: 82%|████████▏ | 1647/2000 [10:27<01:55, 3.07it/s, loss=0.429]" ] }, { @@ -62916,7 +62916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1648/2000 [10:43<01:54, 3.08it/s, loss=0.404]" + "training until 2000: 82%|████████▏ | 1648/2000 [10:27<01:55, 3.04it/s, loss=0.429]" ] }, { @@ -62924,7 +62924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1648/2000 [10:43<01:54, 3.08it/s, loss=0.39] " + "training until 2000: 82%|████████▏ | 1648/2000 [10:27<01:55, 3.04it/s, loss=0.395]" ] }, { @@ -62932,7 +62932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1649/2000 [10:43<01:53, 3.10it/s, loss=0.39]" + "training until 2000: 82%|████████▏ | 1649/2000 [10:27<01:55, 3.03it/s, loss=0.395]" ] }, { @@ -62940,7 +62940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▏ | 1649/2000 [10:43<01:53, 3.10it/s, loss=0.392]" + "training until 2000: 82%|████████▏ | 1649/2000 [10:27<01:55, 3.03it/s, loss=0.45] " ] }, { @@ -62948,7 +62948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▎ | 1650/2000 [10:44<01:52, 3.11it/s, loss=0.392]" + "training until 2000: 82%|████████▎ | 1650/2000 [10:28<01:55, 3.03it/s, loss=0.45]" ] }, { @@ -62956,7 +62956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 82%|████████▎ | 1650/2000 [10:44<01:52, 3.11it/s, loss=0.383]" + "training until 2000: 82%|████████▎ | 1650/2000 [10:28<01:55, 3.03it/s, loss=0.498]" ] }, { @@ -62964,7 +62964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1651/2000 [10:44<01:51, 3.12it/s, loss=0.383]" + "training until 2000: 83%|████████▎ | 1651/2000 [10:28<01:54, 3.06it/s, loss=0.498]" ] }, { @@ -62972,7 +62972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1651/2000 [10:44<01:51, 3.12it/s, loss=0.387]" + "training until 2000: 83%|████████▎ | 1651/2000 [10:28<01:54, 3.06it/s, loss=0.424]" ] }, { @@ -62980,7 +62980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1652/2000 [10:44<01:51, 3.12it/s, loss=0.387]" + "training until 2000: 83%|████████▎ | 1652/2000 [10:28<01:52, 3.09it/s, loss=0.424]" ] }, { @@ -62988,7 +62988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1652/2000 [10:44<01:51, 3.12it/s, loss=0.381]" + "training until 2000: 83%|████████▎ | 1652/2000 [10:28<01:52, 3.09it/s, loss=0.38] " ] }, { @@ -62996,7 +62996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1653/2000 [10:45<01:51, 3.11it/s, loss=0.381]" + "training until 2000: 83%|████████▎ | 1653/2000 [10:29<01:52, 3.08it/s, loss=0.38]" ] }, { @@ -63004,7 +63004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1653/2000 [10:45<01:51, 3.11it/s, loss=0.389]" + "training until 2000: 83%|████████▎ | 1653/2000 [10:29<01:52, 3.08it/s, loss=0.403]" ] }, { @@ -63012,7 +63012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1654/2000 [10:45<01:51, 3.11it/s, loss=0.389]" + "training until 2000: 83%|████████▎ | 1654/2000 [10:29<01:50, 3.12it/s, loss=0.403]" ] }, { @@ -63020,7 +63020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1654/2000 [10:45<01:51, 3.11it/s, loss=0.432]" + "training until 2000: 83%|████████▎ | 1654/2000 [10:29<01:50, 3.12it/s, loss=0.406]" ] }, { @@ -63028,7 +63028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1655/2000 [10:45<01:50, 3.12it/s, loss=0.432]" + "training until 2000: 83%|████████▎ | 1655/2000 [10:29<01:50, 3.13it/s, loss=0.406]" ] }, { @@ -63036,7 +63036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1655/2000 [10:45<01:50, 3.12it/s, loss=0.39] " + "training until 2000: 83%|████████▎ | 1655/2000 [10:29<01:50, 3.13it/s, loss=0.424]" ] }, { @@ -63044,7 +63044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1656/2000 [10:46<01:50, 3.13it/s, loss=0.39]" + "training until 2000: 83%|████████▎ | 1656/2000 [10:30<01:51, 3.09it/s, loss=0.424]" ] }, { @@ -63052,7 +63052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1656/2000 [10:46<01:50, 3.13it/s, loss=0.396]" + "training until 2000: 83%|████████▎ | 1656/2000 [10:30<01:51, 3.09it/s, loss=0.378]" ] }, { @@ -63060,7 +63060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1657/2000 [10:46<01:49, 3.14it/s, loss=0.396]" + "training until 2000: 83%|████████▎ | 1657/2000 [10:30<01:51, 3.08it/s, loss=0.378]" ] }, { @@ -63068,7 +63068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1657/2000 [10:46<01:49, 3.14it/s, loss=0.402]" + "training until 2000: 83%|████████▎ | 1657/2000 [10:30<01:51, 3.08it/s, loss=0.372]" ] }, { @@ -63076,7 +63076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1658/2000 [10:46<01:48, 3.15it/s, loss=0.402]" + "training until 2000: 83%|████████▎ | 1658/2000 [10:30<01:50, 3.09it/s, loss=0.372]" ] }, { @@ -63084,7 +63084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1658/2000 [10:46<01:48, 3.15it/s, loss=0.424]" + "training until 2000: 83%|████████▎ | 1658/2000 [10:30<01:50, 3.09it/s, loss=0.537]" ] }, { @@ -63092,7 +63092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1659/2000 [10:46<01:48, 3.14it/s, loss=0.424]" + "training until 2000: 83%|████████▎ | 1659/2000 [10:31<01:50, 3.08it/s, loss=0.537]" ] }, { @@ -63100,7 +63100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1659/2000 [10:46<01:48, 3.14it/s, loss=0.418]" + "training until 2000: 83%|████████▎ | 1659/2000 [10:31<01:50, 3.08it/s, loss=0.417]" ] }, { @@ -63108,7 +63108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1660/2000 [10:47<01:48, 3.15it/s, loss=0.418]" + "training until 2000: 83%|████████▎ | 1660/2000 [10:31<01:51, 3.04it/s, loss=0.417]" ] }, { @@ -63116,7 +63116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1660/2000 [10:47<01:48, 3.15it/s, loss=0.391]" + "training until 2000: 83%|████████▎ | 1660/2000 [10:31<01:51, 3.04it/s, loss=0.542]" ] }, { @@ -63124,7 +63124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1661/2000 [10:47<01:48, 3.12it/s, loss=0.391]" + "training until 2000: 83%|████████▎ | 1661/2000 [10:31<01:50, 3.08it/s, loss=0.542]" ] }, { @@ -63132,7 +63132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1661/2000 [10:47<01:48, 3.12it/s, loss=0.407]" + "training until 2000: 83%|████████▎ | 1661/2000 [10:31<01:50, 3.08it/s, loss=0.373]" ] }, { @@ -63140,7 +63140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1662/2000 [10:47<01:47, 3.14it/s, loss=0.407]" + "training until 2000: 83%|████████▎ | 1662/2000 [10:32<01:49, 3.07it/s, loss=0.373]" ] }, { @@ -63148,7 +63148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1662/2000 [10:47<01:47, 3.14it/s, loss=0.42] " + "training until 2000: 83%|████████▎ | 1662/2000 [10:32<01:49, 3.07it/s, loss=0.44] " ] }, { @@ -63156,7 +63156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1663/2000 [10:48<01:46, 3.16it/s, loss=0.42]" + "training until 2000: 83%|████████▎ | 1663/2000 [10:32<01:48, 3.09it/s, loss=0.44]" ] }, { @@ -63164,7 +63164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1663/2000 [10:48<01:46, 3.16it/s, loss=0.412]" + "training until 2000: 83%|████████▎ | 1663/2000 [10:32<01:48, 3.09it/s, loss=0.399]" ] }, { @@ -63172,7 +63172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1664/2000 [10:48<01:46, 3.15it/s, loss=0.412]" + "training until 2000: 83%|████████▎ | 1664/2000 [10:32<01:47, 3.13it/s, loss=0.399]" ] }, { @@ -63180,7 +63180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1664/2000 [10:48<01:46, 3.15it/s, loss=0.411]" + "training until 2000: 83%|████████▎ | 1664/2000 [10:32<01:47, 3.13it/s, loss=0.396]" ] }, { @@ -63188,7 +63188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1665/2000 [10:48<01:46, 3.14it/s, loss=0.411]" + "training until 2000: 83%|████████▎ | 1665/2000 [10:33<01:46, 3.13it/s, loss=0.396]" ] }, { @@ -63196,7 +63196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1665/2000 [10:48<01:46, 3.14it/s, loss=0.391]" + "training until 2000: 83%|████████▎ | 1665/2000 [10:33<01:46, 3.13it/s, loss=0.461]" ] }, { @@ -63204,7 +63204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1666/2000 [10:49<01:46, 3.13it/s, loss=0.391]" + "training until 2000: 83%|████████▎ | 1666/2000 [10:33<01:48, 3.09it/s, loss=0.461]" ] }, { @@ -63212,7 +63212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1666/2000 [10:49<01:46, 3.13it/s, loss=0.414]" + "training until 2000: 83%|████████▎ | 1666/2000 [10:33<01:48, 3.09it/s, loss=0.42] " ] }, { @@ -63220,7 +63220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1667/2000 [10:49<01:45, 3.15it/s, loss=0.414]" + "training until 2000: 83%|████████▎ | 1667/2000 [10:33<01:48, 3.07it/s, loss=0.42]" ] }, { @@ -63228,7 +63228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1667/2000 [10:49<01:45, 3.15it/s, loss=0.418]" + "training until 2000: 83%|████████▎ | 1667/2000 [10:33<01:48, 3.07it/s, loss=0.401]" ] }, { @@ -63236,7 +63236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1668/2000 [10:49<01:46, 3.13it/s, loss=0.418]" + "training until 2000: 83%|████████▎ | 1668/2000 [10:34<01:47, 3.08it/s, loss=0.401]" ] }, { @@ -63244,7 +63244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1668/2000 [10:49<01:46, 3.13it/s, loss=0.407]" + "training until 2000: 83%|████████▎ | 1668/2000 [10:34<01:47, 3.08it/s, loss=0.39] " ] }, { @@ -63252,7 +63252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1669/2000 [10:50<01:45, 3.13it/s, loss=0.407]" + "training until 2000: 83%|████████▎ | 1669/2000 [10:34<01:46, 3.11it/s, loss=0.39]" ] }, { @@ -63260,7 +63260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 83%|████████▎ | 1669/2000 [10:50<01:45, 3.13it/s, loss=0.416]" + "training until 2000: 83%|████████▎ | 1669/2000 [10:34<01:46, 3.11it/s, loss=0.4] " ] }, { @@ -63268,7 +63268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1670/2000 [10:50<01:45, 3.13it/s, loss=0.416]" + "training until 2000: 84%|████████▎ | 1670/2000 [10:34<01:47, 3.07it/s, loss=0.4]" ] }, { @@ -63276,7 +63276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1670/2000 [10:50<01:45, 3.13it/s, loss=0.383]" + "training until 2000: 84%|████████▎ | 1670/2000 [10:34<01:47, 3.07it/s, loss=0.568]" ] }, { @@ -63284,7 +63284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1671/2000 [10:50<01:45, 3.12it/s, loss=0.383]" + "training until 2000: 84%|████████▎ | 1671/2000 [10:34<01:46, 3.08it/s, loss=0.568]" ] }, { @@ -63292,7 +63292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1671/2000 [10:50<01:45, 3.12it/s, loss=0.383]" + "training until 2000: 84%|████████▎ | 1671/2000 [10:34<01:46, 3.08it/s, loss=0.415]" ] }, { @@ -63300,7 +63300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1672/2000 [10:51<01:46, 3.09it/s, loss=0.383]" + "training until 2000: 84%|████████▎ | 1672/2000 [10:35<01:46, 3.07it/s, loss=0.415]" ] }, { @@ -63308,7 +63308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1672/2000 [10:51<01:46, 3.09it/s, loss=0.394]" + "training until 2000: 84%|████████▎ | 1672/2000 [10:35<01:46, 3.07it/s, loss=0.428]" ] }, { @@ -63316,7 +63316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1673/2000 [10:51<01:46, 3.08it/s, loss=0.394]" + "training until 2000: 84%|████████▎ | 1673/2000 [10:35<01:46, 3.07it/s, loss=0.428]" ] }, { @@ -63324,7 +63324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1673/2000 [10:51<01:46, 3.08it/s, loss=0.394]" + "training until 2000: 84%|████████▎ | 1673/2000 [10:35<01:46, 3.07it/s, loss=0.427]" ] }, { @@ -63332,7 +63332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1674/2000 [10:51<01:45, 3.08it/s, loss=0.394]" + "training until 2000: 84%|████████▎ | 1674/2000 [10:35<01:46, 3.07it/s, loss=0.427]" ] }, { @@ -63340,7 +63340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▎ | 1674/2000 [10:51<01:45, 3.08it/s, loss=0.384]" + "training until 2000: 84%|████████▎ | 1674/2000 [10:35<01:46, 3.07it/s, loss=0.443]" ] }, { @@ -63348,7 +63348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1675/2000 [10:52<01:47, 3.02it/s, loss=0.384]" + "training until 2000: 84%|████████▍ | 1675/2000 [10:36<01:46, 3.05it/s, loss=0.443]" ] }, { @@ -63356,7 +63356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1675/2000 [10:52<01:47, 3.02it/s, loss=0.413]" + "training until 2000: 84%|████████▍ | 1675/2000 [10:36<01:46, 3.05it/s, loss=0.4] " ] }, { @@ -63364,7 +63364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1676/2000 [10:52<01:46, 3.04it/s, loss=0.413]" + "training until 2000: 84%|████████▍ | 1676/2000 [10:36<01:45, 3.07it/s, loss=0.4]" ] }, { @@ -63372,7 +63372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1676/2000 [10:52<01:46, 3.04it/s, loss=0.377]" + "training until 2000: 84%|████████▍ | 1676/2000 [10:36<01:45, 3.07it/s, loss=0.45]" ] }, { @@ -63380,7 +63380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1677/2000 [10:52<01:45, 3.07it/s, loss=0.377]" + "training until 2000: 84%|████████▍ | 1677/2000 [10:36<01:44, 3.10it/s, loss=0.45]" ] }, { @@ -63388,7 +63388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1677/2000 [10:52<01:45, 3.07it/s, loss=0.405]" + "training until 2000: 84%|████████▍ | 1677/2000 [10:36<01:44, 3.10it/s, loss=0.414]" ] }, { @@ -63396,7 +63396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1678/2000 [10:53<01:46, 3.03it/s, loss=0.405]" + "training until 2000: 84%|████████▍ | 1678/2000 [10:37<01:44, 3.08it/s, loss=0.414]" ] }, { @@ -63404,7 +63404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1678/2000 [10:53<01:46, 3.03it/s, loss=0.388]" + "training until 2000: 84%|████████▍ | 1678/2000 [10:37<01:44, 3.08it/s, loss=0.39] " ] }, { @@ -63412,7 +63412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1679/2000 [10:53<01:47, 2.99it/s, loss=0.388]" + "training until 2000: 84%|████████▍ | 1679/2000 [10:37<01:43, 3.11it/s, loss=0.39]" ] }, { @@ -63420,7 +63420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1679/2000 [10:53<01:47, 2.99it/s, loss=0.399]" + "training until 2000: 84%|████████▍ | 1679/2000 [10:37<01:43, 3.11it/s, loss=0.401]" ] }, { @@ -63428,7 +63428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1680/2000 [10:53<01:47, 2.97it/s, loss=0.399]" + "training until 2000: 84%|████████▍ | 1680/2000 [10:37<01:42, 3.12it/s, loss=0.401]" ] }, { @@ -63436,7 +63436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1680/2000 [10:53<01:47, 2.97it/s, loss=0.413]" + "training until 2000: 84%|████████▍ | 1680/2000 [10:37<01:42, 3.12it/s, loss=0.41] " ] }, { @@ -63444,7 +63444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1681/2000 [10:54<01:46, 2.99it/s, loss=0.413]" + "training until 2000: 84%|████████▍ | 1681/2000 [10:38<01:41, 3.15it/s, loss=0.41]" ] }, { @@ -63452,7 +63452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1681/2000 [10:54<01:46, 2.99it/s, loss=0.383]" + "training until 2000: 84%|████████▍ | 1681/2000 [10:38<01:41, 3.15it/s, loss=0.401]" ] }, { @@ -63460,7 +63460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1682/2000 [10:54<01:45, 3.00it/s, loss=0.383]" + "training until 2000: 84%|████████▍ | 1682/2000 [10:38<01:40, 3.15it/s, loss=0.401]" ] }, { @@ -63468,7 +63468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1682/2000 [10:54<01:45, 3.00it/s, loss=0.467]" + "training until 2000: 84%|████████▍ | 1682/2000 [10:38<01:40, 3.15it/s, loss=0.379]" ] }, { @@ -63476,7 +63476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1683/2000 [10:54<01:45, 2.99it/s, loss=0.467]" + "training until 2000: 84%|████████▍ | 1683/2000 [10:38<01:41, 3.13it/s, loss=0.379]" ] }, { @@ -63484,7 +63484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1683/2000 [10:54<01:45, 2.99it/s, loss=0.417]" + "training until 2000: 84%|████████▍ | 1683/2000 [10:38<01:41, 3.13it/s, loss=0.381]" ] }, { @@ -63492,7 +63492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1684/2000 [10:55<01:44, 3.02it/s, loss=0.417]" + "training until 2000: 84%|████████▍ | 1684/2000 [10:39<01:40, 3.14it/s, loss=0.381]" ] }, { @@ -63500,7 +63500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1684/2000 [10:55<01:44, 3.02it/s, loss=0.4] " + "training until 2000: 84%|████████▍ | 1684/2000 [10:39<01:40, 3.14it/s, loss=0.501]" ] }, { @@ -63508,7 +63508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1685/2000 [10:55<01:45, 3.00it/s, loss=0.4]" + "training until 2000: 84%|████████▍ | 1685/2000 [10:39<01:39, 3.17it/s, loss=0.501]" ] }, { @@ -63516,7 +63516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1685/2000 [10:55<01:45, 3.00it/s, loss=0.384]" + "training until 2000: 84%|████████▍ | 1685/2000 [10:39<01:39, 3.17it/s, loss=0.426]" ] }, { @@ -63524,7 +63524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1686/2000 [10:55<01:43, 3.02it/s, loss=0.384]" + "training until 2000: 84%|████████▍ | 1686/2000 [10:39<01:39, 3.16it/s, loss=0.426]" ] }, { @@ -63532,7 +63532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1686/2000 [10:55<01:43, 3.02it/s, loss=0.39] " + "training until 2000: 84%|████████▍ | 1686/2000 [10:39<01:39, 3.16it/s, loss=0.399]" ] }, { @@ -63540,7 +63540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1687/2000 [10:56<01:42, 3.04it/s, loss=0.39]" + "training until 2000: 84%|████████▍ | 1687/2000 [10:40<01:38, 3.16it/s, loss=0.399]" ] }, { @@ -63548,7 +63548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1687/2000 [10:56<01:42, 3.04it/s, loss=0.403]" + "training until 2000: 84%|████████▍ | 1687/2000 [10:40<01:38, 3.16it/s, loss=0.387]" ] }, { @@ -63556,7 +63556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1688/2000 [10:56<01:41, 3.06it/s, loss=0.403]" + "training until 2000: 84%|████████▍ | 1688/2000 [10:40<02:03, 2.53it/s, loss=0.387]" ] }, { @@ -63564,7 +63564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1688/2000 [10:56<01:41, 3.06it/s, loss=0.387]" + "training until 2000: 84%|████████▍ | 1688/2000 [10:40<02:03, 2.53it/s, loss=0.439]" ] }, { @@ -63572,7 +63572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1689/2000 [10:56<01:41, 3.05it/s, loss=0.387]" + "training until 2000: 84%|████████▍ | 1689/2000 [10:40<01:54, 2.71it/s, loss=0.439]" ] }, { @@ -63580,7 +63580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1689/2000 [10:56<01:41, 3.05it/s, loss=0.419]" + "training until 2000: 84%|████████▍ | 1689/2000 [10:40<01:54, 2.71it/s, loss=0.462]" ] }, { @@ -63588,7 +63588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1690/2000 [10:57<01:40, 3.08it/s, loss=0.419]" + "training until 2000: 84%|████████▍ | 1690/2000 [10:41<01:48, 2.85it/s, loss=0.462]" ] }, { @@ -63596,7 +63596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 84%|████████▍ | 1690/2000 [10:57<01:40, 3.08it/s, loss=0.391]" + "training until 2000: 84%|████████▍ | 1690/2000 [10:41<01:48, 2.85it/s, loss=0.597]" ] }, { @@ -63604,7 +63604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1691/2000 [10:57<01:40, 3.07it/s, loss=0.391]" + "training until 2000: 85%|████████▍ | 1691/2000 [10:41<01:45, 2.92it/s, loss=0.597]" ] }, { @@ -63612,7 +63612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1691/2000 [10:57<01:40, 3.07it/s, loss=0.401]" + "training until 2000: 85%|████████▍ | 1691/2000 [10:41<01:45, 2.92it/s, loss=0.395]" ] }, { @@ -63620,7 +63620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1692/2000 [10:57<01:40, 3.08it/s, loss=0.401]" + "training until 2000: 85%|████████▍ | 1692/2000 [10:41<01:42, 3.00it/s, loss=0.395]" ] }, { @@ -63628,7 +63628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1692/2000 [10:57<01:40, 3.08it/s, loss=0.384]" + "training until 2000: 85%|████████▍ | 1692/2000 [10:41<01:42, 3.00it/s, loss=0.457]" ] }, { @@ -63636,7 +63636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1693/2000 [10:58<02:03, 2.49it/s, loss=0.384]" + "training until 2000: 85%|████████▍ | 1693/2000 [10:42<01:41, 3.03it/s, loss=0.457]" ] }, { @@ -63644,7 +63644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1693/2000 [10:58<02:03, 2.49it/s, loss=0.383]" + "training until 2000: 85%|████████▍ | 1693/2000 [10:42<01:41, 3.03it/s, loss=0.395]" ] }, { @@ -63652,7 +63652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1694/2000 [10:58<01:55, 2.64it/s, loss=0.383]" + "training until 2000: 85%|████████▍ | 1694/2000 [10:42<01:40, 3.06it/s, loss=0.395]" ] }, { @@ -63660,7 +63660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1694/2000 [10:58<01:55, 2.64it/s, loss=0.451]" + "training until 2000: 85%|████████▍ | 1694/2000 [10:42<01:40, 3.06it/s, loss=0.369]" ] }, { @@ -63668,7 +63668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1695/2000 [10:58<01:49, 2.78it/s, loss=0.451]" + "training until 2000: 85%|████████▍ | 1695/2000 [10:42<01:37, 3.12it/s, loss=0.369]" ] }, { @@ -63676,7 +63676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1695/2000 [10:58<01:49, 2.78it/s, loss=0.402]" + "training until 2000: 85%|████████▍ | 1695/2000 [10:42<01:37, 3.12it/s, loss=0.518]" ] }, { @@ -63684,7 +63684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1696/2000 [10:59<01:45, 2.87it/s, loss=0.402]" + "training until 2000: 85%|████████▍ | 1696/2000 [10:43<01:36, 3.13it/s, loss=0.518]" ] }, { @@ -63692,7 +63692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1696/2000 [10:59<01:45, 2.87it/s, loss=0.436]" + "training until 2000: 85%|████████▍ | 1696/2000 [10:43<01:36, 3.13it/s, loss=0.414]" ] }, { @@ -63700,7 +63700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1697/2000 [10:59<01:43, 2.93it/s, loss=0.436]" + "training until 2000: 85%|████████▍ | 1697/2000 [10:43<01:35, 3.17it/s, loss=0.414]" ] }, { @@ -63708,7 +63708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1697/2000 [10:59<01:43, 2.93it/s, loss=0.377]" + "training until 2000: 85%|████████▍ | 1697/2000 [10:43<01:35, 3.17it/s, loss=0.42] " ] }, { @@ -63716,7 +63716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1698/2000 [10:59<01:39, 3.03it/s, loss=0.377]" + "training until 2000: 85%|████████▍ | 1698/2000 [10:43<01:35, 3.16it/s, loss=0.42]" ] }, { @@ -63724,7 +63724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1698/2000 [10:59<01:39, 3.03it/s, loss=0.434]" + "training until 2000: 85%|████████▍ | 1698/2000 [10:43<01:35, 3.16it/s, loss=0.557]" ] }, { @@ -63732,7 +63732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1699/2000 [11:00<01:38, 3.04it/s, loss=0.434]" + "training until 2000: 85%|████████▍ | 1699/2000 [10:44<01:35, 3.14it/s, loss=0.557]" ] }, { @@ -63740,7 +63740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▍ | 1699/2000 [11:00<01:38, 3.04it/s, loss=0.413]" + "training until 2000: 85%|████████▍ | 1699/2000 [10:44<01:35, 3.14it/s, loss=0.506]" ] }, { @@ -63748,7 +63748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1700/2000 [11:00<01:39, 3.00it/s, loss=0.413]" + "training until 2000: 85%|████████▌ | 1700/2000 [10:44<01:36, 3.10it/s, loss=0.506]" ] }, { @@ -63756,7 +63756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1700/2000 [11:00<01:39, 3.00it/s, loss=0.396]" + "training until 2000: 85%|████████▌ | 1700/2000 [10:44<01:36, 3.10it/s, loss=0.397]" ] }, { @@ -63764,7 +63764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1701/2000 [11:00<01:39, 3.00it/s, loss=0.396]" + "training until 2000: 85%|████████▌ | 1701/2000 [10:44<01:37, 3.08it/s, loss=0.397]" ] }, { @@ -63772,7 +63772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1701/2000 [11:00<01:39, 3.00it/s, loss=0.413]" + "training until 2000: 85%|████████▌ | 1701/2000 [10:44<01:37, 3.08it/s, loss=0.361]" ] }, { @@ -63780,7 +63780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1702/2000 [11:01<01:39, 3.00it/s, loss=0.413]" + "training until 2000: 85%|████████▌ | 1702/2000 [10:45<01:36, 3.10it/s, loss=0.361]" ] }, { @@ -63788,7 +63788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1702/2000 [11:01<01:39, 3.00it/s, loss=0.42] " + "training until 2000: 85%|████████▌ | 1702/2000 [10:45<01:36, 3.10it/s, loss=0.39] " ] }, { @@ -63796,7 +63796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1703/2000 [11:01<01:38, 3.01it/s, loss=0.42]" + "training until 2000: 85%|████████▌ | 1703/2000 [10:45<01:35, 3.11it/s, loss=0.39]" ] }, { @@ -63804,7 +63804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1703/2000 [11:01<01:38, 3.01it/s, loss=0.388]" + "training until 2000: 85%|████████▌ | 1703/2000 [10:45<01:35, 3.11it/s, loss=0.383]" ] }, { @@ -63812,7 +63812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1704/2000 [11:01<01:37, 3.05it/s, loss=0.388]" + "training until 2000: 85%|████████▌ | 1704/2000 [10:45<01:35, 3.11it/s, loss=0.383]" ] }, { @@ -63820,7 +63820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1704/2000 [11:01<01:37, 3.05it/s, loss=0.483]" + "training until 2000: 85%|████████▌ | 1704/2000 [10:45<01:35, 3.11it/s, loss=0.374]" ] }, { @@ -63828,7 +63828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1705/2000 [11:02<01:37, 3.03it/s, loss=0.483]" + "training until 2000: 85%|████████▌ | 1705/2000 [10:46<01:35, 3.10it/s, loss=0.374]" ] }, { @@ -63836,7 +63836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1705/2000 [11:02<01:37, 3.03it/s, loss=0.39] " + "training until 2000: 85%|████████▌ | 1705/2000 [10:46<01:35, 3.10it/s, loss=0.399]" ] }, { @@ -63844,7 +63844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1706/2000 [11:02<01:37, 3.00it/s, loss=0.39]" + "training until 2000: 85%|████████▌ | 1706/2000 [10:46<01:34, 3.10it/s, loss=0.399]" ] }, { @@ -63852,7 +63852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1706/2000 [11:02<01:37, 3.00it/s, loss=0.406]" + "training until 2000: 85%|████████▌ | 1706/2000 [10:46<01:34, 3.10it/s, loss=0.385]" ] }, { @@ -63860,7 +63860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1707/2000 [11:02<01:37, 3.01it/s, loss=0.406]" + "training until 2000: 85%|████████▌ | 1707/2000 [10:46<01:34, 3.10it/s, loss=0.385]" ] }, { @@ -63868,7 +63868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1707/2000 [11:02<01:37, 3.01it/s, loss=0.396]" + "training until 2000: 85%|████████▌ | 1707/2000 [10:46<01:34, 3.10it/s, loss=0.402]" ] }, { @@ -63876,7 +63876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1708/2000 [11:03<01:36, 3.03it/s, loss=0.396]" + "training until 2000: 85%|████████▌ | 1708/2000 [10:47<01:33, 3.12it/s, loss=0.402]" ] }, { @@ -63884,7 +63884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1708/2000 [11:03<01:36, 3.03it/s, loss=0.39] " + "training until 2000: 85%|████████▌ | 1708/2000 [10:47<01:33, 3.12it/s, loss=0.398]" ] }, { @@ -63892,7 +63892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1709/2000 [11:03<01:36, 3.01it/s, loss=0.39]" + "training until 2000: 85%|████████▌ | 1709/2000 [10:47<01:34, 3.08it/s, loss=0.398]" ] }, { @@ -63900,7 +63900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 85%|████████▌ | 1709/2000 [11:03<01:36, 3.01it/s, loss=0.406]" + "training until 2000: 85%|████████▌ | 1709/2000 [10:47<01:34, 3.08it/s, loss=0.396]" ] }, { @@ -63908,7 +63908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1710/2000 [11:03<01:36, 3.00it/s, loss=0.406]" + "training until 2000: 86%|████████▌ | 1710/2000 [10:47<01:33, 3.09it/s, loss=0.396]" ] }, { @@ -63916,7 +63916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1710/2000 [11:03<01:36, 3.00it/s, loss=0.385]" + "training until 2000: 86%|████████▌ | 1710/2000 [10:47<01:33, 3.09it/s, loss=0.381]" ] }, { @@ -63924,7 +63924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1711/2000 [11:04<01:35, 3.02it/s, loss=0.385]" + "training until 2000: 86%|████████▌ | 1711/2000 [10:48<01:32, 3.12it/s, loss=0.381]" ] }, { @@ -63932,7 +63932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1711/2000 [11:04<01:35, 3.02it/s, loss=0.415]" + "training until 2000: 86%|████████▌ | 1711/2000 [10:48<01:32, 3.12it/s, loss=0.365]" ] }, { @@ -63940,7 +63940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1712/2000 [11:04<01:36, 2.99it/s, loss=0.415]" + "training until 2000: 86%|████████▌ | 1712/2000 [10:48<01:31, 3.14it/s, loss=0.365]" ] }, { @@ -63948,7 +63948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1712/2000 [11:04<01:36, 2.99it/s, loss=0.4] " + "training until 2000: 86%|████████▌ | 1712/2000 [10:48<01:31, 3.14it/s, loss=0.374]" ] }, { @@ -63956,7 +63956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1713/2000 [11:04<01:34, 3.02it/s, loss=0.4]" + "training until 2000: 86%|████████▌ | 1713/2000 [10:48<01:30, 3.16it/s, loss=0.374]" ] }, { @@ -63964,7 +63964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1713/2000 [11:04<01:34, 3.02it/s, loss=0.404]" + "training until 2000: 86%|████████▌ | 1713/2000 [10:48<01:30, 3.16it/s, loss=0.369]" ] }, { @@ -63972,7 +63972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1714/2000 [11:05<01:34, 3.04it/s, loss=0.404]" + "training until 2000: 86%|████████▌ | 1714/2000 [10:48<01:30, 3.16it/s, loss=0.369]" ] }, { @@ -63980,7 +63980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1714/2000 [11:05<01:34, 3.04it/s, loss=0.478]" + "training until 2000: 86%|████████▌ | 1714/2000 [10:48<01:30, 3.16it/s, loss=0.442]" ] }, { @@ -63988,7 +63988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1715/2000 [11:05<01:34, 3.01it/s, loss=0.478]" + "training until 2000: 86%|████████▌ | 1715/2000 [10:49<01:30, 3.15it/s, loss=0.442]" ] }, { @@ -63996,7 +63996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1715/2000 [11:05<01:34, 3.01it/s, loss=0.427]" + "training until 2000: 86%|████████▌ | 1715/2000 [10:49<01:30, 3.15it/s, loss=0.378]" ] }, { @@ -64004,7 +64004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1716/2000 [11:05<01:33, 3.04it/s, loss=0.427]" + "training until 2000: 86%|████████▌ | 1716/2000 [10:49<01:30, 3.12it/s, loss=0.378]" ] }, { @@ -64012,7 +64012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1716/2000 [11:05<01:33, 3.04it/s, loss=0.388]" + "training until 2000: 86%|████████▌ | 1716/2000 [10:49<01:30, 3.12it/s, loss=0.391]" ] }, { @@ -64020,7 +64020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1717/2000 [11:06<01:32, 3.08it/s, loss=0.388]" + "training until 2000: 86%|████████▌ | 1717/2000 [10:49<01:29, 3.15it/s, loss=0.391]" ] }, { @@ -64028,7 +64028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1717/2000 [11:06<01:32, 3.08it/s, loss=0.377]" + "training until 2000: 86%|████████▌ | 1717/2000 [10:49<01:29, 3.15it/s, loss=0.433]" ] }, { @@ -64036,7 +64036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1718/2000 [11:06<01:32, 3.04it/s, loss=0.377]" + "training until 2000: 86%|████████▌ | 1718/2000 [10:50<01:29, 3.14it/s, loss=0.433]" ] }, { @@ -64044,7 +64044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1718/2000 [11:06<01:32, 3.04it/s, loss=0.419]" + "training until 2000: 86%|████████▌ | 1718/2000 [10:50<01:29, 3.14it/s, loss=0.467]" ] }, { @@ -64052,7 +64052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1719/2000 [11:06<01:31, 3.08it/s, loss=0.419]" + "training until 2000: 86%|████████▌ | 1719/2000 [10:50<01:30, 3.09it/s, loss=0.467]" ] }, { @@ -64060,7 +64060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1719/2000 [11:06<01:31, 3.08it/s, loss=0.399]" + "training until 2000: 86%|████████▌ | 1719/2000 [10:50<01:30, 3.09it/s, loss=0.394]" ] }, { @@ -64068,7 +64068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1720/2000 [11:07<01:30, 3.09it/s, loss=0.399]" + "training until 2000: 86%|████████▌ | 1720/2000 [10:50<01:30, 3.09it/s, loss=0.394]" ] }, { @@ -64076,7 +64076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1720/2000 [11:07<01:30, 3.09it/s, loss=0.393]" + "training until 2000: 86%|████████▌ | 1720/2000 [10:50<01:30, 3.09it/s, loss=0.394]" ] }, { @@ -64084,7 +64084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1721/2000 [11:07<01:31, 3.06it/s, loss=0.393]" + "training until 2000: 86%|████████▌ | 1721/2000 [10:51<01:30, 3.10it/s, loss=0.394]" ] }, { @@ -64092,7 +64092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1721/2000 [11:07<01:31, 3.06it/s, loss=0.377]" + "training until 2000: 86%|████████▌ | 1721/2000 [10:51<01:30, 3.10it/s, loss=0.42] " ] }, { @@ -64100,7 +64100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1722/2000 [11:07<01:31, 3.04it/s, loss=0.377]" + "training until 2000: 86%|████████▌ | 1722/2000 [10:51<01:29, 3.09it/s, loss=0.42]" ] }, { @@ -64108,7 +64108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1722/2000 [11:07<01:31, 3.04it/s, loss=0.374]" + "training until 2000: 86%|████████▌ | 1722/2000 [10:51<01:29, 3.09it/s, loss=0.396]" ] }, { @@ -64116,7 +64116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1723/2000 [11:08<01:31, 3.01it/s, loss=0.374]" + "training until 2000: 86%|████████▌ | 1723/2000 [10:51<01:29, 3.10it/s, loss=0.396]" ] }, { @@ -64124,7 +64124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1723/2000 [11:08<01:31, 3.01it/s, loss=0.401]" + "training until 2000: 86%|████████▌ | 1723/2000 [10:51<01:29, 3.10it/s, loss=0.457]" ] }, { @@ -64132,7 +64132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1724/2000 [11:08<01:31, 3.03it/s, loss=0.401]" + "training until 2000: 86%|████████▌ | 1724/2000 [10:52<01:28, 3.11it/s, loss=0.457]" ] }, { @@ -64140,7 +64140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▌ | 1724/2000 [11:08<01:31, 3.03it/s, loss=0.449]" + "training until 2000: 86%|████████▌ | 1724/2000 [10:52<01:28, 3.11it/s, loss=0.4] " ] }, { @@ -64148,7 +64148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1725/2000 [11:08<01:30, 3.05it/s, loss=0.449]" + "training until 2000: 86%|████████▋ | 1725/2000 [10:52<01:28, 3.11it/s, loss=0.4]" ] }, { @@ -64156,7 +64156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1725/2000 [11:08<01:30, 3.05it/s, loss=0.395]" + "training until 2000: 86%|████████▋ | 1725/2000 [10:52<01:28, 3.11it/s, loss=0.363]" ] }, { @@ -64164,7 +64164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1726/2000 [11:09<01:28, 3.09it/s, loss=0.395]" + "training until 2000: 86%|████████▋ | 1726/2000 [10:52<01:29, 3.07it/s, loss=0.363]" ] }, { @@ -64172,7 +64172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1726/2000 [11:09<01:28, 3.09it/s, loss=0.379]" + "training until 2000: 86%|████████▋ | 1726/2000 [10:52<01:29, 3.07it/s, loss=0.394]" ] }, { @@ -64180,7 +64180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1727/2000 [11:09<01:29, 3.06it/s, loss=0.379]" + "training until 2000: 86%|████████▋ | 1727/2000 [10:53<01:31, 3.00it/s, loss=0.394]" ] }, { @@ -64188,7 +64188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1727/2000 [11:09<01:29, 3.06it/s, loss=0.446]" + "training until 2000: 86%|████████▋ | 1727/2000 [10:53<01:31, 3.00it/s, loss=0.402]" ] }, { @@ -64196,7 +64196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1728/2000 [11:09<01:29, 3.04it/s, loss=0.446]" + "training until 2000: 86%|████████▋ | 1728/2000 [10:53<01:30, 3.01it/s, loss=0.402]" ] }, { @@ -64204,7 +64204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1728/2000 [11:09<01:29, 3.04it/s, loss=0.372]" + "training until 2000: 86%|████████▋ | 1728/2000 [10:53<01:30, 3.01it/s, loss=0.38] " ] }, { @@ -64212,7 +64212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1729/2000 [11:10<01:28, 3.06it/s, loss=0.372]" + "training until 2000: 86%|████████▋ | 1729/2000 [10:53<01:28, 3.06it/s, loss=0.38]" ] }, { @@ -64220,7 +64220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1729/2000 [11:10<01:28, 3.06it/s, loss=0.396]" + "training until 2000: 86%|████████▋ | 1729/2000 [10:53<01:28, 3.06it/s, loss=0.41]" ] }, { @@ -64228,7 +64228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1730/2000 [11:10<01:28, 3.04it/s, loss=0.396]" + "training until 2000: 86%|████████▋ | 1730/2000 [10:54<01:29, 3.03it/s, loss=0.41]" ] }, { @@ -64236,7 +64236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 86%|████████▋ | 1730/2000 [11:10<01:28, 3.04it/s, loss=0.385]" + "training until 2000: 86%|████████▋ | 1730/2000 [10:54<01:29, 3.03it/s, loss=0.386]" ] }, { @@ -64244,7 +64244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1731/2000 [11:10<01:27, 3.06it/s, loss=0.385]" + "training until 2000: 87%|████████▋ | 1731/2000 [10:54<01:27, 3.08it/s, loss=0.386]" ] }, { @@ -64252,7 +64252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1731/2000 [11:10<01:27, 3.06it/s, loss=0.445]" + "training until 2000: 87%|████████▋ | 1731/2000 [10:54<01:27, 3.08it/s, loss=0.43] " ] }, { @@ -64260,7 +64260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1732/2000 [11:11<01:27, 3.06it/s, loss=0.445]" + "training until 2000: 87%|████████▋ | 1732/2000 [10:54<01:27, 3.07it/s, loss=0.43]" ] }, { @@ -64268,7 +64268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1732/2000 [11:11<01:27, 3.06it/s, loss=0.393]" + "training until 2000: 87%|████████▋ | 1732/2000 [10:54<01:27, 3.07it/s, loss=0.378]" ] }, { @@ -64276,7 +64276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1733/2000 [11:11<01:26, 3.07it/s, loss=0.393]" + "training until 2000: 87%|████████▋ | 1733/2000 [10:55<01:26, 3.08it/s, loss=0.378]" ] }, { @@ -64284,7 +64284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1733/2000 [11:11<01:26, 3.07it/s, loss=0.396]" + "training until 2000: 87%|████████▋ | 1733/2000 [10:55<01:26, 3.08it/s, loss=0.396]" ] }, { @@ -64292,7 +64292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1734/2000 [11:11<01:25, 3.09it/s, loss=0.396]" + "training until 2000: 87%|████████▋ | 1734/2000 [10:55<01:25, 3.09it/s, loss=0.396]" ] }, { @@ -64300,7 +64300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1734/2000 [11:11<01:25, 3.09it/s, loss=0.416]" + "training until 2000: 87%|████████▋ | 1734/2000 [10:55<01:25, 3.09it/s, loss=0.4] " ] }, { @@ -64308,7 +64308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1735/2000 [11:12<01:24, 3.12it/s, loss=0.416]" + "training until 2000: 87%|████████▋ | 1735/2000 [10:55<01:27, 3.05it/s, loss=0.4]" ] }, { @@ -64316,7 +64316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1735/2000 [11:12<01:24, 3.12it/s, loss=0.383]" + "training until 2000: 87%|████████▋ | 1735/2000 [10:55<01:27, 3.05it/s, loss=0.376]" ] }, { @@ -64324,7 +64324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1736/2000 [11:12<01:24, 3.13it/s, loss=0.383]" + "training until 2000: 87%|████████▋ | 1736/2000 [10:56<01:26, 3.05it/s, loss=0.376]" ] }, { @@ -64332,7 +64332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1736/2000 [11:12<01:24, 3.13it/s, loss=0.39] " + "training until 2000: 87%|████████▋ | 1736/2000 [10:56<01:26, 3.05it/s, loss=0.494]" ] }, { @@ -64340,7 +64340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1737/2000 [11:12<01:24, 3.13it/s, loss=0.39]" + "training until 2000: 87%|████████▋ | 1737/2000 [10:56<01:24, 3.13it/s, loss=0.494]" ] }, { @@ -64348,7 +64348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1737/2000 [11:12<01:24, 3.13it/s, loss=0.445]" + "training until 2000: 87%|████████▋ | 1737/2000 [10:56<01:24, 3.13it/s, loss=0.401]" ] }, { @@ -64356,7 +64356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1738/2000 [11:13<01:23, 3.15it/s, loss=0.445]" + "training until 2000: 87%|████████▋ | 1738/2000 [10:56<01:23, 3.13it/s, loss=0.401]" ] }, { @@ -64364,7 +64364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1738/2000 [11:13<01:23, 3.15it/s, loss=0.412]" + "training until 2000: 87%|████████▋ | 1738/2000 [10:56<01:23, 3.13it/s, loss=0.365]" ] }, { @@ -64372,7 +64372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1739/2000 [11:13<01:23, 3.12it/s, loss=0.412]" + "training until 2000: 87%|████████▋ | 1739/2000 [10:57<01:23, 3.14it/s, loss=0.365]" ] }, { @@ -64380,7 +64380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1739/2000 [11:13<01:23, 3.12it/s, loss=0.468]" + "training until 2000: 87%|████████▋ | 1739/2000 [10:57<01:23, 3.14it/s, loss=0.402]" ] }, { @@ -64388,7 +64388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1740/2000 [11:13<01:24, 3.07it/s, loss=0.468]" + "training until 2000: 87%|████████▋ | 1740/2000 [10:57<01:22, 3.14it/s, loss=0.402]" ] }, { @@ -64396,7 +64396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1740/2000 [11:13<01:24, 3.07it/s, loss=0.416]" + "training until 2000: 87%|████████▋ | 1740/2000 [10:57<01:22, 3.14it/s, loss=0.513]" ] }, { @@ -64404,7 +64404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1741/2000 [11:13<01:23, 3.10it/s, loss=0.416]" + "training until 2000: 87%|████████▋ | 1741/2000 [10:57<01:22, 3.13it/s, loss=0.513]" ] }, { @@ -64412,7 +64412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1741/2000 [11:13<01:23, 3.10it/s, loss=0.39] " + "training until 2000: 87%|████████▋ | 1741/2000 [10:57<01:22, 3.13it/s, loss=0.379]" ] }, { @@ -64420,7 +64420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1742/2000 [11:14<01:23, 3.09it/s, loss=0.39]" + "training until 2000: 87%|████████▋ | 1742/2000 [10:58<01:22, 3.13it/s, loss=0.379]" ] }, { @@ -64428,7 +64428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1742/2000 [11:14<01:23, 3.09it/s, loss=0.394]" + "training until 2000: 87%|████████▋ | 1742/2000 [10:58<01:22, 3.13it/s, loss=0.376]" ] }, { @@ -64436,7 +64436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1743/2000 [11:14<01:22, 3.11it/s, loss=0.394]" + "training until 2000: 87%|████████▋ | 1743/2000 [10:58<01:22, 3.10it/s, loss=0.376]" ] }, { @@ -64444,7 +64444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1743/2000 [11:14<01:22, 3.11it/s, loss=0.396]" + "training until 2000: 87%|████████▋ | 1743/2000 [10:58<01:22, 3.10it/s, loss=0.375]" ] }, { @@ -64452,7 +64452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1744/2000 [11:14<01:22, 3.09it/s, loss=0.396]" + "training until 2000: 87%|████████▋ | 1744/2000 [10:58<01:21, 3.13it/s, loss=0.375]" ] }, { @@ -64460,7 +64460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1744/2000 [11:14<01:22, 3.09it/s, loss=0.388]" + "training until 2000: 87%|████████▋ | 1744/2000 [10:58<01:21, 3.13it/s, loss=0.47] " ] }, { @@ -64468,7 +64468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1745/2000 [11:15<01:22, 3.08it/s, loss=0.388]" + "training until 2000: 87%|████████▋ | 1745/2000 [10:58<01:21, 3.12it/s, loss=0.47]" ] }, { @@ -64476,7 +64476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1745/2000 [11:15<01:22, 3.08it/s, loss=0.382]" + "training until 2000: 87%|████████▋ | 1745/2000 [10:58<01:21, 3.12it/s, loss=0.39]" ] }, { @@ -64484,7 +64484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1746/2000 [11:15<01:22, 3.07it/s, loss=0.382]" + "training until 2000: 87%|████████▋ | 1746/2000 [10:59<01:20, 3.14it/s, loss=0.39]" ] }, { @@ -64492,7 +64492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1746/2000 [11:15<01:22, 3.07it/s, loss=0.378]" + "training until 2000: 87%|████████▋ | 1746/2000 [10:59<01:20, 3.14it/s, loss=0.415]" ] }, { @@ -64500,7 +64500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1747/2000 [11:15<01:22, 3.08it/s, loss=0.378]" + "training until 2000: 87%|████████▋ | 1747/2000 [10:59<01:21, 3.10it/s, loss=0.415]" ] }, { @@ -64508,7 +64508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1747/2000 [11:15<01:22, 3.08it/s, loss=0.389]" + "training until 2000: 87%|████████▋ | 1747/2000 [10:59<01:21, 3.10it/s, loss=0.393]" ] }, { @@ -64516,7 +64516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1748/2000 [11:16<01:21, 3.08it/s, loss=0.389]" + "training until 2000: 87%|████████▋ | 1748/2000 [10:59<01:21, 3.11it/s, loss=0.393]" ] }, { @@ -64524,7 +64524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1748/2000 [11:16<01:21, 3.08it/s, loss=0.381]" + "training until 2000: 87%|████████▋ | 1748/2000 [10:59<01:21, 3.11it/s, loss=0.393]" ] }, { @@ -64532,7 +64532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1749/2000 [11:16<01:21, 3.09it/s, loss=0.381]" + "training until 2000: 87%|████████▋ | 1749/2000 [11:00<01:20, 3.12it/s, loss=0.393]" ] }, { @@ -64540,7 +64540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 87%|████████▋ | 1749/2000 [11:16<01:21, 3.09it/s, loss=0.378]" + "training until 2000: 87%|████████▋ | 1749/2000 [11:00<01:20, 3.12it/s, loss=0.397]" ] }, { @@ -64548,7 +64548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1750/2000 [11:16<01:21, 3.08it/s, loss=0.378]" + "training until 2000: 88%|████████▊ | 1750/2000 [11:00<01:20, 3.12it/s, loss=0.397]" ] }, { @@ -64556,7 +64556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1750/2000 [11:16<01:21, 3.08it/s, loss=0.414]" + "training until 2000: 88%|████████▊ | 1750/2000 [11:00<01:20, 3.12it/s, loss=0.401]" ] }, { @@ -64564,7 +64564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1751/2000 [11:17<01:20, 3.07it/s, loss=0.414]" + "training until 2000: 88%|████████▊ | 1751/2000 [11:00<01:18, 3.16it/s, loss=0.401]" ] }, { @@ -64572,7 +64572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1751/2000 [11:17<01:20, 3.07it/s, loss=0.4] " + "training until 2000: 88%|████████▊ | 1751/2000 [11:00<01:18, 3.16it/s, loss=0.37] " ] }, { @@ -64580,7 +64580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1752/2000 [11:17<01:20, 3.09it/s, loss=0.4]" + "training until 2000: 88%|████████▊ | 1752/2000 [11:01<01:36, 2.58it/s, loss=0.37]" ] }, { @@ -64588,7 +64588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1752/2000 [11:17<01:20, 3.09it/s, loss=0.385]" + "training until 2000: 88%|████████▊ | 1752/2000 [11:01<01:36, 2.58it/s, loss=0.397]" ] }, { @@ -64596,7 +64596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1753/2000 [11:17<01:19, 3.09it/s, loss=0.385]" + "training until 2000: 88%|████████▊ | 1753/2000 [11:01<01:30, 2.72it/s, loss=0.397]" ] }, { @@ -64604,7 +64604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1753/2000 [11:17<01:19, 3.09it/s, loss=0.394]" + "training until 2000: 88%|████████▊ | 1753/2000 [11:01<01:30, 2.72it/s, loss=0.398]" ] }, { @@ -64612,7 +64612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1754/2000 [11:18<01:19, 3.10it/s, loss=0.394]" + "training until 2000: 88%|████████▊ | 1754/2000 [11:02<01:26, 2.84it/s, loss=0.398]" ] }, { @@ -64620,7 +64620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1754/2000 [11:18<01:19, 3.10it/s, loss=0.375]" + "training until 2000: 88%|████████▊ | 1754/2000 [11:02<01:26, 2.84it/s, loss=0.378]" ] }, { @@ -64628,7 +64628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1755/2000 [11:18<01:18, 3.13it/s, loss=0.375]" + "training until 2000: 88%|████████▊ | 1755/2000 [11:02<01:24, 2.91it/s, loss=0.378]" ] }, { @@ -64636,7 +64636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1755/2000 [11:18<01:18, 3.13it/s, loss=0.399]" + "training until 2000: 88%|████████▊ | 1755/2000 [11:02<01:24, 2.91it/s, loss=0.4] " ] }, { @@ -64644,7 +64644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1756/2000 [11:18<01:18, 3.11it/s, loss=0.399]" + "training until 2000: 88%|████████▊ | 1756/2000 [11:02<01:21, 2.98it/s, loss=0.4]" ] }, { @@ -64652,7 +64652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1756/2000 [11:18<01:18, 3.11it/s, loss=0.454]" + "training until 2000: 88%|████████▊ | 1756/2000 [11:02<01:21, 2.98it/s, loss=0.377]" ] }, { @@ -64660,7 +64660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1757/2000 [11:19<01:18, 3.09it/s, loss=0.454]" + "training until 2000: 88%|████████▊ | 1757/2000 [11:03<01:20, 3.04it/s, loss=0.377]" ] }, { @@ -64668,7 +64668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1757/2000 [11:19<01:18, 3.09it/s, loss=0.4] " + "training until 2000: 88%|████████▊ | 1757/2000 [11:03<01:20, 3.04it/s, loss=0.564]" ] }, { @@ -64676,7 +64676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1758/2000 [11:19<01:18, 3.09it/s, loss=0.4]" + "training until 2000: 88%|████████▊ | 1758/2000 [11:03<01:18, 3.08it/s, loss=0.564]" ] }, { @@ -64684,7 +64684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1758/2000 [11:19<01:18, 3.09it/s, loss=0.384]" + "training until 2000: 88%|████████▊ | 1758/2000 [11:03<01:18, 3.08it/s, loss=0.395]" ] }, { @@ -64692,7 +64692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1759/2000 [11:20<01:36, 2.50it/s, loss=0.384]" + "training until 2000: 88%|████████▊ | 1759/2000 [11:03<01:18, 3.09it/s, loss=0.395]" ] }, { @@ -64700,7 +64700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1759/2000 [11:20<01:36, 2.50it/s, loss=0.383]" + "training until 2000: 88%|████████▊ | 1759/2000 [11:03<01:18, 3.09it/s, loss=0.43] " ] }, { @@ -64708,7 +64708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1760/2000 [11:20<01:30, 2.64it/s, loss=0.383]" + "training until 2000: 88%|████████▊ | 1760/2000 [11:03<01:17, 3.10it/s, loss=0.43]" ] }, { @@ -64716,7 +64716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1760/2000 [11:20<01:30, 2.64it/s, loss=0.383]" + "training until 2000: 88%|████████▊ | 1760/2000 [11:03<01:17, 3.10it/s, loss=0.421]" ] }, { @@ -64724,7 +64724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1761/2000 [11:20<01:26, 2.75it/s, loss=0.383]" + "training until 2000: 88%|████████▊ | 1761/2000 [11:04<01:15, 3.15it/s, loss=0.421]" ] }, { @@ -64732,7 +64732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1761/2000 [11:20<01:26, 2.75it/s, loss=0.406]" + "training until 2000: 88%|████████▊ | 1761/2000 [11:04<01:15, 3.15it/s, loss=0.434]" ] }, { @@ -64740,7 +64740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1762/2000 [11:21<01:25, 2.80it/s, loss=0.406]" + "training until 2000: 88%|████████▊ | 1762/2000 [11:04<01:15, 3.16it/s, loss=0.434]" ] }, { @@ -64748,7 +64748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1762/2000 [11:21<01:25, 2.80it/s, loss=0.405]" + "training until 2000: 88%|████████▊ | 1762/2000 [11:04<01:15, 3.16it/s, loss=0.509]" ] }, { @@ -64756,7 +64756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1763/2000 [11:21<01:22, 2.88it/s, loss=0.405]" + "training until 2000: 88%|████████▊ | 1763/2000 [11:04<01:14, 3.17it/s, loss=0.509]" ] }, { @@ -64764,7 +64764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1763/2000 [11:21<01:22, 2.88it/s, loss=0.396]" + "training until 2000: 88%|████████▊ | 1763/2000 [11:04<01:14, 3.17it/s, loss=0.362]" ] }, { @@ -64772,7 +64772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1764/2000 [11:21<01:19, 2.97it/s, loss=0.396]" + "training until 2000: 88%|████████▊ | 1764/2000 [11:05<01:14, 3.19it/s, loss=0.362]" ] }, { @@ -64780,7 +64780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1764/2000 [11:21<01:19, 2.97it/s, loss=0.397]" + "training until 2000: 88%|████████▊ | 1764/2000 [11:05<01:14, 3.19it/s, loss=0.41] " ] }, { @@ -64788,7 +64788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1765/2000 [11:22<01:18, 3.01it/s, loss=0.397]" + "training until 2000: 88%|████████▊ | 1765/2000 [11:05<01:13, 3.20it/s, loss=0.41]" ] }, { @@ -64796,7 +64796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1765/2000 [11:22<01:18, 3.01it/s, loss=0.377]" + "training until 2000: 88%|████████▊ | 1765/2000 [11:05<01:13, 3.20it/s, loss=0.419]" ] }, { @@ -64804,7 +64804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1766/2000 [11:22<01:17, 3.03it/s, loss=0.377]" + "training until 2000: 88%|████████▊ | 1766/2000 [11:05<01:13, 3.17it/s, loss=0.419]" ] }, { @@ -64812,7 +64812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1766/2000 [11:22<01:17, 3.03it/s, loss=0.454]" + "training until 2000: 88%|████████▊ | 1766/2000 [11:05<01:13, 3.17it/s, loss=0.373]" ] }, { @@ -64820,7 +64820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1767/2000 [11:22<01:16, 3.06it/s, loss=0.454]" + "training until 2000: 88%|████████▊ | 1767/2000 [11:06<01:13, 3.18it/s, loss=0.373]" ] }, { @@ -64828,7 +64828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1767/2000 [11:22<01:16, 3.06it/s, loss=0.38] " + "training until 2000: 88%|████████▊ | 1767/2000 [11:06<01:13, 3.18it/s, loss=0.366]" ] }, { @@ -64836,7 +64836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1768/2000 [11:22<01:14, 3.11it/s, loss=0.38]" + "training until 2000: 88%|████████▊ | 1768/2000 [11:06<01:12, 3.20it/s, loss=0.366]" ] }, { @@ -64844,7 +64844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1768/2000 [11:22<01:14, 3.11it/s, loss=0.386]" + "training until 2000: 88%|████████▊ | 1768/2000 [11:06<01:12, 3.20it/s, loss=0.394]" ] }, { @@ -64852,7 +64852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1769/2000 [11:23<01:14, 3.10it/s, loss=0.386]" + "training until 2000: 88%|████████▊ | 1769/2000 [11:06<01:12, 3.18it/s, loss=0.394]" ] }, { @@ -64860,7 +64860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1769/2000 [11:23<01:14, 3.10it/s, loss=0.38] " + "training until 2000: 88%|████████▊ | 1769/2000 [11:06<01:12, 3.18it/s, loss=0.384]" ] }, { @@ -64868,7 +64868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1770/2000 [11:23<01:14, 3.08it/s, loss=0.38]" + "training until 2000: 88%|████████▊ | 1770/2000 [11:07<01:12, 3.18it/s, loss=0.384]" ] }, { @@ -64876,7 +64876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 88%|████████▊ | 1770/2000 [11:23<01:14, 3.08it/s, loss=0.382]" + "training until 2000: 88%|████████▊ | 1770/2000 [11:07<01:12, 3.18it/s, loss=0.384]" ] }, { @@ -64884,7 +64884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1771/2000 [11:23<01:14, 3.09it/s, loss=0.382]" + "training until 2000: 89%|████████▊ | 1771/2000 [11:07<01:12, 3.15it/s, loss=0.384]" ] }, { @@ -64892,7 +64892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1771/2000 [11:23<01:14, 3.09it/s, loss=0.367]" + "training until 2000: 89%|████████▊ | 1771/2000 [11:07<01:12, 3.15it/s, loss=0.434]" ] }, { @@ -64900,7 +64900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1772/2000 [11:24<01:13, 3.09it/s, loss=0.367]" + "training until 2000: 89%|████████▊ | 1772/2000 [11:07<01:12, 3.16it/s, loss=0.434]" ] }, { @@ -64908,7 +64908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1772/2000 [11:24<01:13, 3.09it/s, loss=0.386]" + "training until 2000: 89%|████████▊ | 1772/2000 [11:07<01:12, 3.16it/s, loss=0.429]" ] }, { @@ -64916,7 +64916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1773/2000 [11:24<01:13, 3.09it/s, loss=0.386]" + "training until 2000: 89%|████████▊ | 1773/2000 [11:08<01:11, 3.16it/s, loss=0.429]" ] }, { @@ -64924,7 +64924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1773/2000 [11:24<01:13, 3.09it/s, loss=0.373]" + "training until 2000: 89%|████████▊ | 1773/2000 [11:08<01:11, 3.16it/s, loss=0.474]" ] }, { @@ -64932,7 +64932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1774/2000 [11:24<01:12, 3.11it/s, loss=0.373]" + "training until 2000: 89%|████████▊ | 1774/2000 [11:08<01:10, 3.19it/s, loss=0.474]" ] }, { @@ -64940,7 +64940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▊ | 1774/2000 [11:24<01:12, 3.11it/s, loss=0.392]" + "training until 2000: 89%|████████▊ | 1774/2000 [11:08<01:10, 3.19it/s, loss=0.577]" ] }, { @@ -64948,7 +64948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1775/2000 [11:25<01:11, 3.15it/s, loss=0.392]" + "training until 2000: 89%|████████▉ | 1775/2000 [11:08<01:10, 3.17it/s, loss=0.577]" ] }, { @@ -64956,7 +64956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1775/2000 [11:25<01:11, 3.15it/s, loss=0.383]" + "training until 2000: 89%|████████▉ | 1775/2000 [11:08<01:10, 3.17it/s, loss=0.416]" ] }, { @@ -64964,7 +64964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1776/2000 [11:25<01:12, 3.11it/s, loss=0.383]" + "training until 2000: 89%|████████▉ | 1776/2000 [11:09<01:10, 3.16it/s, loss=0.416]" ] }, { @@ -64972,7 +64972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1776/2000 [11:25<01:12, 3.11it/s, loss=0.418]" + "training until 2000: 89%|████████▉ | 1776/2000 [11:09<01:10, 3.16it/s, loss=0.411]" ] }, { @@ -64980,7 +64980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1777/2000 [11:25<01:11, 3.10it/s, loss=0.418]" + "training until 2000: 89%|████████▉ | 1777/2000 [11:09<01:10, 3.15it/s, loss=0.411]" ] }, { @@ -64988,7 +64988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1777/2000 [11:25<01:11, 3.10it/s, loss=0.397]" + "training until 2000: 89%|████████▉ | 1777/2000 [11:09<01:10, 3.15it/s, loss=0.395]" ] }, { @@ -64996,7 +64996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1778/2000 [11:26<01:12, 3.05it/s, loss=0.397]" + "training until 2000: 89%|████████▉ | 1778/2000 [11:09<01:11, 3.13it/s, loss=0.395]" ] }, { @@ -65004,7 +65004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1778/2000 [11:26<01:12, 3.05it/s, loss=0.369]" + "training until 2000: 89%|████████▉ | 1778/2000 [11:09<01:11, 3.13it/s, loss=0.406]" ] }, { @@ -65012,7 +65012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1779/2000 [11:26<01:12, 3.07it/s, loss=0.369]" + "training until 2000: 89%|████████▉ | 1779/2000 [11:09<01:10, 3.12it/s, loss=0.406]" ] }, { @@ -65020,7 +65020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1779/2000 [11:26<01:12, 3.07it/s, loss=0.383]" + "training until 2000: 89%|████████▉ | 1779/2000 [11:09<01:10, 3.12it/s, loss=0.381]" ] }, { @@ -65028,7 +65028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1780/2000 [11:26<01:12, 3.04it/s, loss=0.383]" + "training until 2000: 89%|████████▉ | 1780/2000 [11:10<01:10, 3.13it/s, loss=0.381]" ] }, { @@ -65036,7 +65036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1780/2000 [11:26<01:12, 3.04it/s, loss=0.375]" + "training until 2000: 89%|████████▉ | 1780/2000 [11:10<01:10, 3.13it/s, loss=0.387]" ] }, { @@ -65044,7 +65044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1781/2000 [11:27<01:11, 3.05it/s, loss=0.375]" + "training until 2000: 89%|████████▉ | 1781/2000 [11:10<01:09, 3.13it/s, loss=0.387]" ] }, { @@ -65052,7 +65052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1781/2000 [11:27<01:11, 3.05it/s, loss=0.377]" + "training until 2000: 89%|████████▉ | 1781/2000 [11:10<01:09, 3.13it/s, loss=0.366]" ] }, { @@ -65060,7 +65060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1782/2000 [11:27<01:12, 3.02it/s, loss=0.377]" + "training until 2000: 89%|████████▉ | 1782/2000 [11:10<01:09, 3.12it/s, loss=0.366]" ] }, { @@ -65068,7 +65068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1782/2000 [11:27<01:12, 3.02it/s, loss=0.383]" + "training until 2000: 89%|████████▉ | 1782/2000 [11:10<01:09, 3.12it/s, loss=0.362]" ] }, { @@ -65076,7 +65076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1783/2000 [11:27<01:12, 2.97it/s, loss=0.383]" + "training until 2000: 89%|████████▉ | 1783/2000 [11:11<01:09, 3.11it/s, loss=0.362]" ] }, { @@ -65084,7 +65084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1783/2000 [11:27<01:12, 2.97it/s, loss=0.39] " + "training until 2000: 89%|████████▉ | 1783/2000 [11:11<01:09, 3.11it/s, loss=0.37] " ] }, { @@ -65092,7 +65092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1784/2000 [11:28<01:12, 2.97it/s, loss=0.39]" + "training until 2000: 89%|████████▉ | 1784/2000 [11:11<01:09, 3.13it/s, loss=0.37]" ] }, { @@ -65100,7 +65100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1784/2000 [11:28<01:12, 2.97it/s, loss=0.42]" + "training until 2000: 89%|████████▉ | 1784/2000 [11:11<01:09, 3.13it/s, loss=0.376]" ] }, { @@ -65108,7 +65108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1785/2000 [11:28<01:11, 3.00it/s, loss=0.42]" + "training until 2000: 89%|████████▉ | 1785/2000 [11:11<01:08, 3.13it/s, loss=0.376]" ] }, { @@ -65116,7 +65116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1785/2000 [11:28<01:11, 3.00it/s, loss=0.371]" + "training until 2000: 89%|████████▉ | 1785/2000 [11:11<01:08, 3.13it/s, loss=0.409]" ] }, { @@ -65124,7 +65124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1786/2000 [11:28<01:10, 3.03it/s, loss=0.371]" + "training until 2000: 89%|████████▉ | 1786/2000 [11:12<01:07, 3.15it/s, loss=0.409]" ] }, { @@ -65132,7 +65132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1786/2000 [11:28<01:10, 3.03it/s, loss=0.413]" + "training until 2000: 89%|████████▉ | 1786/2000 [11:12<01:07, 3.15it/s, loss=0.384]" ] }, { @@ -65140,7 +65140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1787/2000 [11:29<01:10, 3.01it/s, loss=0.413]" + "training until 2000: 89%|████████▉ | 1787/2000 [11:12<01:07, 3.17it/s, loss=0.384]" ] }, { @@ -65148,7 +65148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1787/2000 [11:29<01:10, 3.01it/s, loss=0.412]" + "training until 2000: 89%|████████▉ | 1787/2000 [11:12<01:07, 3.17it/s, loss=0.388]" ] }, { @@ -65156,7 +65156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1788/2000 [11:29<01:11, 2.95it/s, loss=0.412]" + "training until 2000: 89%|████████▉ | 1788/2000 [11:12<01:06, 3.18it/s, loss=0.388]" ] }, { @@ -65164,7 +65164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1788/2000 [11:29<01:11, 2.95it/s, loss=0.368]" + "training until 2000: 89%|████████▉ | 1788/2000 [11:12<01:06, 3.18it/s, loss=0.387]" ] }, { @@ -65172,7 +65172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1789/2000 [11:29<01:10, 2.98it/s, loss=0.368]" + "training until 2000: 89%|████████▉ | 1789/2000 [11:13<01:06, 3.16it/s, loss=0.387]" ] }, { @@ -65180,7 +65180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 89%|████████▉ | 1789/2000 [11:29<01:10, 2.98it/s, loss=0.389]" + "training until 2000: 89%|████████▉ | 1789/2000 [11:13<01:06, 3.16it/s, loss=0.385]" ] }, { @@ -65188,7 +65188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1790/2000 [11:30<01:09, 3.02it/s, loss=0.389]" + "training until 2000: 90%|████████▉ | 1790/2000 [11:13<01:06, 3.17it/s, loss=0.385]" ] }, { @@ -65196,7 +65196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1790/2000 [11:30<01:09, 3.02it/s, loss=0.396]" + "training until 2000: 90%|████████▉ | 1790/2000 [11:13<01:06, 3.17it/s, loss=0.361]" ] }, { @@ -65204,7 +65204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1791/2000 [11:30<01:08, 3.05it/s, loss=0.396]" + "training until 2000: 90%|████████▉ | 1791/2000 [11:13<01:06, 3.13it/s, loss=0.361]" ] }, { @@ -65212,7 +65212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1791/2000 [11:30<01:08, 3.05it/s, loss=0.388]" + "training until 2000: 90%|████████▉ | 1791/2000 [11:13<01:06, 3.13it/s, loss=0.372]" ] }, { @@ -65220,7 +65220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1792/2000 [11:30<01:07, 3.06it/s, loss=0.388]" + "training until 2000: 90%|████████▉ | 1792/2000 [11:14<01:06, 3.15it/s, loss=0.372]" ] }, { @@ -65228,7 +65228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1792/2000 [11:30<01:07, 3.06it/s, loss=0.37] " + "training until 2000: 90%|████████▉ | 1792/2000 [11:14<01:06, 3.15it/s, loss=0.406]" ] }, { @@ -65236,7 +65236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1793/2000 [11:31<01:06, 3.09it/s, loss=0.37]" + "training until 2000: 90%|████████▉ | 1793/2000 [11:14<01:05, 3.16it/s, loss=0.406]" ] }, { @@ -65244,7 +65244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1793/2000 [11:31<01:06, 3.09it/s, loss=0.429]" + "training until 2000: 90%|████████▉ | 1793/2000 [11:14<01:05, 3.16it/s, loss=0.372]" ] }, { @@ -65252,7 +65252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1794/2000 [11:31<01:07, 3.07it/s, loss=0.429]" + "training until 2000: 90%|████████▉ | 1794/2000 [11:14<01:04, 3.17it/s, loss=0.372]" ] }, { @@ -65260,7 +65260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1794/2000 [11:31<01:07, 3.07it/s, loss=0.446]" + "training until 2000: 90%|████████▉ | 1794/2000 [11:14<01:04, 3.17it/s, loss=0.394]" ] }, { @@ -65268,7 +65268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1795/2000 [11:31<01:06, 3.07it/s, loss=0.446]" + "training until 2000: 90%|████████▉ | 1795/2000 [11:15<01:05, 3.14it/s, loss=0.394]" ] }, { @@ -65276,7 +65276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1795/2000 [11:31<01:06, 3.07it/s, loss=0.38] " + "training until 2000: 90%|████████▉ | 1795/2000 [11:15<01:05, 3.14it/s, loss=0.374]" ] }, { @@ -65284,7 +65284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1796/2000 [11:32<01:07, 3.01it/s, loss=0.38]" + "training until 2000: 90%|████████▉ | 1796/2000 [11:15<01:04, 3.17it/s, loss=0.374]" ] }, { @@ -65292,7 +65292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1796/2000 [11:32<01:07, 3.01it/s, loss=0.369]" + "training until 2000: 90%|████████▉ | 1796/2000 [11:15<01:04, 3.17it/s, loss=0.348]" ] }, { @@ -65300,7 +65300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1797/2000 [11:32<01:08, 2.97it/s, loss=0.369]" + "training until 2000: 90%|████████▉ | 1797/2000 [11:15<01:04, 3.16it/s, loss=0.348]" ] }, { @@ -65308,7 +65308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1797/2000 [11:32<01:08, 2.97it/s, loss=0.373]" + "training until 2000: 90%|████████▉ | 1797/2000 [11:15<01:04, 3.16it/s, loss=0.375]" ] }, { @@ -65316,7 +65316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1798/2000 [11:32<01:06, 3.03it/s, loss=0.373]" + "training until 2000: 90%|████████▉ | 1798/2000 [11:16<01:04, 3.11it/s, loss=0.375]" ] }, { @@ -65324,7 +65324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1798/2000 [11:32<01:06, 3.03it/s, loss=0.378]" + "training until 2000: 90%|████████▉ | 1798/2000 [11:16<01:04, 3.11it/s, loss=0.413]" ] }, { @@ -65332,7 +65332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1799/2000 [11:33<01:05, 3.08it/s, loss=0.378]" + "training until 2000: 90%|████████▉ | 1799/2000 [11:16<01:04, 3.13it/s, loss=0.413]" ] }, { @@ -65340,7 +65340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|████████▉ | 1799/2000 [11:33<01:05, 3.08it/s, loss=0.404]" + "training until 2000: 90%|████████▉ | 1799/2000 [11:16<01:04, 3.13it/s, loss=0.502]" ] }, { @@ -65348,7 +65348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1800/2000 [11:33<01:05, 3.06it/s, loss=0.404]" + "training until 2000: 90%|█████████ | 1800/2000 [11:16<01:04, 3.12it/s, loss=0.502]" ] }, { @@ -65356,7 +65356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1800/2000 [11:33<01:05, 3.06it/s, loss=0.407]" + "training until 2000: 90%|█████████ | 1800/2000 [11:16<01:04, 3.12it/s, loss=0.508]" ] }, { @@ -65364,7 +65364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1801/2000 [11:33<01:05, 3.04it/s, loss=0.407]" + "training until 2000: 90%|█████████ | 1801/2000 [11:16<01:03, 3.12it/s, loss=0.508]" ] }, { @@ -65372,7 +65372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1801/2000 [11:33<01:05, 3.04it/s, loss=0.399]" + "training until 2000: 90%|█████████ | 1801/2000 [11:16<01:03, 3.12it/s, loss=0.418]" ] }, { @@ -65380,7 +65380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1802/2000 [11:34<01:04, 3.05it/s, loss=0.399]" + "training until 2000: 90%|█████████ | 1802/2000 [11:17<01:02, 3.15it/s, loss=0.418]" ] }, { @@ -65388,7 +65388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1802/2000 [11:34<01:04, 3.05it/s, loss=0.368]" + "training until 2000: 90%|█████████ | 1802/2000 [11:17<01:02, 3.15it/s, loss=0.422]" ] }, { @@ -65396,7 +65396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1803/2000 [11:34<01:04, 3.05it/s, loss=0.368]" + "training until 2000: 90%|█████████ | 1803/2000 [11:17<01:02, 3.15it/s, loss=0.422]" ] }, { @@ -65404,7 +65404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1803/2000 [11:34<01:04, 3.05it/s, loss=0.4] " + "training until 2000: 90%|█████████ | 1803/2000 [11:17<01:02, 3.15it/s, loss=0.389]" ] }, { @@ -65412,7 +65412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1804/2000 [11:34<01:04, 3.05it/s, loss=0.4]" + "training until 2000: 90%|█████████ | 1804/2000 [11:17<01:01, 3.17it/s, loss=0.389]" ] }, { @@ -65420,7 +65420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1804/2000 [11:34<01:04, 3.05it/s, loss=0.375]" + "training until 2000: 90%|█████████ | 1804/2000 [11:17<01:01, 3.17it/s, loss=0.539]" ] }, { @@ -65428,7 +65428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1805/2000 [11:35<01:04, 3.01it/s, loss=0.375]" + "training until 2000: 90%|█████████ | 1805/2000 [11:18<01:01, 3.17it/s, loss=0.539]" ] }, { @@ -65436,7 +65436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1805/2000 [11:35<01:04, 3.01it/s, loss=0.391]" + "training until 2000: 90%|█████████ | 1805/2000 [11:18<01:01, 3.17it/s, loss=0.376]" ] }, { @@ -65444,7 +65444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1806/2000 [11:35<01:04, 3.01it/s, loss=0.391]" + "training until 2000: 90%|█████████ | 1806/2000 [11:18<01:00, 3.18it/s, loss=0.376]" ] }, { @@ -65452,7 +65452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1806/2000 [11:35<01:04, 3.01it/s, loss=0.429]" + "training until 2000: 90%|█████████ | 1806/2000 [11:18<01:00, 3.18it/s, loss=0.371]" ] }, { @@ -65460,7 +65460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1807/2000 [11:35<01:03, 3.05it/s, loss=0.429]" + "training until 2000: 90%|█████████ | 1807/2000 [11:18<01:00, 3.19it/s, loss=0.371]" ] }, { @@ -65468,7 +65468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1807/2000 [11:35<01:03, 3.05it/s, loss=0.373]" + "training until 2000: 90%|█████████ | 1807/2000 [11:18<01:00, 3.19it/s, loss=0.363]" ] }, { @@ -65476,7 +65476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1808/2000 [11:36<01:02, 3.06it/s, loss=0.373]" + "training until 2000: 90%|█████████ | 1808/2000 [11:19<01:01, 3.13it/s, loss=0.363]" ] }, { @@ -65484,7 +65484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1808/2000 [11:36<01:02, 3.06it/s, loss=0.37] " + "training until 2000: 90%|█████████ | 1808/2000 [11:19<01:01, 3.13it/s, loss=0.354]" ] }, { @@ -65492,7 +65492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1809/2000 [11:36<01:02, 3.07it/s, loss=0.37]" + "training until 2000: 90%|█████████ | 1809/2000 [11:19<01:00, 3.16it/s, loss=0.354]" ] }, { @@ -65500,7 +65500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1809/2000 [11:36<01:02, 3.07it/s, loss=0.468]" + "training until 2000: 90%|█████████ | 1809/2000 [11:19<01:00, 3.16it/s, loss=0.486]" ] }, { @@ -65508,7 +65508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1810/2000 [11:36<01:01, 3.07it/s, loss=0.468]" + "training until 2000: 90%|█████████ | 1810/2000 [11:19<00:59, 3.18it/s, loss=0.486]" ] }, { @@ -65516,7 +65516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 90%|█████████ | 1810/2000 [11:36<01:01, 3.07it/s, loss=0.376]" + "training until 2000: 90%|█████████ | 1810/2000 [11:19<00:59, 3.18it/s, loss=0.389]" ] }, { @@ -65524,7 +65524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1811/2000 [11:37<01:02, 3.02it/s, loss=0.376]" + "training until 2000: 91%|█████████ | 1811/2000 [11:20<00:59, 3.17it/s, loss=0.389]" ] }, { @@ -65532,7 +65532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1811/2000 [11:37<01:02, 3.02it/s, loss=0.391]" + "training until 2000: 91%|█████████ | 1811/2000 [11:20<00:59, 3.17it/s, loss=0.37] " ] }, { @@ -65540,7 +65540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1812/2000 [11:37<01:02, 3.02it/s, loss=0.391]" + "training until 2000: 91%|█████████ | 1812/2000 [11:20<00:59, 3.16it/s, loss=0.37]" ] }, { @@ -65548,7 +65548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1812/2000 [11:37<01:02, 3.02it/s, loss=0.394]" + "training until 2000: 91%|█████████ | 1812/2000 [11:20<00:59, 3.16it/s, loss=0.359]" ] }, { @@ -65556,7 +65556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1813/2000 [11:37<01:02, 3.01it/s, loss=0.394]" + "training until 2000: 91%|█████████ | 1813/2000 [11:20<00:58, 3.18it/s, loss=0.359]" ] }, { @@ -65564,7 +65564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1813/2000 [11:37<01:02, 3.01it/s, loss=0.384]" + "training until 2000: 91%|█████████ | 1813/2000 [11:20<00:58, 3.18it/s, loss=0.398]" ] }, { @@ -65572,7 +65572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1814/2000 [11:38<01:00, 3.07it/s, loss=0.384]" + "training until 2000: 91%|█████████ | 1814/2000 [11:21<00:58, 3.16it/s, loss=0.398]" ] }, { @@ -65580,7 +65580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1814/2000 [11:38<01:00, 3.07it/s, loss=0.396]" + "training until 2000: 91%|█████████ | 1814/2000 [11:21<00:58, 3.16it/s, loss=0.427]" ] }, { @@ -65588,7 +65588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1815/2000 [11:38<01:00, 3.06it/s, loss=0.396]" + "training until 2000: 91%|█████████ | 1815/2000 [11:21<00:58, 3.16it/s, loss=0.427]" ] }, { @@ -65596,7 +65596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1815/2000 [11:38<01:00, 3.06it/s, loss=0.365]" + "training until 2000: 91%|█████████ | 1815/2000 [11:21<00:58, 3.16it/s, loss=0.411]" ] }, { @@ -65604,7 +65604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1816/2000 [11:38<00:59, 3.08it/s, loss=0.365]" + "training until 2000: 91%|█████████ | 1816/2000 [11:21<00:58, 3.15it/s, loss=0.411]" ] }, { @@ -65612,7 +65612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1816/2000 [11:38<00:59, 3.08it/s, loss=0.37] " + "training until 2000: 91%|█████████ | 1816/2000 [11:21<00:58, 3.15it/s, loss=0.37] " ] }, { @@ -65620,7 +65620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1817/2000 [11:39<00:59, 3.10it/s, loss=0.37]" + "training until 2000: 91%|█████████ | 1817/2000 [11:22<00:58, 3.10it/s, loss=0.37]" ] }, { @@ -65628,7 +65628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1817/2000 [11:39<00:59, 3.10it/s, loss=0.397]" + "training until 2000: 91%|█████████ | 1817/2000 [11:22<00:58, 3.10it/s, loss=0.404]" ] }, { @@ -65636,7 +65636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1818/2000 [11:39<00:59, 3.07it/s, loss=0.397]" + "training until 2000: 91%|█████████ | 1818/2000 [11:22<00:58, 3.12it/s, loss=0.404]" ] }, { @@ -65644,7 +65644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1818/2000 [11:39<00:59, 3.07it/s, loss=0.412]" + "training until 2000: 91%|█████████ | 1818/2000 [11:22<00:58, 3.12it/s, loss=0.386]" ] }, { @@ -65652,7 +65652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1819/2000 [11:39<00:58, 3.08it/s, loss=0.412]" + "training until 2000: 91%|█████████ | 1819/2000 [11:22<01:10, 2.57it/s, loss=0.386]" ] }, { @@ -65660,7 +65660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1819/2000 [11:39<00:58, 3.08it/s, loss=0.371]" + "training until 2000: 91%|█████████ | 1819/2000 [11:22<01:10, 2.57it/s, loss=0.4] " ] }, { @@ -65668,7 +65668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1820/2000 [11:40<00:58, 3.06it/s, loss=0.371]" + "training until 2000: 91%|█████████ | 1820/2000 [11:23<01:06, 2.71it/s, loss=0.4]" ] }, { @@ -65676,7 +65676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1820/2000 [11:40<00:58, 3.06it/s, loss=0.36] " + "training until 2000: 91%|█████████ | 1820/2000 [11:23<01:06, 2.71it/s, loss=0.467]" ] }, { @@ -65684,7 +65684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1821/2000 [11:40<01:00, 2.97it/s, loss=0.36]" + "training until 2000: 91%|█████████ | 1821/2000 [11:23<01:03, 2.82it/s, loss=0.467]" ] }, { @@ -65692,7 +65692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1821/2000 [11:40<01:00, 2.97it/s, loss=0.366]" + "training until 2000: 91%|█████████ | 1821/2000 [11:23<01:03, 2.82it/s, loss=0.367]" ] }, { @@ -65700,7 +65700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1822/2000 [11:40<00:59, 2.97it/s, loss=0.366]" + "training until 2000: 91%|█████████ | 1822/2000 [11:23<01:01, 2.91it/s, loss=0.367]" ] }, { @@ -65708,7 +65708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1822/2000 [11:40<00:59, 2.97it/s, loss=0.364]" + "training until 2000: 91%|█████████ | 1822/2000 [11:23<01:01, 2.91it/s, loss=0.364]" ] }, { @@ -65716,7 +65716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1823/2000 [11:41<00:59, 2.99it/s, loss=0.364]" + "training until 2000: 91%|█████████ | 1823/2000 [11:24<00:59, 2.98it/s, loss=0.364]" ] }, { @@ -65724,7 +65724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1823/2000 [11:41<00:59, 2.99it/s, loss=0.399]" + "training until 2000: 91%|█████████ | 1823/2000 [11:24<00:59, 2.98it/s, loss=0.437]" ] }, { @@ -65732,7 +65732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1824/2000 [11:41<00:58, 2.99it/s, loss=0.399]" + "training until 2000: 91%|█████████ | 1824/2000 [11:24<00:58, 3.02it/s, loss=0.437]" ] }, { @@ -65740,7 +65740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████ | 1824/2000 [11:41<00:58, 2.99it/s, loss=0.384]" + "training until 2000: 91%|█████████ | 1824/2000 [11:24<00:58, 3.02it/s, loss=0.371]" ] }, { @@ -65748,7 +65748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1825/2000 [11:41<01:11, 2.45it/s, loss=0.384]" + "training until 2000: 91%|█████████▏| 1825/2000 [11:24<00:58, 2.99it/s, loss=0.371]" ] }, { @@ -65756,7 +65756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1825/2000 [11:41<01:11, 2.45it/s, loss=0.409]" + "training until 2000: 91%|█████████▏| 1825/2000 [11:24<00:58, 2.99it/s, loss=0.367]" ] }, { @@ -65764,7 +65764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1826/2000 [11:42<01:07, 2.59it/s, loss=0.409]" + "training until 2000: 91%|█████████▏| 1826/2000 [11:25<00:57, 3.02it/s, loss=0.367]" ] }, { @@ -65772,7 +65772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1826/2000 [11:42<01:07, 2.59it/s, loss=0.418]" + "training until 2000: 91%|█████████▏| 1826/2000 [11:25<00:57, 3.02it/s, loss=0.369]" ] }, { @@ -65780,7 +65780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1827/2000 [11:42<01:04, 2.70it/s, loss=0.418]" + "training until 2000: 91%|█████████▏| 1827/2000 [11:25<00:56, 3.07it/s, loss=0.369]" ] }, { @@ -65788,7 +65788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1827/2000 [11:42<01:04, 2.70it/s, loss=0.418]" + "training until 2000: 91%|█████████▏| 1827/2000 [11:25<00:56, 3.07it/s, loss=0.403]" ] }, { @@ -65796,7 +65796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1828/2000 [11:42<01:01, 2.81it/s, loss=0.418]" + "training until 2000: 91%|█████████▏| 1828/2000 [11:25<00:55, 3.10it/s, loss=0.403]" ] }, { @@ -65804,7 +65804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1828/2000 [11:42<01:01, 2.81it/s, loss=0.384]" + "training until 2000: 91%|█████████▏| 1828/2000 [11:25<00:55, 3.10it/s, loss=0.367]" ] }, { @@ -65812,7 +65812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1829/2000 [11:43<00:59, 2.89it/s, loss=0.384]" + "training until 2000: 91%|█████████▏| 1829/2000 [11:26<00:54, 3.11it/s, loss=0.367]" ] }, { @@ -65820,7 +65820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 91%|█████████▏| 1829/2000 [11:43<00:59, 2.89it/s, loss=0.407]" + "training until 2000: 91%|█████████▏| 1829/2000 [11:26<00:54, 3.11it/s, loss=0.428]" ] }, { @@ -65828,7 +65828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1830/2000 [11:43<00:57, 2.96it/s, loss=0.407]" + "training until 2000: 92%|█████████▏| 1830/2000 [11:26<00:55, 3.05it/s, loss=0.428]" ] }, { @@ -65836,7 +65836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1830/2000 [11:43<00:57, 2.96it/s, loss=0.436]" + "training until 2000: 92%|█████████▏| 1830/2000 [11:26<00:55, 3.05it/s, loss=0.386]" ] }, { @@ -65844,7 +65844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1831/2000 [11:43<00:56, 2.99it/s, loss=0.436]" + "training until 2000: 92%|█████████▏| 1831/2000 [11:26<00:55, 3.06it/s, loss=0.386]" ] }, { @@ -65852,7 +65852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1831/2000 [11:43<00:56, 2.99it/s, loss=0.362]" + "training until 2000: 92%|█████████▏| 1831/2000 [11:26<00:55, 3.06it/s, loss=0.373]" ] }, { @@ -65860,7 +65860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1832/2000 [11:44<00:55, 3.03it/s, loss=0.362]" + "training until 2000: 92%|█████████▏| 1832/2000 [11:27<00:54, 3.11it/s, loss=0.373]" ] }, { @@ -65868,7 +65868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1832/2000 [11:44<00:55, 3.03it/s, loss=0.357]" + "training until 2000: 92%|█████████▏| 1832/2000 [11:27<00:54, 3.11it/s, loss=0.425]" ] }, { @@ -65876,7 +65876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1833/2000 [11:44<00:54, 3.07it/s, loss=0.357]" + "training until 2000: 92%|█████████▏| 1833/2000 [11:27<00:53, 3.12it/s, loss=0.425]" ] }, { @@ -65884,7 +65884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1833/2000 [11:44<00:54, 3.07it/s, loss=0.438]" + "training until 2000: 92%|█████████▏| 1833/2000 [11:27<00:53, 3.12it/s, loss=0.429]" ] }, { @@ -65892,7 +65892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1834/2000 [11:44<00:53, 3.09it/s, loss=0.438]" + "training until 2000: 92%|█████████▏| 1834/2000 [11:27<00:52, 3.14it/s, loss=0.429]" ] }, { @@ -65900,7 +65900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1834/2000 [11:44<00:53, 3.09it/s, loss=0.373]" + "training until 2000: 92%|█████████▏| 1834/2000 [11:27<00:52, 3.14it/s, loss=0.376]" ] }, { @@ -65908,7 +65908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1835/2000 [11:45<00:53, 3.07it/s, loss=0.373]" + "training until 2000: 92%|█████████▏| 1835/2000 [11:28<00:52, 3.15it/s, loss=0.376]" ] }, { @@ -65916,7 +65916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1835/2000 [11:45<00:53, 3.07it/s, loss=0.383]" + "training until 2000: 92%|█████████▏| 1835/2000 [11:28<00:52, 3.15it/s, loss=0.394]" ] }, { @@ -65924,7 +65924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1836/2000 [11:45<00:53, 3.07it/s, loss=0.383]" + "training until 2000: 92%|█████████▏| 1836/2000 [11:28<00:52, 3.14it/s, loss=0.394]" ] }, { @@ -65932,7 +65932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1836/2000 [11:45<00:53, 3.07it/s, loss=0.403]" + "training until 2000: 92%|█████████▏| 1836/2000 [11:28<00:52, 3.14it/s, loss=0.35] " ] }, { @@ -65940,7 +65940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1837/2000 [11:45<00:52, 3.10it/s, loss=0.403]" + "training until 2000: 92%|█████████▏| 1837/2000 [11:28<00:51, 3.14it/s, loss=0.35]" ] }, { @@ -65948,7 +65948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1837/2000 [11:45<00:52, 3.10it/s, loss=0.391]" + "training until 2000: 92%|█████████▏| 1837/2000 [11:28<00:51, 3.14it/s, loss=0.377]" ] }, { @@ -65956,7 +65956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1838/2000 [11:46<00:52, 3.11it/s, loss=0.391]" + "training until 2000: 92%|█████████▏| 1838/2000 [11:29<00:51, 3.14it/s, loss=0.377]" ] }, { @@ -65964,7 +65964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1838/2000 [11:46<00:52, 3.11it/s, loss=0.425]" + "training until 2000: 92%|█████████▏| 1838/2000 [11:29<00:51, 3.14it/s, loss=0.385]" ] }, { @@ -65972,7 +65972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1839/2000 [11:46<00:52, 3.06it/s, loss=0.425]" + "training until 2000: 92%|█████████▏| 1839/2000 [11:29<00:51, 3.16it/s, loss=0.385]" ] }, { @@ -65980,7 +65980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1839/2000 [11:46<00:52, 3.06it/s, loss=0.387]" + "training until 2000: 92%|█████████▏| 1839/2000 [11:29<00:51, 3.16it/s, loss=0.377]" ] }, { @@ -65988,7 +65988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1840/2000 [11:46<00:52, 3.06it/s, loss=0.387]" + "training until 2000: 92%|█████████▏| 1840/2000 [11:29<00:50, 3.17it/s, loss=0.377]" ] }, { @@ -65996,7 +65996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1840/2000 [11:46<00:52, 3.06it/s, loss=0.401]" + "training until 2000: 92%|█████████▏| 1840/2000 [11:29<00:50, 3.17it/s, loss=0.364]" ] }, { @@ -66004,7 +66004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1841/2000 [11:47<00:52, 3.05it/s, loss=0.401]" + "training until 2000: 92%|█████████▏| 1841/2000 [11:29<00:50, 3.17it/s, loss=0.364]" ] }, { @@ -66012,7 +66012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1841/2000 [11:47<00:52, 3.05it/s, loss=0.419]" + "training until 2000: 92%|█████████▏| 1841/2000 [11:29<00:50, 3.17it/s, loss=0.369]" ] }, { @@ -66020,7 +66020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1842/2000 [11:47<00:51, 3.04it/s, loss=0.419]" + "training until 2000: 92%|█████████▏| 1842/2000 [11:30<00:49, 3.18it/s, loss=0.369]" ] }, { @@ -66028,7 +66028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1842/2000 [11:47<00:51, 3.04it/s, loss=0.373]" + "training until 2000: 92%|█████████▏| 1842/2000 [11:30<00:49, 3.18it/s, loss=0.526]" ] }, { @@ -66036,7 +66036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1843/2000 [11:47<00:51, 3.03it/s, loss=0.373]" + "training until 2000: 92%|█████████▏| 1843/2000 [11:30<00:50, 3.13it/s, loss=0.526]" ] }, { @@ -66044,7 +66044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1843/2000 [11:47<00:51, 3.03it/s, loss=0.364]" + "training until 2000: 92%|█████████▏| 1843/2000 [11:30<00:50, 3.13it/s, loss=0.403]" ] }, { @@ -66052,7 +66052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1844/2000 [11:48<00:50, 3.06it/s, loss=0.364]" + "training until 2000: 92%|█████████▏| 1844/2000 [11:30<00:49, 3.15it/s, loss=0.403]" ] }, { @@ -66060,7 +66060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1844/2000 [11:48<00:50, 3.06it/s, loss=0.395]" + "training until 2000: 92%|█████████▏| 1844/2000 [11:30<00:49, 3.15it/s, loss=0.513]" ] }, { @@ -66068,7 +66068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1845/2000 [11:48<00:50, 3.08it/s, loss=0.395]" + "training until 2000: 92%|█████████▏| 1845/2000 [11:31<00:49, 3.15it/s, loss=0.513]" ] }, { @@ -66076,7 +66076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1845/2000 [11:48<00:50, 3.08it/s, loss=0.375]" + "training until 2000: 92%|█████████▏| 1845/2000 [11:31<00:49, 3.15it/s, loss=0.386]" ] }, { @@ -66084,7 +66084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1846/2000 [11:48<00:49, 3.13it/s, loss=0.375]" + "training until 2000: 92%|█████████▏| 1846/2000 [11:31<00:48, 3.16it/s, loss=0.386]" ] }, { @@ -66092,7 +66092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1846/2000 [11:48<00:49, 3.13it/s, loss=0.378]" + "training until 2000: 92%|█████████▏| 1846/2000 [11:31<00:48, 3.16it/s, loss=0.345]" ] }, { @@ -66100,7 +66100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1847/2000 [11:49<00:49, 3.09it/s, loss=0.378]" + "training until 2000: 92%|█████████▏| 1847/2000 [11:31<00:49, 3.11it/s, loss=0.345]" ] }, { @@ -66108,7 +66108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1847/2000 [11:49<00:49, 3.09it/s, loss=0.41] " + "training until 2000: 92%|█████████▏| 1847/2000 [11:31<00:49, 3.11it/s, loss=0.361]" ] }, { @@ -66116,7 +66116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1848/2000 [11:49<00:48, 3.10it/s, loss=0.41]" + "training until 2000: 92%|█████████▏| 1848/2000 [11:32<00:49, 3.10it/s, loss=0.361]" ] }, { @@ -66124,7 +66124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1848/2000 [11:49<00:48, 3.10it/s, loss=0.369]" + "training until 2000: 92%|█████████▏| 1848/2000 [11:32<00:49, 3.10it/s, loss=0.342]" ] }, { @@ -66132,7 +66132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1849/2000 [11:49<00:48, 3.11it/s, loss=0.369]" + "training until 2000: 92%|█████████▏| 1849/2000 [11:32<00:48, 3.13it/s, loss=0.342]" ] }, { @@ -66140,7 +66140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▏| 1849/2000 [11:49<00:48, 3.11it/s, loss=0.363]" + "training until 2000: 92%|█████████▏| 1849/2000 [11:32<00:48, 3.13it/s, loss=0.371]" ] }, { @@ -66148,7 +66148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▎| 1850/2000 [11:50<00:48, 3.11it/s, loss=0.363]" + "training until 2000: 92%|█████████▎| 1850/2000 [11:32<00:48, 3.12it/s, loss=0.371]" ] }, { @@ -66156,7 +66156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 92%|█████████▎| 1850/2000 [11:50<00:48, 3.11it/s, loss=0.387]" + "training until 2000: 92%|█████████▎| 1850/2000 [11:32<00:48, 3.12it/s, loss=0.397]" ] }, { @@ -66164,7 +66164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1851/2000 [11:50<00:48, 3.06it/s, loss=0.387]" + "training until 2000: 93%|█████████▎| 1851/2000 [11:33<00:48, 3.07it/s, loss=0.397]" ] }, { @@ -66172,7 +66172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1851/2000 [11:50<00:48, 3.06it/s, loss=0.385]" + "training until 2000: 93%|█████████▎| 1851/2000 [11:33<00:48, 3.07it/s, loss=0.381]" ] }, { @@ -66180,7 +66180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1852/2000 [11:50<00:47, 3.09it/s, loss=0.385]" + "training until 2000: 93%|█████████▎| 1852/2000 [11:33<00:47, 3.09it/s, loss=0.381]" ] }, { @@ -66188,7 +66188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1852/2000 [11:50<00:47, 3.09it/s, loss=0.366]" + "training until 2000: 93%|█████████▎| 1852/2000 [11:33<00:47, 3.09it/s, loss=0.372]" ] }, { @@ -66196,7 +66196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1853/2000 [11:51<00:48, 3.05it/s, loss=0.366]" + "training until 2000: 93%|█████████▎| 1853/2000 [11:33<00:47, 3.13it/s, loss=0.372]" ] }, { @@ -66204,7 +66204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1853/2000 [11:51<00:48, 3.05it/s, loss=0.357]" + "training until 2000: 93%|█████████▎| 1853/2000 [11:33<00:47, 3.13it/s, loss=0.366]" ] }, { @@ -66212,7 +66212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1854/2000 [11:51<00:47, 3.06it/s, loss=0.357]" + "training until 2000: 93%|█████████▎| 1854/2000 [11:34<00:46, 3.14it/s, loss=0.366]" ] }, { @@ -66220,7 +66220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1854/2000 [11:51<00:47, 3.06it/s, loss=0.434]" + "training until 2000: 93%|█████████▎| 1854/2000 [11:34<00:46, 3.14it/s, loss=0.37] " ] }, { @@ -66228,7 +66228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1855/2000 [11:51<00:47, 3.05it/s, loss=0.434]" + "training until 2000: 93%|█████████▎| 1855/2000 [11:34<00:45, 3.16it/s, loss=0.37]" ] }, { @@ -66236,7 +66236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1855/2000 [11:51<00:47, 3.05it/s, loss=0.369]" + "training until 2000: 93%|█████████▎| 1855/2000 [11:34<00:45, 3.16it/s, loss=0.375]" ] }, { @@ -66244,7 +66244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1856/2000 [11:52<00:47, 3.05it/s, loss=0.369]" + "training until 2000: 93%|█████████▎| 1856/2000 [11:34<00:45, 3.19it/s, loss=0.375]" ] }, { @@ -66252,7 +66252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1856/2000 [11:52<00:47, 3.05it/s, loss=0.394]" + "training until 2000: 93%|█████████▎| 1856/2000 [11:34<00:45, 3.19it/s, loss=0.446]" ] }, { @@ -66260,7 +66260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1857/2000 [11:52<00:46, 3.09it/s, loss=0.394]" + "training until 2000: 93%|█████████▎| 1857/2000 [11:35<00:45, 3.16it/s, loss=0.446]" ] }, { @@ -66268,7 +66268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1857/2000 [11:52<00:46, 3.09it/s, loss=0.382]" + "training until 2000: 93%|█████████▎| 1857/2000 [11:35<00:45, 3.16it/s, loss=0.356]" ] }, { @@ -66276,7 +66276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1858/2000 [11:52<00:45, 3.09it/s, loss=0.382]" + "training until 2000: 93%|█████████▎| 1858/2000 [11:35<00:45, 3.13it/s, loss=0.356]" ] }, { @@ -66284,7 +66284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1858/2000 [11:52<00:45, 3.09it/s, loss=0.366]" + "training until 2000: 93%|█████████▎| 1858/2000 [11:35<00:45, 3.13it/s, loss=0.475]" ] }, { @@ -66292,7 +66292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1859/2000 [11:53<00:45, 3.09it/s, loss=0.366]" + "training until 2000: 93%|█████████▎| 1859/2000 [11:35<00:45, 3.11it/s, loss=0.475]" ] }, { @@ -66300,7 +66300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1859/2000 [11:53<00:45, 3.09it/s, loss=0.399]" + "training until 2000: 93%|█████████▎| 1859/2000 [11:35<00:45, 3.11it/s, loss=0.355]" ] }, { @@ -66308,7 +66308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1860/2000 [11:53<00:44, 3.11it/s, loss=0.399]" + "training until 2000: 93%|█████████▎| 1860/2000 [11:36<00:44, 3.14it/s, loss=0.355]" ] }, { @@ -66316,7 +66316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1860/2000 [11:53<00:44, 3.11it/s, loss=0.379]" + "training until 2000: 93%|█████████▎| 1860/2000 [11:36<00:44, 3.14it/s, loss=0.365]" ] }, { @@ -66324,7 +66324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1861/2000 [11:53<00:44, 3.11it/s, loss=0.379]" + "training until 2000: 93%|█████████▎| 1861/2000 [11:36<00:44, 3.14it/s, loss=0.365]" ] }, { @@ -66332,7 +66332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1861/2000 [11:53<00:44, 3.11it/s, loss=0.373]" + "training until 2000: 93%|█████████▎| 1861/2000 [11:36<00:44, 3.14it/s, loss=0.361]" ] }, { @@ -66340,7 +66340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1862/2000 [11:53<00:45, 3.07it/s, loss=0.373]" + "training until 2000: 93%|█████████▎| 1862/2000 [11:36<00:43, 3.15it/s, loss=0.361]" ] }, { @@ -66348,7 +66348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1862/2000 [11:53<00:45, 3.07it/s, loss=0.361]" + "training until 2000: 93%|█████████▎| 1862/2000 [11:36<00:43, 3.15it/s, loss=0.388]" ] }, { @@ -66356,7 +66356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1863/2000 [11:54<00:44, 3.07it/s, loss=0.361]" + "training until 2000: 93%|█████████▎| 1863/2000 [11:36<00:43, 3.16it/s, loss=0.388]" ] }, { @@ -66364,7 +66364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1863/2000 [11:54<00:44, 3.07it/s, loss=0.35] " + "training until 2000: 93%|█████████▎| 1863/2000 [11:36<00:43, 3.16it/s, loss=0.365]" ] }, { @@ -66372,7 +66372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1864/2000 [11:54<00:44, 3.07it/s, loss=0.35]" + "training until 2000: 93%|█████████▎| 1864/2000 [11:37<00:42, 3.17it/s, loss=0.365]" ] }, { @@ -66380,7 +66380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1864/2000 [11:54<00:44, 3.07it/s, loss=0.358]" + "training until 2000: 93%|█████████▎| 1864/2000 [11:37<00:42, 3.17it/s, loss=0.404]" ] }, { @@ -66388,7 +66388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1865/2000 [11:54<00:43, 3.08it/s, loss=0.358]" + "training until 2000: 93%|█████████▎| 1865/2000 [11:37<00:42, 3.15it/s, loss=0.404]" ] }, { @@ -66396,7 +66396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1865/2000 [11:54<00:43, 3.08it/s, loss=0.381]" + "training until 2000: 93%|█████████▎| 1865/2000 [11:37<00:42, 3.15it/s, loss=0.386]" ] }, { @@ -66404,7 +66404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1866/2000 [11:55<00:43, 3.09it/s, loss=0.381]" + "training until 2000: 93%|█████████▎| 1866/2000 [11:37<00:42, 3.17it/s, loss=0.386]" ] }, { @@ -66412,7 +66412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1866/2000 [11:55<00:43, 3.09it/s, loss=0.401]" + "training until 2000: 93%|█████████▎| 1866/2000 [11:37<00:42, 3.17it/s, loss=0.405]" ] }, { @@ -66420,7 +66420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1867/2000 [11:55<00:43, 3.08it/s, loss=0.401]" + "training until 2000: 93%|█████████▎| 1867/2000 [11:38<00:41, 3.18it/s, loss=0.405]" ] }, { @@ -66428,7 +66428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1867/2000 [11:55<00:43, 3.08it/s, loss=0.377]" + "training until 2000: 93%|█████████▎| 1867/2000 [11:38<00:41, 3.18it/s, loss=0.353]" ] }, { @@ -66436,7 +66436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1868/2000 [11:55<00:43, 3.07it/s, loss=0.377]" + "training until 2000: 93%|█████████▎| 1868/2000 [11:38<00:41, 3.20it/s, loss=0.353]" ] }, { @@ -66444,7 +66444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1868/2000 [11:55<00:43, 3.07it/s, loss=0.393]" + "training until 2000: 93%|█████████▎| 1868/2000 [11:38<00:41, 3.20it/s, loss=0.385]" ] }, { @@ -66452,7 +66452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1869/2000 [11:56<00:42, 3.07it/s, loss=0.393]" + "training until 2000: 93%|█████████▎| 1869/2000 [11:38<00:41, 3.18it/s, loss=0.385]" ] }, { @@ -66460,7 +66460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 93%|█████████▎| 1869/2000 [11:56<00:42, 3.07it/s, loss=0.387]" + "training until 2000: 93%|█████████▎| 1869/2000 [11:38<00:41, 3.18it/s, loss=0.408]" ] }, { @@ -66468,7 +66468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1870/2000 [11:56<00:41, 3.10it/s, loss=0.387]" + "training until 2000: 94%|█████████▎| 1870/2000 [11:39<00:40, 3.20it/s, loss=0.408]" ] }, { @@ -66476,7 +66476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1870/2000 [11:56<00:41, 3.10it/s, loss=0.401]" + "training until 2000: 94%|█████████▎| 1870/2000 [11:39<00:40, 3.20it/s, loss=0.383]" ] }, { @@ -66484,7 +66484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1871/2000 [11:56<00:41, 3.11it/s, loss=0.401]" + "training until 2000: 94%|█████████▎| 1871/2000 [11:39<00:40, 3.22it/s, loss=0.383]" ] }, { @@ -66492,7 +66492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1871/2000 [11:56<00:41, 3.11it/s, loss=0.393]" + "training until 2000: 94%|█████████▎| 1871/2000 [11:39<00:40, 3.22it/s, loss=0.411]" ] }, { @@ -66500,7 +66500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1872/2000 [11:57<00:41, 3.11it/s, loss=0.393]" + "training until 2000: 94%|█████████▎| 1872/2000 [11:39<00:40, 3.18it/s, loss=0.411]" ] }, { @@ -66508,7 +66508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1872/2000 [11:57<00:41, 3.11it/s, loss=0.375]" + "training until 2000: 94%|█████████▎| 1872/2000 [11:39<00:40, 3.18it/s, loss=0.41] " ] }, { @@ -66516,7 +66516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1873/2000 [11:57<00:41, 3.06it/s, loss=0.375]" + "training until 2000: 94%|█████████▎| 1873/2000 [11:40<00:39, 3.20it/s, loss=0.41]" ] }, { @@ -66524,7 +66524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1873/2000 [11:57<00:41, 3.06it/s, loss=0.372]" + "training until 2000: 94%|█████████▎| 1873/2000 [11:40<00:39, 3.20it/s, loss=0.349]" ] }, { @@ -66532,7 +66532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1874/2000 [11:57<00:40, 3.09it/s, loss=0.372]" + "training until 2000: 94%|█████████▎| 1874/2000 [11:40<00:39, 3.21it/s, loss=0.349]" ] }, { @@ -66540,7 +66540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▎| 1874/2000 [11:57<00:40, 3.09it/s, loss=0.39] " + "training until 2000: 94%|█████████▎| 1874/2000 [11:40<00:39, 3.21it/s, loss=0.458]" ] }, { @@ -66548,7 +66548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1875/2000 [11:58<00:40, 3.08it/s, loss=0.39]" + "training until 2000: 94%|█████████▍| 1875/2000 [11:40<00:39, 3.19it/s, loss=0.458]" ] }, { @@ -66556,7 +66556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1875/2000 [11:58<00:40, 3.08it/s, loss=0.361]" + "training until 2000: 94%|█████████▍| 1875/2000 [11:40<00:39, 3.19it/s, loss=0.364]" ] }, { @@ -66564,7 +66564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1876/2000 [11:58<00:40, 3.06it/s, loss=0.361]" + "training until 2000: 94%|█████████▍| 1876/2000 [11:41<00:38, 3.20it/s, loss=0.364]" ] }, { @@ -66572,7 +66572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1876/2000 [11:58<00:40, 3.06it/s, loss=0.391]" + "training until 2000: 94%|█████████▍| 1876/2000 [11:41<00:38, 3.20it/s, loss=0.365]" ] }, { @@ -66580,7 +66580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1877/2000 [11:58<00:40, 3.06it/s, loss=0.391]" + "training until 2000: 94%|█████████▍| 1877/2000 [11:41<00:38, 3.20it/s, loss=0.365]" ] }, { @@ -66588,7 +66588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1877/2000 [11:58<00:40, 3.06it/s, loss=0.401]" + "training until 2000: 94%|█████████▍| 1877/2000 [11:41<00:38, 3.20it/s, loss=0.363]" ] }, { @@ -66596,7 +66596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1878/2000 [11:59<00:39, 3.07it/s, loss=0.401]" + "training until 2000: 94%|█████████▍| 1878/2000 [11:41<00:38, 3.19it/s, loss=0.363]" ] }, { @@ -66604,7 +66604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1878/2000 [11:59<00:39, 3.07it/s, loss=0.412]" + "training until 2000: 94%|█████████▍| 1878/2000 [11:41<00:38, 3.19it/s, loss=0.36] " ] }, { @@ -66612,7 +66612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1879/2000 [11:59<00:39, 3.09it/s, loss=0.412]" + "training until 2000: 94%|█████████▍| 1879/2000 [11:41<00:38, 3.18it/s, loss=0.36]" ] }, { @@ -66620,7 +66620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1879/2000 [11:59<00:39, 3.09it/s, loss=0.415]" + "training until 2000: 94%|█████████▍| 1879/2000 [11:41<00:38, 3.18it/s, loss=0.393]" ] }, { @@ -66628,7 +66628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1880/2000 [11:59<00:38, 3.09it/s, loss=0.415]" + "training until 2000: 94%|█████████▍| 1880/2000 [11:42<00:37, 3.18it/s, loss=0.393]" ] }, { @@ -66636,7 +66636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1880/2000 [11:59<00:38, 3.09it/s, loss=0.438]" + "training until 2000: 94%|█████████▍| 1880/2000 [11:42<00:37, 3.18it/s, loss=0.395]" ] }, { @@ -66644,7 +66644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1881/2000 [12:00<00:38, 3.10it/s, loss=0.438]" + "training until 2000: 94%|█████████▍| 1881/2000 [11:42<00:37, 3.19it/s, loss=0.395]" ] }, { @@ -66652,7 +66652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1881/2000 [12:00<00:38, 3.10it/s, loss=0.527]" + "training until 2000: 94%|█████████▍| 1881/2000 [11:42<00:37, 3.19it/s, loss=0.361]" ] }, { @@ -66660,7 +66660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1882/2000 [12:00<00:37, 3.11it/s, loss=0.527]" + "training until 2000: 94%|█████████▍| 1882/2000 [11:42<00:36, 3.21it/s, loss=0.361]" ] }, { @@ -66668,7 +66668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1882/2000 [12:00<00:37, 3.11it/s, loss=0.357]" + "training until 2000: 94%|█████████▍| 1882/2000 [11:42<00:36, 3.21it/s, loss=0.381]" ] }, { @@ -66676,7 +66676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1883/2000 [12:00<00:38, 3.08it/s, loss=0.357]" + "training until 2000: 94%|█████████▍| 1883/2000 [11:43<00:36, 3.18it/s, loss=0.381]" ] }, { @@ -66684,7 +66684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1883/2000 [12:00<00:38, 3.08it/s, loss=0.413]" + "training until 2000: 94%|█████████▍| 1883/2000 [11:43<00:36, 3.18it/s, loss=0.404]" ] }, { @@ -66692,7 +66692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1884/2000 [12:01<00:37, 3.07it/s, loss=0.413]" + "training until 2000: 94%|█████████▍| 1884/2000 [11:43<00:36, 3.14it/s, loss=0.404]" ] }, { @@ -66700,7 +66700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1884/2000 [12:01<00:37, 3.07it/s, loss=0.424]" + "training until 2000: 94%|█████████▍| 1884/2000 [11:43<00:36, 3.14it/s, loss=0.4] " ] }, { @@ -66708,7 +66708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1885/2000 [12:01<00:37, 3.06it/s, loss=0.424]" + "training until 2000: 94%|█████████▍| 1885/2000 [11:43<00:36, 3.14it/s, loss=0.4]" ] }, { @@ -66716,7 +66716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1885/2000 [12:01<00:37, 3.06it/s, loss=0.474]" + "training until 2000: 94%|█████████▍| 1885/2000 [11:43<00:36, 3.14it/s, loss=0.416]" ] }, { @@ -66724,7 +66724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1886/2000 [12:01<00:37, 3.05it/s, loss=0.474]" + "training until 2000: 94%|█████████▍| 1886/2000 [11:44<00:36, 3.15it/s, loss=0.416]" ] }, { @@ -66732,7 +66732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1886/2000 [12:01<00:37, 3.05it/s, loss=0.386]" + "training until 2000: 94%|█████████▍| 1886/2000 [11:44<00:36, 3.15it/s, loss=0.386]" ] }, { @@ -66740,7 +66740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1887/2000 [12:02<00:36, 3.07it/s, loss=0.386]" + "training until 2000: 94%|█████████▍| 1887/2000 [11:44<00:44, 2.53it/s, loss=0.386]" ] }, { @@ -66748,7 +66748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1887/2000 [12:02<00:36, 3.07it/s, loss=0.392]" + "training until 2000: 94%|█████████▍| 1887/2000 [11:44<00:44, 2.53it/s, loss=0.377]" ] }, { @@ -66756,7 +66756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1888/2000 [12:02<00:36, 3.08it/s, loss=0.392]" + "training until 2000: 94%|█████████▍| 1888/2000 [11:45<00:41, 2.68it/s, loss=0.377]" ] }, { @@ -66764,7 +66764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1888/2000 [12:02<00:36, 3.08it/s, loss=0.36] " + "training until 2000: 94%|█████████▍| 1888/2000 [11:45<00:41, 2.68it/s, loss=0.381]" ] }, { @@ -66772,7 +66772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1889/2000 [12:02<00:36, 3.08it/s, loss=0.36]" + "training until 2000: 94%|█████████▍| 1889/2000 [11:45<00:39, 2.80it/s, loss=0.381]" ] }, { @@ -66780,7 +66780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1889/2000 [12:02<00:36, 3.08it/s, loss=0.56]" + "training until 2000: 94%|█████████▍| 1889/2000 [11:45<00:39, 2.80it/s, loss=0.374]" ] }, { @@ -66788,7 +66788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1890/2000 [12:03<00:43, 2.50it/s, loss=0.56]" + "training until 2000: 94%|█████████▍| 1890/2000 [11:45<00:38, 2.87it/s, loss=0.374]" ] }, { @@ -66796,7 +66796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 94%|█████████▍| 1890/2000 [12:03<00:43, 2.50it/s, loss=0.386]" + "training until 2000: 94%|█████████▍| 1890/2000 [11:45<00:38, 2.87it/s, loss=0.372]" ] }, { @@ -66804,7 +66804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1891/2000 [12:03<00:41, 2.64it/s, loss=0.386]" + "training until 2000: 95%|█████████▍| 1891/2000 [11:46<00:36, 2.98it/s, loss=0.372]" ] }, { @@ -66812,7 +66812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1891/2000 [12:03<00:41, 2.64it/s, loss=0.52] " + "training until 2000: 95%|█████████▍| 1891/2000 [11:46<00:36, 2.98it/s, loss=0.37] " ] }, { @@ -66820,7 +66820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1892/2000 [12:03<00:39, 2.75it/s, loss=0.52]" + "training until 2000: 95%|█████████▍| 1892/2000 [11:46<00:36, 2.98it/s, loss=0.37]" ] }, { @@ -66828,7 +66828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1892/2000 [12:03<00:39, 2.75it/s, loss=0.372]" + "training until 2000: 95%|█████████▍| 1892/2000 [11:46<00:36, 2.98it/s, loss=0.393]" ] }, { @@ -66836,7 +66836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1893/2000 [12:04<00:37, 2.84it/s, loss=0.372]" + "training until 2000: 95%|█████████▍| 1893/2000 [11:46<00:35, 2.98it/s, loss=0.393]" ] }, { @@ -66844,7 +66844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1893/2000 [12:04<00:37, 2.84it/s, loss=0.37] " + "training until 2000: 95%|█████████▍| 1893/2000 [11:46<00:35, 2.98it/s, loss=0.384]" ] }, { @@ -66852,7 +66852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1894/2000 [12:04<00:37, 2.86it/s, loss=0.37]" + "training until 2000: 95%|█████████▍| 1894/2000 [11:47<00:34, 3.06it/s, loss=0.384]" ] }, { @@ -66860,7 +66860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1894/2000 [12:04<00:37, 2.86it/s, loss=0.384]" + "training until 2000: 95%|█████████▍| 1894/2000 [11:47<00:34, 3.06it/s, loss=0.37] " ] }, { @@ -66868,7 +66868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1895/2000 [12:04<00:36, 2.90it/s, loss=0.384]" + "training until 2000: 95%|█████████▍| 1895/2000 [11:47<00:33, 3.12it/s, loss=0.37]" ] }, { @@ -66876,7 +66876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1895/2000 [12:04<00:36, 2.90it/s, loss=0.387]" + "training until 2000: 95%|█████████▍| 1895/2000 [11:47<00:33, 3.12it/s, loss=0.404]" ] }, { @@ -66884,7 +66884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1896/2000 [12:05<00:35, 2.95it/s, loss=0.387]" + "training until 2000: 95%|█████████▍| 1896/2000 [11:47<00:32, 3.16it/s, loss=0.404]" ] }, { @@ -66892,7 +66892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1896/2000 [12:05<00:35, 2.95it/s, loss=0.406]" + "training until 2000: 95%|█████████▍| 1896/2000 [11:47<00:32, 3.16it/s, loss=0.342]" ] }, { @@ -66900,7 +66900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1897/2000 [12:05<00:34, 2.98it/s, loss=0.406]" + "training until 2000: 95%|█████████▍| 1897/2000 [11:47<00:33, 3.12it/s, loss=0.342]" ] }, { @@ -66908,7 +66908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1897/2000 [12:05<00:34, 2.98it/s, loss=0.376]" + "training until 2000: 95%|█████████▍| 1897/2000 [11:47<00:33, 3.12it/s, loss=0.358]" ] }, { @@ -66916,7 +66916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1898/2000 [12:05<00:33, 3.04it/s, loss=0.376]" + "training until 2000: 95%|█████████▍| 1898/2000 [11:48<00:32, 3.15it/s, loss=0.358]" ] }, { @@ -66924,7 +66924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1898/2000 [12:05<00:33, 3.04it/s, loss=0.357]" + "training until 2000: 95%|█████████▍| 1898/2000 [11:48<00:32, 3.15it/s, loss=0.411]" ] }, { @@ -66932,7 +66932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1899/2000 [12:06<00:32, 3.08it/s, loss=0.357]" + "training until 2000: 95%|█████████▍| 1899/2000 [11:48<00:32, 3.15it/s, loss=0.411]" ] }, { @@ -66940,7 +66940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▍| 1899/2000 [12:06<00:32, 3.08it/s, loss=0.375]" + "training until 2000: 95%|█████████▍| 1899/2000 [11:48<00:32, 3.15it/s, loss=0.387]" ] }, { @@ -66948,7 +66948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1900/2000 [12:06<00:32, 3.09it/s, loss=0.375]" + "training until 2000: 95%|█████████▌| 1900/2000 [11:48<00:31, 3.13it/s, loss=0.387]" ] }, { @@ -66956,7 +66956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1900/2000 [12:06<00:32, 3.09it/s, loss=0.378]" + "training until 2000: 95%|█████████▌| 1900/2000 [11:48<00:31, 3.13it/s, loss=0.396]" ] }, { @@ -66964,7 +66964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1901/2000 [12:06<00:32, 3.06it/s, loss=0.378]" + "training until 2000: 95%|█████████▌| 1901/2000 [11:49<00:32, 3.09it/s, loss=0.396]" ] }, { @@ -66972,7 +66972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1901/2000 [12:06<00:32, 3.06it/s, loss=0.361]" + "training until 2000: 95%|█████████▌| 1901/2000 [11:49<00:32, 3.09it/s, loss=0.36] " ] }, { @@ -66980,7 +66980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1902/2000 [12:07<00:32, 3.04it/s, loss=0.361]" + "training until 2000: 95%|█████████▌| 1902/2000 [11:49<00:31, 3.10it/s, loss=0.36]" ] }, { @@ -66988,7 +66988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1902/2000 [12:07<00:32, 3.04it/s, loss=0.383]" + "training until 2000: 95%|█████████▌| 1902/2000 [11:49<00:31, 3.10it/s, loss=0.328]" ] }, { @@ -66996,7 +66996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1903/2000 [12:07<00:32, 3.01it/s, loss=0.383]" + "training until 2000: 95%|█████████▌| 1903/2000 [11:49<00:31, 3.08it/s, loss=0.328]" ] }, { @@ -67004,7 +67004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1903/2000 [12:07<00:32, 3.01it/s, loss=0.401]" + "training until 2000: 95%|█████████▌| 1903/2000 [11:49<00:31, 3.08it/s, loss=0.455]" ] }, { @@ -67012,7 +67012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1904/2000 [12:07<00:31, 3.01it/s, loss=0.401]" + "training until 2000: 95%|█████████▌| 1904/2000 [11:50<00:30, 3.10it/s, loss=0.455]" ] }, { @@ -67020,7 +67020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1904/2000 [12:07<00:31, 3.01it/s, loss=0.373]" + "training until 2000: 95%|█████████▌| 1904/2000 [11:50<00:30, 3.10it/s, loss=0.399]" ] }, { @@ -67028,7 +67028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1905/2000 [12:08<00:31, 3.02it/s, loss=0.373]" + "training until 2000: 95%|█████████▌| 1905/2000 [11:50<00:30, 3.10it/s, loss=0.399]" ] }, { @@ -67036,7 +67036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1905/2000 [12:08<00:31, 3.02it/s, loss=0.366]" + "training until 2000: 95%|█████████▌| 1905/2000 [11:50<00:30, 3.10it/s, loss=0.364]" ] }, { @@ -67044,7 +67044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1906/2000 [12:08<00:30, 3.06it/s, loss=0.366]" + "training until 2000: 95%|█████████▌| 1906/2000 [11:50<00:30, 3.09it/s, loss=0.364]" ] }, { @@ -67052,7 +67052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1906/2000 [12:08<00:30, 3.06it/s, loss=0.417]" + "training until 2000: 95%|█████████▌| 1906/2000 [11:50<00:30, 3.09it/s, loss=0.38] " ] }, { @@ -67060,7 +67060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1907/2000 [12:08<00:30, 3.05it/s, loss=0.417]" + "training until 2000: 95%|█████████▌| 1907/2000 [11:51<00:29, 3.10it/s, loss=0.38]" ] }, { @@ -67068,7 +67068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1907/2000 [12:08<00:30, 3.05it/s, loss=0.476]" + "training until 2000: 95%|█████████▌| 1907/2000 [11:51<00:29, 3.10it/s, loss=0.339]" ] }, { @@ -67076,7 +67076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1908/2000 [12:09<00:29, 3.10it/s, loss=0.476]" + "training until 2000: 95%|█████████▌| 1908/2000 [11:51<00:29, 3.08it/s, loss=0.339]" ] }, { @@ -67084,7 +67084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1908/2000 [12:09<00:29, 3.10it/s, loss=0.365]" + "training until 2000: 95%|█████████▌| 1908/2000 [11:51<00:29, 3.08it/s, loss=0.401]" ] }, { @@ -67092,7 +67092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1909/2000 [12:09<00:29, 3.07it/s, loss=0.365]" + "training until 2000: 95%|█████████▌| 1909/2000 [11:51<00:29, 3.05it/s, loss=0.401]" ] }, { @@ -67100,7 +67100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 95%|█████████▌| 1909/2000 [12:09<00:29, 3.07it/s, loss=0.359]" + "training until 2000: 95%|█████████▌| 1909/2000 [11:51<00:29, 3.05it/s, loss=0.364]" ] }, { @@ -67108,7 +67108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1910/2000 [12:09<00:29, 3.03it/s, loss=0.359]" + "training until 2000: 96%|█████████▌| 1910/2000 [11:52<00:29, 3.04it/s, loss=0.364]" ] }, { @@ -67116,7 +67116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1910/2000 [12:09<00:29, 3.03it/s, loss=0.357]" + "training until 2000: 96%|█████████▌| 1910/2000 [11:52<00:29, 3.04it/s, loss=0.58] " ] }, { @@ -67124,7 +67124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1911/2000 [12:10<00:29, 3.04it/s, loss=0.357]" + "training until 2000: 96%|█████████▌| 1911/2000 [11:52<00:29, 3.02it/s, loss=0.58]" ] }, { @@ -67132,7 +67132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1911/2000 [12:10<00:29, 3.04it/s, loss=0.397]" + "training until 2000: 96%|█████████▌| 1911/2000 [11:52<00:29, 3.02it/s, loss=0.397]" ] }, { @@ -67140,7 +67140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1912/2000 [12:10<00:28, 3.04it/s, loss=0.397]" + "training until 2000: 96%|█████████▌| 1912/2000 [11:52<00:29, 3.02it/s, loss=0.397]" ] }, { @@ -67148,7 +67148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1912/2000 [12:10<00:28, 3.04it/s, loss=0.35] " + "training until 2000: 96%|█████████▌| 1912/2000 [11:52<00:29, 3.02it/s, loss=0.397]" ] }, { @@ -67156,7 +67156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1913/2000 [12:10<00:28, 3.02it/s, loss=0.35]" + "training until 2000: 96%|█████████▌| 1913/2000 [11:53<00:28, 3.03it/s, loss=0.397]" ] }, { @@ -67164,7 +67164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1913/2000 [12:10<00:28, 3.02it/s, loss=0.369]" + "training until 2000: 96%|█████████▌| 1913/2000 [11:53<00:28, 3.03it/s, loss=0.43] " ] }, { @@ -67172,7 +67172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1914/2000 [12:11<00:28, 3.03it/s, loss=0.369]" + "training until 2000: 96%|█████████▌| 1914/2000 [11:53<00:28, 3.06it/s, loss=0.43]" ] }, { @@ -67180,7 +67180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1914/2000 [12:11<00:28, 3.03it/s, loss=0.391]" + "training until 2000: 96%|█████████▌| 1914/2000 [11:53<00:28, 3.06it/s, loss=0.388]" ] }, { @@ -67188,7 +67188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1915/2000 [12:11<00:27, 3.08it/s, loss=0.391]" + "training until 2000: 96%|█████████▌| 1915/2000 [11:53<00:27, 3.07it/s, loss=0.388]" ] }, { @@ -67196,7 +67196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1915/2000 [12:11<00:27, 3.08it/s, loss=0.371]" + "training until 2000: 96%|█████████▌| 1915/2000 [11:53<00:27, 3.07it/s, loss=0.35] " ] }, { @@ -67204,7 +67204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1916/2000 [12:11<00:27, 3.08it/s, loss=0.371]" + "training until 2000: 96%|█████████▌| 1916/2000 [11:54<00:27, 3.06it/s, loss=0.35]" ] }, { @@ -67212,7 +67212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1916/2000 [12:11<00:27, 3.08it/s, loss=0.437]" + "training until 2000: 96%|█████████▌| 1916/2000 [11:54<00:27, 3.06it/s, loss=0.38]" ] }, { @@ -67220,7 +67220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1917/2000 [12:12<00:26, 3.08it/s, loss=0.437]" + "training until 2000: 96%|█████████▌| 1917/2000 [11:54<00:26, 3.09it/s, loss=0.38]" ] }, { @@ -67228,7 +67228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1917/2000 [12:12<00:26, 3.08it/s, loss=0.421]" + "training until 2000: 96%|█████████▌| 1917/2000 [11:54<00:26, 3.09it/s, loss=0.379]" ] }, { @@ -67236,7 +67236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1918/2000 [12:12<00:26, 3.06it/s, loss=0.421]" + "training until 2000: 96%|█████████▌| 1918/2000 [11:54<00:26, 3.08it/s, loss=0.379]" ] }, { @@ -67244,7 +67244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1918/2000 [12:12<00:26, 3.06it/s, loss=0.372]" + "training until 2000: 96%|█████████▌| 1918/2000 [11:54<00:26, 3.08it/s, loss=0.359]" ] }, { @@ -67252,7 +67252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1919/2000 [12:12<00:26, 3.07it/s, loss=0.372]" + "training until 2000: 96%|█████████▌| 1919/2000 [11:55<00:26, 3.07it/s, loss=0.359]" ] }, { @@ -67260,7 +67260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1919/2000 [12:12<00:26, 3.07it/s, loss=0.391]" + "training until 2000: 96%|█████████▌| 1919/2000 [11:55<00:26, 3.07it/s, loss=0.42] " ] }, { @@ -67268,7 +67268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1920/2000 [12:13<00:26, 3.07it/s, loss=0.391]" + "training until 2000: 96%|█████████▌| 1920/2000 [11:55<00:26, 3.07it/s, loss=0.42]" ] }, { @@ -67276,7 +67276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1920/2000 [12:13<00:26, 3.07it/s, loss=0.393]" + "training until 2000: 96%|█████████▌| 1920/2000 [11:55<00:26, 3.07it/s, loss=0.373]" ] }, { @@ -67284,7 +67284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1921/2000 [12:13<00:25, 3.08it/s, loss=0.393]" + "training until 2000: 96%|█████████▌| 1921/2000 [11:55<00:25, 3.12it/s, loss=0.373]" ] }, { @@ -67292,7 +67292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1921/2000 [12:13<00:25, 3.08it/s, loss=0.388]" + "training until 2000: 96%|█████████▌| 1921/2000 [11:55<00:25, 3.12it/s, loss=0.357]" ] }, { @@ -67300,7 +67300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1922/2000 [12:13<00:25, 3.10it/s, loss=0.388]" + "training until 2000: 96%|█████████▌| 1922/2000 [11:56<00:25, 3.12it/s, loss=0.357]" ] }, { @@ -67308,7 +67308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1922/2000 [12:13<00:25, 3.10it/s, loss=0.362]" + "training until 2000: 96%|█████████▌| 1922/2000 [11:56<00:25, 3.12it/s, loss=0.628]" ] }, { @@ -67316,7 +67316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1923/2000 [12:14<00:24, 3.11it/s, loss=0.362]" + "training until 2000: 96%|█████████▌| 1923/2000 [11:56<00:24, 3.13it/s, loss=0.628]" ] }, { @@ -67324,7 +67324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1923/2000 [12:14<00:24, 3.11it/s, loss=0.377]" + "training until 2000: 96%|█████████▌| 1923/2000 [11:56<00:24, 3.13it/s, loss=0.398]" ] }, { @@ -67332,7 +67332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1924/2000 [12:14<00:24, 3.13it/s, loss=0.377]" + "training until 2000: 96%|█████████▌| 1924/2000 [11:56<00:24, 3.10it/s, loss=0.398]" ] }, { @@ -67340,7 +67340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▌| 1924/2000 [12:14<00:24, 3.13it/s, loss=0.352]" + "training until 2000: 96%|█████████▌| 1924/2000 [11:56<00:24, 3.10it/s, loss=0.347]" ] }, { @@ -67348,7 +67348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1925/2000 [12:14<00:24, 3.10it/s, loss=0.352]" + "training until 2000: 96%|█████████▋| 1925/2000 [11:57<00:23, 3.13it/s, loss=0.347]" ] }, { @@ -67356,7 +67356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1925/2000 [12:14<00:24, 3.10it/s, loss=0.367]" + "training until 2000: 96%|█████████▋| 1925/2000 [11:57<00:23, 3.13it/s, loss=0.381]" ] }, { @@ -67364,7 +67364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1926/2000 [12:15<00:23, 3.12it/s, loss=0.367]" + "training until 2000: 96%|█████████▋| 1926/2000 [11:57<00:23, 3.13it/s, loss=0.381]" ] }, { @@ -67372,7 +67372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1926/2000 [12:15<00:23, 3.12it/s, loss=0.376]" + "training until 2000: 96%|█████████▋| 1926/2000 [11:57<00:23, 3.13it/s, loss=0.386]" ] }, { @@ -67380,7 +67380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1927/2000 [12:15<00:23, 3.05it/s, loss=0.376]" + "training until 2000: 96%|█████████▋| 1927/2000 [11:57<00:23, 3.12it/s, loss=0.386]" ] }, { @@ -67388,7 +67388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1927/2000 [12:15<00:23, 3.05it/s, loss=0.558]" + "training until 2000: 96%|█████████▋| 1927/2000 [11:57<00:23, 3.12it/s, loss=0.371]" ] }, { @@ -67396,7 +67396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1928/2000 [12:15<00:23, 3.09it/s, loss=0.558]" + "training until 2000: 96%|█████████▋| 1928/2000 [11:58<00:23, 3.12it/s, loss=0.371]" ] }, { @@ -67404,7 +67404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1928/2000 [12:15<00:23, 3.09it/s, loss=0.411]" + "training until 2000: 96%|█████████▋| 1928/2000 [11:58<00:23, 3.12it/s, loss=0.36] " ] }, { @@ -67412,7 +67412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1929/2000 [12:16<00:22, 3.10it/s, loss=0.411]" + "training until 2000: 96%|█████████▋| 1929/2000 [11:58<00:22, 3.11it/s, loss=0.36]" ] }, { @@ -67420,7 +67420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1929/2000 [12:16<00:22, 3.10it/s, loss=0.373]" + "training until 2000: 96%|█████████▋| 1929/2000 [11:58<00:22, 3.11it/s, loss=0.34]" ] }, { @@ -67428,7 +67428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1930/2000 [12:16<00:22, 3.10it/s, loss=0.373]" + "training until 2000: 96%|█████████▋| 1930/2000 [11:58<00:22, 3.16it/s, loss=0.34]" ] }, { @@ -67436,7 +67436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 96%|█████████▋| 1930/2000 [12:16<00:22, 3.10it/s, loss=0.407]" + "training until 2000: 96%|█████████▋| 1930/2000 [11:58<00:22, 3.16it/s, loss=0.39]" ] }, { @@ -67444,7 +67444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1931/2000 [12:16<00:22, 3.09it/s, loss=0.407]" + "training until 2000: 97%|█████████▋| 1931/2000 [11:58<00:21, 3.16it/s, loss=0.39]" ] }, { @@ -67452,7 +67452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1931/2000 [12:16<00:22, 3.09it/s, loss=0.431]" + "training until 2000: 97%|█████████▋| 1931/2000 [11:58<00:21, 3.16it/s, loss=0.365]" ] }, { @@ -67460,7 +67460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1932/2000 [12:17<00:22, 3.05it/s, loss=0.431]" + "training until 2000: 97%|█████████▋| 1932/2000 [11:59<00:21, 3.17it/s, loss=0.365]" ] }, { @@ -67468,7 +67468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1932/2000 [12:17<00:22, 3.05it/s, loss=0.371]" + "training until 2000: 97%|█████████▋| 1932/2000 [11:59<00:21, 3.17it/s, loss=0.359]" ] }, { @@ -67476,7 +67476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1933/2000 [12:17<00:21, 3.07it/s, loss=0.371]" + "training until 2000: 97%|█████████▋| 1933/2000 [11:59<00:21, 3.14it/s, loss=0.359]" ] }, { @@ -67484,7 +67484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1933/2000 [12:17<00:21, 3.07it/s, loss=0.377]" + "training until 2000: 97%|█████████▋| 1933/2000 [11:59<00:21, 3.14it/s, loss=0.366]" ] }, { @@ -67492,7 +67492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1934/2000 [12:17<00:21, 3.08it/s, loss=0.377]" + "training until 2000: 97%|█████████▋| 1934/2000 [11:59<00:21, 3.12it/s, loss=0.366]" ] }, { @@ -67500,7 +67500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1934/2000 [12:17<00:21, 3.08it/s, loss=0.362]" + "training until 2000: 97%|█████████▋| 1934/2000 [11:59<00:21, 3.12it/s, loss=0.386]" ] }, { @@ -67508,7 +67508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1935/2000 [12:18<00:21, 3.08it/s, loss=0.362]" + "training until 2000: 97%|█████████▋| 1935/2000 [12:00<00:20, 3.11it/s, loss=0.386]" ] }, { @@ -67516,7 +67516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1935/2000 [12:18<00:21, 3.08it/s, loss=0.38] " + "training until 2000: 97%|█████████▋| 1935/2000 [12:00<00:20, 3.11it/s, loss=0.398]" ] }, { @@ -67524,7 +67524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1936/2000 [12:18<00:20, 3.11it/s, loss=0.38]" + "training until 2000: 97%|█████████▋| 1936/2000 [12:00<00:20, 3.15it/s, loss=0.398]" ] }, { @@ -67532,7 +67532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1936/2000 [12:18<00:20, 3.11it/s, loss=0.377]" + "training until 2000: 97%|█████████▋| 1936/2000 [12:00<00:20, 3.15it/s, loss=0.357]" ] }, { @@ -67540,7 +67540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1937/2000 [12:18<00:20, 3.09it/s, loss=0.377]" + "training until 2000: 97%|█████████▋| 1937/2000 [12:00<00:20, 3.13it/s, loss=0.357]" ] }, { @@ -67548,7 +67548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1937/2000 [12:18<00:20, 3.09it/s, loss=0.432]" + "training until 2000: 97%|█████████▋| 1937/2000 [12:00<00:20, 3.13it/s, loss=0.402]" ] }, { @@ -67556,7 +67556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1938/2000 [12:18<00:20, 3.06it/s, loss=0.432]" + "training until 2000: 97%|█████████▋| 1938/2000 [12:01<00:19, 3.15it/s, loss=0.402]" ] }, { @@ -67564,7 +67564,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1938/2000 [12:18<00:20, 3.06it/s, loss=0.358]" + "training until 2000: 97%|█████████▋| 1938/2000 [12:01<00:19, 3.15it/s, loss=0.39] " ] }, { @@ -67572,7 +67572,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1939/2000 [12:19<00:20, 3.04it/s, loss=0.358]" + "training until 2000: 97%|█████████▋| 1939/2000 [12:01<00:19, 3.15it/s, loss=0.39]" ] }, { @@ -67580,7 +67580,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1939/2000 [12:19<00:20, 3.04it/s, loss=0.401]" + "training until 2000: 97%|█████████▋| 1939/2000 [12:01<00:19, 3.15it/s, loss=0.478]" ] }, { @@ -67588,7 +67588,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1940/2000 [12:19<00:19, 3.07it/s, loss=0.401]" + "training until 2000: 97%|█████████▋| 1940/2000 [12:01<00:19, 3.14it/s, loss=0.478]" ] }, { @@ -67596,7 +67596,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1940/2000 [12:19<00:19, 3.07it/s, loss=0.374]" + "training until 2000: 97%|█████████▋| 1940/2000 [12:01<00:19, 3.14it/s, loss=0.346]" ] }, { @@ -67604,7 +67604,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1941/2000 [12:19<00:19, 3.07it/s, loss=0.374]" + "training until 2000: 97%|█████████▋| 1941/2000 [12:02<00:18, 3.11it/s, loss=0.346]" ] }, { @@ -67612,7 +67612,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1941/2000 [12:19<00:19, 3.07it/s, loss=0.396]" + "training until 2000: 97%|█████████▋| 1941/2000 [12:02<00:18, 3.11it/s, loss=0.39] " ] }, { @@ -67620,7 +67620,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1942/2000 [12:20<00:18, 3.07it/s, loss=0.396]" + "training until 2000: 97%|█████████▋| 1942/2000 [12:02<00:18, 3.15it/s, loss=0.39]" ] }, { @@ -67628,7 +67628,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1942/2000 [12:20<00:18, 3.07it/s, loss=0.359]" + "training until 2000: 97%|█████████▋| 1942/2000 [12:02<00:18, 3.15it/s, loss=0.385]" ] }, { @@ -67636,7 +67636,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1943/2000 [12:20<00:18, 3.08it/s, loss=0.359]" + "training until 2000: 97%|█████████▋| 1943/2000 [12:02<00:18, 3.12it/s, loss=0.385]" ] }, { @@ -67644,7 +67644,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1943/2000 [12:20<00:18, 3.08it/s, loss=0.355]" + "training until 2000: 97%|█████████▋| 1943/2000 [12:02<00:18, 3.12it/s, loss=0.368]" ] }, { @@ -67652,7 +67652,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1944/2000 [12:20<00:18, 3.08it/s, loss=0.355]" + "training until 2000: 97%|█████████▋| 1944/2000 [12:03<00:17, 3.11it/s, loss=0.368]" ] }, { @@ -67660,7 +67660,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1944/2000 [12:20<00:18, 3.08it/s, loss=0.458]" + "training until 2000: 97%|█████████▋| 1944/2000 [12:03<00:17, 3.11it/s, loss=0.39] " ] }, { @@ -67668,7 +67668,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1945/2000 [12:21<00:18, 3.03it/s, loss=0.458]" + "training until 2000: 97%|█████████▋| 1945/2000 [12:03<00:17, 3.15it/s, loss=0.39]" ] }, { @@ -67676,7 +67676,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1945/2000 [12:21<00:18, 3.03it/s, loss=0.353]" + "training until 2000: 97%|█████████▋| 1945/2000 [12:03<00:17, 3.15it/s, loss=0.469]" ] }, { @@ -67684,7 +67684,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1946/2000 [12:21<00:17, 3.04it/s, loss=0.353]" + "training until 2000: 97%|█████████▋| 1946/2000 [12:03<00:17, 3.16it/s, loss=0.469]" ] }, { @@ -67692,7 +67692,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1946/2000 [12:21<00:17, 3.04it/s, loss=0.431]" + "training until 2000: 97%|█████████▋| 1946/2000 [12:03<00:17, 3.16it/s, loss=0.358]" ] }, { @@ -67700,7 +67700,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1947/2000 [12:21<00:17, 3.08it/s, loss=0.431]" + "training until 2000: 97%|█████████▋| 1947/2000 [12:04<00:16, 3.16it/s, loss=0.358]" ] }, { @@ -67708,7 +67708,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1947/2000 [12:21<00:17, 3.08it/s, loss=0.361]" + "training until 2000: 97%|█████████▋| 1947/2000 [12:04<00:16, 3.16it/s, loss=0.378]" ] }, { @@ -67716,7 +67716,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1948/2000 [12:22<00:16, 3.10it/s, loss=0.361]" + "training until 2000: 97%|█████████▋| 1948/2000 [12:04<00:16, 3.14it/s, loss=0.378]" ] }, { @@ -67724,7 +67724,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1948/2000 [12:22<00:16, 3.10it/s, loss=0.356]" + "training until 2000: 97%|█████████▋| 1948/2000 [12:04<00:16, 3.14it/s, loss=0.379]" ] }, { @@ -67732,7 +67732,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1949/2000 [12:22<00:16, 3.09it/s, loss=0.356]" + "training until 2000: 97%|█████████▋| 1949/2000 [12:04<00:16, 3.15it/s, loss=0.379]" ] }, { @@ -67740,7 +67740,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 97%|█████████▋| 1949/2000 [12:22<00:16, 3.09it/s, loss=0.347]" + "training until 2000: 97%|█████████▋| 1949/2000 [12:04<00:16, 3.15it/s, loss=0.338]" ] }, { @@ -67748,7 +67748,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1950/2000 [12:22<00:16, 3.11it/s, loss=0.347]" + "training until 2000: 98%|█████████▊| 1950/2000 [12:05<00:15, 3.13it/s, loss=0.338]" ] }, { @@ -67756,7 +67756,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1950/2000 [12:22<00:16, 3.11it/s, loss=0.367]" + "training until 2000: 98%|█████████▊| 1950/2000 [12:05<00:15, 3.13it/s, loss=0.38] " ] }, { @@ -67764,7 +67764,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1951/2000 [12:23<00:15, 3.14it/s, loss=0.367]" + "training until 2000: 98%|█████████▊| 1951/2000 [12:05<00:15, 3.15it/s, loss=0.38]" ] }, { @@ -67772,7 +67772,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1951/2000 [12:23<00:15, 3.14it/s, loss=0.358]" + "training until 2000: 98%|█████████▊| 1951/2000 [12:05<00:15, 3.15it/s, loss=0.391]" ] }, { @@ -67780,7 +67780,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1952/2000 [12:23<00:15, 3.13it/s, loss=0.358]" + "training until 2000: 98%|█████████▊| 1952/2000 [12:05<00:15, 3.13it/s, loss=0.391]" ] }, { @@ -67788,7 +67788,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1952/2000 [12:23<00:15, 3.13it/s, loss=0.366]" + "training until 2000: 98%|█████████▊| 1952/2000 [12:05<00:15, 3.13it/s, loss=0.381]" ] }, { @@ -67796,7 +67796,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1953/2000 [12:23<00:15, 3.10it/s, loss=0.366]" + "training until 2000: 98%|█████████▊| 1953/2000 [12:06<00:18, 2.53it/s, loss=0.381]" ] }, { @@ -67804,7 +67804,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1953/2000 [12:23<00:15, 3.10it/s, loss=0.354]" + "training until 2000: 98%|█████████▊| 1953/2000 [12:06<00:18, 2.53it/s, loss=0.377]" ] }, { @@ -67812,7 +67812,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1954/2000 [12:24<00:14, 3.09it/s, loss=0.354]" + "training until 2000: 98%|█████████▊| 1954/2000 [12:06<00:17, 2.70it/s, loss=0.377]" ] }, { @@ -67820,7 +67820,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1954/2000 [12:24<00:14, 3.09it/s, loss=0.354]" + "training until 2000: 98%|█████████▊| 1954/2000 [12:06<00:17, 2.70it/s, loss=0.447]" ] }, { @@ -67828,7 +67828,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1955/2000 [12:24<00:14, 3.10it/s, loss=0.354]" + "training until 2000: 98%|█████████▊| 1955/2000 [12:06<00:15, 2.82it/s, loss=0.447]" ] }, { @@ -67836,7 +67836,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1955/2000 [12:24<00:14, 3.10it/s, loss=0.384]" + "training until 2000: 98%|█████████▊| 1955/2000 [12:06<00:15, 2.82it/s, loss=0.386]" ] }, { @@ -67844,7 +67844,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1956/2000 [12:24<00:14, 3.10it/s, loss=0.384]" + "training until 2000: 98%|█████████▊| 1956/2000 [12:07<00:15, 2.89it/s, loss=0.386]" ] }, { @@ -67852,7 +67852,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1956/2000 [12:24<00:14, 3.10it/s, loss=0.404]" + "training until 2000: 98%|█████████▊| 1956/2000 [12:07<00:15, 2.89it/s, loss=0.369]" ] }, { @@ -67860,7 +67860,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1957/2000 [12:25<00:13, 3.12it/s, loss=0.404]" + "training until 2000: 98%|█████████▊| 1957/2000 [12:07<00:14, 2.98it/s, loss=0.369]" ] }, { @@ -67868,7 +67868,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1957/2000 [12:25<00:13, 3.12it/s, loss=0.493]" + "training until 2000: 98%|█████████▊| 1957/2000 [12:07<00:14, 2.98it/s, loss=0.348]" ] }, { @@ -67876,7 +67876,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1958/2000 [12:25<00:13, 3.15it/s, loss=0.493]" + "training until 2000: 98%|█████████▊| 1958/2000 [12:07<00:13, 3.05it/s, loss=0.348]" ] }, { @@ -67884,7 +67884,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1958/2000 [12:25<00:13, 3.15it/s, loss=0.361]" + "training until 2000: 98%|█████████▊| 1958/2000 [12:07<00:13, 3.05it/s, loss=0.41] " ] }, { @@ -67892,7 +67892,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1959/2000 [12:25<00:13, 3.14it/s, loss=0.361]" + "training until 2000: 98%|█████████▊| 1959/2000 [12:08<00:13, 3.08it/s, loss=0.41]" ] }, { @@ -67900,7 +67900,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1959/2000 [12:25<00:13, 3.14it/s, loss=0.357]" + "training until 2000: 98%|█████████▊| 1959/2000 [12:08<00:13, 3.08it/s, loss=0.368]" ] }, { @@ -67908,7 +67908,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1960/2000 [12:26<00:12, 3.12it/s, loss=0.357]" + "training until 2000: 98%|█████████▊| 1960/2000 [12:08<00:12, 3.10it/s, loss=0.368]" ] }, { @@ -67916,7 +67916,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1960/2000 [12:26<00:12, 3.12it/s, loss=0.393]" + "training until 2000: 98%|█████████▊| 1960/2000 [12:08<00:12, 3.10it/s, loss=0.424]" ] }, { @@ -67924,7 +67924,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1961/2000 [12:26<00:15, 2.54it/s, loss=0.393]" + "training until 2000: 98%|█████████▊| 1961/2000 [12:08<00:12, 3.12it/s, loss=0.424]" ] }, { @@ -67932,7 +67932,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1961/2000 [12:26<00:15, 2.54it/s, loss=0.37] " + "training until 2000: 98%|█████████▊| 1961/2000 [12:08<00:12, 3.12it/s, loss=0.358]" ] }, { @@ -67940,7 +67940,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1962/2000 [12:26<00:14, 2.68it/s, loss=0.37]" + "training until 2000: 98%|█████████▊| 1962/2000 [12:09<00:12, 3.11it/s, loss=0.358]" ] }, { @@ -67948,7 +67948,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1962/2000 [12:26<00:14, 2.68it/s, loss=0.398]" + "training until 2000: 98%|█████████▊| 1962/2000 [12:09<00:12, 3.11it/s, loss=0.327]" ] }, { @@ -67956,7 +67956,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1963/2000 [12:27<00:13, 2.77it/s, loss=0.398]" + "training until 2000: 98%|█████████▊| 1963/2000 [12:09<00:11, 3.13it/s, loss=0.327]" ] }, { @@ -67964,7 +67964,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1963/2000 [12:27<00:13, 2.77it/s, loss=0.36] " + "training until 2000: 98%|█████████▊| 1963/2000 [12:09<00:11, 3.13it/s, loss=0.372]" ] }, { @@ -67972,7 +67972,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1964/2000 [12:27<00:12, 2.87it/s, loss=0.36]" + "training until 2000: 98%|█████████▊| 1964/2000 [12:09<00:11, 3.16it/s, loss=0.372]" ] }, { @@ -67980,7 +67980,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1964/2000 [12:27<00:12, 2.87it/s, loss=0.365]" + "training until 2000: 98%|█████████▊| 1964/2000 [12:09<00:11, 3.16it/s, loss=0.349]" ] }, { @@ -67988,7 +67988,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1965/2000 [12:27<00:11, 2.95it/s, loss=0.365]" + "training until 2000: 98%|█████████▊| 1965/2000 [12:10<00:11, 3.14it/s, loss=0.349]" ] }, { @@ -67996,7 +67996,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1965/2000 [12:27<00:11, 2.95it/s, loss=0.351]" + "training until 2000: 98%|█████████▊| 1965/2000 [12:10<00:11, 3.14it/s, loss=0.343]" ] }, { @@ -68004,7 +68004,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1966/2000 [12:28<00:11, 2.98it/s, loss=0.351]" + "training until 2000: 98%|█████████▊| 1966/2000 [12:10<00:10, 3.11it/s, loss=0.343]" ] }, { @@ -68012,7 +68012,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1966/2000 [12:28<00:11, 2.98it/s, loss=0.445]" + "training until 2000: 98%|█████████▊| 1966/2000 [12:10<00:10, 3.11it/s, loss=0.429]" ] }, { @@ -68020,7 +68020,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1967/2000 [12:28<00:10, 3.01it/s, loss=0.445]" + "training until 2000: 98%|█████████▊| 1967/2000 [12:10<00:10, 3.14it/s, loss=0.429]" ] }, { @@ -68028,7 +68028,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1967/2000 [12:28<00:10, 3.01it/s, loss=0.358]" + "training until 2000: 98%|█████████▊| 1967/2000 [12:10<00:10, 3.14it/s, loss=0.386]" ] }, { @@ -68036,7 +68036,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1968/2000 [12:28<00:10, 3.05it/s, loss=0.358]" + "training until 2000: 98%|█████████▊| 1968/2000 [12:10<00:10, 3.12it/s, loss=0.386]" ] }, { @@ -68044,7 +68044,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1968/2000 [12:28<00:10, 3.05it/s, loss=0.355]" + "training until 2000: 98%|█████████▊| 1968/2000 [12:10<00:10, 3.12it/s, loss=0.391]" ] }, { @@ -68052,7 +68052,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1969/2000 [12:29<00:10, 3.05it/s, loss=0.355]" + "training until 2000: 98%|█████████▊| 1969/2000 [12:11<00:09, 3.13it/s, loss=0.391]" ] }, { @@ -68060,7 +68060,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1969/2000 [12:29<00:10, 3.05it/s, loss=0.383]" + "training until 2000: 98%|█████████▊| 1969/2000 [12:11<00:09, 3.13it/s, loss=0.385]" ] }, { @@ -68068,7 +68068,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1970/2000 [12:29<00:09, 3.06it/s, loss=0.383]" + "training until 2000: 98%|█████████▊| 1970/2000 [12:11<00:09, 3.11it/s, loss=0.385]" ] }, { @@ -68076,7 +68076,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 98%|█████████▊| 1970/2000 [12:29<00:09, 3.06it/s, loss=0.349]" + "training until 2000: 98%|█████████▊| 1970/2000 [12:11<00:09, 3.11it/s, loss=0.445]" ] }, { @@ -68084,7 +68084,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1971/2000 [12:29<00:09, 3.09it/s, loss=0.349]" + "training until 2000: 99%|█████████▊| 1971/2000 [12:11<00:09, 3.12it/s, loss=0.445]" ] }, { @@ -68092,7 +68092,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1971/2000 [12:29<00:09, 3.09it/s, loss=0.405]" + "training until 2000: 99%|█████████▊| 1971/2000 [12:11<00:09, 3.12it/s, loss=0.359]" ] }, { @@ -68100,7 +68100,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1972/2000 [12:30<00:09, 3.08it/s, loss=0.405]" + "training until 2000: 99%|█████████▊| 1972/2000 [12:12<00:08, 3.14it/s, loss=0.359]" ] }, { @@ -68108,7 +68108,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1972/2000 [12:30<00:09, 3.08it/s, loss=0.394]" + "training until 2000: 99%|█████████▊| 1972/2000 [12:12<00:08, 3.14it/s, loss=0.509]" ] }, { @@ -68116,7 +68116,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1973/2000 [12:30<00:08, 3.10it/s, loss=0.394]" + "training until 2000: 99%|█████████▊| 1973/2000 [12:12<00:08, 3.16it/s, loss=0.509]" ] }, { @@ -68124,7 +68124,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1973/2000 [12:30<00:08, 3.10it/s, loss=0.359]" + "training until 2000: 99%|█████████▊| 1973/2000 [12:12<00:08, 3.16it/s, loss=0.386]" ] }, { @@ -68132,7 +68132,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1974/2000 [12:30<00:08, 3.10it/s, loss=0.359]" + "training until 2000: 99%|█████████▊| 1974/2000 [12:12<00:08, 3.15it/s, loss=0.386]" ] }, { @@ -68140,7 +68140,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▊| 1974/2000 [12:30<00:08, 3.10it/s, loss=0.383]" + "training until 2000: 99%|█████████▊| 1974/2000 [12:12<00:08, 3.15it/s, loss=0.349]" ] }, { @@ -68148,7 +68148,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1975/2000 [12:31<00:07, 3.13it/s, loss=0.383]" + "training until 2000: 99%|█████████▉| 1975/2000 [12:13<00:07, 3.13it/s, loss=0.349]" ] }, { @@ -68156,7 +68156,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1975/2000 [12:31<00:07, 3.13it/s, loss=0.346]" + "training until 2000: 99%|█████████▉| 1975/2000 [12:13<00:07, 3.13it/s, loss=0.352]" ] }, { @@ -68164,7 +68164,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1976/2000 [12:31<00:07, 3.13it/s, loss=0.346]" + "training until 2000: 99%|█████████▉| 1976/2000 [12:13<00:07, 3.17it/s, loss=0.352]" ] }, { @@ -68172,7 +68172,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1976/2000 [12:31<00:07, 3.13it/s, loss=0.395]" + "training until 2000: 99%|█████████▉| 1976/2000 [12:13<00:07, 3.17it/s, loss=0.357]" ] }, { @@ -68180,7 +68180,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1977/2000 [12:31<00:07, 3.12it/s, loss=0.395]" + "training until 2000: 99%|█████████▉| 1977/2000 [12:13<00:07, 3.15it/s, loss=0.357]" ] }, { @@ -68188,7 +68188,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1977/2000 [12:31<00:07, 3.12it/s, loss=0.378]" + "training until 2000: 99%|█████████▉| 1977/2000 [12:13<00:07, 3.15it/s, loss=0.358]" ] }, { @@ -68196,7 +68196,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1978/2000 [12:32<00:06, 3.15it/s, loss=0.378]" + "training until 2000: 99%|█████████▉| 1978/2000 [12:14<00:06, 3.15it/s, loss=0.358]" ] }, { @@ -68204,7 +68204,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1978/2000 [12:32<00:06, 3.15it/s, loss=0.393]" + "training until 2000: 99%|█████████▉| 1978/2000 [12:14<00:06, 3.15it/s, loss=0.369]" ] }, { @@ -68212,7 +68212,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1979/2000 [12:32<00:06, 3.14it/s, loss=0.393]" + "training until 2000: 99%|█████████▉| 1979/2000 [12:14<00:06, 3.10it/s, loss=0.369]" ] }, { @@ -68220,7 +68220,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1979/2000 [12:32<00:06, 3.14it/s, loss=0.372]" + "training until 2000: 99%|█████████▉| 1979/2000 [12:14<00:06, 3.10it/s, loss=0.416]" ] }, { @@ -68228,7 +68228,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1980/2000 [12:32<00:06, 3.15it/s, loss=0.372]" + "training until 2000: 99%|█████████▉| 1980/2000 [12:14<00:06, 3.12it/s, loss=0.416]" ] }, { @@ -68236,7 +68236,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1980/2000 [12:32<00:06, 3.15it/s, loss=0.366]" + "training until 2000: 99%|█████████▉| 1980/2000 [12:14<00:06, 3.12it/s, loss=0.404]" ] }, { @@ -68244,7 +68244,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1981/2000 [12:33<00:06, 3.15it/s, loss=0.366]" + "training until 2000: 99%|█████████▉| 1981/2000 [12:15<00:05, 3.17it/s, loss=0.404]" ] }, { @@ -68252,7 +68252,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1981/2000 [12:33<00:06, 3.15it/s, loss=0.361]" + "training until 2000: 99%|█████████▉| 1981/2000 [12:15<00:05, 3.17it/s, loss=0.363]" ] }, { @@ -68260,7 +68260,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1982/2000 [12:33<00:05, 3.10it/s, loss=0.361]" + "training until 2000: 99%|█████████▉| 1982/2000 [12:15<00:05, 3.15it/s, loss=0.363]" ] }, { @@ -68268,7 +68268,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1982/2000 [12:33<00:05, 3.10it/s, loss=0.379]" + "training until 2000: 99%|█████████▉| 1982/2000 [12:15<00:05, 3.15it/s, loss=0.334]" ] }, { @@ -68276,7 +68276,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1983/2000 [12:33<00:05, 3.10it/s, loss=0.379]" + "training until 2000: 99%|█████████▉| 1983/2000 [12:15<00:05, 3.16it/s, loss=0.334]" ] }, { @@ -68284,7 +68284,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1983/2000 [12:33<00:05, 3.10it/s, loss=0.36] " + "training until 2000: 99%|█████████▉| 1983/2000 [12:15<00:05, 3.16it/s, loss=0.361]" ] }, { @@ -68292,7 +68292,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1984/2000 [12:34<00:05, 3.09it/s, loss=0.36]" + "training until 2000: 99%|█████████▉| 1984/2000 [12:16<00:05, 3.15it/s, loss=0.361]" ] }, { @@ -68300,7 +68300,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1984/2000 [12:34<00:05, 3.09it/s, loss=0.391]" + "training until 2000: 99%|█████████▉| 1984/2000 [12:16<00:05, 3.15it/s, loss=0.361]" ] }, { @@ -68308,7 +68308,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1985/2000 [12:34<00:04, 3.06it/s, loss=0.391]" + "training until 2000: 99%|█████████▉| 1985/2000 [12:16<00:04, 3.11it/s, loss=0.361]" ] }, { @@ -68316,7 +68316,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1985/2000 [12:34<00:04, 3.06it/s, loss=0.357]" + "training until 2000: 99%|█████████▉| 1985/2000 [12:16<00:04, 3.11it/s, loss=0.345]" ] }, { @@ -68324,7 +68324,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1986/2000 [12:34<00:04, 3.09it/s, loss=0.357]" + "training until 2000: 99%|█████████▉| 1986/2000 [12:16<00:04, 3.12it/s, loss=0.345]" ] }, { @@ -68332,7 +68332,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1986/2000 [12:34<00:04, 3.09it/s, loss=0.365]" + "training until 2000: 99%|█████████▉| 1986/2000 [12:16<00:04, 3.12it/s, loss=0.562]" ] }, { @@ -68340,7 +68340,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1987/2000 [12:35<00:04, 3.10it/s, loss=0.365]" + "training until 2000: 99%|█████████▉| 1987/2000 [12:17<00:04, 3.13it/s, loss=0.562]" ] }, { @@ -68348,7 +68348,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1987/2000 [12:35<00:04, 3.10it/s, loss=0.357]" + "training until 2000: 99%|█████████▉| 1987/2000 [12:17<00:04, 3.13it/s, loss=0.363]" ] }, { @@ -68356,7 +68356,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1988/2000 [12:35<00:03, 3.11it/s, loss=0.357]" + "training until 2000: 99%|█████████▉| 1988/2000 [12:17<00:03, 3.12it/s, loss=0.363]" ] }, { @@ -68364,7 +68364,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1988/2000 [12:35<00:03, 3.11it/s, loss=0.355]" + "training until 2000: 99%|█████████▉| 1988/2000 [12:17<00:03, 3.12it/s, loss=0.393]" ] }, { @@ -68372,7 +68372,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1989/2000 [12:35<00:03, 3.11it/s, loss=0.355]" + "training until 2000: 99%|█████████▉| 1989/2000 [12:17<00:03, 3.13it/s, loss=0.393]" ] }, { @@ -68380,7 +68380,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 99%|█████████▉| 1989/2000 [12:35<00:03, 3.11it/s, loss=0.354]" + "training until 2000: 99%|█████████▉| 1989/2000 [12:17<00:03, 3.13it/s, loss=0.465]" ] }, { @@ -68388,7 +68388,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1990/2000 [12:35<00:03, 3.12it/s, loss=0.354]" + "training until 2000: 100%|█████████▉| 1990/2000 [12:17<00:03, 3.14it/s, loss=0.465]" ] }, { @@ -68396,7 +68396,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1990/2000 [12:35<00:03, 3.12it/s, loss=0.348]" + "training until 2000: 100%|█████████▉| 1990/2000 [12:17<00:03, 3.14it/s, loss=0.344]" ] }, { @@ -68404,7 +68404,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1991/2000 [12:36<00:02, 3.11it/s, loss=0.348]" + "training until 2000: 100%|█████████▉| 1991/2000 [12:18<00:02, 3.15it/s, loss=0.344]" ] }, { @@ -68412,7 +68412,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1991/2000 [12:36<00:02, 3.11it/s, loss=0.361]" + "training until 2000: 100%|█████████▉| 1991/2000 [12:18<00:02, 3.15it/s, loss=0.365]" ] }, { @@ -68420,7 +68420,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1992/2000 [12:36<00:02, 3.10it/s, loss=0.361]" + "training until 2000: 100%|█████████▉| 1992/2000 [12:18<00:02, 3.13it/s, loss=0.365]" ] }, { @@ -68428,7 +68428,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1992/2000 [12:36<00:02, 3.10it/s, loss=0.364]" + "training until 2000: 100%|█████████▉| 1992/2000 [12:18<00:02, 3.13it/s, loss=0.362]" ] }, { @@ -68436,7 +68436,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1993/2000 [12:36<00:02, 3.13it/s, loss=0.364]" + "training until 2000: 100%|█████████▉| 1993/2000 [12:18<00:02, 3.08it/s, loss=0.362]" ] }, { @@ -68444,7 +68444,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1993/2000 [12:36<00:02, 3.13it/s, loss=0.368]" + "training until 2000: 100%|█████████▉| 1993/2000 [12:18<00:02, 3.08it/s, loss=0.36] " ] }, { @@ -68452,7 +68452,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1994/2000 [12:37<00:01, 3.15it/s, loss=0.368]" + "training until 2000: 100%|█████████▉| 1994/2000 [12:19<00:01, 3.11it/s, loss=0.36]" ] }, { @@ -68460,7 +68460,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1994/2000 [12:37<00:01, 3.15it/s, loss=0.418]" + "training until 2000: 100%|█████████▉| 1994/2000 [12:19<00:01, 3.11it/s, loss=0.365]" ] }, { @@ -68468,7 +68468,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1995/2000 [12:37<00:01, 3.07it/s, loss=0.418]" + "training until 2000: 100%|█████████▉| 1995/2000 [12:19<00:01, 3.07it/s, loss=0.365]" ] }, { @@ -68476,7 +68476,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1995/2000 [12:37<00:01, 3.07it/s, loss=0.364]" + "training until 2000: 100%|█████████▉| 1995/2000 [12:19<00:01, 3.07it/s, loss=0.446]" ] }, { @@ -68484,7 +68484,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1996/2000 [12:37<00:01, 3.07it/s, loss=0.364]" + "training until 2000: 100%|█████████▉| 1996/2000 [12:19<00:01, 3.07it/s, loss=0.446]" ] }, { @@ -68492,7 +68492,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1996/2000 [12:37<00:01, 3.07it/s, loss=0.419]" + "training until 2000: 100%|█████████▉| 1996/2000 [12:19<00:01, 3.07it/s, loss=0.413]" ] }, { @@ -68500,7 +68500,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1997/2000 [12:38<00:00, 3.06it/s, loss=0.419]" + "training until 2000: 100%|█████████▉| 1997/2000 [12:20<00:00, 3.09it/s, loss=0.413]" ] }, { @@ -68508,7 +68508,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1997/2000 [12:38<00:00, 3.06it/s, loss=0.39] " + "training until 2000: 100%|█████████▉| 1997/2000 [12:20<00:00, 3.09it/s, loss=0.367]" ] }, { @@ -68516,7 +68516,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1998/2000 [12:38<00:00, 3.09it/s, loss=0.39]" + "training until 2000: 100%|█████████▉| 1998/2000 [12:20<00:00, 3.12it/s, loss=0.367]" ] }, { @@ -68524,7 +68524,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1998/2000 [12:38<00:00, 3.09it/s, loss=0.355]" + "training until 2000: 100%|█████████▉| 1998/2000 [12:20<00:00, 3.12it/s, loss=0.59] " ] }, { @@ -68532,7 +68532,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1999/2000 [12:38<00:00, 3.10it/s, loss=0.355]" + "training until 2000: 100%|█████████▉| 1999/2000 [12:20<00:00, 3.08it/s, loss=0.59]" ] }, { @@ -68540,7 +68540,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|█████████▉| 1999/2000 [12:38<00:00, 3.10it/s, loss=0.379]" + "training until 2000: 100%|█████████▉| 1999/2000 [12:20<00:00, 3.08it/s, loss=0.358]" ] }, { @@ -68548,7 +68548,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|██████████| 2000/2000 [12:39<00:00, 3.10it/s, loss=0.379]" + "training until 2000: 100%|██████████| 2000/2000 [12:21<00:00, 3.08it/s, loss=0.358]" ] }, { @@ -68556,7 +68556,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|██████████| 2000/2000 [12:39<00:00, 3.10it/s, loss=0.345]" + "training until 2000: 100%|██████████| 2000/2000 [12:21<00:00, 3.08it/s, loss=0.434]" ] }, { @@ -68644,7 +68644,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:10, 20.63blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:10, 20.66blocks/s, ⧗=0, ▶=1, ✔=1, ✗=0, ∅=0]" ] }, { @@ -68666,7 +68666,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:21, 10.11blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 0%| | 1/216 [00:00<00:23, 9.02blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" ] }, { @@ -68688,7 +68688,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 19.87blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:11, 17.88blocks/s, ⧗=0, ▶=0, ✔=2, ✗=0, ∅=0]" ] }, { @@ -68710,7 +68710,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 19.87blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:11, 17.88blocks/s, ⧗=0, ▶=1, ✔=2, ✗=0, ∅=0]" ] }, { @@ -68732,7 +68732,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:10, 19.87blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%| | 2/216 [00:00<00:11, 17.88blocks/s, ⧗=0, ▶=0, ✔=3, ✗=0, ∅=0]" ] }, { @@ -68754,7 +68754,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 19.87blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:11, 17.88blocks/s, ⧗=0, ▶=1, ✔=3, ✗=0, ∅=0]" ] }, { @@ -68776,7 +68776,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:10, 19.87blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 1%|▏ | 3/216 [00:00<00:11, 17.88blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" ] }, { @@ -68798,7 +68798,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 18.89blocks/s, ⧗=0, ▶=0, ✔=4, ✗=0, ∅=0]" ] }, { @@ -68820,7 +68820,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 18.89blocks/s, ⧗=0, ▶=1, ✔=4, ✗=0, ∅=0]" ] }, { @@ -68842,7 +68842,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 4/216 [00:00<00:11, 18.89blocks/s, ⧗=0, ▶=0, ✔=5, ✗=0, ∅=0]" ] }, { @@ -68864,7 +68864,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:11, 18.89blocks/s, ⧗=0, ▶=1, ✔=5, ✗=0, ∅=0]" ] }, { @@ -68886,7 +68886,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 2%|▏ | 5/216 [00:00<00:11, 18.89blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -68908,7 +68908,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 17.76blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=0, ✔=6, ✗=0, ∅=0]" ] }, { @@ -68930,7 +68930,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.62blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=1, ✔=6, ✗=0, ∅=0]" ] }, { @@ -68952,7 +68952,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.62blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 6/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=0, ✔=7, ✗=0, ∅=0]" ] }, { @@ -68974,7 +68974,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.62blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=1, ✔=7, ✗=0, ∅=0]" ] }, { @@ -68996,7 +68996,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.62blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 3%|▎ | 7/216 [00:00<00:11, 18.43blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" ] }, { @@ -69018,7 +69018,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.06blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 18.84blocks/s, ⧗=0, ▶=0, ✔=8, ✗=0, ∅=0]" ] }, { @@ -69040,7 +69040,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.06blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 18.84blocks/s, ⧗=0, ▶=1, ✔=8, ✗=0, ∅=0]" ] }, { @@ -69062,7 +69062,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:10, 19.06blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▎ | 8/216 [00:00<00:11, 18.84blocks/s, ⧗=0, ▶=0, ✔=9, ✗=0, ∅=0]" ] }, { @@ -69084,7 +69084,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.06blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 18.84blocks/s, ⧗=0, ▶=1, ✔=9, ✗=0, ∅=0]" ] }, { @@ -69106,7 +69106,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 19.06blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 4%|▍ | 9/216 [00:00<00:10, 18.84blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" ] }, { @@ -69128,7 +69128,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.69blocks/s, ⧗=0, ▶=0, ✔=10, ✗=0, ∅=0]" ] }, { @@ -69150,7 +69150,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.69blocks/s, ⧗=0, ▶=1, ✔=10, ✗=0, ∅=0]" ] }, { @@ -69172,7 +69172,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▍ | 10/216 [00:00<00:11, 18.69blocks/s, ⧗=0, ▶=0, ✔=11, ✗=0, ∅=0]" ] }, { @@ -69194,7 +69194,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 18.69blocks/s, ⧗=0, ▶=1, ✔=11, ✗=0, ∅=0]" ] }, { @@ -69216,7 +69216,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 18.86blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 5%|▌ | 11/216 [00:00<00:10, 18.69blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" ] }, { @@ -69238,7 +69238,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.73blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.88blocks/s, ⧗=0, ▶=0, ✔=12, ✗=0, ∅=0]" ] }, { @@ -69260,7 +69260,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.73blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.88blocks/s, ⧗=0, ▶=1, ✔=12, ✗=0, ∅=0]" ] }, { @@ -69282,7 +69282,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.73blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 12/216 [00:00<00:10, 18.88blocks/s, ⧗=0, ▶=0, ✔=13, ✗=0, ∅=0]" ] }, { @@ -69304,7 +69304,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 18.73blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 18.88blocks/s, ⧗=0, ▶=1, ✔=13, ✗=0, ∅=0]" ] }, { @@ -69326,7 +69326,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 18.73blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▌ | 13/216 [00:00<00:10, 18.88blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" ] }, { @@ -69348,7 +69348,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:11, 17.57blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 18.80blocks/s, ⧗=0, ▶=0, ✔=14, ✗=0, ∅=0]" ] }, { @@ -69370,7 +69370,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:11, 17.57blocks/s, ⧗=0, ▶=1, ✔=14, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 18.80blocks/s, ⧗=0, ▶=1, ✔=14, ✗=0, ∅=0]" ] }, { @@ -69392,7 +69392,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:11, 17.57blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 6%|▋ | 14/216 [00:00<00:10, 18.80blocks/s, ⧗=0, ▶=0, ✔=15, ✗=0, ∅=0]" ] }, { @@ -69414,7 +69414,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 17.57blocks/s, ⧗=0, ▶=1, ✔=15, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:10, 18.80blocks/s, ⧗=0, ▶=1, ✔=15, ✗=0, ∅=0]" ] }, { @@ -69436,7 +69436,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:11, 17.57blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 15/216 [00:00<00:10, 18.80blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" ] }, { @@ -69458,7 +69458,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.25blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:10, 18.19blocks/s, ⧗=0, ▶=0, ✔=16, ✗=0, ∅=0]" ] }, { @@ -69480,7 +69480,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.25blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:10, 18.19blocks/s, ⧗=0, ▶=1, ✔=16, ✗=0, ∅=0]" ] }, { @@ -69502,7 +69502,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:11, 17.25blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 7%|▋ | 16/216 [00:00<00:10, 18.19blocks/s, ⧗=0, ▶=0, ✔=17, ✗=0, ∅=0]" ] }, { @@ -69524,7 +69524,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:11, 17.25blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:10, 18.19blocks/s, ⧗=0, ▶=1, ✔=17, ✗=0, ∅=0]" ] }, { @@ -69546,7 +69546,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:01<00:11, 17.25blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 17/216 [00:00<00:10, 18.19blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" ] }, { @@ -69568,7 +69568,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.16blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:00<00:11, 17.86blocks/s, ⧗=0, ▶=0, ✔=18, ✗=0, ∅=0]" ] }, { @@ -69590,7 +69590,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.16blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:00<00:11, 17.86blocks/s, ⧗=0, ▶=1, ✔=18, ✗=0, ∅=0]" ] }, { @@ -69612,7 +69612,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.16blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 8%|▊ | 18/216 [00:01<00:11, 17.86blocks/s, ⧗=0, ▶=0, ✔=19, ✗=0, ∅=0]" ] }, { @@ -69634,7 +69634,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.16blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.86blocks/s, ⧗=0, ▶=1, ✔=19, ✗=0, ∅=0]" ] }, { @@ -69656,7 +69656,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.16blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 19/216 [00:01<00:11, 17.86blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" ] }, { @@ -69678,7 +69678,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.62blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.75blocks/s, ⧗=0, ▶=0, ✔=20, ✗=0, ∅=0]" ] }, { @@ -69700,7 +69700,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.62blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.75blocks/s, ⧗=0, ▶=1, ✔=20, ✗=0, ∅=0]" ] }, { @@ -69722,7 +69722,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.62blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 9%|▉ | 20/216 [00:01<00:11, 17.75blocks/s, ⧗=0, ▶=0, ✔=21, ✗=0, ∅=0]" ] }, { @@ -69744,7 +69744,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 17.62blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:10, 17.75blocks/s, ⧗=0, ▶=1, ✔=21, ✗=0, ∅=0]" ] }, { @@ -69766,7 +69766,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:11, 17.62blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|▉ | 21/216 [00:01<00:10, 17.75blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" ] }, { @@ -69788,7 +69788,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.77blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.92blocks/s, ⧗=0, ▶=0, ✔=22, ✗=0, ∅=0]" ] }, { @@ -69810,7 +69810,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.77blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.92blocks/s, ⧗=0, ▶=1, ✔=22, ✗=0, ∅=0]" ] }, { @@ -69832,7 +69832,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.77blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 10%|█ | 22/216 [00:01<00:10, 17.92blocks/s, ⧗=0, ▶=0, ✔=23, ✗=0, ∅=0]" ] }, { @@ -69854,7 +69854,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.77blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.92blocks/s, ⧗=0, ▶=1, ✔=23, ✗=0, ∅=0]" ] }, { @@ -69876,7 +69876,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.77blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 23/216 [00:01<00:10, 17.92blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" ] }, { @@ -69898,7 +69898,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.96blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.62blocks/s, ⧗=0, ▶=0, ✔=24, ✗=0, ∅=0]" ] }, { @@ -69920,7 +69920,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.96blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.62blocks/s, ⧗=0, ▶=1, ✔=24, ✗=0, ∅=0]" ] }, { @@ -69942,7 +69942,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:11, 16.96blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 11%|█ | 24/216 [00:01<00:10, 17.62blocks/s, ⧗=0, ▶=0, ✔=25, ✗=0, ∅=0]" ] }, { @@ -69964,7 +69964,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:11, 16.96blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.62blocks/s, ⧗=0, ▶=1, ✔=25, ✗=0, ∅=0]" ] }, { @@ -69986,7 +69986,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:11, 16.96blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 25/216 [00:01<00:10, 17.62blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" ] }, { @@ -70008,7 +70008,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:11, 16.84blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=0, ✔=26, ✗=0, ∅=0]" ] }, { @@ -70030,7 +70030,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:11, 16.84blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=1, ✔=26, ✗=0, ∅=0]" ] }, { @@ -70052,7 +70052,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:11, 16.84blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▏ | 26/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=0, ✔=27, ✗=0, ∅=0]" ] }, { @@ -70074,7 +70074,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:11, 16.84blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=1, ✔=27, ✗=0, ∅=0]" ] }, { @@ -70096,7 +70096,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:11, 16.84blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 12%|█▎ | 27/216 [00:01<00:10, 18.07blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" ] }, { @@ -70118,7 +70118,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.18blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.12blocks/s, ⧗=0, ▶=0, ✔=28, ✗=0, ∅=0]" ] }, { @@ -70140,7 +70140,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.18blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.12blocks/s, ⧗=0, ▶=1, ✔=28, ✗=0, ∅=0]" ] }, { @@ -70162,7 +70162,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 17.18blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 28/216 [00:01<00:10, 18.12blocks/s, ⧗=0, ▶=0, ✔=29, ✗=0, ∅=0]" ] }, { @@ -70184,7 +70184,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.18blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 18.12blocks/s, ⧗=0, ▶=1, ✔=29, ✗=0, ∅=0]" ] }, { @@ -70206,7 +70206,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 17.18blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 13%|█▎ | 29/216 [00:01<00:10, 18.12blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" ] }, { @@ -70228,7 +70228,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.67blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.50blocks/s, ⧗=0, ▶=0, ✔=30, ✗=0, ∅=0]" ] }, { @@ -70250,7 +70250,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.67blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.50blocks/s, ⧗=0, ▶=1, ✔=30, ✗=0, ∅=0]" ] }, { @@ -70272,7 +70272,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 17.67blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 30/216 [00:01<00:10, 18.50blocks/s, ⧗=0, ▶=0, ✔=31, ✗=0, ∅=0]" ] }, { @@ -70294,7 +70294,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 17.67blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.50blocks/s, ⧗=0, ▶=1, ✔=31, ✗=0, ∅=0]" ] }, { @@ -70316,7 +70316,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 17.67blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 14%|█▍ | 31/216 [00:01<00:10, 18.50blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" ] }, { @@ -70338,7 +70338,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 18.28blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.92blocks/s, ⧗=0, ▶=0, ✔=32, ✗=0, ∅=0]" ] }, { @@ -70360,7 +70360,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 18.28blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.92blocks/s, ⧗=0, ▶=1, ✔=32, ✗=0, ∅=0]" ] }, { @@ -70382,7 +70382,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:10, 18.28blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▍ | 32/216 [00:01<00:09, 18.92blocks/s, ⧗=0, ▶=0, ✔=33, ✗=0, ∅=0]" ] }, { @@ -70404,7 +70404,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 18.28blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.92blocks/s, ⧗=0, ▶=1, ✔=33, ✗=0, ∅=0]" ] }, { @@ -70426,7 +70426,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:10, 18.28blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 15%|█▌ | 33/216 [00:01<00:09, 18.92blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" ] }, { @@ -70448,7 +70448,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.38blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 17.64blocks/s, ⧗=0, ▶=0, ✔=34, ✗=0, ∅=0]" ] }, { @@ -70470,7 +70470,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.38blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 17.64blocks/s, ⧗=0, ▶=1, ✔=34, ✗=0, ∅=0]" ] }, { @@ -70492,7 +70492,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:09, 18.38blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 34/216 [00:01<00:10, 17.64blocks/s, ⧗=0, ▶=0, ✔=35, ✗=0, ∅=0]" ] }, { @@ -70514,7 +70514,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.38blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 17.64blocks/s, ⧗=0, ▶=1, ✔=35, ✗=0, ∅=0]" ] }, { @@ -70536,7 +70536,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:09, 18.38blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 16%|█▌ | 35/216 [00:01<00:10, 17.64blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" ] }, { @@ -70558,7 +70558,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.38blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.10blocks/s, ⧗=0, ▶=0, ✔=36, ✗=0, ∅=0]" ] }, { @@ -70580,7 +70580,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.80blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:01<00:09, 18.10blocks/s, ⧗=0, ▶=1, ✔=36, ✗=0, ∅=0]" ] }, { @@ -70602,7 +70602,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.80blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 36/216 [00:02<00:09, 18.10blocks/s, ⧗=0, ▶=0, ✔=37, ✗=0, ∅=0]" ] }, { @@ -70624,7 +70624,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.80blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.10blocks/s, ⧗=0, ▶=1, ✔=37, ✗=0, ∅=0]" ] }, { @@ -70646,7 +70646,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.80blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 17%|█▋ | 37/216 [00:02<00:09, 18.10blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" ] }, { @@ -70668,7 +70668,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.97blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.25blocks/s, ⧗=0, ▶=0, ✔=38, ✗=0, ∅=0]" ] }, { @@ -70690,7 +70690,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.97blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.25blocks/s, ⧗=0, ▶=1, ✔=38, ✗=0, ∅=0]" ] }, { @@ -70712,7 +70712,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.97blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 38/216 [00:02<00:09, 18.25blocks/s, ⧗=0, ▶=0, ✔=39, ✗=0, ∅=0]" ] }, { @@ -70734,7 +70734,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.97blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.25blocks/s, ⧗=0, ▶=1, ✔=39, ✗=0, ∅=0]" ] }, { @@ -70756,7 +70756,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.97blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 18%|█▊ | 39/216 [00:02<00:09, 18.25blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" ] }, { @@ -70778,7 +70778,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.47blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:10, 17.44blocks/s, ⧗=0, ▶=0, ✔=40, ✗=0, ∅=0]" ] }, { @@ -70800,7 +70800,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.47blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:10, 17.44blocks/s, ⧗=0, ▶=1, ✔=40, ✗=0, ∅=0]" ] }, { @@ -70822,7 +70822,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:09, 18.47blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▊ | 40/216 [00:02<00:10, 17.44blocks/s, ⧗=0, ▶=0, ✔=41, ✗=0, ∅=0]" ] }, { @@ -70844,7 +70844,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.47blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:10, 17.44blocks/s, ⧗=0, ▶=1, ✔=41, ✗=0, ∅=0]" ] }, { @@ -70866,7 +70866,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:09, 18.47blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 41/216 [00:02<00:10, 17.44blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" ] }, { @@ -70888,7 +70888,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.53blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 17.51blocks/s, ⧗=0, ▶=0, ✔=42, ✗=0, ∅=0]" ] }, { @@ -70910,7 +70910,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.53blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 17.51blocks/s, ⧗=0, ▶=1, ✔=42, ✗=0, ∅=0]" ] }, { @@ -70932,7 +70932,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 18.53blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 19%|█▉ | 42/216 [00:02<00:09, 17.51blocks/s, ⧗=0, ▶=0, ✔=43, ✗=0, ∅=0]" ] }, { @@ -70954,7 +70954,7 @@ "output_type": "stream", "text": [ "\r", - 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"predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.68blocks/s, ⧗=0, ▶=0, ✔=44, ✗=0, ∅=0]" ] }, { @@ -71020,7 +71020,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.41blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.68blocks/s, ⧗=0, ▶=1, ✔=44, ✗=0, ∅=0]" ] }, { @@ -71042,7 +71042,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 18.41blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 20%|██ | 44/216 [00:02<00:09, 17.68blocks/s, ⧗=0, ▶=0, ✔=45, ✗=0, ∅=0]" ] }, { @@ -71064,7 +71064,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 18.41blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.68blocks/s, ⧗=0, ▶=1, ✔=45, ✗=0, ∅=0]" ] }, { @@ -71086,7 +71086,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 18.41blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██ | 45/216 [00:02<00:09, 17.68blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" ] }, { @@ -71108,7 +71108,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.84blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=0, ✔=46, ✗=0, ∅=0]" ] }, { @@ -71130,7 +71130,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.84blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=1, ✔=46, ✗=0, ∅=0]" ] }, { @@ -71152,7 +71152,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:09, 17.84blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 21%|██▏ | 46/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=0, ✔=47, ✗=0, ∅=0]" ] }, { @@ -71174,7 +71174,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.84blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=1, ✔=47, ✗=0, ∅=0]" ] }, { @@ -71196,7 +71196,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:09, 17.84blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 47/216 [00:02<00:10, 16.61blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" ] }, { @@ -71218,7 +71218,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=0, ✔=48, ✗=0, ∅=0]" ] }, { @@ -71240,7 +71240,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=1, ✔=48, ✗=0, ∅=0]" ] }, { @@ -71262,7 +71262,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 22%|██▏ | 48/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=0, ✔=49, ✗=0, ∅=0]" ] }, { @@ -71284,7 +71284,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=1, ✔=49, ✗=0, ∅=0]" ] }, { @@ -71306,7 +71306,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:09, 17.88blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 49/216 [00:02<00:10, 16.25blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" ] }, { @@ -71328,7 +71328,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.04blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 15.78blocks/s, ⧗=0, ▶=0, ✔=50, ✗=0, ∅=0]" ] }, { @@ -71350,7 +71350,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.04blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 15.78blocks/s, ⧗=0, ▶=1, ✔=50, ✗=0, ∅=0]" ] }, { @@ -71372,7 +71372,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 16.04blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 23%|██▎ | 50/216 [00:02<00:10, 15.78blocks/s, ⧗=0, ▶=0, ✔=51, ✗=0, ∅=0]" ] }, { @@ -71394,7 +71394,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.04blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 15.78blocks/s, ⧗=0, ▶=1, ✔=51, ✗=0, ∅=0]" ] }, { @@ -71416,7 +71416,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 16.04blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▎ | 51/216 [00:02<00:10, 15.78blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" ] }, { @@ -71438,7 +71438,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.24blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:11, 14.60blocks/s, ⧗=0, ▶=0, ✔=52, ✗=0, ∅=0]" ] }, { @@ -71460,7 +71460,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:10, 15.24blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:02<00:11, 14.60blocks/s, ⧗=0, ▶=1, ✔=52, ✗=0, ∅=0]" ] }, { @@ -71482,7 +71482,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 24%|██▍ | 52/216 [00:03<00:11, 14.60blocks/s, ⧗=0, ▶=0, ✔=53, ✗=0, ∅=0]" ] }, { @@ -71504,7 +71504,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:11, 14.60blocks/s, ⧗=0, ▶=1, ✔=53, ✗=0, ∅=0]" ] }, { @@ -71526,7 +71526,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:10, 15.24blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▍ | 53/216 [00:03<00:11, 14.60blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" ] }, { @@ -71548,7 +71548,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.22blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:11, 14.41blocks/s, ⧗=0, ▶=0, ✔=54, ✗=0, ∅=0]" ] }, { @@ -71570,7 +71570,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.22blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:11, 14.41blocks/s, ⧗=0, ▶=1, ✔=54, ✗=0, ∅=0]" ] }, { @@ -71592,7 +71592,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:10, 15.22blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 54/216 [00:03<00:11, 14.41blocks/s, ⧗=0, ▶=0, ✔=55, ✗=0, ∅=0]" ] }, { @@ -71614,7 +71614,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 15.22blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:11, 14.41blocks/s, ⧗=0, ▶=1, ✔=55, ✗=0, ∅=0]" ] }, { @@ -71636,7 +71636,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:10, 15.22blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 25%|██▌ | 55/216 [00:03<00:11, 14.41blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" ] }, { @@ -71658,7 +71658,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.80blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 14.89blocks/s, ⧗=0, ▶=0, ✔=56, ✗=0, ∅=0]" ] }, { @@ -71680,7 +71680,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.80blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 14.89blocks/s, ⧗=0, ▶=1, ✔=56, ✗=0, ∅=0]" ] }, { @@ -71702,7 +71702,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 15.80blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▌ | 56/216 [00:03<00:10, 14.89blocks/s, ⧗=0, ▶=0, ✔=57, ✗=0, ∅=0]" ] }, { @@ -71724,7 +71724,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.80blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 14.89blocks/s, ⧗=0, ▶=1, ✔=57, ✗=0, ∅=0]" ] }, { @@ -71746,7 +71746,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 15.80blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 26%|██▋ | 57/216 [00:03<00:10, 14.89blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" ] }, { @@ -71768,7 +71768,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 15.92blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 14.88blocks/s, ⧗=0, ▶=0, ✔=58, ✗=0, ∅=0]" ] }, { @@ -71790,7 +71790,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 15.92blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 14.88blocks/s, ⧗=0, ▶=1, ✔=58, ✗=0, ∅=0]" ] }, { @@ -71812,7 +71812,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:09, 15.92blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 58/216 [00:03<00:10, 14.88blocks/s, ⧗=0, ▶=0, ✔=59, ✗=0, ∅=0]" ] }, { @@ -71834,7 +71834,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:09, 15.92blocks/s, ⧗=0, ▶=1, ✔=59, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:10, 14.88blocks/s, ⧗=0, ▶=1, ✔=59, ✗=0, ∅=0]" ] }, { @@ -71856,7 +71856,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:09, 15.92blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 27%|██▋ | 59/216 [00:03<00:10, 14.88blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" ] }, { @@ -71878,7 +71878,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:09, 16.54blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 28%|██▊ | 60/216 [00:03<00:10, 15.54blocks/s, ⧗=0, ▶=0, ✔=60, ✗=0, ∅=0]" ] }, { @@ -71900,7 +71900,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 28%|██▊ | 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[00:03<00:09, 15.54blocks/s, ⧗=0, ▶=1, ✔=61, ✗=0, ∅=0]" ] }, { @@ -71966,7 +71966,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 16.54blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 28%|██▊ | 61/216 [00:03<00:09, 15.54blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" ] }, { @@ -71988,7 +71988,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.89blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.23blocks/s, ⧗=0, ▶=0, ✔=62, ✗=0, ∅=0]" ] }, { @@ -72010,7 +72010,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.89blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.23blocks/s, ⧗=0, ▶=1, ✔=62, ✗=0, ∅=0]" ] }, { @@ -72032,7 +72032,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.89blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▊ | 62/216 [00:03<00:09, 16.23blocks/s, ⧗=0, ▶=0, ✔=63, ✗=0, ∅=0]" ] }, { @@ -72054,7 +72054,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.89blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.23blocks/s, ⧗=0, ▶=1, ✔=63, ✗=0, ∅=0]" ] }, { @@ -72076,7 +72076,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.89blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 29%|██▉ | 63/216 [00:03<00:09, 16.23blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" ] }, { @@ -72098,7 +72098,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.91blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 16.31blocks/s, ⧗=0, ▶=0, ✔=64, ✗=0, ∅=0]" ] }, { @@ -72120,7 +72120,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.91blocks/s, ⧗=0, ▶=1, ✔=64, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 16.31blocks/s, ⧗=0, ▶=1, ✔=64, ✗=0, ∅=0]" ] }, { @@ -72142,7 +72142,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 15.91blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 30%|██▉ | 64/216 [00:03<00:09, 16.31blocks/s, ⧗=0, ▶=0, ✔=65, ✗=0, ∅=0]" ] }, { @@ -72164,7 +72164,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 30%|███ | 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[00:03<00:08, 16.85blocks/s, ⧗=0, ▶=0, ✔=66, ✗=0, ∅=0]" ] }, { @@ -72230,7 +72230,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.35blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:08, 16.85blocks/s, ⧗=0, ▶=1, ✔=66, ✗=0, ∅=0]" ] }, { @@ -72252,7 +72252,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:09, 16.35blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 66/216 [00:03<00:08, 16.85blocks/s, ⧗=0, ▶=0, ✔=67, ✗=0, ∅=0]" ] }, { @@ -72274,7 +72274,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:09, 16.35blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.85blocks/s, ⧗=0, ▶=1, ✔=67, ✗=0, ∅=0]" ] }, { @@ -72296,7 +72296,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:09, 16.35blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███ | 67/216 [00:03<00:08, 16.85blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" ] }, { @@ -72318,7 +72318,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:09, 16.24blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 17.27blocks/s, ⧗=0, ▶=0, ✔=68, ✗=0, ∅=0]" ] }, { @@ -72340,7 +72340,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:09, 16.24blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:03<00:08, 17.27blocks/s, ⧗=0, ▶=1, ✔=68, ✗=0, ∅=0]" ] }, { @@ -72362,7 +72362,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 31%|███▏ | 68/216 [00:04<00:08, 17.27blocks/s, ⧗=0, ▶=0, ✔=69, ✗=0, ∅=0]" ] }, { @@ -72384,7 +72384,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 17.27blocks/s, ⧗=0, ▶=1, ✔=69, ✗=0, ∅=0]" ] }, { @@ -72406,7 +72406,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:09, 16.24blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 69/216 [00:04<00:08, 17.27blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" ] }, { @@ -72428,7 +72428,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 16.13blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 17.21blocks/s, ⧗=0, ▶=0, ✔=70, ✗=0, ∅=0]" ] }, { @@ -72450,7 +72450,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 16.13blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 17.21blocks/s, ⧗=0, ▶=1, ✔=70, ✗=0, ∅=0]" ] }, { @@ -72472,7 +72472,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:09, 16.13blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 32%|███▏ | 70/216 [00:04<00:08, 17.21blocks/s, ⧗=0, ▶=0, ✔=71, ✗=0, ∅=0]" ] }, { @@ -72494,7 +72494,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.13blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 17.21blocks/s, ⧗=0, ▶=1, ✔=71, ✗=0, ∅=0]" ] }, { @@ -72516,7 +72516,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 16.13blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 71/216 [00:04<00:08, 17.21blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" ] }, { @@ -72538,7 +72538,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.54blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=0, ✔=72, ✗=0, ∅=0]" ] }, { @@ -72560,7 +72560,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.54blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=1, ✔=72, ✗=0, ∅=0]" ] }, { @@ -72582,7 +72582,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 16.54blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 33%|███▎ | 72/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=0, ✔=73, ✗=0, ∅=0]" ] }, { @@ -72604,7 +72604,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 16.54blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=1, ✔=73, ✗=0, ∅=0]" ] }, { @@ -72626,7 +72626,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 16.54blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 73/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" ] }, { @@ -72648,7 +72648,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.24blocks/s, ⧗=0, ▶=0, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" ] }, { @@ -72670,7 +72670,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.24blocks/s, ⧗=0, ▶=1, ✔=74, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 34%|███▍ | 74/216 [00:04<00:08, 17.59blocks/s, ⧗=0, ▶=0, ✔=75, ✗=0, ∅=0]" ] }, { @@ -72692,7 +72692,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 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35%|███▍ | 75/216 [00:04<00:07, 18.34blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" ] }, { @@ -72758,7 +72758,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 16.99blocks/s, ⧗=0, ▶=0, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 18.34blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" ] }, { @@ -72780,7 +72780,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 16.99blocks/s, ⧗=0, ▶=1, ✔=76, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:07, 18.34blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" ] }, { @@ -72802,7 +72802,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 35%|███▌ | 76/216 [00:04<00:08, 16.99blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.68blocks/s, ⧗=0, ▶=0, ✔=77, ✗=0, ∅=0]" ] }, { @@ -72824,7 +72824,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:08, 16.99blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.68blocks/s, ⧗=0, ▶=1, ✔=77, ✗=0, ∅=0]" ] }, { @@ -72846,7 +72846,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:08, 16.99blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 77/216 [00:04<00:07, 17.68blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" ] }, { @@ -72868,7 +72868,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.53blocks/s, ⧗=0, ▶=0, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.68blocks/s, ⧗=0, ▶=1, ✔=78, ✗=0, ∅=0]" ] }, { @@ -72890,7 +72890,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.53blocks/s, ⧗=0, ▶=1, ✔=78, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.68blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" ] }, { @@ -72912,7 +72912,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 36%|███▌ | 78/216 [00:04<00:07, 17.53blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:08, 17.03blocks/s, ⧗=0, ▶=0, ✔=79, ✗=0, ∅=0]" ] }, { @@ -72934,7 +72934,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.53blocks/s, ⧗=0, ▶=1, ✔=79, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:08, 17.03blocks/s, ⧗=0, ▶=1, ✔=79, ✗=0, ∅=0]" ] }, { @@ -72956,7 +72956,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:07, 17.53blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 79/216 [00:04<00:08, 17.03blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" ] }, { @@ -72978,7 +72978,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.84blocks/s, ⧗=0, ▶=0, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.03blocks/s, ⧗=0, ▶=1, ✔=80, ✗=0, ∅=0]" ] }, { @@ -73000,7 +73000,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.84blocks/s, ⧗=0, ▶=1, ✔=80, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.03blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" ] }, { @@ -73022,7 +73022,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 37%|███▋ | 80/216 [00:04<00:07, 17.84blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:08, 16.42blocks/s, ⧗=0, ▶=0, ✔=81, ✗=0, ∅=0]" ] }, { @@ -73044,7 +73044,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 17.84blocks/s, ⧗=0, ▶=1, ✔=81, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:08, 16.42blocks/s, ⧗=0, ▶=1, ✔=81, ✗=0, ∅=0]" ] }, { @@ -73066,7 +73066,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:07, 17.84blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 81/216 [00:04<00:08, 16.42blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" ] }, { @@ -73088,7 +73088,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.54blocks/s, ⧗=0, ▶=0, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:08, 16.42blocks/s, ⧗=0, ▶=1, ✔=82, ✗=0, ∅=0]" ] }, { @@ -73110,7 +73110,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.54blocks/s, ⧗=0, ▶=1, ✔=82, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:08, 16.42blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" ] }, { @@ -73132,7 +73132,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 82/216 [00:04<00:07, 17.54blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 16.85blocks/s, ⧗=0, ▶=0, ✔=83, ✗=0, ∅=0]" ] }, { @@ -73154,7 +73154,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.54blocks/s, ⧗=0, ▶=1, ✔=83, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 16.85blocks/s, ⧗=0, ▶=1, ✔=83, ✗=0, ∅=0]" ] }, { @@ -73176,7 +73176,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 17.54blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 38%|███▊ | 83/216 [00:04<00:07, 16.85blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" ] }, { @@ -73198,7 +73198,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=0, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 16.85blocks/s, ⧗=0, ▶=1, ✔=84, ✗=0, ∅=0]" ] }, { @@ -73220,7 +73220,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=1, ✔=84, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 16.85blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" ] }, { @@ -73242,7 +73242,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 84/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.30blocks/s, ⧗=0, ▶=0, ✔=85, ✗=0, ∅=0]" ] }, { @@ -73264,7 +73264,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=1, ✔=85, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.30blocks/s, ⧗=0, ▶=1, ✔=85, ✗=0, ∅=0]" ] }, { @@ -73286,7 +73286,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:04<00:07, 17.95blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 39%|███▉ | 85/216 [00:05<00:07, 17.30blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" ] }, { @@ -73308,7 +73308,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:04<00:07, 17.14blocks/s, ⧗=0, ▶=0, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.30blocks/s, ⧗=0, ▶=1, ✔=86, ✗=0, ∅=0]" ] }, { @@ -73330,7 +73330,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:04<00:07, 17.14blocks/s, ⧗=0, ▶=1, ✔=86, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.30blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" ] }, { @@ -73352,7 +73352,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|███▉ | 86/216 [00:05<00:07, 17.14blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 16.37blocks/s, ⧗=0, ▶=0, ✔=87, ✗=0, ∅=0]" ] }, { @@ -73374,7 +73374,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.14blocks/s, ⧗=0, ▶=1, ✔=87, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 16.37blocks/s, ⧗=0, ▶=1, ✔=87, ✗=0, ∅=0]" ] }, { @@ -73396,7 +73396,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 17.14blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 40%|████ | 87/216 [00:05<00:07, 16.37blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" ] }, { @@ -73418,7 +73418,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.59blocks/s, ⧗=0, ▶=0, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.37blocks/s, ⧗=0, ▶=1, ✔=88, ✗=0, ∅=0]" ] }, { @@ -73440,7 +73440,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.59blocks/s, ⧗=0, ▶=1, ✔=88, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.37blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" ] }, { @@ -73462,7 +73462,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 88/216 [00:05<00:07, 16.59blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=0, ✔=89, ✗=0, ∅=0]" ] }, { @@ -73484,7 +73484,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.59blocks/s, ⧗=0, ▶=1, ✔=89, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=1, ✔=89, ✗=0, ∅=0]" ] }, { @@ -73506,7 +73506,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.59blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 41%|████ | 89/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" ] }, { @@ -73528,7 +73528,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.61blocks/s, ⧗=0, ▶=0, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" ] }, { @@ -73550,7 +73550,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.61blocks/s, ⧗=0, ▶=1, ✔=90, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.49blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" ] }, { @@ -73572,7 +73572,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 90/216 [00:05<00:07, 16.61blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=0, ✔=91, ✗=0, ∅=0]" ] }, { @@ -73594,7 +73594,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.61blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=1, ✔=91, ✗=0, ∅=0]" ] }, { @@ -73616,7 +73616,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 16.61blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 42%|████▏ | 91/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" ] }, { @@ -73638,7 +73638,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.17blocks/s, ⧗=0, ▶=0, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" ] }, { @@ -73660,7 +73660,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.17blocks/s, ⧗=0, ▶=1, ✔=92, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.00blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" ] }, { @@ -73682,7 +73682,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 92/216 [00:05<00:07, 17.17blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=0, ✔=93, ✗=0, ∅=0]" ] }, { @@ -73704,7 +73704,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.17blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=1, ✔=93, ✗=0, ∅=0]" ] }, { @@ -73726,7 +73726,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.17blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 43%|████▎ | 93/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" ] }, { @@ -73748,7 +73748,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=0, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" ] }, { @@ -73770,7 +73770,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=1, ✔=94, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" ] }, { @@ -73792,7 +73792,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▎ | 94/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.43blocks/s, ⧗=0, ▶=0, ✔=95, ✗=0, ∅=0]" ] }, { @@ -73814,7 +73814,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.43blocks/s, ⧗=0, ▶=1, ✔=95, ✗=0, ∅=0]" ] }, { @@ -73836,7 +73836,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:07, 17.22blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 95/216 [00:05<00:06, 17.43blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" ] }, { @@ -73858,7 +73858,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.56blocks/s, ⧗=0, ▶=0, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.43blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" ] }, { @@ -73880,7 +73880,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.56blocks/s, ⧗=0, ▶=1, ✔=96, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.43blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" ] }, { @@ -73902,7 +73902,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 44%|████▍ | 96/216 [00:05<00:06, 17.56blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.92blocks/s, ⧗=0, ▶=0, ✔=97, ✗=0, ∅=0]" ] }, { @@ -73924,7 +73924,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.56blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.92blocks/s, ⧗=0, ▶=1, ✔=97, ✗=0, ∅=0]" ] }, { @@ -73946,7 +73946,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.56blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▍ | 97/216 [00:05<00:06, 17.92blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" ] }, { @@ -73968,7 +73968,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.54blocks/s, ⧗=0, ▶=0, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.92blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" ] }, { @@ -73990,7 +73990,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.54blocks/s, ⧗=0, ▶=1, ✔=98, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.92blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" ] }, { @@ -74012,7 +74012,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 45%|████▌ | 98/216 [00:05<00:06, 17.54blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.88blocks/s, ⧗=0, ▶=0, ✔=99, ✗=0, ∅=0]" ] }, { @@ -74034,7 +74034,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.54blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.88blocks/s, ⧗=0, ▶=1, ✔=99, ✗=0, ∅=0]" ] }, { @@ -74056,7 +74056,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.54blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▌ | 99/216 [00:05<00:06, 17.88blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" ] }, { @@ -74078,7 +74078,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:07, 16.51blocks/s, ⧗=0, ▶=0, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 17.88blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" ] }, { @@ -74100,7 +74100,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:07, 16.51blocks/s, ⧗=0, ▶=1, ✔=100, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:06, 17.88blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" ] }, { @@ -74122,7 +74122,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 46%|████▋ | 100/216 [00:05<00:07, 16.51blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.83blocks/s, ⧗=0, ▶=0, ✔=101, ✗=0, ∅=0]" ] }, { @@ -74144,7 +74144,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.51blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.83blocks/s, ⧗=0, ▶=1, ✔=101, ✗=0, ∅=0]" ] }, { @@ -74166,7 +74166,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 16.51blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 101/216 [00:05<00:06, 17.83blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" ] }, { @@ -74188,7 +74188,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.09blocks/s, ⧗=0, ▶=0, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.83blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" ] }, { @@ -74210,7 +74210,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.09blocks/s, ⧗=0, ▶=1, ✔=102, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.83blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" ] }, { @@ -74232,7 +74232,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 47%|████▋ | 102/216 [00:05<00:06, 17.09blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 18.35blocks/s, ⧗=0, ▶=0, ✔=103, ✗=0, ∅=0]" ] }, { @@ -74254,7 +74254,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 17.09blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:05<00:06, 18.35blocks/s, ⧗=0, ▶=1, ✔=103, ✗=0, ∅=0]" ] }, { @@ -74276,7 +74276,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 17.09blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 103/216 [00:06<00:06, 18.35blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" ] }, { @@ -74298,7 +74298,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 16.84blocks/s, ⧗=0, ▶=0, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 18.35blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" ] }, { @@ -74320,7 +74320,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 16.84blocks/s, ⧗=0, ▶=1, ✔=104, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 18.35blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" ] }, { @@ -74342,7 +74342,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 48%|████▊ | 104/216 [00:06<00:06, 16.84blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 18.34blocks/s, ⧗=0, ▶=0, ✔=105, ✗=0, ∅=0]" ] }, { @@ -74364,7 +74364,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 16.84blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 18.34blocks/s, ⧗=0, ▶=1, ✔=105, ✗=0, ∅=0]" ] }, { @@ -74386,7 +74386,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 16.84blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▊ | 105/216 [00:06<00:06, 18.34blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" ] }, { @@ -74408,7 +74408,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 15.99blocks/s, ⧗=0, ▶=0, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:05, 18.34blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" ] }, { @@ -74430,7 +74430,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 15.99blocks/s, ⧗=0, ▶=1, ✔=106, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:05, 18.34blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" ] }, { @@ -74452,7 +74452,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 49%|████▉ | 106/216 [00:06<00:06, 15.99blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:05, 18.60blocks/s, ⧗=0, ▶=0, ✔=107, ✗=0, ∅=0]" ] }, { @@ -74474,7 +74474,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 15.99blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:05, 18.60blocks/s, ⧗=0, ▶=1, ✔=107, ✗=0, ∅=0]" ] }, { @@ -74496,7 +74496,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:06, 15.99blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|████▉ | 107/216 [00:06<00:05, 18.60blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" ] }, { @@ -74518,7 +74518,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 15.97blocks/s, ⧗=0, ▶=0, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:05, 18.60blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" ] }, { @@ -74540,7 +74540,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 15.97blocks/s, ⧗=0, ▶=1, ✔=108, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:05, 18.60blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" ] }, { @@ -74562,7 +74562,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 108/216 [00:06<00:06, 15.97blocks/s, ⧗=0, ▶=0, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 18.60blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" ] }, { @@ -74584,7 +74584,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:06, 15.97blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 18.98blocks/s, ⧗=0, ▶=1, ✔=109, ✗=0, ∅=0]" ] }, { @@ -74606,7 +74606,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:06, 15.97blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 50%|█████ | 109/216 [00:06<00:05, 18.98blocks/s, ⧗=0, ▶=0, ✔=110, ✗=0, ∅=0]" ] }, { @@ -74628,7 +74628,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:06, 15.97blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 18.98blocks/s, ⧗=0, ▶=1, ✔=110, ✗=0, ∅=0]" ] }, { @@ -74650,7 +74650,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:06, 15.97blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████ | 110/216 [00:06<00:05, 18.98blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" ] }, { @@ -74672,7 +74672,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:06, 17.39blocks/s, ⧗=0, ▶=0, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.98blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" ] }, { @@ -74694,7 +74694,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:06, 17.39blocks/s, ⧗=0, ▶=1, ✔=111, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:05, 18.98blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" ] }, { @@ -74716,7 +74716,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 51%|█████▏ | 111/216 [00:06<00:06, 17.39blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 19.61blocks/s, ⧗=0, ▶=0, ✔=112, ✗=0, ∅=0]" ] }, { @@ -74738,7 +74738,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 17.39blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 19.61blocks/s, ⧗=0, ▶=1, ✔=112, ✗=0, ∅=0]" ] }, { @@ -74760,7 +74760,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 17.39blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 112/216 [00:06<00:05, 19.61blocks/s, ⧗=0, ▶=0, ✔=113, ✗=0, ∅=0]" ] }, { @@ -74782,7 +74782,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 17.39blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.61blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" ] }, { @@ -74804,7 +74804,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=1, ✔=113, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 19.61blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" ] }, { @@ -74826,7 +74826,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 52%|█████▏ | 113/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.38blocks/s, ⧗=0, ▶=0, ✔=114, ✗=0, ∅=0]" ] }, { @@ -74848,7 +74848,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.38blocks/s, ⧗=0, ▶=1, ✔=114, ✗=0, ∅=0]" ] }, { @@ -74870,7 +74870,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 114/216 [00:06<00:05, 18.38blocks/s, ⧗=0, ▶=0, ✔=115, ✗=0, ∅=0]" ] }, { @@ -74892,7 +74892,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.38blocks/s, ⧗=0, ▶=1, ✔=115, ✗=0, ∅=0]" ] }, { @@ -74914,7 +74914,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 17.96blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 53%|█████▎ | 115/216 [00:06<00:05, 18.38blocks/s, ⧗=0, ▶=0, ✔=116, ✗=0, ∅=0]" ] }, { @@ -75046,7 +75046,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.57blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.64blocks/s, ⧗=0, ▶=0, ✔=118, ✗=0, ∅=0]" ] }, { @@ -75068,7 +75068,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.57blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.64blocks/s, ⧗=0, ▶=1, ✔=118, ✗=0, ∅=0]" ] }, { @@ -75090,7 +75090,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.57blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▍ | 118/216 [00:06<00:05, 18.64blocks/s, ⧗=0, ▶=0, ✔=119, ✗=0, ∅=0]" ] }, { @@ -75112,7 +75112,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.57blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.64blocks/s, ⧗=0, ▶=1, ✔=119, ✗=0, ∅=0]" ] }, { @@ -75134,7 +75134,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.57blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 55%|█████▌ | 119/216 [00:06<00:05, 18.64blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" ] }, { @@ -75156,7 +75156,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.77blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.29blocks/s, ⧗=0, ▶=0, ✔=120, ✗=0, ∅=0]" ] }, { @@ -75178,7 +75178,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.77blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.29blocks/s, ⧗=0, ▶=1, ✔=120, ✗=0, ∅=0]" ] }, { @@ -75200,7 +75200,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.77blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 120/216 [00:06<00:05, 18.29blocks/s, ⧗=0, ▶=0, ✔=121, ✗=0, ∅=0]" ] }, { @@ -75222,7 +75222,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.77blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.29blocks/s, ⧗=0, ▶=1, ✔=121, ✗=0, ∅=0]" ] }, { @@ -75244,7 +75244,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:07<00:05, 18.77blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▌ | 121/216 [00:06<00:05, 18.29blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" ] }, { @@ -75266,7 +75266,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.19blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:06<00:05, 18.35blocks/s, ⧗=0, ▶=0, ✔=122, ✗=0, ∅=0]" ] }, { @@ -75288,7 +75288,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.19blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:06<00:05, 18.35blocks/s, ⧗=0, ▶=1, ✔=122, ✗=0, ∅=0]" ] }, { @@ -75310,7 +75310,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.19blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 56%|█████▋ | 122/216 [00:07<00:05, 18.35blocks/s, ⧗=0, ▶=0, ✔=123, ✗=0, ∅=0]" ] }, { @@ -75332,7 +75332,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.19blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.35blocks/s, ⧗=0, ▶=1, ✔=123, ✗=0, ∅=0]" ] }, { @@ -75354,7 +75354,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.19blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 123/216 [00:07<00:05, 18.35blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" ] }, { @@ -75376,7 +75376,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.58blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.82blocks/s, ⧗=0, ▶=0, ✔=124, ✗=0, ∅=0]" ] }, { @@ -75398,7 +75398,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.58blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.82blocks/s, ⧗=0, ▶=1, ✔=124, ✗=0, ∅=0]" ] }, { @@ -75420,7 +75420,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.58blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 57%|█████▋ | 124/216 [00:07<00:05, 17.82blocks/s, ⧗=0, ▶=0, ✔=125, ✗=0, ∅=0]" ] }, { @@ -75442,7 +75442,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.58blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.82blocks/s, ⧗=0, ▶=1, ✔=125, ✗=0, ∅=0]" ] }, { @@ -75464,7 +75464,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.58blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 125/216 [00:07<00:05, 17.82blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" ] }, { @@ -75486,7 +75486,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.43blocks/s, ⧗=0, ▶=0, ✔=126, ✗=0, ∅=0]" ] }, { @@ -75508,7 +75508,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.43blocks/s, ⧗=0, ▶=1, ✔=126, ✗=0, ∅=0]" ] }, { @@ -75530,7 +75530,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 58%|█████▊ | 126/216 [00:07<00:05, 17.43blocks/s, ⧗=0, ▶=0, ✔=127, ✗=0, ∅=0]" ] }, { @@ -75552,7 +75552,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 17.43blocks/s, ⧗=0, ▶=1, ✔=127, ✗=0, ∅=0]" ] }, { @@ -75574,7 +75574,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 17.33blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 59%|█████▉ | 127/216 [00:07<00:05, 17.43blocks/s, ⧗=0, ▶=0, ✔=128, ✗=0, ∅=0]" ] }, { @@ -75706,7 +75706,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.90blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.35blocks/s, ⧗=0, ▶=0, ✔=130, ✗=0, ∅=0]" ] }, { @@ -75728,7 +75728,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.90blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.35blocks/s, ⧗=0, ▶=1, ✔=130, ✗=0, ∅=0]" ] }, { @@ -75750,7 +75750,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.90blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 60%|██████ | 130/216 [00:07<00:04, 17.35blocks/s, ⧗=0, ▶=0, ✔=131, ✗=0, ∅=0]" ] }, { @@ -75772,7 +75772,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.90blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.35blocks/s, ⧗=0, ▶=1, ✔=131, ✗=0, ∅=0]" ] }, { @@ -75794,7 +75794,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.90blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 131/216 [00:07<00:04, 17.35blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" ] }, { @@ -75816,7 +75816,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.34blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:05, 16.78blocks/s, ⧗=0, ▶=0, ✔=132, ✗=0, ∅=0]" ] }, { @@ -75838,7 +75838,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.34blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:05, 16.78blocks/s, ⧗=0, ▶=1, ✔=132, ✗=0, ∅=0]" ] }, { @@ -75860,7 +75860,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:04, 18.34blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 61%|██████ | 132/216 [00:07<00:05, 16.78blocks/s, ⧗=0, ▶=0, ✔=133, ✗=0, ∅=0]" ] }, { @@ -75882,7 +75882,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.34blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 16.78blocks/s, ⧗=0, ▶=1, ✔=133, ✗=0, ∅=0]" ] }, { @@ -75904,7 +75904,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 18.34blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 133/216 [00:07<00:04, 16.78blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" ] }, { @@ -75926,7 +75926,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.63blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 16.81blocks/s, ⧗=0, ▶=0, ✔=134, ✗=0, ∅=0]" ] }, { @@ -75948,7 +75948,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.63blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 16.81blocks/s, ⧗=0, ▶=1, ✔=134, ✗=0, ∅=0]" ] }, { @@ -75970,7 +75970,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 18.63blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▏ | 134/216 [00:07<00:04, 16.81blocks/s, ⧗=0, ▶=0, ✔=135, ✗=0, ∅=0]" ] }, { @@ -75992,7 +75992,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.63blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 16.81blocks/s, ⧗=0, ▶=1, ✔=135, ✗=0, ∅=0]" ] }, { @@ -76014,7 +76014,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 18.63blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 62%|██████▎ | 135/216 [00:07<00:04, 16.81blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" ] }, { @@ -76036,7 +76036,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=0, ✔=136, ✗=0, ∅=0]" ] }, { @@ -76058,7 +76058,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=1, ✔=136, ✗=0, ∅=0]" ] }, { @@ -76080,7 +76080,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 136/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=0, ✔=137, ✗=0, ∅=0]" ] }, { @@ -76102,7 +76102,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=1, ✔=137, ✗=0, ∅=0]" ] }, { @@ -76124,7 +76124,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 18.46blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 63%|██████▎ | 137/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" ] }, { @@ -76146,7 +76146,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 18.69blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=0, ✔=138, ✗=0, ∅=0]" ] }, { @@ -76168,7 +76168,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 18.69blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=1, ✔=138, ✗=0, ∅=0]" ] }, { @@ -76190,7 +76190,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 18.69blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 138/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=0, ✔=139, ✗=0, ∅=0]" ] }, { @@ -76212,7 +76212,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 18.69blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:07<00:04, 16.95blocks/s, ⧗=0, ▶=1, ✔=139, ✗=0, ∅=0]" ] }, { @@ -76234,7 +76234,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 18.69blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 64%|██████▍ | 139/216 [00:08<00:04, 16.95blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" ] }, { @@ -76256,7 +76256,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:03, 19.04blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.09blocks/s, ⧗=0, ▶=0, ✔=140, ✗=0, ∅=0]" ] }, { @@ -76278,7 +76278,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:03, 19.04blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.09blocks/s, ⧗=0, ▶=1, ✔=140, ✗=0, ∅=0]" ] }, { @@ -76300,7 +76300,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:03, 19.04blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▍ | 140/216 [00:08<00:04, 17.09blocks/s, ⧗=0, ▶=0, ✔=141, ✗=0, ∅=0]" ] }, { @@ -76322,7 +76322,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:03, 19.04blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.09blocks/s, ⧗=0, ▶=1, ✔=141, ✗=0, ∅=0]" ] }, { @@ -76344,7 +76344,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:03, 19.04blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 65%|██████▌ | 141/216 [00:08<00:04, 17.09blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" ] }, { @@ -76366,7 +76366,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:03, 18.74blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 16.85blocks/s, ⧗=0, ▶=0, ✔=142, ✗=0, ∅=0]" ] }, { @@ -76388,7 +76388,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:03, 18.74blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 16.85blocks/s, ⧗=0, ▶=1, ✔=142, ✗=0, ∅=0]" ] }, { @@ -76410,7 +76410,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:03, 18.74blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 142/216 [00:08<00:04, 16.85blocks/s, ⧗=0, ▶=0, ✔=143, ✗=0, ∅=0]" ] }, { @@ -76432,7 +76432,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.74blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 16.85blocks/s, ⧗=0, ▶=1, ✔=143, ✗=0, ∅=0]" ] }, { @@ -76454,7 +76454,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:03, 18.74blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 66%|██████▌ | 143/216 [00:08<00:04, 16.85blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" ] }, { @@ -76476,7 +76476,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.99blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.17blocks/s, ⧗=0, ▶=0, ✔=144, ✗=0, ∅=0]" ] }, { @@ -76498,7 +76498,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.99blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.17blocks/s, ⧗=0, ▶=1, ✔=144, ✗=0, ∅=0]" ] }, { @@ -76520,7 +76520,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:03, 18.99blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 144/216 [00:08<00:04, 17.17blocks/s, ⧗=0, ▶=0, ✔=145, ✗=0, ∅=0]" ] }, { @@ -76542,7 +76542,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.99blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:04, 17.17blocks/s, ⧗=0, ▶=1, ✔=145, ✗=0, ∅=0]" ] }, { @@ -76564,7 +76564,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:03, 18.99blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 67%|██████▋ | 145/216 [00:08<00:04, 17.17blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" ] }, { @@ -76586,7 +76586,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.21blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:04, 17.10blocks/s, ⧗=0, ▶=0, ✔=146, ✗=0, ∅=0]" ] }, { @@ -76608,7 +76608,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.21blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:04, 17.10blocks/s, ⧗=0, ▶=1, ✔=146, ✗=0, ∅=0]" ] }, { @@ -76630,7 +76630,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:03, 18.21blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 146/216 [00:08<00:04, 17.10blocks/s, ⧗=0, ▶=0, ✔=147, ✗=0, ∅=0]" ] }, { @@ -76652,7 +76652,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.21blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:04, 17.10blocks/s, ⧗=0, ▶=1, ✔=147, ✗=0, ∅=0]" ] }, { @@ -76674,7 +76674,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:03, 18.21blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 68%|██████▊ | 147/216 [00:08<00:04, 17.10blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" ] }, { @@ -76696,7 +76696,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:04, 16.83blocks/s, ⧗=0, ▶=0, ✔=148, ✗=0, ∅=0]" ] }, { @@ -76718,7 +76718,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:04, 16.83blocks/s, ⧗=0, ▶=1, ✔=148, ✗=0, ∅=0]" ] }, { @@ -76740,7 +76740,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▊ | 148/216 [00:08<00:04, 16.83blocks/s, ⧗=0, ▶=0, ✔=149, ✗=0, ∅=0]" ] }, { @@ -76762,7 +76762,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 16.83blocks/s, ⧗=0, ▶=1, ✔=149, ✗=0, ∅=0]" ] }, { @@ -76784,7 +76784,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 149/216 [00:08<00:03, 16.83blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" ] }, { @@ -76806,7 +76806,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.52blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=0, ✔=150, ✗=0, ∅=0]" ] }, { @@ -76828,7 +76828,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.52blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=1, ✔=150, ✗=0, ∅=0]" ] }, { @@ -76850,7 +76850,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.52blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 69%|██████▉ | 150/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=0, ✔=151, ✗=0, ∅=0]" ] }, { @@ -76872,7 +76872,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.52blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=1, ✔=151, ✗=0, ∅=0]" ] }, { @@ -76894,7 +76894,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.52blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|██████▉ | 151/216 [00:08<00:03, 17.23blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" ] }, { @@ -76916,7 +76916,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.84blocks/s, ⧗=0, ▶=0, ✔=152, ✗=0, ∅=0]" ] }, { @@ -76938,7 +76938,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.84blocks/s, ⧗=0, ▶=1, ✔=152, ✗=0, ∅=0]" ] }, { @@ -76960,7 +76960,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 70%|███████ | 152/216 [00:08<00:03, 16.84blocks/s, ⧗=0, ▶=0, ✔=153, ✗=0, ∅=0]" ] }, { @@ -76982,7 +76982,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.84blocks/s, ⧗=0, ▶=1, ✔=153, ✗=0, ∅=0]" ] }, { @@ -77004,7 +77004,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 17.57blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████ | 153/216 [00:08<00:03, 16.84blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" ] }, { @@ -77026,7 +77026,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 17.05blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.68blocks/s, ⧗=0, ▶=0, ✔=154, ✗=0, ∅=0]" ] }, { @@ -77048,7 +77048,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 17.05blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.68blocks/s, ⧗=0, ▶=1, ✔=154, ✗=0, ∅=0]" ] }, { @@ -77070,7 +77070,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 17.05blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 71%|███████▏ | 154/216 [00:08<00:03, 16.68blocks/s, ⧗=0, ▶=0, ✔=155, ✗=0, ∅=0]" ] }, { @@ -77092,7 +77092,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 17.05blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.68blocks/s, ⧗=0, ▶=1, ✔=155, ✗=0, ∅=0]" ] }, { @@ -77114,7 +77114,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 17.05blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 155/216 [00:08<00:03, 16.68blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" ] }, { @@ -77136,7 +77136,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.19blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.99blocks/s, ⧗=0, ▶=0, ✔=156, ✗=0, ∅=0]" ] }, { @@ -77158,7 +77158,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.19blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:08<00:03, 16.99blocks/s, ⧗=0, ▶=1, ✔=156, ✗=0, ∅=0]" ] }, { @@ -77180,7 +77180,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:09<00:03, 16.19blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 72%|███████▏ | 156/216 [00:09<00:03, 16.99blocks/s, ⧗=0, ▶=0, ✔=157, ✗=0, ∅=0]" ] }, { @@ -77202,7 +77202,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.19blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.99blocks/s, ⧗=0, ▶=1, ✔=157, ✗=0, ∅=0]" ] }, { @@ -77224,7 +77224,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.19blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 157/216 [00:09<00:03, 16.99blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" ] }, { @@ -77246,7 +77246,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.61blocks/s, ⧗=0, ▶=0, ✔=158, ✗=0, ∅=0]" ] }, { @@ -77268,7 +77268,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.61blocks/s, ⧗=0, ▶=1, ✔=158, ✗=0, ∅=0]" ] }, { @@ -77290,7 +77290,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 73%|███████▎ | 158/216 [00:09<00:03, 16.61blocks/s, ⧗=0, ▶=0, ✔=159, ✗=0, ∅=0]" ] }, { @@ -77312,7 +77312,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 16.61blocks/s, ⧗=0, ▶=1, ✔=159, ✗=0, ∅=0]" ] }, { @@ -77334,7 +77334,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▎ | 159/216 [00:09<00:03, 16.61blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" ] }, { @@ -77356,7 +77356,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=0, ✔=160, ✗=0, ∅=0]" ] }, { @@ -77378,7 +77378,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=1, ✔=160, ✗=0, ∅=0]" ] }, { @@ -77400,7 +77400,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 74%|███████▍ | 160/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=0, ✔=161, ✗=0, ∅=0]" ] }, { @@ -77422,7 +77422,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=1, ✔=161, ✗=0, ∅=0]" ] }, { @@ -77444,7 +77444,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 14.87blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▍ | 161/216 [00:09<00:03, 15.34blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" ] }, { @@ -77466,7 +77466,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.40blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.03blocks/s, ⧗=0, ▶=0, ✔=162, ✗=0, ∅=0]" ] }, { @@ -77488,7 +77488,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.40blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.03blocks/s, ⧗=0, ▶=1, ✔=162, ✗=0, ∅=0]" ] }, { @@ -77510,7 +77510,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 14.40blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 162/216 [00:09<00:03, 15.03blocks/s, ⧗=0, ▶=0, ✔=163, ✗=0, ∅=0]" ] }, { @@ -77532,7 +77532,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 14.40blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.03blocks/s, ⧗=0, ▶=1, ✔=163, ✗=0, ∅=0]" ] }, { @@ -77554,7 +77554,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 14.40blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 75%|███████▌ | 163/216 [00:09<00:03, 15.03blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" ] }, { @@ -77576,7 +77576,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.29blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=0, ✔=164, ✗=0, ∅=0]" ] }, { @@ -77598,7 +77598,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.29blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=1, ✔=164, ✗=0, ∅=0]" ] }, { @@ -77620,7 +77620,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.29blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▌ | 164/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=0, ✔=165, ✗=0, ∅=0]" ] }, { @@ -77642,7 +77642,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.29blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=1, ✔=165, ✗=0, ∅=0]" ] }, { @@ -77664,7 +77664,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.29blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 76%|███████▋ | 165/216 [00:09<00:03, 15.64blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" ] }, { @@ -77686,7 +77686,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.52blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=0, ✔=166, ✗=0, ∅=0]" ] }, { @@ -77708,7 +77708,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.52blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=1, ✔=166, ✗=0, ∅=0]" ] }, { @@ -77730,7 +77730,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.52blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 166/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=0, ✔=167, ✗=0, ∅=0]" ] }, { @@ -77752,7 +77752,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.52blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=1, ✔=167, ✗=0, ∅=0]" ] }, { @@ -77774,7 +77774,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.52blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 77%|███████▋ | 167/216 [00:09<00:03, 15.74blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" ] }, { @@ -77796,7 +77796,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.23blocks/s, ⧗=0, ▶=0, ✔=168, ✗=0, ∅=0]" ] }, { @@ -77818,7 +77818,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.23blocks/s, ⧗=0, ▶=1, ✔=168, ✗=0, ∅=0]" ] }, { @@ -77840,7 +77840,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 168/216 [00:09<00:03, 15.23blocks/s, ⧗=0, ▶=0, ✔=169, ✗=0, ∅=0]" ] }, { @@ -77862,7 +77862,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.23blocks/s, ⧗=0, ▶=1, ✔=169, ✗=0, ∅=0]" ] }, { @@ -77884,7 +77884,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.25blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 78%|███████▊ | 169/216 [00:09<00:03, 15.23blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" ] }, { @@ -77906,7 +77906,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.66blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.87blocks/s, ⧗=0, ▶=0, ✔=170, ✗=0, ∅=0]" ] }, { @@ -77928,7 +77928,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.66blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.87blocks/s, ⧗=0, ▶=1, ✔=170, ✗=0, ∅=0]" ] }, { @@ -77950,7 +77950,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.66blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▊ | 170/216 [00:09<00:02, 15.87blocks/s, ⧗=0, ▶=0, ✔=171, ✗=0, ∅=0]" ] }, { @@ -77972,7 +77972,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.66blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:09<00:02, 15.87blocks/s, ⧗=0, ▶=1, ✔=171, ✗=0, ∅=0]" ] }, { @@ -77994,7 +77994,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 15.66blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 79%|███████▉ | 171/216 [00:10<00:02, 15.87blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" ] }, { @@ -78016,7 +78016,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.93blocks/s, ⧗=0, ▶=0, ✔=172, ✗=0, ∅=0]" ] }, { @@ -78038,7 +78038,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.93blocks/s, ⧗=0, ▶=1, ✔=172, ✗=0, ∅=0]" ] }, { @@ -78060,7 +78060,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|███████▉ | 172/216 [00:10<00:02, 15.93blocks/s, ⧗=0, ▶=0, ✔=173, ✗=0, ∅=0]" ] }, { @@ -78082,7 +78082,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.93blocks/s, ⧗=0, ▶=1, ✔=173, ✗=0, ∅=0]" ] }, { @@ -78104,7 +78104,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.75blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 80%|████████ | 173/216 [00:10<00:02, 15.93blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" ] }, { @@ -78126,7 +78126,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.88blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.39blocks/s, ⧗=0, ▶=0, ✔=174, ✗=0, ∅=0]" ] }, { @@ -78148,7 +78148,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.88blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.39blocks/s, ⧗=0, ▶=1, ✔=174, ✗=0, ∅=0]" ] }, { @@ -78170,7 +78170,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 15.88blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 174/216 [00:10<00:02, 16.39blocks/s, ⧗=0, ▶=0, ✔=175, ✗=0, ∅=0]" ] }, { @@ -78192,7 +78192,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 15.88blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.39blocks/s, ⧗=0, ▶=1, ✔=175, ✗=0, ∅=0]" ] }, { @@ -78214,7 +78214,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 15.88blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████ | 175/216 [00:10<00:02, 16.39blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" ] }, { @@ -78236,7 +78236,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.40blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.53blocks/s, ⧗=0, ▶=0, ✔=176, ✗=0, ∅=0]" ] }, { @@ -78258,7 +78258,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.40blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.53blocks/s, ⧗=0, ▶=1, ✔=176, ✗=0, ∅=0]" ] }, { @@ -78280,7 +78280,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.40blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 81%|████████▏ | 176/216 [00:10<00:02, 16.53blocks/s, ⧗=0, ▶=0, ✔=177, ✗=0, ∅=0]" ] }, { @@ -78302,7 +78302,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.40blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.53blocks/s, ⧗=0, ▶=1, ✔=177, ✗=0, ∅=0]" ] }, { @@ -78324,7 +78324,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.40blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 177/216 [00:10<00:02, 16.53blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" ] }, { @@ -78346,7 +78346,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.32blocks/s, ⧗=0, ▶=0, ✔=178, ✗=0, ∅=0]" ] }, { @@ -78368,7 +78368,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.32blocks/s, ⧗=0, ▶=1, ✔=178, ✗=0, ∅=0]" ] }, { @@ -78390,7 +78390,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 82%|████████▏ | 178/216 [00:10<00:02, 16.32blocks/s, ⧗=0, ▶=0, ✔=179, ✗=0, ∅=0]" ] }, { @@ -78412,7 +78412,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.32blocks/s, ⧗=0, ▶=1, ✔=179, ✗=0, ∅=0]" ] }, { @@ -78434,7 +78434,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.23blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 179/216 [00:10<00:02, 16.32blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" ] }, { @@ -78456,7 +78456,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.60blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.85blocks/s, ⧗=0, ▶=0, ✔=180, ✗=0, ∅=0]" ] }, { @@ -78478,7 +78478,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.60blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.85blocks/s, ⧗=0, ▶=1, ✔=180, ✗=0, ∅=0]" ] }, { @@ -78500,7 +78500,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.60blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 83%|████████▎ | 180/216 [00:10<00:02, 16.85blocks/s, ⧗=0, ▶=0, ✔=181, ✗=0, ∅=0]" ] }, { @@ -78522,7 +78522,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.60blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.85blocks/s, ⧗=0, ▶=1, ✔=181, ✗=0, ∅=0]" ] }, { @@ -78544,7 +78544,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.60blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 84%|████████▍ | 181/216 [00:10<00:02, 16.85blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" ] }, { @@ -78566,7 +78566,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.39blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 84%|████████▍ | 182/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=0, ✔=182, ✗=0, ∅=0]" ] }, { @@ -78588,7 +78588,7 @@ "output_type": "stream", "text": 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17.39blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=1, ✔=183, ✗=0, ∅=0]" ] }, { @@ -78654,7 +78654,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.39blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 85%|████████▍ | 183/216 [00:10<00:01, 17.60blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" ] }, { @@ -78676,7 +78676,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 85%|████████▌ | 184/216 [00:10<00:01, 17.64blocks/s, ⧗=0, ▶=0, ✔=184, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 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17.55blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 18.20blocks/s, ⧗=0, ▶=0, ✔=186, ✗=0, ∅=0]" ] }, { @@ -78808,7 +78808,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.55blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 18.20blocks/s, ⧗=0, ▶=1, ✔=186, ✗=0, ∅=0]" ] }, { @@ -78830,7 +78830,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 17.55blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 86%|████████▌ | 186/216 [00:10<00:01, 18.20blocks/s, ⧗=0, ▶=0, ✔=187, ✗=0, ∅=0]" ] }, { @@ -78852,7 +78852,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.55blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 18.20blocks/s, ⧗=0, ▶=1, ✔=187, ✗=0, ∅=0]" ] }, { @@ -78874,7 +78874,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 17.55blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 187/216 [00:10<00:01, 18.20blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" ] }, { @@ -78896,7 +78896,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 17.26blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 18.61blocks/s, ⧗=0, ▶=0, ✔=188, ✗=0, ∅=0]" ] }, { @@ -78918,7 +78918,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 17.26blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 18.61blocks/s, ⧗=0, ▶=1, ✔=188, ✗=0, ∅=0]" ] }, { @@ -78940,7 +78940,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:11<00:01, 17.26blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 87%|████████▋ | 188/216 [00:10<00:01, 18.61blocks/s, ⧗=0, ▶=0, ✔=189, ✗=0, ∅=0]" ] }, { @@ -78962,7 +78962,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.26blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:10<00:01, 18.61blocks/s, ⧗=0, ▶=1, ✔=189, ✗=0, ∅=0]" ] }, { @@ -78984,7 +78984,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 17.26blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 189/216 [00:11<00:01, 18.61blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" ] }, { @@ -79006,7 +79006,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.29blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.12blocks/s, ⧗=0, ▶=0, ✔=190, ✗=0, ∅=0]" ] }, { @@ -79028,7 +79028,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.29blocks/s, ⧗=0, ▶=1, ✔=190, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.12blocks/s, ⧗=0, ▶=1, ✔=190, ✗=0, ∅=0]" ] }, { @@ -79050,7 +79050,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.29blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 190/216 [00:11<00:01, 17.12blocks/s, ⧗=0, ▶=0, ✔=191, ✗=0, ∅=0]" ] }, { @@ -79072,7 +79072,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.29blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.12blocks/s, ⧗=0, ▶=1, ✔=191, ✗=0, ∅=0]" ] }, { @@ -79094,7 +79094,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.29blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 88%|████████▊ | 191/216 [00:11<00:01, 17.12blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" ] }, { @@ -79116,7 +79116,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.64blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.33blocks/s, ⧗=0, ▶=0, ✔=192, ✗=0, ∅=0]" ] }, { @@ -79138,7 +79138,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.64blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.33blocks/s, ⧗=0, ▶=1, ✔=192, ✗=0, ∅=0]" ] }, { @@ -79160,7 +79160,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.64blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 192/216 [00:11<00:01, 17.33blocks/s, ⧗=0, ▶=0, ✔=193, ✗=0, ∅=0]" ] }, { @@ -79182,7 +79182,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.64blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 89%|████████▉ | 193/216 [00:11<00:01, 17.33blocks/s, ⧗=0, ▶=1, ✔=193, ✗=0, ∅=0]" ] }, { @@ -79204,7 +79204,7 @@ "output_type": "stream", "text": 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17.48blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.78blocks/s, ⧗=0, ▶=1, ✔=194, ✗=0, ∅=0]" ] }, { @@ -79270,7 +79270,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 17.48blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 90%|████████▉ | 194/216 [00:11<00:01, 16.78blocks/s, ⧗=0, ▶=0, ✔=195, ✗=0, ∅=0]" ] }, { @@ -79292,7 +79292,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 17.48blocks/s, ⧗=0, ▶=1, ✔=195, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 16.78blocks/s, ⧗=0, ▶=1, ✔=195, ✗=0, ∅=0]" ] }, { @@ -79314,7 +79314,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 17.48blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 90%|█████████ | 195/216 [00:11<00:01, 16.78blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" ] }, { @@ -79336,7 +79336,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.63blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 91%|█████████ | 196/216 [00:11<00:01, 16.42blocks/s, ⧗=0, ▶=0, ✔=196, ✗=0, ∅=0]" ] }, { @@ -79358,7 +79358,7 @@ "output_type": "stream", "text": 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16.63blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.42blocks/s, ⧗=0, ▶=1, ✔=197, ✗=0, ∅=0]" ] }, { @@ -79424,7 +79424,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.63blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 91%|█████████ | 197/216 [00:11<00:01, 16.42blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" ] }, { @@ -79446,7 +79446,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.73blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.74blocks/s, ⧗=0, ▶=0, ✔=198, ✗=0, ∅=0]" ] }, { @@ -79468,7 +79468,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.73blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.74blocks/s, ⧗=0, ▶=1, ✔=198, ✗=0, ∅=0]" ] }, { @@ -79490,7 +79490,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.73blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 198/216 [00:11<00:01, 16.74blocks/s, ⧗=0, ▶=0, ✔=199, ✗=0, ∅=0]" ] }, { @@ -79512,7 +79512,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.73blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.74blocks/s, ⧗=0, ▶=1, ✔=199, ✗=0, ∅=0]" ] }, { @@ -79534,7 +79534,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.73blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 92%|█████████▏| 199/216 [00:11<00:01, 16.74blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" ] }, { @@ -79556,7 +79556,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.27blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 16.80blocks/s, ⧗=0, ▶=0, ✔=200, ✗=0, ∅=0]" ] }, { @@ -79578,7 +79578,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.27blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 16.80blocks/s, ⧗=0, ▶=1, ✔=200, ✗=0, ∅=0]" ] }, { @@ -79600,7 +79600,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 17.27blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 200/216 [00:11<00:00, 16.80blocks/s, ⧗=0, ▶=0, ✔=201, ✗=0, ∅=0]" ] }, { @@ -79622,7 +79622,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.27blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.80blocks/s, ⧗=0, ▶=1, ✔=201, ✗=0, ∅=0]" ] }, { @@ -79644,7 +79644,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 17.27blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 93%|█████████▎| 201/216 [00:11<00:00, 16.80blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" ] }, { @@ -79666,7 +79666,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.66blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.83blocks/s, ⧗=0, ▶=0, ✔=202, ✗=0, ∅=0]" ] }, { @@ -79688,7 +79688,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.66blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.83blocks/s, ⧗=0, ▶=1, ✔=202, ✗=0, ∅=0]" ] }, { @@ -79710,7 +79710,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.66blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▎| 202/216 [00:11<00:00, 16.83blocks/s, ⧗=0, ▶=0, ✔=203, ✗=0, ∅=0]" ] }, { @@ -79732,7 +79732,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.66blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.83blocks/s, ⧗=0, ▶=1, ✔=203, ✗=0, ∅=0]" ] }, { @@ -79754,7 +79754,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.66blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 203/216 [00:11<00:00, 16.83blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" ] }, { @@ -79776,7 +79776,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.12blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.03blocks/s, ⧗=0, ▶=0, ✔=204, ✗=0, ∅=0]" ] }, { @@ -79798,7 +79798,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.12blocks/s, ⧗=0, ▶=1, ✔=204, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.03blocks/s, ⧗=0, ▶=1, ✔=204, ✗=0, ∅=0]" ] }, { @@ -79820,7 +79820,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.12blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 94%|█████████▍| 204/216 [00:11<00:00, 17.03blocks/s, ⧗=0, ▶=0, ✔=205, ✗=0, ∅=0]" ] }, { @@ -79842,7 +79842,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.12blocks/s, ⧗=0, ▶=1, ✔=205, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:11<00:00, 17.03blocks/s, ⧗=0, ▶=1, ✔=205, ✗=0, ∅=0]" ] }, { @@ -79864,7 +79864,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:12<00:00, 17.12blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▍| 205/216 [00:12<00:00, 17.03blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" ] }, { @@ -79886,7 +79886,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 16.28blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.17blocks/s, ⧗=0, ▶=0, ✔=206, ✗=0, ∅=0]" ] }, { @@ -79908,7 +79908,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 16.28blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.17blocks/s, ⧗=0, ▶=1, ✔=206, ✗=0, ∅=0]" ] }, { @@ -79930,7 +79930,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 16.28blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 95%|█████████▌| 206/216 [00:12<00:00, 17.17blocks/s, ⧗=0, ▶=0, ✔=207, ✗=0, ∅=0]" ] }, { @@ -79952,7 +79952,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 16.28blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 96%|█████████▌| 207/216 [00:12<00:00, 17.17blocks/s, ⧗=0, ▶=1, ✔=207, ✗=0, ∅=0]" ] }, { @@ -79974,7 +79974,7 @@ "output_type": "stream", "text": 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16.54blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 16.69blocks/s, ⧗=0, ▶=1, ✔=208, ✗=0, ∅=0]" ] }, { @@ -80040,7 +80040,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 16.54blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 96%|█████████▋| 208/216 [00:12<00:00, 16.69blocks/s, ⧗=0, ▶=0, ✔=209, ✗=0, ∅=0]" ] }, { @@ -80062,7 +80062,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 97%|█████████▋| 209/216 [00:12<00:00, 16.54blocks/s, ⧗=0, ▶=1, ✔=209, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 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[ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 16.81blocks/s, ⧗=0, ▶=1, ✔=210, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.21blocks/s, ⧗=0, ▶=1, ✔=210, ✗=0, ∅=0]" ] }, { @@ -80150,7 +80150,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 16.81blocks/s, ⧗=0, ▶=0, ✔=211, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 97%|█████████▋| 210/216 [00:12<00:00, 17.21blocks/s, ⧗=0, ▶=0, ✔=211, ✗=0, ∅=0]" ] }, { @@ -80172,7 +80172,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 16.81blocks/s, ⧗=0, ▶=1, ✔=211, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 17.21blocks/s, ⧗=0, ▶=1, ✔=211, ✗=0, ∅=0]" ] }, { @@ -80194,7 +80194,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 16.81blocks/s, ⧗=0, ▶=0, ✔=212, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 211/216 [00:12<00:00, 17.21blocks/s, ⧗=0, ▶=0, ✔=212, ✗=0, ∅=0]" ] }, { @@ -80216,7 +80216,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 17.25blocks/s, ⧗=0, ▶=0, ✔=212, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 16.73blocks/s, ⧗=0, ▶=0, ✔=212, ✗=0, ∅=0]" ] }, { @@ -80238,7 +80238,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 17.25blocks/s, ⧗=0, ▶=1, ✔=212, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 16.73blocks/s, ⧗=0, ▶=1, ✔=212, ✗=0, ∅=0]" ] }, { @@ -80260,7 +80260,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 17.25blocks/s, ⧗=0, ▶=0, ✔=213, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 98%|█████████▊| 212/216 [00:12<00:00, 16.73blocks/s, ⧗=0, ▶=0, ✔=213, ✗=0, ∅=0]" ] }, { @@ -80282,7 +80282,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 17.25blocks/s, ⧗=0, ▶=1, ✔=213, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 16.73blocks/s, ⧗=0, ▶=1, ✔=213, ✗=0, ∅=0]" ] }, { @@ -80304,7 +80304,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 17.25blocks/s, ⧗=0, ▶=0, ✔=214, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▊| 213/216 [00:12<00:00, 16.73blocks/s, ⧗=0, ▶=0, ✔=214, ✗=0, ∅=0]" ] }, { @@ -80326,7 +80326,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 16.72blocks/s, ⧗=0, ▶=0, ✔=214, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 16.13blocks/s, ⧗=0, ▶=0, ✔=214, ✗=0, ∅=0]" ] }, { @@ -80348,7 +80348,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 16.72blocks/s, ⧗=0, ▶=1, ✔=214, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 16.13blocks/s, ⧗=0, ▶=1, ✔=214, ✗=0, ∅=0]" ] }, { @@ -80370,7 +80370,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 16.72blocks/s, ⧗=0, ▶=0, ✔=215, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 99%|█████████▉| 214/216 [00:12<00:00, 16.13blocks/s, ⧗=0, ▶=0, ✔=215, ✗=0, ∅=0]" ] }, { @@ -80392,7 +80392,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 16.72blocks/s, ⧗=0, ▶=1, ✔=215, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 16.13blocks/s, ⧗=0, ▶=1, ✔=215, ✗=0, ∅=0]" ] }, { @@ -80414,7 +80414,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 16.72blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 100%|█████████▉| 215/216 [00:12<00:00, 16.13blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -80436,7 +80436,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 100%|██████████| 216/216 [00:12<00:00, 17.35blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ▶: 100%|██████████| 216/216 [00:12<00:00, 16.48blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -80458,7 +80458,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.35blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 16.48blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -80473,7 +80473,7 @@ "output_type": "stream", "text": [ "\r", - "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.15blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" + "predict_/home/runner/dacapo/example_run/validation.zarr_2000/ds_example_dataset/prediction ✔: 100%|██████████| 216/216 [00:12<00:00, 17.12blocks/s, ⧗=0, ▶=0, ✔=216, ✗=0, ∅=0]" ] }, { @@ -80520,7 +80520,7 @@ "output_type": "stream", "text": [ "\r", - "training until 2000: 100%|██████████| 2000/2000 [13:12<00:00, 2.52it/s, loss=0.345]" + "training until 2000: 100%|██████████| 2000/2000 [12:52<00:00, 2.59it/s, loss=0.434]" ] }, { @@ -80557,7 +80557,7 @@ }, { "cell_type": "markdown", - "id": "c20f64b7", + "id": "0d3054f4", "metadata": {}, "source": [ "## Visualize\n", @@ -80568,13 +80568,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "eb04b1f8", + "id": "229e4509", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:49:36.285107Z", - "iopub.status.busy": "2024-11-07T15:49:36.284320Z", - "iopub.status.idle": "2024-11-07T15:49:36.427597Z", - "shell.execute_reply": "2024-11-07T15:49:36.426996Z" + "iopub.execute_input": "2024-11-07T16:12:39.976866Z", + "iopub.status.busy": "2024-11-07T16:12:39.976269Z", + "iopub.status.idle": "2024-11-07T16:12:40.093153Z", + "shell.execute_reply": "2024-11-07T16:12:40.092453Z" } }, "outputs": [ @@ -80585,15 +80585,15 @@ "Creating FileStatsStore:\n", "\tpath : /home/runner/dacapo/stats\n", "\n", - "array([0.65979844, 0.61128896, 0.58934087, ..., 0.35486192, 0.37893647,\n", - " 0.34515283])\n", + "array([0.78323734, 0.73412889, 0.75879186, ..., 0.59049326, 0.35759771,\n", + " 0.4338592 ])\n", "Coordinates:\n", " * iterations (iterations) int64 0 1 2 3 4 5 ... 1994 1995 1996 1997 1998 1999\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -80617,13 +80617,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "407bb556", + "id": "49f4728b", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:49:36.429646Z", - "iopub.status.busy": "2024-11-07T15:49:36.429258Z", - "iopub.status.idle": "2024-11-07T15:49:37.420672Z", - "shell.execute_reply": "2024-11-07T15:49:37.419947Z" + "iopub.execute_input": "2024-11-07T16:12:40.095421Z", + "iopub.status.busy": "2024-11-07T16:12:40.095054Z", + "iopub.status.idle": "2024-11-07T16:12:41.043087Z", + "shell.execute_reply": "2024-11-07T16:12:41.042147Z" }, "lines_to_next_cell": 0 }, @@ -80640,7 +80640,7 @@ }, { "data": { - "image/png": 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ee3DUUUfhgQceMEr4eswxx+Dyyy/Pfl+yZIlRP7a2tuLBBx/ETjvtlG339ddfb7RvvlBEJ4QQouTMRycAAM574lMsvPXbZW4NIYQQQgghhBDSvpk7dy4aGhpw/PHHS8tbWlqw//77AwB++MMf4j//+Q9uvfVWPPDAA9h5552lbe+77z48/vjjWLx4MRobG9HS0oL99ttP2mbPPfdEIpEzIBk4cCD22muv7PdkMol+/fph9erV0n6usA8AVVVVOOiggzBz5kzlsUybNg2ff/45/vWvf2WXOY4D27axYMEC7L777qH9cdBBB4Vuo6Jbt25ZAR0ABg8e7DuWuKGITgghhBBCCCGEEEIIabd0rU5ixvUnlK1uU+rq6gAAb7zxBrbddltpXW1tLYCMVcmkSZOQTCYxZ84caZvnn38eV1xxBe68806MGDECPXv2xO23344JEyZI21VXV0vfLctSLrNt27jtqmO56KKL8Otf/9q3bvvttzcqo3v37tJ3V/gXLXJaW1t9+6mOJaqtTlQoohNCCCGEEEIIIYQQQtotlmUZW6qUkz322AO1tbVYvHgxjjrqKOU2l19+ORKJBN566y1861vfwre//W0cc8wxAICPP/4Yhx12GH7xi19kt583b15s7fvkk09w5JFHAgBSqRQmTZqk9Tg/4IADMGPGDAwfPjy2+rfeemsAwIoVK9C3b18AmcSilUDljy5CCCGEEEIIIYQQQghp5/Ts2RNXXHEFfve738G2bRx++OHYtGkTPv74Y/Tq1Qv9+/fH448/jvHjx+OAAw7A73//e/z0pz/F559/jr59+2LnnXfGU089hVGjRmHYsGF4+umn8emnn2LYsGGxtO++++7DzjvvjN133x1/+9vfsGHDBvzsZz9TbvuHP/wBhx56KC699FJccMEF6N69O2bMmIF33nkH9957b171Dx8+HEOGDMG1116Lm266CV999RXuvPPOQg4pNhLhmxBCCCGEEEIIIYQQQggplBtuuAFXXXUVbrnlFuy+++448cQT8cYbb2Do0KE4//zzce211+KAAw4AAFx33XUYOHAgLr74YgDARRddhNNOOw2nn346DjnkEKxbt06KSi+UW2+9Fbfeeiv23XdfjB07Fq+99hr69++v3HafffbBBx98gK+++gpHHHEE9t9/f1x99dXYZptt8q6/uroazz33HGbNmoV99tkHf/3rX3HjjTfmXV6cWE6xDWMqjM2bN6N3797YtGkTevXqVe7mEEJIxTL0j29kPzOxKCGEEEIIIYSQSqGpqQkLFizAsGHD0KVLl3I3p92zcOFCDBs2DFOmTPElKe0IBI0XU62YkeiEEEIIIYQQQgghhBBCiAaK6IQQQgghhBBCCCGEEEJi4aOPPkKPHj20/9ojTCxKCCGEEEIIIYQQQgghnZShQ4ciTsfvgw46CFOnTo2tvEqAIjohhBBCCCGEEEIIIYSQWOjatSuGDx9e7mbECu1cCCGEEEIIIYQQQgghhBANFNEJIYQQQgghhBBCCCHtjjgtSEjHJY5xQhGdEEIIIYQQQgghhBDSbqiurgYANDQ0lLklpD3gjhN33OQDPdEJIYQQQgghhBBCCCHthmQyiT59+mD16tUAgG7dusGyrDK3ilQajuOgoaEBq1evRp8+fZBMJvMuiyI6IYQQQgghhBBCCCGkXTFo0CAAyArphOjo06dPdrzkC0V0QgghhBBCCCGEEEJIu8KyLAwePBgDBgxAa2truZtDKpTq6uqCItBdKKITQgghhBBCCCGEEELaJclkMhaRlJAgmFiUEEIIIYQQQgghhBBCCNFAEZ0QQgghhBBCCCGEEEII0UARnRBCCCGEEEIIIYQQQgjRQBGdEEIIIYQQQgghhBBCCNFAEZ0QQgghhBBCCCGEEEII0UARnRBCCCGEEEIIIYQQQgjRQBGdEEIIIYQQQgghhBBCCNFAEZ0QQgghhBBCCCGEEEII0UARnZQUx3HK3QRCCCGEEEIIIYQQQggxhiI6KRm/+NcknHj3R2hJ2eVuCiGEEEIIIYQQQgghhBhBEZ2UjDe/WInZq7bg04Xry90UQgghhBBCCCGEEEIIMYIiOiGEEEIIIYQQQgghhBCigSI6IYQQQgghhBBCCCGEEKKBIjohhBBCCCGEEEIIIYQQooEiOiGEEEIIIYQQQgghhBCigSI6IYQQQgghhBBCCCGEEKKBIjopOY5T7hYQQgghhBBCCCGEEEKIGRTRSUlwqJwTQgghhBBCCCGEEELaIRTRSUmwBQ3dssrXDkIIIYQQQgghhBBCCIkCRXTi49+Tl+La16bDtuOLHreFSHQGpRNCCCGEEEIIIYQQQtoLVeVuAKk8LntxGgDgsJ364Zt7DoqlTArnhBBCCCGEEEIIIYSQ9ggj0YmWjQ2tsZVlU0UnhBBCCCGEEEIIIYS0Qyiik5JADZ0QUghMTkwIIYQQQgghhJByQRGd6IkxAagDCmCbm1pxz+g5WLC2vtxNIaRdcckzk3DyvWORStvlbgohhBBCCCGEEEI6IRTRSUmIMUdpu+Xa/07HXe98hRPu/rDcTSGkXfHWlyvx5bLNmLZ0U8nrfv3z5Xjh08Ulr5cQQgghhBBCCCGVAxOLEi0xBqLTEx3AxIXrAQAtKUbTEtIecBwHlz47BQBw9K4DMLBXlzK3iBBCCCGEEEIIIeWAkeikJJRDQ1+8rgE3vj4Dyzc2lr5yQki7R7xvbW6ML9EyIYQQQgghhBBC2heMRCdaLCu+WPRyJAU845FPsGxjIz5ZsA6v/+qIktdPCGnf8P0ZQgghhBBCCCGEAIxEJwHEa+cSY2GGLGuLQP9y2ebSV04IaffQhooQQgghhBBCCCEARXQSQIyB6BSjEG9/EkKKD29bhBBCCCGEEEIIASiikxIhilFOJzVJoCBHSPuis96rCCGEEEIIIYQQIkMRnWiJM3Ja9EQvh7ULIYRERZ78I4QQQgghhBBCSGeFIjopCaJw3lmtXWjnQkh0xAk4XkOEEEIIIYQQQggpBxTRSUkQbRGcTiqiE0KiU87bRWed8COEEEIIIYQQQogMRXSixUJ8YZ9iJDp1KUKIKeW8XfBeRQghhBBCCCGEEIAiOvFQLOsE26Ynekdh3Ny1+PN/vkB9c6rcTSGdgHK+uSLWTEGdEEIIIYQQQgjpvFSVuwGksiiFwF1qiwT6KMfLmY9OAAD06lqNP5y4W5lbQzo65Y1Ep3JOCCGEEEIIIYQQRqITD8USuMVyS61LVYqGHqc9TiWwZH1DuZtAOgHl1LEpoRNCCCGEEEIIIQSgiE482JKdS7E80UsrTSUqJBTd6WCSXJzjgxAd5bxuHLtsVRNCCCGEEEIIIaSCoIhOJER9O06JVBTOS+2JTq2XkPZLse5JRnV3sIkvQgghhBBCCCGE5AdFdCJRPDuX4teho1JsVCqlHXHRsY6GED/irYqCOiGEEEIIIYQQ0nmhiE4kihUlLkail1qKYiR6cWC/klJAT3RCCCGEEEIIIYSUG4roRMKRPNFjLFdTR9CyuKDYWxwqxWuedGzK6olexoTIhBBCCCGEEEIIqRwoohMJW/IfjjOxqKP8DADj5q3FQTe+i5FfroitPpGOZqNSKbBXSSkop3gtJ0QuXzsIIYQQQgghhBBSXiiiE4liRaLbtvozAJz16ASsq2/Bxc9Mjq9CgQTV3uLAfi0bxXxzo9Io55GKUfD0RCeEEEIIIYQQQjovFNGJRNE80aH3RC9WnS5WhdiOVEgzSDvntWnLcfDNozFp0YZYy33zixU45d6xWLyuIdZyC6WsEwaMRCeEEEIIIYQQQggoohMPXquVt6evxPKNjQWXKxbrraPYVIp23dFEONrklIdfPzcFa7Y046KnP4u13F/8azKmLd2EP7zyeazlFop42ZR6QkzO5VDSqgEAExesx/h560pfMSGEEEIIIYQQQiSqyt0AUlmIAvd/py7DqOmrAAALb/12bOWWPLKUWm9RYGR9eWlNF+c62tLcWpRy86WsgehiJHqJ7VyaWtP40UPjAQDTrzsB3Wv555oQQgghhBBCCCkXjEQnEqJo9OFXa2Mr13bUn0tBokLU3gppRmx0sMNpd3QaX/SQw0ylbVz57y/w36nLYq9aTogce/GBNLfmkkfUN6dKWzkhhBBCCCGEEEIkKKITCSliPMbIS0eKRI+tWCM6mnhdauatqVOKeOxXP3UUO2Mn7D70yuSleG7iYvzm+alFqFv43FkmLQghhBBCCCGEEOKDIjqRcIqUSM+mJ3q7ZMriDTj2zg9w3F0f+NbRE13m/Vmrsdc1o/DXkbNKUl9nkXTDbhfLNhSes0Fftz4hMiGEEEIIIYQQQjoPFNGJhF2kiHGnjJ7opbJzWbOlucNFq478ciUAYMWmJt86RqLLXPe/6QCAB8bMK02FHWuoGaG6vhpa0kWsL7huQgghhBBCCCGEdA4qQkS/7777MHToUHTp0gWHHHIIJk6cqN326KOPhmVZvn/f/nZhiS9JhmIl0hNLKrW3cCnE3lcmLcXXbnoXt7xVmijkUmEFdB5FdBlKrMUhrF8bW4snokvt4AkmhBBCCCGEEEI6LWUX0V944QVcdtlluOaaazB58mTsu+++OOGEE7B69Wrl9v/+97+xYsWK7L8vv/wSyWQSP/zhD0vc8o5JsRLp2Xb5ItFLYehy/eszAAAPfzi/jK2In0Rgo9vjEalpTqWxeos/2r4SWLGpEZsaW33LO4umG3a/KKaIXs7EooQQQgghhBBCCKkcyi6i33XXXbjwwgtx3nnnYY899sCDDz6Ibt264fHHH1duv9VWW2HQoEHZf++88w66detGET0PHMfBliZZnLOLZF8ge6LHVqwRpYiYDhabM7QXDW75xpxoG9R3HSkS/aS7P8LBN43G3NV15W6KxLq6Zoy45T3se93b5W5K2QiNRO+gdi5xvglECCGEEEIIIYSQwiiriN7S0oJJkybhuOOOyy5LJBI47rjjMH78eKMyHnvsMfz4xz9G9+7dleubm5uxefNm6R/JcN3/ZmDva9/GuHlrs8uKFXkpCkIdMbFoqXzXi82aLc047NacaBuUPLSYR7yxoQWptF3EGmTmr60HAIyavjLvMooxrGeu2BJ/oSFUWsJY2WLKTzEj0ctpQ0X7GEIIIYQQQgghpHIoq4i+du1apNNpDBw4UFo+cOBArFwZLmZNnDgRX375JS644ALtNrfccgt69+6d/TdkyJCC291ReGLcQgDAnW9/lV1WrGhLOaKzKFVoKUkkukEoemVJk2q+XLZJ+h50WMXq18XrGrDf9e/gtAfGFaeCdkRQ/xftWq2wCGixPapDLm5iUaHuEveLo/lMCCGEEEIIIYSQ0lN2O5dCeOyxx7D33nvj4IMP1m5z5ZVXYtOmTdl/S5YsKWEL2weiTlesaEu7jGJUKSJrTexc2gO+twSCEosWqV//9/lyAMDnSzeFbBk/pffrDyFIRC9dK8qLo/0CAGgqqid6YNVFpeLGIiGEEEIIIYQQ0okpq4jev39/JJNJrFq1Slq+atUqDBo0KHDf+vp6PP/88zj//PMDt6utrUWvXr2kf0RPsaxWxGJLbYsQl8A9d3Ud/u/laVi0rl5Rh7+SVNrGY2MXYMby9mMh5D39HWVywJRChn8xJoeCbIKKpbFWnJ2L+FlxzMX0RIdkQ1XEahTYZXx7hxBCCCGEEEIIITJlFdFrampw4IEHYvTo0dlltm1j9OjRGDFiROC+L730Epqbm3HWWWcVu5kdHlGnK5ZYI3utlzgSPSbfkR88OA4vfrYUFzz5mW+dSux8buJi3PD6DHzrno9iqb8UeM9NkIhbLDuX9movX4xhLfZ/Z41MDvNEL66di1h3+RKLVprFDiGEEEIIIYQQ0tkou53LZZddhkceeQRPPvkkZs6ciUsuuQT19fU477zzAADnnHMOrrzySt9+jz32GE499VT069ev1E3u0JQiEr29aoEbG1oBAHNW1/nWqYTfqUtKb0fiZW1dM377/BRMmL/OaHvvqQn0RM+/WRVLpQ1Nsf/TnlDoziKshh1nMe1cyplYFB3gntmRSaVtnPHwJ7jx9RnlbgohhBBCCCGEkBJQVe4GnH766VizZg2uvvpqrFy5Evvttx9GjhyZTTa6ePFiJBKy1j979myMHTsWb7/9djma3OEQ7RuKJdaIQtizExajR20VfnrY0OJU5iFRgqkiVcR2JUQOX/3fL/HmFyvx6tTlWHjrt0O391uiB0WiV46M3pxK49GPFuDoXbfGntv0LndzYkPs4rTjSDfsChheJSFsAq6xmCK6VHdpO1wU7Uv99g4J54Ov1mD8/HUYP38d/vKdPcrdHEIIIYQQQgghRabsIjoAXHrppbj00kuV68aMGeNbtuuuu1aEQNlhEIS6Yok1tp37vGxjI655bTpO2nsQBvTsUpT6ROL2eK5O+stLKkK2K0H4WrSuIdL23usqSCevIA0dj360ALePmo3bR802mizQUZAnehFOtzhRIV5DnQnZE93fycUU0aWEyKVOLIry1U3CaU3zpBBCCCGEEEJIZ6Lsdi6ksiiWZYFKUN7c2FqcyjzELfZWKULbVXWU3P5BQVTxzbt50AREJSWg/HzpxnI3oSiIbzikOqmKLgrnquFcTIG5rJ7oFXD/IIQQQgghhBBCSAaK6ESSQosWia4otjlVGlEwKDlmPlQpItFVdXj7shz2J1HPpz+xaJytMSMfcT4d01CqNJ9xsf+9GnpltbR4lDOfQjmjweUI/NLWTQghhBBCCCGEEBmK6ESieDY5/nJbiiii24JqbyrJ3j9mLv7w8uehfVCd9F82KrG5EuxcouJtctAERCXZucTV15V2ysT+T1da48pAOaPBS/1miXgPa4/3EkIIIYQQQgghpCNBEZ1IYmjx7Fz8y4opokuCo6HYe9vI2XjhsyWYvHhj4HZVCsVcGYleAe4bUbU32QPakceG7UgTDBWkoSNVCd45RSbtPcaOf8gAPGO4jMdczjwcneRUtzN4VgghhBBCCCGkM0ERnUjYRRIjVZGULXF5cChI5xGJ7tIckqhQJaKrrFq8gnR7w3bk40o7jjQZUkmR6HH1b6WdJTkSWm5dpVnPFItyHqfY56Wep5FtbDrHuSaEEEIIIYQQQioViuhE8qAumplLiSPRRfErqid6mHd5lcLORbFIEt0KnTB4e/pKzF9TF3m/qAKkV/gX5wvStiOtL5bHez7F+qK0Owji+euoxxiGnNyzfHWXunbx3HfSU1/hVNAsIiGEEEIIIYSQokMRnXjsXEoYiV4kEb2pNY3WlCj2Rts/bHvTxKKi6FbIsX40Zw1+/vQkHHPnB751T45biFPuHYt1dc3KfaOKb14PaPGofCJ6tKKLSmwCc4VF/IrN8R5jhTW1aJQzwWY567bLKOATQgghhBBCCCFEhiI6kYgiFE1atB4rNjXmXW4x7Fy2NLVi96tH4qS/f5hdZhnIvVG8vqsT/stGFZUt+nQXIqJPDfBov+a16Zi2dBNuenOmcn1UGwhRuFu0rh43vpErN+048nmsIBW9EhIvFsNyQ7YT8dq5dA7Efi19YtFy2rmIb4WUtm5CCCGEEEIIIYTIUEQneUWiL17XgO8/MB6XPDPZaHuV+FWMSPRPF66H4wDLNzVll5lEootRvgmF57mIOhLdv11za+74mgs4VpMzohPao2pvonD344c/kSYCbF8keuWo6HFFoleaVim2p1R2LsVw6WlOpZHKc9KsYiLRS27nov5MKgWeFUIIIYQQQgjpTFBEJ5gwfz2u/98MNLakjaMtl7dFoK/ZorYR8WIr9LNChOW4SUeIRFd5oqvsXJpSuQSlhUwYmAiH89fWa3aOWJfweV19i7QuY+eS+15JiUXTHdPNJTCxaHuhqTWN/a57B8fe5bcjMqG8nuiVEYneXs89IYQQQgghhBDSUagqdwNI+UnZDh7/eAF6dKnC/tv3MdqnsTUjEJtaWKhEoGKI6KroaJMmiiJ/mDhcrQg7V0WiNwmR6C1p29eylpSN6qRVtASdQGGR6F7STmk80fMp166AzIvFaYGYWNSzpp0Iq7NXbkFjaxqL1jXkWYKc7LaUSAJ+WesuadXEiAqaRSSEEEIIIYQQUnQYiU6yLFxbbywUNbeJ6KbapWqzZiFSu5iYRHFKkeghorbKzkW1j9tHgD8SfX19C/a+dhQuenpSaNuCbCS61ySD942ovgVtbtuyWF1Rkeix2blUllopHpYvsWiJ25IvhbaznAJyxVjJtJeTTQghhBBCCCGEdFAoohMJle2Ki+M4mL+mDmnbyUaim9oMqMRcMVK7mBiJ6KI4HLJttcLOJamycxFE9GUbGyXLldemLkNzysbbM1aFti2o+WGCf1TtLUiLTtm2bOdSQZGYlWB3UYwmdAQ7F5F8ornL6Q0uW8mULxK9I5z7jgfPCSGEEEIIIYR0JiiikywOgmWB5yYuwTF3foDfvTAVjS0ZAdw0AFi1Xaki0U2aKEZYPz9xiSSAA7L4V6Wyc1FcSU1C9Lk34jwVU+R0mIwdVXsLEgptu3LFvNgi0Svs8MRx54tEr7C26ijUV9yJoKLHbbkillfq/q7Ua40QQgghhBBCCOmMUEQnEkHCzX3vzwUAvDZteVZkNhWtVJs1lygS3aSJop3LC58twe2jZsvrBfXPNLFoc6t+kqA1QibMwC1DVPSo0bNBIqfXE72SrE/SMQmOlXNEGcT2xHWM5SQfYVgcZ2FjLu4uEq+H0icWVX8mlUd7yU9ACCGEEEIIISR/KKKTLI7jGIsBUe1c1IlFZZFZlZwzDkza6E1MOWb2aul7i5DVURWJrrJVCRSkg3xzPASdk7gj0YN2SNtOSYS9fLzW40osWsgx5TupsLmpFUff/j5uemOGb504dsuZPDUu//u8RPQIYy7uHpIE/FInFo0weUDKQe6ioIZOCCGEEEIIIR0fiuhEIkinE4W0yIlFDTzRw/y98yVqJLqK1lRYJLpYX2bbIMEwNjuXME/0iNUENcv2RaJXDnHpy+UQK1+YuAQL1zXgkY8WqBqUJS7Lmnwo5MosNEFmFBE96JqzbQezVm6ONhlRxmhw2RO9tHVXOvXNKUxatKFiIsBpvUMIIYQQQgghHR+K6EQiSAwQ9dpsJLqhuqPayhuJXqw0lVETiwJ+cVqMRE8qGiraubhFBVUbRRANTiwqbmde5pfLNmF9fUukdqXSjiTmVZJuVE6BuVCCxiftXGTC9g4q/qY3Z+LEuz/CX0fNyqu+ciYWrRSxuFL40UPj8f0HxuHFz5aUsRWVOaFICCGEEEIIIaQ4UEQnWRyERKILMndkOxdFwf5IdKOiglGUYdJEnbuK4zi48+3ZeG7i4twyxXaiiJ6y3aSr8USiB4l3Yr0qIVklvk1ZvAHf+cdYHHLzu751gZG8jiOdx1KIiqbiYWwieiF2LnnuGzTupWjk0qQQiJ1CI6qjWKoEjd/HxmYi/R/6YL553Yq2L1nfoJyAihuHIq2W6cs3AwBembysLPUvXteAi5+ZnP3OOQ5CCCGEEEII6fhUlbsBpLII9N8WxD5XADcVxVTbeYXk4tm5GESia7aZsmQj/vHeXGmZ6lhEOxdX0A2qNS7RV+wxVZGqWj74ag0AdXLToHZ5PdFLoew5jtnkSiERzuWO8rUC3sEQj6sjRKLn09flPGzJvsgB1tY144jb3gcALLz120Wtu1j5B9bXt6Bvt+qi3W9LSpnGxq+fn+JpRvu/NgkhhBBCCCGEBMNIdJLDMRcj40gs6k2uWTw7F5NtPIJ+2/8bFBGnKiFQFREe1Det6SiJRfXrRB1MVZ9q3yALniChNu3xRC+WD7AoKhcyvkyRxMq8S8mfwEh04XM5E4sWhjhm4ipJsz7mLvLaucxeuSXeCkzrjunAPvhqDQ644R1c8dLnsZTXWVmxqVH63gHmtwghhBBCCCGEhEARnUgEWUaIWl9TS0ZEL0Q88PuQ519WECYCq06gVO2qWpYQrqRMxLYT2DeRPNED1wYLzt4IydWbm7C5KaUtLUiotW3HF5lbbEy7qZDI/rgmA4rRHaJ4WtbEojFdnIVGoheSWDQfHCe+CYCoFCOJ7z2j5wAAXpm8NKYSOycUzQkhhBBCCCGk80E7F5LFCXkpXRTSmlKFR6J7BfsgWwtTVCWYiF/eCGz3UFW7qnpJbHvKa3uiIJInegFCsrjvyk1NOPSW0SFlBJdfDGEvCFObhCj96a9D+FwGdSxIoBZb0xHsXAr1RA8bdXH3kFSe4xTtbRll3UWycyHxU6y3cgghwazY1IjGljR23LpHuZtCCCGEEEI6AYxEJxLGdi4tUUV0/zKvKJgomkJl4IleoKgtCn22HS79phV+5Nr6AhOL5j6rDkFcNmHButC6Au1cbEcqr1i6kagpm9ZRSFtKHV3vRTqHnpMoRUKXMxK9gH3FZucjNpYzEh1S2+MtOkrlFGnVlMuL3DvvxdNDSHkYcct7OObOD7CxofjJngkhhBBCCKGITrI4TkhiUeFzY8TEoqpi/XYuxVHRjTzRNRHYqv4I8x5PeSK2VbQG+eZEwAoQYPMhzC9djkQvvnJkKh4WYnWSjwC2ZkszWlLyOcxXSBNHvXdciGWWKhI97qtQHFN5ieiaz8ptY+4ieYKltEppKSasSDzw9BBSXpasbwzfiBBCCCGEkAKhiE4kgrRIUbBtbkssCpiJS0o7F00yz0JQCfEmwp3PzqWtNUo7F2XEt+xdHVZnJNHXcFN1ndGirEMj0QWNtxTCnrGVTQkTi85fU4ev3fQuTvr7h3nXKSKO2ZTnDQVJRG+niUXTTrQx6MUJ2N9374k7EL2MkejyuIyn8lLa0ZSCSplcKIcNFCGEEEIIIYSQ0kJP9E7GpEXrcf3/Zmj9I4PEX1HsaxREdNsBkiHqjEpk8AqGcQSiq+oxEo8jKGSqTb1iZ6ye6IbbqYTkqEJcYGJRbyR6CYQjY7uggjzRo+371pcrAQDz1tTnXaeIOO59IrrwuZyWHoVcm7IQXVgkelDZ+ZZvWre3ZMdxivb2jFu+tvJ8y4ynGOKB/UoIIYQQQgghHR+K6J2M+uY0pi3dhFaFJ7fjmEdbNkkiuoNkSIxjWPQ2EI+di0k9KnTbqBeroupzn40SiwZl8PS1IUjYFrdTrY8YiR5k52JHj9oulEImEIzriGiboRfsC++RFs+4EM+fV2BvL6QlO5fo+wdNBPmE7ejFh9StnzRynHgm/rR1C5/b6UsIRaeY/R8FJx53LkJInpQrPwIhhBBCCOlc0M6lk1HVlsVQJZY6cCJ4ossiehjKxKI+T/TQYgzqiTsSXS2Y3/f+XFz72nTldiZ2LlEE0aCiwqJ8oz5WhicWLcyaIyqm4lRciUWN6jLYpjVt4+KnJ+GxsQtCtxXHXirAE73UkehxvWkgtju/NwbM7VzifjtCikR3IN0Ei30+ipF/oEI059go18sZfhchCniElBraKBFCCCGEkFLDSPRORrJNRNclttSJXBnrgtz3plZbWBder0pw8vuQF47aaiV/GxPVro7j4PZRswEAPzlke+w8sKdUr4mI7o04zhfx2MIsaQqLslaI6BWUWLQQZMuO/N9aEHnzixUYOX0lRk5fifMPHxa4rSSit02uOI6Dcx6fiI/nrhW2C602VsTDtAq4OgudeAl6+8E7XOOO2HYCxnvRo8MjviFBygfPDyHlhdcgIYQQQggpBYxE72RUtZmXt2oUOZ0wFPSAEiQqurYlqi28gm0ihlB0dQLT8P18gn5AU0Q/czci3ytmh9Wp638VQUX5ImW96yParwS1O+3Ix1WSSPTiVyFFu5sck24bcfmWppRx/eJ4cidXFq9vwEdz1sqTMyVWCWxZRc+/HKF/C/VE90We++xdYo5El94EkNcVe4JHtnOhQlTJ8OwQUnp4WySEEEIIIaWGInonI5nInPLWlCbyWrNfxslAraTphNc/vvI59rnubazc1ITGFr+omPEOz+3sFa5fnrQUx945BgvWmidwVEWdmwhQuihu1VKVFYvsiW6HqioqT3odwXYuwZHoJolAG1vSuPe9OZi1cnOgUGt7zlcpnl9LE4kev51LFFSR6KrhWEjy1HyIqzbJziUfET1w/Jtvmw9Bk1TFHpqlzj9A8oe2EoSUHl51hBBCCCGk1FBE72S4nuiqSGjH0YsBtsfOxbtOxfOfLkFDSxqH3jIaj3y0wL+fL2JbruCKl6Zh3pp6XPnvz9UVK9viX5av3YxuX7Hv3PXeqNFwT/R4vDnE41XWaXDs97w3B3e8/RVOvPujUDsXUfAtlnBUah/wqNq0yXFHKVKclAl6QyHMridu4up6WUTPpx16O5iii+iSkO1IE4ml9ESnWqSmUrqlUtpBSGei1JP6hBBCCCGEUETvZLie6CpPbgfRxOTsujz14LRHbNaJ9A0tafUKVVuUOrJJJLqmPMW+6gkIOZo43BM9QiS60IaNDS3aesMSi6pqdBwH05ZszH4PEmpLZeci9XkpLGMMD2RdXTMcx9HbueRZf1rwOwkS0UueWDSmzpfHTGF2Lv51RbZzCUhqWko7F9Vx/W/actz73pyitoGYQbudzsHUJRvx6+emYPnGxnI3hYDCOSGEEEIIKT0U0TsZbiR6SyqiJzocWBqVO18BIW3Lom1CI6Kr7FN05O2JHiFEVhTApy7Z6BNWY/dEF8ra7/p3sKG+Rbku9BBUFiGeZUF2LmmvnUuRnmDtKMcUA7Jlh4Om1rRP7P3v1GU48MZ3cevIWWbjPULniJ7o7meV2Gw6RuuaU1iyvsG4fh1xnV+x3flFogufvaK5T9iOXn4QQRMAxR6b0gSZ4nbxq+em4I63v8JUYRIsjBjSTlQUFWOjUiHNaG9UzPkz5NT7PsZr05bjty9MLXdTCOiJTgghhBBCSg9F9E5GMsDOBQiORNfpL/mK6LZHfNZ7rhcqoofv793GnTAIs3O55rXpePGzJT7LirCI2ELsXD5btCH7WaxFJbKGiRTefcLsXOwAQTMugkTTQlmxqRFPjluI5ycuzkYTiudu8foG7HbVSFz24jRpv+v/NwMA8NAH84vqiR5o52J4HRxy07s44rb3MX9NXcFtcylEey3YEz1CNHjcopzjua5FEbrYAqCpm8sGz9sppPRQy4vOnW/PxqG3jMaqzU3lbkpkFkbI00KKR7F+gxBCCCGEEKKDInono6otsag24jxgud4TPb+2pG1HEgajeq6rUNq5OJno3Kte/RLj563TtkVZnmKZV+h86bOlPh/vsCZHSywqb1vfnFKuC4vCVz1wetsa1Cyv13sp7FzijvY95d6Pcc1r0/HHf3+BY+/8wK0wy/uz1wAA/jNlmbRfIhHuhZ2vP2tKEtH1e5omFq1vsz/6WDPWTSmOJ3o+oejCR0e7Srk+Tood9e6rT5o80FeW6Gjh5e0QRsRG5x/vzcWqzc145MP55W5KWWlv0fiVBLuOEEIIIYSUGoronYxkUi+4OI5eqAuK+Mn3ITAT2SyI6JrtUhHUKp1I9/AH8/D0J4twxiOfGO3ntkV1bK0eK5xkwvIJhWFioZycNFr/bRFEdMn6RBHEHBTFm9nfkUXr0Ej0/ITiKEgTEjErlau3NGc/N7ZmxGaTKpJSCHKsTZImcNw3FFTWSTHlojUmLp9ncVwWqKGHiubFTCzqnXAquie6YSQ6JfTyw4jY/ClxvuSK4qXPlmDf697GpEXry92Udg8nIwghhBBCSCmgiN7JqNIZjwMA9F7eXisD77p8SNuOJJJqPdcjiejq5StDXhnP1xMdAKqTCZ/QF8UTPWxb72opEl2K2lbZuejLAfzHHZhY1C6NiFjqh2ETASwRUUOPcggpKbFoQP9H7Bfv1TR65ip8NGeN8f5ibYUEO6ed4DEa2g4pEj04FL2oiUURfL3NX1OH+96fK12fBdUddNwCUc6NzjKLFEZnFoILJdmJf4X+/uXPsbkphYufmVzuprRLqJsTQgghhJBS04kfXzonyUARXS9COY4Ti2e5SNqRRXudGBRFPNS1pWeX6uC2RFBBvHYumQhV2XohTAgWk6WGbetdXdekiURXieiBJfuj18MSi9qm4bEFEHZMcWNShWTnEsH6xwQpEr3thKjGRNSofHHrDfUtOP/Jz3D2YxPDx3o2H0A8fe/1FRf5ctkm/OXVL7B4XYO2Pq+QLeIdH7EnFhWvD69Nk6euY+78ALePmo2/jpwVT90B1kliX1WKnUvadvDzpz7D3e9+VbI6K0VDYxRs/iRCfpNUInFfchw++cE3QAghhBBCSKmhiN7JCI5ED/BKRzye5dJ+tmMkXqtsSnToxIyeXaoCt4lyDF4Rfdy8dfh04YZcWXb4Q3GLaOcSUp/3QbFOjHQNE5xD1vtEyIDzYTtej/XiIEe7F6kSAZNzL04+mUWimzdcnFBxx5byrYEClBYx+WRTm41NGFIkujCBtnpzE8585BO88fkKo3KCJkW+84+xeOaTxTjy9vfx51e/VLcjQLj29ojY75saW3HbyFmYs2qLUTuVdQufM+NfPyHgMklI/FsIAYct3TcrREPHuHlr8faMVbj73TnlbkrJoQgaDfFvaLJSBnAE+EZHZcDrjhBCCCGElBqK6J2MoEh0x9GLf46j997N90Em7Yvg1mynUauen7gYt4+Soz51ZYiR6FsUdgs6v2mVwOoV0VX7RPNED9zUt75e8kQPFvVEAf6a/073rfcKs0FCrT+xaHGeYE0TKsbVDqNIdEHoiTs6Xhzfrp2L0r++gBkFsc3GIrqmuttHzca4eevwy2fNLAjE4ws6V89OWIxU2sbqLbL1UtBRe8sTu+iqV7/E/WPm4Tv/GGvUzrDyHY/dlW4cxKUJBlkniddppUSiR8ldQTo34t+wShm/5YXXTj6w1/TMXb0Ft42chU0NreVuCiGEEEJIh4IieiejKqE/5Q70wpDjOFp1KF9R0XFk4UUnsOnK/+O/v8B978/D50s3Ctuq61ojJJTcUN/iW+8Vj91DVenlQb7VbhvC9KQoliXetXWSJ3oO1WSDWLRK5PIKs0FvBti2Z9JDs93aumac/8SneHfGKm1ZQch9E759IZp2a9o2FNHD68vX6UY8L6msiO4v4Ytlm3D+E59GKDlHY0tuIDelbGxskK8B5bWnOYi6iJ7fQXYuXn7y6AQcfNNoTFmci+b2Ctki/vJyC8bOXQsAaE7ln5HVG4kuH4tGRI8tSlU/wSi+nVMpEmSNYG7d2exNTA539sotuPjpSZi9Mv83IzoK9S25ibzWKK+aVQjU/SsDk98jnZXj7voQ94+Zh6v+q37DixBCCCGE5AdF9E5GmCe61s4l4AmlkADEVgNbE9txMGnReq34sKVJHZkt8uAH87KfNygic3RRvvlNEIR7oheCJKKHiHphrcgIg+J3/R5px5EmFXSb3vzGTIyetRoXPPVZSO0aIka7F9LTDc1pI19V8brx9tErk5bimU8WyW2K0Cg5Et1uq8O/3bh56zB61mrjcsUrvaElN2ae+WQR9rv+HfxjdM52I+wtBpGu1UnjNgDy8YVF009YsB4A8NzExUI7hDZ5dve2UVy/qTGGCDxxYsRzrejOcVwWz0HWSVIkeoEVFvKGg4hoFdYS8rZOXFSKVm9yD/nxw+MxcvpKnP7w+BK0qLIRI9GbW9ufiE4qgwq5/CuaaUKQCSGEEEIIKRyK6J2McE90TSQ69BGPUZJyemmVEmyqt1lb14LvPzAeJ9z9oXK92C4T0XVDgyIS3XMMbqRZPgKTSSS6SGF2LkK9Ci0irD98di4hkei2QeTXaiHqPx8czWcdhdir1LWkjM6VaDkgRfenbVz+0jT85dUvJdE2WiR67sTlPNHjlQcaBAuXB8ZkJpTufCeXAFI1TsRFYuRll5poIrrYvze/OTPUDslbtyRk+zbU11XIfSlXnjyhY4dMWgGILUxVFuw916lw3yxEQ//gqzXY9/q38eYXZv72QVRX5X5OFBL93x4xGWru5O1G2itIE8GNLWb2Uh2ZSpkMam+w38JhHxFCCCGExAtF9E5GImEFajy6H9y242j3cwWelOTzbfbLXRTUTMTQMFHbSMxQ2Lno6s5Hh7OdaJHo4cctr3cFCL8fdB6R6J4DDHqz3p9YsThPZ1HrCJ+E0G9Q35wyOleSiC70alMMYqEo9rrWLnE7HIQJVaoeEPtevPS7RYxEF8uZtnQTnheizE2QPfK9Zeu3jQOvli+/taHeJz4zF/1xi5NfVgGi/U8fn4gtTSn84l9m/vZBiBO0nS26uLPZ1xRKQ3PuftRomKOhkqCbS4XAyy6UuP8mEkIIIYR0diiid0J00eiO42hF6qDEoraTSWK0xzWjcNvITKJP0yhQEzsXEWWEo9AwE9FVVYauvUGJNnVEjkQPW+8V0NoKDxLWdPt6CUpY6CWTCFYsPLjsfDGxzBAJOudj56zFwTePxuiZan/2uuaU9jCeHr8Qh90yGrNXbvHYueS2adYIQFFENdETvTVl49a3ZuFP//nCeH8dYgsaQkT0sAkY8XNXIRLd5Di9ZS/d0Bi6j3bCzmvf4vVIj1m79UaDm0zwxGXnInvsm70xkrYdX2LWctCcan/CaCFQpoqGFIneDkX0uOH4yQ8KxIQQQgghpNRQRO+E6HzRHehFVAeONuLRdhzcNnI2WlI27m+zilCVc8p+2/iWSSK6wfOQ+8CtT0IaXoZK/PIlFm1T5vOJMHQcJ9LDXVgd2kSWEctR4RXjAu1cHK+di1l989fUSYldw4iSdBUIHjdnPTYBa7Y04/wn1f7sQZHoV/13OpZvasK/JiySfKfFzeOORK9rSeHBD+Zh6pKNBZcrEiZUKfOKaiYzugiR6E0GEcf52GPr6vZ5ohtEovfqUhW9AYryvJNjunFTSGS4iGwlE7Qu9/nsxzKJWScLiVnlxsXSNCXS5FKJ7FwqRUJjIHo0REuypnYoosd1jZPCiDrh3hlhvxBCCCGExAtF9E5IVUJ/2rWCbWAkun8nVUTot/ce7FvWkooW2uyKgaJgY0EUOPMTXXUR+Pn4KjtOtIjYfK2bvf0+edFGnPHwJ5i+fFPeZQT1X8YTXdxWvZ0oPK7a3IRj7vwAX7vpXeM2BdlYhG2f2ce8QzMievA22/bpKkUXi+XrItGjkBL8rddu8VsN5Yt4vTYKiUVVKEV0zfVYK3hf14eUC/jHWFQBKuj8+CeS/Nv06lodqT4R/3h3lOtEiiGveesS70viMY+btw4A8K9PolnmxIEtXRedy86lcuT89oGY6Lg9iuikMuBVFw5FdEIIIYSQeKGI3gnRRaIDAYlFnQCLBcUuKYWKXJ30DzdxOxMx2fV21onbRqKrKhLd09xsYtE8HkDEiO0axTH7GxS22uxY731/LsbPX4ezHp1g0kwACnEuSER3gqNjVcxYsdm4LS6iAGdm5xL8PYiGlnTo9l1rkkhqEouaRGKHJncVGrCuvrCkrDp0di6jpq8EAHXke4CdiIsYUarDe/wmGrpuas3bCu/kl1uVuLxXl/xFdPFkZ65rSN9VxBWk6rWSERHHjGr8lsPmQGxjSz6vH7RjYshh26kQ31SgnQs99fOF/UZcUp3sbw4hhBBCygdF9E6I3hM9QJwOEGVMI9Grkv56WyMmI23KRqLntrUieqKrDlEnHoclMtWV7xZXUxV+iYUJXiYR3yIbGlpD63RJ23IpQRH0aVtOmFoMoe7VKcvwxLiFufZoDv6vI2fhuLs+wJamVt+4iepHHnYctu1Idi5im5o03s9RXjMXJ5LW1hVHRNclFr3o6UkAgDMe+URuU9rWvnUg2c8YiOi+CarQPWScCJ3pnss6IdK1Z0F2LjKSJ7rmWokaab9gbT0ufOoz30SGbJ0kI0eiV4aQZJIroKNSIaeg3SDdQzvdWwt+OHzigL3YWVmyvgF7XD0K1742vWxtsG0Hn8xfhy1N5r+/CSGEENI+oYjeCQnyRNdpxrYj26Z413lRidIqG5nWtF4oUtGoENHD2uLfRiX668rLR0TPCbPViokDL2FV+CwrDPczwTtpEnS8/kjcwuv38tsXphq154Ex8zB3dR2en7jE144ozbJtJ7Qf0w7kSHRhXVNIwk4gfAyJ5yBOOxeRoMSi3jHQmrJx1O1j8MOHxmWXNadsTFq0AYB83uubox+/icYsbhIUia7zSBffZjCZyNLhSONdHiu6yRex7c2pND6asybQsuLCpz7DOzNW4dT7PtbW7a1KvL8qr8My6EniPbRUnuiVAhMcRkMcs7oJvkqGluiVgXSL5CWopFImWYvJ/WPmoSVtSwEYpeaZCYvw44c/wekPfRK+MSGEEELaNRTROyG6SHRA/4PbCTBFV4mEKjsXVST6n//zpVCHtllZVHYuYqmxR6Ln8fzhCGKziYAX1uYgi51CcRzIth0BZdqOg5TGi7lYhFWRdhzfmI0y8ZF2nNDtHceRJp7E7U2sCMJaI/ZpoXYuYl+IQk+QiN7iETxnrNiMZRsbsWR9o7T8+w9kRHXx+PPyRDeIRdeJI0EJNsXvURMWa9vhsS8ysTMS+/26/83A2Y9NxBUvTdPWsXBtvbpu4bPvOMXrUDHCyiGblCOxaKUoZxXSjHaDbInV/kR0UhnIk5qElI9XJi8DkJ+FISGEEELaFxTROyHJgOjoQE90zT4q4V1p56IQ70X7ChPxU5VYNKwtJtvEGYkuim21VUmcf/iw4O0NyourbV68kwdBZaZtr+9k8R9bw86nBX00sgmi9Y5+G0cSRsVEoDoRXRQ2o0Sii29m5INOcG5s1YvdUUUs8VpxPdGbWtM49b6PcctbM/3b5xGJLiMK145mjfxdnBgoJEpYsrSBHImu9UQX7pTPTsgk+Hz98xWR65atk2TyncwqZgCtlHBXY3PUUaGIHg3x2vFO4rUHGIleGURNQt4Z6RzdUv6j5C2BEEII6TxQRO+EqGxVXIJy83gfHMXkm96fsKrIblViUQmD38FZT3QxEt0So4TDy1CJX15LC7fE/D3Rnba2AVd9Zw/cctre2u1NRFx5B+m/gvAed1BiUcdxJAG5JJHoIXVYVmGTCSZ2LrYjWyCJoo+JFUG4J3p8Hanri6B2RokabmhJSWPEjXB/Z8YqTF2yEQ99MN+3j/eeMm9NHd6dscq4zqBoQ50fvhiJHuTzH1q38NmbWFd32qIKbLqzHxSBnw4R0cvxCr/siS53+vr6Frw3a5U250a+lF86yUA7l2iI41P3N6clZeO/U5dhzZbi5ImoJCgA54nBpGZnh91SGjixRgghhHQeKKJ3QrSe6AprDBfbcXw2DN2qk5l1CmEkrYioVdm5SPUHrs0wZ1Ud/vyfLzBfY4Fg8iD1yEcLcPTt72PFppxdhe5BPkhU1iF6JyfaflkHOOiECl7e7nWPMQ6hzC9C6rdN2w5abTHCt/iEaW4WrMDkj2GkDRKLpm1H8kQXBVptJHqAAKoqPy50JQUJ9VGihtduaZGud7cvRNsi7/3AO8be/GIlLnjqM3y2cL1RnUG94xfVM/+3pP2R6Pk85HrtXIwi0aOK6Lq3f8QoS8+RymK+f/9y6CZiG70TMyf/Yyx+9sRneGr8whK3qjRQqIqGeIvQ3f/ue38ufvP8VJxy79gStap8dAbf6mIg9hq7UA0n+EoDNXRCCCGk80ARvRMS5IkexX+7W21V2z7+H5CmiUXlOsJ/7N/7/lz8a8JinPFILnmPV+gKY82WZixc14DbR87OLtPbuYSX50X0RHcFNStAWQurwu/7nH/bvERLLApPJHrm83MTF+PyF6d5rF7i4bNF65XtdFFFokezc3FC+9Hx2LmIAq0uwttrA5JK23h2wmLMX1Pn27a1gH4z9YNPBdjENLWa17+mrkk6Njcqv1eX6uyyLc2ydYzu3H25bJNRnUETEjp7F9EWR1X9i58twX3vzzWqX6xL9kTXjMmIj9MmkejeY5Ai0UP2LRWyJ7p8XSzbmJmwfOuLlaVsUmyE/W2igBcNk8mod2dm3lZZvqmpFE2KRNRrnBQHx/N3lnROeP8lhBBCSCmhiN4J0UeiB3iNwx9h2b0m2bafIhJdUVB1SCR6FFFYtNSQBMsIv6bFZIs+0b/tYPO1c3GFgVwkelAy1+DytEJpHCK6I0diB4vojiSUu11z5b+/wCuTl+K/U5e3tTe3z3n//DT7OZ9ou9tGzsaIW0bjxLs/1ArWGrcbI9K2/u0LF+8QEKNsdZHoXtuPpz9ZhD/95wscc+cHyjbki3dX8VDEIRf0RkVdc3hyUJc1W1qkstwJAPHa3tjQEtjGbFsN65Qjsj3rvG9ptFUmTUy0bSNeg//38ue4fdRszFm1Jbhuj4AfJGy7RI9E1ywP2CgsEr0ciO3QWQS1R6Hrf9OW42s3vYtPA96caI/HVU7EsaJ7SyZosp8QwHPd8RJUUiF/Hjo8QYEyhBBCCOlYUETvhOgeTm3H0Ypt3uSKANDFtXNR7KJKJhgkJAP5CxGOR7A0JSVYk+iCgfNLLJqLWHWPOEgPeGfmKpz0948wa+VmdRs8bXMcYOSXK3DWYxMit83fVvm7yoYnu8520BoQAXv5S9Pw5hf6BIr5asUrNjVhzuo6/HfqMuV6n5Aa4Zyp/Pz92zie5J8mdi5yxHKQAFeIJ7r/LQXN9RtQR30UEb2uWZkUUOyfDQ2tRm0KOk266HPTxKKt0iSbfC2KbGqU27qhvgWPjV2Q9WGWIs/hmXDSvh0Rz8O0XLeM9GaBohnl0E0cxbgA/JMq8dZZtKKz/Oq5KVhb14LznxAmBMvQjo6E3ybJ34GJChbRqZdVBk7wbZCA/VIqeEsghBBCOg8U0Tshukh0O8AT3bs4mbCyorhKJPPaRyQsS1uvrg5TRC0rioAqWT5oEovm44n+32nLccPrMwGYRaJf9eqXmLliMy55ZrJyvbcNtuPg4mcm4wtDO4wgvFHQrQFiq1dMdjzfAeC3L0wN3L8QPpyzxrcsYVkF27mERqLbDsThLAqETQbR8bYT3KbCItG9Y0NTR0yR6OvrZE90ty/Eejf4ItH1E3MmBG2lm0SQPdEzqC5Bb9m/eWEqbnh9Bi548lPf+swbJvJ3FXE9TEtjKCABsNITvQyqrjjZJ9q57Hf9O9nP7VlsDrpO2/FhlQVvf6n6NtmJlGqOn/yQ78/sRVI+OtHtihBCCOn0VJW7AaT06LzJP5kflOjPkYTgqoQFtxjVw4t3WcIoEj3DpwvXY1j/7oHb6uqKokeKD+66aOB8nsumLN6Y/ZzzRA/fb0tTq3K5qe91PnjF1SBfc9txfP7d9S2yAFub1M/LFdruLU1+sdeyFAJExDEQ1izbEynZYmTnIrcnqI5CvOR//tQkVCctPHz2QUgk/BMK2fYEXBhRRPSUbcue6G2zC2K9mwwj0U0JynngK7rte4siEl2Ft18+/CozUTNt6SZf+Rm7K3kSSUVcD9OO5jMgC9aVoh1Jdi5tPvthbw60JwLzWlTKSWgneK/JlO2gKilvEzbp3qHg8MmLqPlwCCkWzJNACCGEdB4oondC8nk49WpwVUIkuuOER5ZZVk501+E4DsbOWYuzHpuALtXmL0mYCFsqREE47fFMcfWSQqKEAbNI9DC8TYgjoaiLt7+CElDatjexKFDnEbarq/Tn7auVddh7u955tlT9kGzBL4RGEW3TdnhiUW8Evhjl3KBNLBocKextQ7580Cb6TliwHiN26gdHo8cHRaJHsXNJ256+yEai55Z5I9F1cwT5iB5eyyedH74qsWjmITeaqCvW98rkpXhl8lJfuV4KudalugMEotBIdMM6LCs+8UnsD/fe6vfs75hKV5z35M6A/2+aIhK9gkX0ym1Z54J2LuF00FuuREUcI28KhBBCSKeBdi6dkKqQBJ8qHEeOxEskrOx3pZ2L5yk5YYW/nu04wJjZqwEATa3m0bmydYZc72n7b6vdT2yjTjsuNIrWPeTCRHSvcBhjJLqnm1u9Buzito4j+cg7cHxRzDUBkegn3zsWXyzdhCZN9HZ4W82OO0rvmNi5PPzhfMxfU5/9Ltm5mHiiI9jvvxBPdBf3utHVExTsHkVEF5PmAjmxNMgTXWsRZXimdP7omfaoJ1DkxKJZFd1H2PUdtFq3bzHsXPyTlH67GnlndZnFfOVcPM9Pjl+E1z9f7r93Fa/6MtNxj6wYqCLRvVS0iE7vhoqjo07QkfYB7wiEEEJI54Eieickn4dTr+CVsKxsskzb8f+A9Nu5GHiiIz+RJ8jOZfjAHujfo1a536RFG3DMHWOwsaHFF4meLS+2SPRo+4n1FjMS3StMB0WiO47j8ZH3W6zUBESiAxkhfferR/osP0xQiZaW0hM93kj0lO1g5eam7Peodi4ZoT73/fSHxuPt6SulNgCFCZzz1tT56pXaE2jnYj6pYTuO1N9uX4jH5307QTf5YXqaoiQcdssU3xbIRaIrCw8pL+B60CyPzc4l4A0beY4g/xtCnA/+3lZc+uyUgvIVmNVZGcIZ9buIeP+mtTMRPW44fPKDkegmsGcIIYQQQuKEInonpCofOxdbFlyqQhKLeoWzhGUhEZpYNL8f+2JV3iISloXqgMj7+Wvr8cS4hVrxuFDB2j3kKJFrf3vnKxxw4ztYtC4T/ZyPJ7rXu1yHV+ANiopO247k3+3A8UUxh4noQOYcfTRXThKa8SaPHhWcsBSWEaEtyGE70YU4UUTXCdBpSQCV2zRhwXr8/OlJbeucbJ9XB0Txh+Feb7anXpdUwBsGdc3mExoZO5fcd1UkeoPHJ183pIJ6XTwnQUKJLjK91TNOdfWFnfm8ItHjsnMJaIec4Fe1b+mFE3WC0+LWuamxFZMXbyhJFGrQWaVMFQ3vWFFNtOXzO6VUVG7LOhfy3wlehSrYLaWBL6cQQgghnQeK6J2QfCPRxR+JiUQuEl318OL3RA+3c7Gd/ASoIP/ppGWF2tfYjj5aNshL2gQrj0j0v4+eg40NrfjbO19l2uD1/DZQ9ptTEUR0w3alHaDVI9557VxMhWAxCVNr2sY3//YBznpsQmhbM/UKLbYs5STD7JVbsGR9Q2g7bJWhfwhi3+qsUMRztrauGe/MWKWpP/c5yAonDHcOSJcfIGjI1EeIRPdOdjSn/Z7o3jGhTXZqeG0F2bn4PNLb/m8VE4vanpWaspV1B7ZLI6IHF2mMNDkI/xhXbZfdvgzCiaodxbZzWbK+EafdPw6jZ66OuWQFASe2UoSqu975Cn8dOavczQjFO1ZUf3/jyi3QHqAAnB9BfxsIKSVMLEoIIYR0Hiiid0KqwjJ8KvA+oFRJnujyutc/X45z/zlRWpZMhNu5APkJUEG2B5YFVIccb9KyfBHYbjuCHm7PPWxoaNsK8UR3I/fzsURoNvQdz9iZmIqZ3kh0v3VHTVXCqH1id0xfvhnz1tTj47nrgtvaVrBvUsFT3/r6Fpxw94c44rb3Q9thYufiRbQKMRHRz3zkE21ZYoS4SRS/DndiRRe1HeQn7xW9A+txwhOLNrSkPQK+um5zOxfxc/C5d1eLtkNOdlt/hQV5omvmqUwu9UmLNuDq/36JzU36twCCJkGkSPQCpOk4vZ1V90pfbosiKV0jBXukclBo7ow4aGxJ457Rc/DAmHlYs6W53M0JxPdmhaL/8sndQjoXQW/rkAzsltLQieb8CCGEkE5PVbkbQEpPXpHojvyQInuiyzLOpc9O8e2fsKyi/ciU/af99YY9jFcl/b7a2fI0QtkPD9wOvbtWh7Yt64mehz5alVBPUpgINs0p2yi6zXYcY9/39fUtGD9vS26BA2zxCLC1htHU4hA0fW3fbaYouFhuQwQWrVNHoKuE5LTtFCRC6gTooESbuu2CbIfCUNm5iOc1SESPkljUO+nQmo1Ezy17b9Zq7H/DO3j47INw8LCtCrZEkifJ9Osy7ch8Fyc63G1UQl1Y04LGhq6vTSLSvv/AOACy2B/YjgARvZBI9Dhvyar70hbPJEGxBJ1y6xfFEPDW1TVjS1MKQ/t3N9penJALsm+qBEzsXCo6Er2Cm9aZ8CbwJn46w1sOlZAbo5JvV4QQQgiJF0aid0Ly8Rr1So1JyRM9fP+EgZ1Lpp7oBNq5JCwkQxTshGVpPdF1di6mkwL5eKK7uO32C4Xh+z4xbqHRdrZtblkzfflmKWLfgeOLRK+uMjtOsT9MxRK3H0R9KGFZyokTFSqf+OnLN0ke51ExiUQPIiWJ6IXYubgiem6ZN7mpjigiuu3IZbl95z3ejQ2t2bdRdJM0pg/3QVv5AtEduV3uMsfj/e8SNiEVdBrFdeI1FGXCbMaKzdp18lsFetFR1Y86UcEr8Mf54K/Sbb2Jh4ul50Q9ji1NrXjzixVobDG3MgqiGCLOgTe+i6PvGIOVm5rCN0b7EhH9iXIVkegV7IkeN+3p3FUSciQ6e1EFe6U00M6FEEII6TxQRO+E1FbnZ+ciPqSIIrrJw4tlmdm55PMgFByJHh7hm0yoPNz1SVMB1xPewJ4m64lu8gNb3kYXiW4i2IyeucpIyE07jnYCIQzbARo9tjGmvt7ikZq+GWFnhWIh6tfynyNdaSoR/dOFG3DJM5ON6le3Sb3cdGIiLfR9HHYuthShbBaJ7n2bIKwesdxWhSe6S0ObQKn3RA+oyFF/9t4fdJHo4rn2Js/V1qNcr9/A0fRvlIfpoAkcXZJYwJ+4thJQnecgu5o4iSpg/PLZKfjFvybjL69+GaGOAGI+B3NX12U/zwyYaJGaIAylShd0jDzRhb8LqbSNX/5rMh75cH6xm2ZEZfdu50G85RT6xhMhhcBIdEIIIaTzQBG9E1JblYy8jwOvnUvuR6PtOKEPlZnti/Mr0xWzNtS34LmJi6V1lmWFRrQlLEv7+rsuijaZMHuQdrcxicL316EW8k0eFjc1poxsX7yiaBQcx0Ha02/vz16D8fODvc0BeSyYBmC7VXkFam/zdV2ts85oUYjrhWJqkSNFoufj+dOG0qrEMBI9SjRu2sAT3bePNhI9vL7NTa34v1c+1673JxrN0Orx7s83uWnQWm2kf4RLvSWl7/ugKEtxfKkm1cohrKvq9EWiV0hc5IdfrQEAvDJ5qfE+QX+/4jyqWSs347i7Psh+N31DRfwbVumCTlCiXBfxb+a7M1fjjS9W4KY3Zxa9beWgUibC2jfsRBUcW4QQQggh8UIRvRPSpTq6iJ7xPVdHopvYr5pbdkRuWvYB/OrXpvvWJRMWqkJEiGTC8gl9WesQTXuSliVFyunIeqIbihqiWJaNRNe0LYhNjS1mIrpjbj3ixQF8CVlNyedN/WwkuhT1a+avC0BKilpsTPvFbWtVwmw86chGojtqcTXoHEexs/FOurS0TUwEda2uahNB9dGPFsj7aERz73pxwsR2HO2bAWGnKTCxqDYS3ZygCRzJzsXTDnF8qe6/pldlnBHLqvPpFdGLRQHzT7EQp1D1n8nLpO+6N6k+W7geJ/39I3y6cD0AeSKtEhKdBuG97lT3SzGXiddbv6NRKZNL7Y/KeyOn0qDNDSGEEEJIvFBE74R0ydvOJfc9mUhkhVAT6wqviH70rlvjx18b4tsuH03WFZE+X7pRUW+4nUtVwvI9xKuSWIqYRtW74o7p9qL454r//sSi+v236l6TLUeX9FIk7Th5vwbtOOYR117E7jDVtl1hSBIsrWCBUaQYEec6zCPRM23KTErlX9+0pZtw5b+/0FobxSWipx1HEm1bUmm0pGzfGwkiOkHPKBK9MTgxpW6CSTrXjr6uoHZndtU3Us7FkFseJSFiUN+Ldbufnp2wGGPnrPVMloSTth28OmUZlmzwJN2N0xNdGYnuOX9F03PKG3odp2i9bGOj9F1n8/SDB8dj5orN+OGD4zNtEIZSpVtbmHiii9dRpU0KFOutOhINOW8EIeWD9wRCCCGk80ARvROSTyQ6IEegJhOI6Ikuf7cd4EcKET0f3HapLFMSlkFi0YBIdN2xJQ090YMi0Qf0rPUtaxLsHaq0di76/u7dtTo7abC+viW0fbbtlCUSXYyANRVI3KrEKr2TO4Da+zyzvHSP2cae6GIkeoEPYc9NXKxNsht0jpsjTC6kbTmqe3NTCofc/C7+8MoX2n30Irq+TWmVGA6DSPS2/1tTsie6rg1hhx40vCW7HM/EjimBIrpnnE9bshF/+s8XOOuxCdL5VB2bd9GLny3Bb1+YiqUbZIE2zsd+VTtKlVi03MR5WMs9IropUiS6wX05k2y3PCfEW61qLku0YSvh/KcRlMsqA3EYddR7S6F0hm6phHPPewIhhBDSeSi7iH7fffdh6NCh6NKlCw455BBMnDgxcPuNGzfil7/8JQYPHoza2lrssssuePPNN0vU2o5BlzwSGNreSHTBzsR2wn+oewVCx3GUonc+rzW77VLZYSQsC9UGnug+Eb3tf534mExYkcQyb9tqqxLK/ZuERJ3uLr7EogFdVJWw0LtrJhrdSER38hfRESBOhiEeu7mI7vi2dxT764TJSrRzcbdLFmjn4iKKYmK3BIn6USLRM5MWubLWbGnGhoZgqwW9nYsed0w2t3pEdDiYv6YOz3yyCK1p2y+qt31v8Xmiq+vR5ULwlqdCmqTI8zpoDhTRZaFcjFCWrlll1fLCTxR5CuIWUFV9HPYmQVyUIggwqI44+3Llpibpu+n9Me2xMArCth2c9sA4/PjhT8oipHvbp7oOE5KIXmEqOqkITPN+dGrYLSWBgeiEEEJI56GqnJW/8MILuOyyy/Dggw/ikEMOwd13340TTjgBs2fPxoABA3zbt7S04Pjjj8eAAQPw8ssvY9ttt8WiRYvQp0+f0je+HZNPJLrjEcoTgv2EycOLSh9MxiAaivWrEogmErK3qm5/r5CcE2zV+yQsM/sNXSR699oqZeSxKBq22sHR8Cqqkwn06VaNtXXNRiJ6upDEogBSeUZ3W5aFr1ZtwRufr8CInfqZ1ddWlTcK19uC9mTnko1ETyYKsnPJlZf7LImwMR16OuKbC40taW1fmAjUqkj0Y+7MJF1sTtnYfXBP5X7i2whBE0Xi2FdNsgTbuQifxUSfES4J40h06AWjfK/fuC0/VPcpfyR6cRQd00tnU0MrutTkFzsQVEcxdSrTe6w4kRN2ja7Y3IQpizcCAOpb0uhRW9qfgn6LsuD25vvGU3sh7LL4y6tfYMHaejz1s0Ni+93UEVBZXhFSDnhVEkIIIZ2Hsorod911Fy688EKcd955AIAHH3wQb7zxBh5//HH88Y9/9G3/+OOPY/369Rg3bhyqq6sBAEOHDi1lkzsEtXl4oq/Y1CgJIElLSCxq8PTij0RXewf/8+OFkdvm1q8qL2GFJxa1bcf3kO4eqk4ATCbMkvK5z7tev8TutUkpctBFjER3Rb0oIll10kLXmsy1UexI9KCEjSb7fvNvHwJQR8mqcNspi+iKqEaNWC72bbEx7VNXIDO1BwotzxbFY6E9MYmXUT30NwYkuA0a1+5htKa8keg5Ji1aj90G9VSub0nJEwg68VYUKE9/+BP/BgHHKpYp9m+Ung4SB6VVvsjd4Pq8h6saWY7jxBo9p7pXivZUxcSygI0NLfjf5ytw8j6D0adbjW+b9fUtOOCGd7C1wkarYIqo4EW1hgJMEubmNiiH+OM9JNUtW3qTpoOL6F7mranDjOWb8Z19BsOyLDzzyWIAwKRFG3DwsK3K3LrKQba86lxjxBT2SmmgJzohhBDSeSibnUtLSwsmTZqE4447LteYRALHHXccxo8fr9zntddew4gRI/DLX/4SAwcOxF577YWbb74Z6bT+Qb25uRmbN2+W/nV2ulRFj0T/zfNT8enCDdnvouiXjye6Ayf2SHRVeSZ2LilFdG3Of1t9bAnLzM7F7SPvpt1rqpQ/upuESHRXKIuiH1QlE+jTNSOir6szEdHzj2R1nPwTi4pjZsZys2vSbaf3wdnb/BZN5KY3KraYmIo+oie6yt4oKnJyQfPIVPPyo725kErrJ2mCxEGdJ7qIZVkKOxf1frrDF49l0qINvvVBR6oT+eKyFRCjLDOWWeLEQHB9Ji2IW5dUleeNoi6WzmXBwmUvTsNVr36JXz03RbnNxAXrAWQsiOImTisJX5R2xMTLJu0pt97o/c2gsnPRHU++f3PipBh6Wdp2MG7uWmxpasWxd36AXz03Be/OXC1tQ8sSGXZHOJxcIIQQQgiJl7KJ6GvXrkU6ncbAgQOl5QMHDsTKlSuV+8yfPx8vv/wy0uk03nzzTVx11VW48847ceONN2rrueWWW9C7d+/svyFD4klm2Z7JL7GojOgJbtvhCcrUkegFN6OtrEzdOk/0sMSiKosKt0zdm/QJyyxy2NJGolepPdFTYiR6m4geQTSoSljo3c08Ej2qPYeI4+T/mr2ocXY3tBLI2rl4fL99goxGeN1cQhHd3BM909aoHvs6WhWR6HGKTumIby6oJqiyZQXYVLhtbvZGMgvnOmFZPrsVd3VTS26/oMSiYecpqO9kYU9sQ0wiujhZBMcj2qu307VBNWHnwDF6m8YUVR97k/zmk/PCBMsC3puVERw/mrO2KHUEEadO5e1H00j0VARPdJFySGy+ZNmKW7ZktSRch3G9VVMIcV43QOYc/PPjBTjz0Qn4sfBGzOdLN0rXciFvK325bBPOfOQTfL50YwEtrSwkO5fyDwtSJirh1DMOnRBCCOk8lD2xaBRs28aAAQPw8MMP48ADD8Tpp5+OP//5z3jwwQe1+1x55ZXYtGlT9t+SJUtK2OLKJB87Fy9iJLrthEe6eh/+bMeJJZGiWz+g9kRPJjIWJ8H76+1cdIKYqehp6SLRdSK6aOdi52PnkkDvrhHsXAoR0YW27bh190j7isfUvdZsUsfdxxv1622+V7hz8SY5LCbGyQDFSPQYrgexb3ITQf629O9Rg8OH98+r/CjjMW3bWqE6SMDORqIH2LkkLUXS3bYtGoXrKOiNibAJBnft0H7d/PuKonYR/OfFlnlzUsgTSflFosctOqnKq3Qv6yiaZNDr+nEepV9gNis9ypsnOn/9UuFtnuoeJV1fafNja6+8PGkpAGC6580s8XgL+RNx+kPjMW7eOnz/gXH5F1JhlHsctwfYK6WBbi6EEEJI56FsInr//v2RTCaxatUqafmqVaswaNAg5T6DBw/GLrvsgmQyJ7rtvvvuWLlyJVpa1IJhbW0tevXqJf3r7MQViS4mFg3Lfeazc3EQi32FWz+gLs+yrNDEomk7I/apytQ9mJl6WOc80eXl3WuSymg20c6l1Y1Ej/AUVJW00Kdrxg94XX24bUHKjuZxLeI4Tjb6sTok2t9LfXMuKtw0qZ3qnNiKSHSdncumEoroxp7obdvF5YkuTiBkI/cVbamtSmJAHt7QGUHafPugSHTdZAeQa7NPRBeKSiQs37l329bQkhtfqokWsX1BuMWftPdgX3/pRMu4oq3FY3M838X7Vb61OU68D/6q4w46x3nXo7gnWwC6xvB3Dcicy8mLN/jegghMLFpEOxfTiQh5Ai2sjvJG8Hqr9P79BfQ5BypBRI9dMHP050E8/4X4Lte3vZ3Tmmcy8EqHGroa9kupoIpOCCGEdBbKJqLX1NTgwAMPxOjRo7PLbNvG6NGjMWLECOU+X//61zF37lzYwgPXV199hcGDB6Omxp9IjKjJxxPdi5hY1MQbW2XnEp8nelsditGctCxUhQi8ykj0tv+DHtijeKJ7MYpEzyOxaFUigT5tdi4b6sNF45RtFyRKuG2rrop2LkVfYtNx4OpxsgDkF0i1di4lFNGjCl9ViUQswozKm1t1fpMJC/Ut0e1tokaip9L+a8slSGB1q2j2RaKLUZmWT4xzv4uTUeIbE15CI3bbSrTgH6eiyOed2ImDoChLybpDUaHJKbIdJ9bH/lJ5oqvKsCwL3Wqi/11THf/978/FafePw2UvTjNvU+Sa9fjsXEzvJVEi0cXPRVLZlm9sxC1vzcTyjY2+df5j9O8v6uri8VT62w35opqEchz52BntKiNbXhEVxbLQKhW27WDM7NVYWxd/Los44bVJCCGEdB7Kaudy2WWX4ZFHHsGTTz6JmTNn4pJLLkF9fT3OO+88AMA555yDK6+8Mrv9JZdcgvXr1+M3v/kNvvrqK7zxxhu4+eab8ctf/rJch9Au6RKDnUsiYWWjomwDn2SVjh2XnYsrAigTixrYuaiSH+aintX7ZBKLRvBE98g15x42VCmwiyJ6a1vlUTSO6qSVFdHXGdi5/GvCYsn6Igri5EPYRIUXUUQXBc8gHIUobDuO7yFRJ7KUMhLd1IJBjESPY1JJ9kXO/K+ySkgmLCxa1xC5/LTjRPIkznjuq89vUESkSSR60lJEord9l+1c9Pcn0wSMqhwIkt2E8OWdGatw8dOTsKmhsPEmjWtPM8XEqaoj8C5Tjay4pRVVX3oTRi5a31BwJLFu724htlCmIsejYxcAAN74fIV5myIcUlg7dH+LwvAmm522ZCMe/GCesr/lCSCj4iNz/pOf4aEP5uOcxycG1g+oRX+dJ3olJBYtBtpIdOE+GcfbSh0J2RM92rj4eO5a3PD6DH/eDVJRvDp1Gc7956c45o4x5W5KILwyCSGEkM6DmY9CkTj99NOxZs0aXH311Vi5ciX2228/jBw5MptsdPHixUgI4tyQIUMwatQo/O53v8M+++yDbbfdFr/5zW/whz/8oVyH0C6Jxc7FEu1cwh/0fZHocGK3c1E9YFoGiUWbU2lFYtG2sjUP7AnL7Edz1hNd2PjD338D2/frptxfFMdcH9golghVyQR6ZT3RwyN3NhYg9IlRcmETFV7WCFFFpg+x2YkNT1JF79DT9VchxxqVzxZtCN0mI+5m2lqVjMfORRSbnGx/xSei2xHtf1K2PhK9JcjOpa3t/kj0HImEQnhq+y7auajGiNg+QH+du31oWf6JwCAP6pHTV2LrALucmqqEb4LAX7fw2fNdHONu1VFFJNtxCrKH8KKq3jtR0pKy8dsXpuIfZ+yfdz2qvzWO46B7TZX0Pd9j61qdjDzhFmc0t7eoqNZQQKaPvnd/xvu6d9dqnHHw9nIdwudieUnPXJHx9p67us63zuQYxUXi20UdMRLdPxWcQ5yIiisZe0ehkEj0nzw6AQCwdc9aXHzUTvE1qsJo73Yuo2dmEkaXMjF8PnB+ixBCCOk8lFVEB4BLL70Ul156qXLdmDFjfMtGjBiBTz75pMit6tjE54mei0QPexD3Chq2o45Ozwf3mVoVyZu0rFCB9/4x83zLsgKk5rgsyywqTLXNVj3arIcUu8uvrWcenoPERi/VCQt92kT0YmsNDkQRPdrJFEUq00h0VWS1HUFEL2UkuglpO+cpL+YYKKxMf4Sy0s7FsjC0f/es0GVcvuNEigRNB3miB4jIbh3esS9bG1i+c+8g0z5xTAXdn3T15Mprqwuq5MjiZ3/5Kzc3KcsEMolkw94TEct3PG9ctKb8kyWOZ3sJxdiKW1xRjQtvJDoA/G/a8oJEdFW7U7aDroKdS2NrGt1qwn/eqIR2nS1M0O0+SleGXea+xKJ5RaLnls9euSVw23KIbD47F83EiIs44VQZnujxKmaZiT71caU055V4rrs8+2bx+uiTye2JzjBk2vtEASGEEELaF2W1cyHlIS47F1cED7JLyG7vSyzqxPZqcs5XWmHnYuCJrsI9HF30iwXL0BNdv0x1/KK44UZxhkWsilQlLfTpVpr8AOJ5r4ooojc056LPlyk8c1WoE4v6BVKvD7NLpYnoYtLNqpgSi6psD3R2Lv84Y7/I5dsBori6Pbb2fAQmFnUj0T1WQ6LdUVLhiW47QJPnzQYHeiHS7a9mzUROdjeFnYuc6DNYBPRiYt0jiube4lUJZKNGFMfthW3iiR4Hqpjdf01YjCmLN2a/m751ojoLXbXe6vpzFmdXmt7PvOg80VW3FXHbYnmiB+EdK6oJGLEfpDe0OqhipjoqB/KbPJUwgVBJ6PJSEFJqvJaNhBBCCOm4UETvhNTGkFi0SvBEv+PtrzBZEDBU+O1cEJudi/sgpRIhE1ZGWI5cJhxMmL9O+So64Eaih5fjbiI2LejHtqiXZCPRI4noCfRui0QvFjVVmduGGIleE7GP80lq6QotchSlX0T3Wkj075Gx1QgT0ft1r8Hhw/tHble+pAWrk2RMIroosoyduxavTFoKlSV5VdLC8AE9sePW3SOVb2LdJKLKN+AS5ImuixAXxe6E5W+L4wCNLbKIbgckQ3XraU6rLYXExKLe6907meMru1BNR4osl4Va0eYmLH8DoL7nOE68Pq6qPohiRWWKyfAznTBTXXJd83hTa8Ums4nATJ3Bve69Xk1FY1VSYbNtjYqPFa9wr7JoEReJ94p0ESZmTBDbXEq5TDzeckx4VDKO5jMRYMeUhEq0c5m6ZCN+8a9JWNLB37YghBBCSg1F9E5IMmFhWP9o4pmXhBXNfsIvQMWXWDTIziWRCLdzUZZpA18s26RdbxkmFnWFUVEgdT+qHoi9keii0GpCdcJCry7BNga/OmZ4QR6gvzl258wHJyfwRI32b2iJnswra+ci9Mfc1XX4eO5aaTuvcNe/zT4nTFjbd0gfPHPBIdhtUM/IbcsHORI9EUtiUVFsmrVyCy5/aRpmr/LbObjjMepEVjpAkNZtr/VED7JzcdTbiFHmacfxCarvzlyF+WvrpWUO9EKhaSR6eGJR1TGETxIEIe7vQD5WMSLfEbbxtju0/Bgf/FX3s3L5VxcSsauPRNdz4xszMX+NesLVi6mdy97b9s58NzwWSRgXxqNqAkVcX6wI3kD7m4iR6OKxlSoS3XEc/O2dr/DqlGVt7cm/rJaUjXdnrAr+G6Qpv9WuLCubSkK2sCpfOyoZvdt+x6UcyYcrUUQ/9b6P8eYXK/HLZyeXuymEEEJIh4Iieifl7d8difevODrv/ZMJM09wF5/g7DixiIZAsAiQSjt52bmElWvBUH9q20jVVariRYEglbYjRaEDGW/yqmQiGy2u4rLjd8GQrbpGKlfEtc2xBTuX6oD6VNQ35xGJ3tY3Yh+9OnU5HvlogbSdV0R3EzzWhdTpnqK4bIbC8Eaix1FtWhF2vnaLP8Gsew6jXoMZ+xzz7TMTBeoxHJhY1H3rwFOXKB5nbC7kDd6btRo/fHC8tMwJiJ53x5KuLW79luXvK7HIsMSIunqD8ApE4jGoItHlJHsG5SPeiFpVjcWwczERfQsR700i0VVNGDV9Vd51irjH575BFWck+uiZq/Cdf3yEWSs3h25bKCp7NV2dExasxw8eGIcvhYlrcRPZ0iT+txtUTF68EX8fPQe/fWEqgML66Z7Rc3DBU5/hp49PVK7338lylPutgcpGnmjMqwT2aYfi/jFzse91bytzQRSTSrZzWeAJLCCEEEJIYVBE76RUJxOBYsEFhw8L3D+q/YTPEx3x2bmoIpRdtunTJS87l4xArF+fMEwsqnpAc/dTPbtJEXe2E1lEd73JawM8yi3LipwIVMQVEx0IiUUjirFeyw0T3L4Me+j12oS4InoY7kRPXAlvw0il7awgFJcnusoiRTWh4r4FErXOtB0tseizExahXnOulwd44esEK1E8bk2bCfqO4ygtbYCcVYIuEh2CnYs/OXKwaBlkvWCiA0pWBR7bomYhIl91XZgIQ/mIR2Nmr8ajH81Xv0VTQXYuqr8FqpGuEj7ESHRxrIddKk2tZve04Ajt3Jh279Gm15sUue2o233+k5/hy2Wb8fuXPxfqNCo+MkETdN5DemXyUny2aAPOemyCsI1aOC/V2w3r63Opf3/y6Ce47/252e9Rb9UvT1oKIGOvoMJxHOU15TjyNUTfbxlGoofTGfpFnEC5beRsbGlO4YbXZ5S2EZWroVdy0wghhJB2SbDvA+nQBD0I7tCvW+C+vbvWIBVBIPEn5YtPrHQfPsUHzB9/bQi+s8822HHrHvhk/vrIZaoSVopk7FzCy3Ef+EWxJoqdi86rWYdrXVNTlQD8AchZajwiejJhGb8qnhXRHUFEjyjK5+WJ7kaih7RTF4kehnteogrLvbtW55W0NOWJRI/jzQwxwtRF1VtulGjUazAdcl14eX/2Gu261YoI+Ww9mnMsRaLbdqBAUFOVQEvKDvRxD4tEd3dT5UAISywa1E+RI9Ehi4dNguivuv+Z4L3/rNnSHHqtnPvPTwEAuw/uha978geoTllRRHSDbQoRG8XJZfE+FXZ1ehPa5oPYbPcebSoai10t+miHtbtYIlt1IoEmaK4rzT5iQljxHIpvNJTD0uTjuevw8dx1ee9vcg5NItFp5yLjnWgkftgrpaGShWqOAUIIISReGIneiQnSCnt3q9GuS1jAJUfvlI16NsGfWNSJzTbDfdgWH1QH9e6Cw3fOiDyqSPRkwsLlx++iLVMUiFVYhpHo7kSDnFjUbbd/e8nOxY5u5+Ja1wTZuQB+0bt7BB/gXCS64OkdMdo/Hy3A7ZswATLlCfPt1aXayBc/kaeIvl3f/Kxx7h8zF3/+z5cAMv0Xx+XwzCeLfctaFWPIPYdR3waxbacknsRpRx3xLorHrWk78BX+bm1j2nYcrUjujt9mTRRxTkT3T3JsacoJrKo+CYo2D7q3rNzU1Cb+C1YFjjzBpopE924vorSTghxd/7Wb3tU32IPXd95bv0sx7FxMxLJC6hXvjfXN5sK4/m0GmaDX/sU+zNq52LnJww++WqOdsBPve1EE/WJFNycD7rkm51C8RFJlEJKD2hj1Xm1iQaOrTjx2RqIDDS0pnPT3j3DzmzOl5fn3DPu0kumMnu6EEEIIqWwoondigh7me3et1q577sJD0btrNWoiCKcKS/Si2rmIIopKQE1YwYlN7TARHWYP0rlIdGFfK+cp7qvXcwwmIroYVe4KL2GR4d4+6VFr/lJK1o7GyYmHhdjDmOI4rjVHWCS6vL5LdRI9u+jHs4t7PUQNCB/SN/itDR2i4J1MJIrmxX7fmIwNgfI6iOyJbmZFUiiOIyfUc/HauQRpSt1rMmPagd5qw73GtZHorp2L5bdzeejD+VlLmrDEiNLygPE7a+VmHHrLaHz33rG+slKSiC5aPLht9bc7iEIEuSaFRY+qONU5LBQTDdX42BTDX5wQCcujIGJq5xIUsijW7U6Iusfy6Efz8dPHJ+Inj36i3Fc8ZtEuK+y2Uk5P9KDbjyhil0VEj7Ess0h09TbibxmK6MB/pizDzBWb8fCH82nnYkBnjdAvdaJPX96nCqJyW0YIIYS0Tyiid2KCHmCDRHT3x2IU4dQrENpOdAFPxwNj5uG8f06UHq5F8UaVWDRhWYEP+YDao9QlY+diHokuRoa7e6mKF0WUVtuWxDIdtULZkp1LAN5EoD26mIvoVUo7l9L8TM8kWAzexmsh0aU6gZ4Gx5evnUu/Hvq3NkypSlixTSp5mb8mEzUsTi64xxi1zrQdzc4lX9K2o/R3FyOwU2k7sC1dqjNj3HEcrcA5f009Fq6tV0YRp20HL36W8TK2YEE1xP8zZVl2Wy+6pgVF8rvlzVq5Rbr/OJ46xPa64psjHIIvEl1Rl+PkLzQ0KvpTNTlQjEh0E3WzEAsMsd+jJEA2FtED6859rqlyI9Ez31+ZnBmLXy7zWzaJ23nbEnb4xbqagxJ6u3+eg95mE9ttl0FEj5OwNgetFd8wKFFO1YpGDCyQ7pGdVCwOo7P2SqmHQyUL1Z11DBBCCCHFgiJ6JyZIBO4TKKJn/o9m5yJ/j/uB5/3Za6REhaJ4oxLLEwp7BhHbCRa7LMvsR7MbgbZDv+444+AhuOjIHQMnD0QhJJV2lB7XXkRBPGvnEnJuvOtNIrVdElkR3UG+nuj5Ygt16vAKd7VVSfQyOD5XWI4qosfhZZ5MWIH+5METPtHJThhEjkQP738d39p7kHJ5/x5+H25dUl1RPA6L7qypyti5OA7Q2KJWn8bPX4ej7xiDzU1+i4y3vlyR/ayzb3KvI+VbJTof9oB2i17WUmS5580YcTLBXRx1csN2HOU97OdPfYYfPjguMGJeKaIrNi9GEkiTKHv33j1j+Wbsd93bePjDecrtVMcv9nODJqJbdYtQ9YlpnS7iOXTvqa4ViHh6VflIxPMltiXMl75Y4mPQfdE9h0EJqSVP9DIkFg3qlqC3+FSEtTnzlpVmX9EPnkKxdJ+R374hKjhkSoP4N4ETOoQQQkjHJi/la8mSJVi6dGn2+8SJE/Hb3/4WDz/8cGwNI8UnSD/rFuCR7e4XJfq4WFYVImLkqigyqMT+ZMIKbFNGLNTXZSF4fxdRkLnltH1w5bd2l+pQ1euyYlMTfvfCtNA6RLHC/eyNNPfi80SPYOeSjURH7viiTKhE4dAdt8I7vzsy+/2T+etDxUKvaJRK20aR6MgKy9HaGMfYrgoZj3EI9SJuaflFoudX51G7bK1crjo023GU4l+TRxwMSyzqlhUWJbxqsz/J6YI1Od9vC+rz7N4DVSKZ1s4loNE6/2PHY+ciesO7nRAkKKnO2bMT/P75tu3g7Rmr8OnCDfjLf79UbgPIdiGq9kZhS1Mr3p6+UpoY0JFK25i7ui50O3cy4i+vfoEtzSnc/OYs5Xaq4S8l6NQMdtWhNhl6ogchVudOiLrtEav84UPjffuK40OcNAqzBItyPbekbHw0Z41R1H3Q7wO3TtNIdMnSpAIi0aN7ohvYuejeXLHl+0BnR44+zy2vgGFByoXi3JfczkX4zMuUEEII6djkpXydeeaZeP/99wEAK1euxPHHH4+JEyfiz3/+M66//vpYG0iKR1AkeiJh4bfH7azbE0C06GNvXcX4kdnQknv1/kdfG5L9rEp6aVkhkXJOsCiUsAw90QPsDFTF5yMQyD/eM/vXRvRE76kR0Xcf3MsXQZxU2LlE8cc35f9O3BXPXXgoBvfJJe4867EJoaKQ1996Q0OrkYheSCT6u5cdiT8LEyRRCZvUiT8S3crWq16v3s928rdz0d0vVG3QRqJ7PNGD2uKOSQfhUcKNLX7bjoG9u2Q/1zWnlJMr7mSVqq26SzlIUBMjbr1+v7rEospIdE8VqsSG94+Z52ujGOn67ITF+NN/vlAeWxzWJS6/em4Kfv70JNz6llroFrn8pWn4wYN+AdmLexz5CGuyF7e5MG7aJ0G3F3FsZO1c3EkSoV1TFm/07yusFxOLhovo5p104xszcPZjE/HHVz4P3Tb476v7BpO8jbiLzhO9VJHoccY2m4no6m1kP/jc8kmLNuDv785RvpXQkZFvc4VPMFDwrGzay/kRn3GYu4AQQgjp2OQlon/55Zc4+OCDAQAvvvgi9tprL4wbNw7/+te/8MQTT8TZPlJEgh7mE5aF3x63S+B+0TzR5e8mr+Tr6tXhvnp/+w/2wZ7b9M4ur1aoXxn7jLBIdH0bM/YOIQ1GcGI97w/ttXXNeHTsgvBCPYjiq1tiqCe6LxJd/ebBeYcNxe0/2FdZnwOnqJHoO2zVHZZl+fp5bZ0/aljEO3Gx/VbdDBOLZogqolclLAwf0BMXHrljpP28ZQSNp7gj0V10h6qzA7KFyaWoPvi6Manqb9tRJ/sUBcFUSCS6O8YdJ1zgVInsYq6BZRsa1ZHoCb2IrhN1gnRZKepULAuOJ0o9t27+2jo4juOJypTr1s3leT2/Vfc8t2/E41H1Z1ThoCVlw3EcjJm9BgDwz48Xatt44t0f4raRs/DfqcuNynYnHLy+8l5UthyiGC3eS8IsPJoM8leElSO2N5tY1D2WkHIlOxfhTYHmEJE1is/2U+MXAQBeNTgPQZ7o7mF6t9EJUeLnkiUWrQAdzIH8Vp3YD99/YBz+9u5X+JfmbZGOivTbsQLOESFe+FYEIYQQ0rEx93AQaG1tRW1txsf23XffxXe/+10AwG677YYVK1YE7UoqiNoAoTVIs3PFpELsXPL5kVlblQh8Zd6NUt26p+yxrIpET1ghiRxDItFNE4sGPfDH9Ttb9mLM/B8monvX96hVi8yqiH0pEr2twrgjpYHcw7J37KzZEiaiZ8bBt/YehIN22Aon7TUIny1aH1qfewhRXwOOI0Fu/x61WN/Qol0ft4juFqcrt7YqoUxqKyYWrU4m0Jo2j0jWTbqp+ltn5yIK661pJzBSWKxPZT8i0qBYL9pDLd3YqLxf/mfKMsxfW4/BQtS6izYSPcjORfRE90aia/Z784uVeG7iEhy3+wChbo+Iruknb1Sv6n7V3JoGulZL69Se6OZ3tE2NrTj81vdw8LCtQrd9edJSzFq5BbNWbjEuf8WmprY25ZaZRqpKCaojRPk2hYwxEyQ7l6QciR72B0Nst2R7FCLu5zOhbYLq766LO1a824jfxCEr+YJXgEJVDIFdV6TO4sllzmrz66IjoPVEL/+wIJ0Y6Y1Qzu4QQgghHZq8wkf33HNPPPjgg/joo4/wzjvv4MQTTwQALF++HP369Yu1gaR41FYF+J4HiHbumkiR6J5NgzzXAbUIHNReEW90my6xaJDwaztOoLWKBflH87D+3bH74F6+7YLtXOL5oa2KXg2b4PBGGvfQRKInLL/NSFZER8572Hu+zj1sKG48da+wpgfido+3/lWbmwL3a2lr0wHb98XPDh+GRMIyi0QPsTjREdVXXMWZh2wfGAEff06BtmPVlNulWj0ebDsX8RzNzinapJvOzkVkxorN+MMrX2jXi+1rCIlEr/NEZFuWHAG673a9lZNm4+evw4MfzMPbM1b61unEvmA7F7U9ge0E21jc8fZsSVxKO8DqLU1ZMdVUeFTV4Qrmkue2YjIzirY5avpKbGlOYfSs1Xm1KYzrX5+BFz5d7IlmNttXvC2LkzZhl2CTgad7WDniecolFjWMRHfE8yPYuYQmFg0pOE+C/75m/vfeQxKaSPSyJBYNXBd/G7SJRYVjV13HFTCnUFK8k4vZzxQuSTlRBLMQQgghpGOSl4j+17/+FQ899BCOPvponHHGGdh334zdw2uvvZa1eSHtmyDRLh87F1eA+scZ+2P4gB6484f7arf95Td2wgHb9/Et71JtVp9XsFdZjSSs4IkC2wmOGLU84vKfvrU7LjximG+7oEjZuH5oqxKP1YRMOHgTj/bQeIYnFTYjrvAqR6Lnytt/+z646jt74LjdB5odgAb3qLz1rwmzc2nrc1EM72XgiZ6vnUuhkegPnX0g+vWoDX77I3ZP9OBydW8ypAWboyjXf1VC/+aGMkmlrY5Ej4LrKQ0Az00MtjzY3NgqfbcAtArX1W+O2yXw/IhJSF3ySSwqHrNXIAqa1KtrSkki0rINDTj4ptE4+vYxmLemDqOmr9I3XkAl0rlv/4jtVkVdR5kUjDKa8x351742QxIYVf2uTiwqRqKrj0l1rGFvO5jglpuwcvehfCZAxLa4k1G6cvLx7zX5Wyzef739pZ3s1QhRYdHYxSComuJEoqsLlZKqKjbpbIKdlHAZwX1jQmfrv45IJZxC0aaLnuiEEEJIxyYvO5ejjz4aa9euxebNm9G3b9/s8p///Ofo1q1bbI0j5cPEziXodW3dPifvuw1O3nebwG3PPnQoPl+6ybdcFx3rxSQSPZmw4NUAd+jXDWcfugNufGNmm9+3vg4LsgAjih4iQZHocf3QVomNOk9rF694EcnOxU3Y6OR8msXyjtx5a6X4HpWcoCQXtHqzmSd6lSSim0eiR213oZHoe27Tq61efTlxRLuLuKXpytWJ6KLtiSqZbDJhKcW6ZEJvn7Sl2Z/UM+04qGsuTJQURf6NDa0BWwJbmryR6FY2Ev07+wxGj9qqwHOwIaR8kSBRVH6rRPYtCIrAbUnbkoi0ti5jDbRycxNOuffjgtqmjkRXTXwYVyNNqNRUJQLfOsh36KcdxxfNb7qfi65dqqLcvmtsSaPVtrX3nIaWNP49eSlOO2A73zq3jeLfp2wkesjfC53djnsMumPJR3zsoUlELSJOXqdsJ/s34tkJizFtycbMNl5PdKldwrkrS2JRPaVqgpi8G1AnHp+4YB0e/Wg+zj98mHaicsWmxqK1sdRIeQ40UemElBrx0quAWxQhhBBCikhekeiNjY1obm7OCuiLFi3C3XffjdmzZ2PAgAEhe5NKQvcwbBKNGybUyuUZb4pkwlJGuQZ5uMv7y99VZalsSqoSFk7aezCATPTlonX+6NLs/gm5jxKWpRR7gh744/qdLUeiZ/4Xo3BVeM+dLrFoQuH9no1ER+6hXuxjt19MPOODyNq5eAbP6hBPdNe+ICkIND1NItGznujR2h1lMklFtu8CJ64KqsKHe4g66xqTa9v7NgOgF+WrEgntPUUl7jW0pPHTxyeGtiGwfRHuT5sa/SK4OxnjlhM0LlSicj52Lq2aqFMnZD9ALbABfquaINSR6Glf+YUmFhWHXc8QQTZfK6OWlI0Fa3P3cJUIrSpZPE5xglIn8HqX7XPdKOxz7dtSH3nrvuzFaco2uwK+JeTscMsNtXPRnB/3ftissZsR22Ya9d7dQEQXJ1bFfvzTf75QbgME2bmIbSzsDZU4cPvss4XrccubM0MTF5uVqV4uvhGjGnfz1tTjxjdm4t2ZemukEbe8V3D7KgWtJ3pFxCOTzkrY3wdCCCGEdBzyEtFPOeUUPPXUUwCAjRs34pBDDsGdd96JU089FQ888ECsDSTF5Zt7qi034rZziSKEVCUsHL+Hv12mnug+cVwhcqoirKuTCemH8Lh567R1WLDkX82WWmQLTCwa0+9syUe57UEyPBJdXt9VE+Xv9qXYV+7nuqaUEImeENa7+wa3e9s+XbFtn67a9bqHYpXgKeIKNmIkuoknejbZZlQ7lwInC7J9GzAeCp2Q8JXXNnjFCYodt+6e/RyWmBbQTE5pdksmLO26YqFqny4Xw+YmeUw5jpMVH91xFOF2ByA/OxdRIBQ3swUbnWKisrBqUkSiq7aL0jxxOIcJslGGvvtWh4uYHNdU2BC301kKqYpy2rZ3J0KWbmgQyjSqOiuEi3ZjuUj04H3Fc6KKRNcl5XbbNmr6Sux1zSiM/NLv7++le41BJLpwwbem1I333md00ZxyYtHQqkvGDx4cj4c+nI8HP5hXcFnaRMTCAQdZzC1cq5/070jI0efqqPRI5VF8JzEg3rucCrpHAagMvxtCCCGkA5GXrDF58mQcccQRAICXX34ZAwcOxKJFi/DUU0/hnnvuibWBpLhcf8pe+NUxw33L47ZzifIKdjJp4fSDhuCRcw7CoTtulV1uGoluaufiXV6V9Een67AsfyS6qs+CfJ3jilYR+7ZvtxoA4SKoV2Cs1Yromf9FEd09bjFhnTgWcrYowX05ZKuueOp8fQ6FvB+KHX+bjCLRs8JyeB3iWIwqrnpxx2HQ4caeV7QN8RL+++n7Zz+bTJCptvFee7nl8rW1//Z9AidQ4kBlN9NNI/5tbpSjtW1HsAVqO86okyW68xkkhr8/e42wvywQhd1D47ifpBX2U6rkpKpjiOaJnutLcWJDFS0dpdeDbJtUAczKiU/hMOTEosGet7btSBHJ4rVgem7czZJC4mu3r8PKkOxcWqJHol/09CQ0tqZx8TOTQtupe3NJROza5rS6bu/EuHg2xPFUjkj0IHHVey6+WrWlaO2Q/eD12xXrb0SlIXuik45OewzqLtfEzPTlm7BkfUP4hoQQQggpiLzkn4aGBvTs2RMA8Pbbb+O0005DIpHAoYceikWLFsXaQFJcetRW4fJv7uqLXI47Ej1K0rXqRAKJtmj0Qb26ZJebeqKbJRZV2bkkIj2IiptqPdGDnnpj+p2dth38/cf74UcHbYdT998WgImILrdVN0HhCkdidLZq8qRGYecSKqL37RYY9V3ow5OUWLSriSe6+3/4IBD7K7ZI9AK3iYJ7jYhtF+swsXPprojq1jUz6RHRE5aFbfp0UW8cE1Ei0VV2LO4EmHutRD3POnsVY7HbY1sQlFgUiEdsUCVCdiOYl23M+Sqrcj1EqV7sSvE8uW+Z3PrWLDzUFt0b5S0MlcWQizKxqGo70c4lJQqYGh+JNtK2I0V7i9dT1Cj4hGX5ItHDELcT2+FGojcX6Iku2i6Z2LmIh6xL0Or9OyXbuYhtVAvqxSRKYlHd8UWsUblUEtFptiz3kjhG2DekjMiJRUtf/4pNjfj2PWNxxG3v+1d2kgk2QgghpFTkJaIPHz4cr776KpYsWYJRo0bhm9/8JgBg9erV6NWrV8jepBLxinReH2oR98eiV4gFgJ0H9FDu09Bi7ssrRT0Ln7tU5xeJXq04loTSzkXta64SML0ivAV1JHoqIBI9zt/Zp+y3LW77wb5ZQSpsgsMrTOlEdPeYxGNTiYmisJ61+A45Xdv17RYoDhcaWStOuph5opuLpWLkfqECtztegyJ5CxXqvajsXMRzGDYJ8+tjhqO3YmJC1xdVnkS+CQsYPqBnlCZHRnUMQQksfdtm7VzaItEjnmfdg7SpCCglVhSS+Jpsny+qOhpb01i6oQGn3T9O2M7fj1HqF+8/4jlparExf00dHvxgHm55a1bbtsbFKt8+cDFtns7OxXs+vLR6ItHzSXqY80TPTVy6y6LYuYgR9FkRXWPnYvIGgeM4+OCr3FsSuskoEakfNdedb7JOsnPRnIcKEEu951/1d37U9JU45b6PJV/+INQWQbKNU9A19urUZbh91KxIb4S0RyQLF/FtnbzLK7BBpOxU2pgvhyf6nFV1+pWV1T2EEEJIuycvEf3qq6/GFVdcgaFDh+Lggw/GiBEjAGSi0vfff/+QvUkl8uODhxhvm08ken2ESPQqhXUIYO6J7m1WpEh0RciGyg7Ga9+S+aywBgh44Df9of3O74402k7ExNNaROxbVf+LfaXqD1Vi0TDht3ttMlCALuQ5pH+PWhw+vH/2e5TEoiZaqTjpoOqPKLh9EHS8sb+q79r0SBNBOcLGzyn7b6s8d0mNnUsyKSeoTVgW/u+EXbHvdr1x7cl7ZJfrEh3ng+r+VB9hMq+5TRDNRaLn1u0yUD1ZKKJ7pds0sljazAm/X+g8r6OgmmRoak1j7Jy10jLVMUTyRBc+i28BtNo2GoS/FbbtKO/JOnR2Qpn2KRrYVvSmhlZ8PHctbFsWLWURPbeb6lBTaVuyTBEnGsztXDLbJRNW9voyFY11CVFdQb1JY+diUvxr05bjwqc+y343OSfiIbdoJpNrq80i0cVzUqpI9KBz5l2jatNFT0/CtCUbcfmLU43q09Umnsuge8eXyzbjvvfnYcKC9Xjj8xVYLrw50pGQ7FzymKginYO488iEIf69Z2JRQgghpGOTl4j+gx/8AIsXL8Znn32GUaNGZZcfe+yx+Nvf/hZb40jp+ONJu+HY3QYYbeuKSSqRSve7tTGCeCVGfCYlEd1suHqFPJX9SDJh+USujCe6vzztcYrbWmrxtUeAeGv6Q7tbHsKiiR2HiBjlL0YZul0pnleVeCq+lZAT0UPaWJUIFKAL8ZU8fHg/yXJANQEz3PPWhDvWTBKLSnYuBUeih+8fRyT6f3/59exntzSx7eJDpy7RrEvP2irlONAdS1UiIfVrwrLQt3sN/nvp4Tj368Ny2yWt2KxrVBMB39lnsPH+2Uj0pH9cmEzoqfzFAbU3t3J/T8LgMPFwfX2LWcEBqMTOxta0T2hVtSVf4UCM3m5N29K9ZurSjZHuA8F2Lvr9fvTQePzk0Ql4edJS6TjE/ghLYmg7kCcA2jZasakRf3jlC5PmZ9uYsHLXgTuMwqItxXMiJ+LMfC4kEn3V5iZPO8P3kfrRMBJdvPTFdoX58ReDwEP02bnoL+oNDbmkxcmEBcdx8NjYBRgfkLjcZcGaeulcmlxiD384H798djKOul1h69AB0AnnTBBaOTiOgzVbmsvdjJLCCR1CCCGk85B3SrxBgwZh//33x/Lly7F06VIAwMEHH4zddtsttsaR0lFblcQ39xxouLXezkUXoVbfbB6JLiLq4d6oNR1eEVQl7FmWBe9zb3UyoRQrVaLeLgN7KBKL5r6fM2IH7DaoJx4952vadpr+0Fb1cxgmkeiiECzak4jic9biRPN2QK6NYiS6fjvvPkECdCEPIiaevTd/b28pca0rlpp5ogt2LgUK3G4fBB1uoXV8c4+B2HdIH3+5wjDp3bUaP/v6MJx72FD071EbWF6PLmoRXSeA+zzRNcMzYVl5jXcV3nL+9K3dMGSrbsb7uKKjG90sjgsTa6lWj9jnioKu7ca2fbpi4p+O1e8vCbjhCRXPemxCaJvCUNluNLXaPoFK6Yke4YIVo7RFET2VliPPT7t/HD6eK0fBBxE0doKE39ltiSHf/HKFJLaL4q+JrUZdc26y2D19v3p2Cv43bblvW5UY7C6zRBG9ra/CeleMRG9R2J+0aJJ7mmjS3r+VQX1p2w4cxzHyMff+Tdclby2HiB72Fpn41kHQtuJYTyYsjJm9Bje8PgNnPPKJtJ3q+nl7xirc+/7cXD0G19h7s1YDyN+n3bYdXPvadLymGLOVgO6NkLwTkRfUGqLiipc+x9duehfvzFhVcFntZXJEbCUj0QkhhJCOTV4ium3buP7669G7d2/ssMMO2GGHHdCnTx/ccMMNsE3D7EjFYfravM7OZY/Bej/8KJ7oIuJwMrZzSXpFdP8wTyb8P3SrEmpPdFGEv+67e+LZCw/B8AE9PYlFLUkYPHb3gRj52yOx93a9te00/ZldHWBRoMMkEn1Q71xiR1FQF6OQ3Qd7XQJK1TJXFA7TfauTYZHo+WMioicseWy4bTGagKiOLxLdxdTOZdo138TpB5nbLwH+CF2V/3vCAq4+eQ9c+909fZZIXrpWJyPZ2FQl5OtDN8GSsKy8xrsK7/1pQM8uoZMRrwrR+s1tyqE7HsTDNYpE94no8vJEAhjQqwve+s0RofvbjlMS8VAlvDUpItHrmlNYuqEBTa3pbNLoKH/6JR90IULaG4kOAG9+sdK43KD7nkqk9I6GHrVVedu5APJksVvO50s3KbdVRS/nEovmrhGT827bjiSwih7d7nKdqHrWYxOwri44atQ7gaNrUkvKxgl3f4gLnvxM2kYXqV2TlK8j8XyI+0tR9qUS0QNuyCnbwddufDf7PUiwFsd6VcLConVqj3SzyYziH/vI6SvxxLiF+PVzU4peVz5IPuiO+jMpL69MzgRW/eO9OWVuSekQh18FpG0ghBCiYfy8dZjTFjxDSL7kpVb8+c9/xr333otbb70VU6ZMwZQpU3DzzTfjH//4B6666qq420hKhUJf+nab/cHBQ3MRu+7DvVekeuCsA7RFN0TwRBcRhe4uIRYTLl6hTBWdmLAsn2hZnUwoo5BFO5jhA3rgsJ0yXtuyFYYcRWcSTWv60KeyowlDJwSLlh4De+VEdNnCJVefKwCILdAlWvV+Do9EtwIF6EKieVSJ77x9Ylly37re+Sa2QWGe6Dv0C454jorYl6qEnmF4xUXL87+3jrBzZ1nqc7ezxivcF4muKT+ZCLbkiIL3/lRTlQi1iulek5t8yUWit9m5RExy7E026I5n93/3PrXT1uo+E8XCTCR6KUR0tSe6aobn8L++j92uGomDbnwHqbQd6XptFoRFMWo6ZTsF+f8H3StN+q9nlypPQkt19Lnu3l3XnLPu8J5vLyrrHHdT0RPd3Syoe9OOI0WLi4fq6t+qtwdc7nrnK33hQhty+RvUZU1evAFzVtdh9KzV0jY6Ed00El0kzsSikxdvwCuTlirXBf19Xr25GZubcoEBQXYu4iRRMmFp33QyTfAahcY8fnetDZlQKTeyhYv6MyGlRje5QwghpHJYuLYeZzzyCY7/24flbgpp5+SlVjz55JN49NFHcckll2CfffbBPvvsg1/84hd45JFH8MQTT8TcRFJO7vnx/nj/iqPxw4O2yy5zHwFFofihsw/EDv26S/uKz4rNGk/Uu0/fD8ftrreREaPBzD3R5YdU1UNrwrL8kehJXSR6rl5xtSxAyt9Nkq6a27nkEYmu6Ktv7Lq1ZOmx09a58yVGn4v94p43sQ9NRfQwLaw6mQiMDD5xz0EhJegRxVCX/t1rpO+WZUkCuPvZxDaoRrRzUfTHwUO3wtG7bm3cXiD4tWVvcHbUCYYg7+FcHeKkkP68uAKyavLgW3sPxnXf3RO7DeopLa9KyF7nOi07WUQ7lxqNXZOI+AZDzhPdb+dSazCh542Ydb/mItGD39gQbSAclEZEF4Vd9x7S2OKPRBepb0ljfUNLpOg7nUd2kBhpQlBiUZVobVmWJDT27FId4IkO5WeRuibRziVYRFdZ52THhmVl741hYry7n87ux/0bmgp4VWBTY6t2nViGe83rzrUocIvb/GfyMjz0wTzf9r7JPfFa0NQRZyT6afePw+UvTcNnC9f71gUNRe+5CLo2RbuiQnNbRL08Vm9pCt/IQzx333iwbQfj562TxqdOoKRuWXmU6pyoqin5ODb4+1Ci6gkhhGiYv7au6HW0pm18uWwTJ1Q7OHmJ6OvXr1d6n++2225Yv97/MELaB6ofncmEhWH9u0sCmMrOxb1PiM+IX1x7Qmidp+6/LR796UHKyGGxXCCCJ7pCoRt9+VG49bS9s98TlsrORS2ydRXaJgpp3s/ivkYieugWbruiPw6oRGSvMPr7E3bDvkP64IZT9pTWNQmikhuNKzZB1R5xURRPdNW5OuPgIZh1w4noF+LLHUS3Wv942qqHLKInLEsS3dy2mFh1VId4xO+xTa9YkoFm2+YpK6qOVF2lnliypMkPob6A4euOLdW5cxwHPz1sKPbcRrYxSiYsORmrzs4lYQUKoVGoSiSk+5FJJHqPWjESPXMduGK82CcmE3p+Eb1NDLXlSHRdX7RKCQXDE4vGgSti77R1d/zhxMzf+KaUHfpD8Hv3jYsk2OkmVVvThdnWBJ1fleWGZQHr6nORt9VJOVeGKHSLfy90gvYW0RM9K36bt8ct17IgRKJnlgX1iu2JRBdx9w+yHAmblHPHrPt3Tbd9ylb30UuTluKWt2Zh9kr59Vnf20EGbSrGZNK8Nf6HqiA7F2/bgiZ/vH2i+7NgclRRJ09Xba7sqPIwXvhsCc545BN8776Ps8skWyUpKp0PqyRHqUeDWB890QkhpPPym+en4Dv/GItHPppf7qaQIpKXWrHvvvvi3nvv9S2/9957sc8++xTcKFIegqJPkwrRMMxmpEdtFU7ZbxsAwPf23zZwW9cixYv4wGwqJquElJ227iH5kycTlk/cqE5ayghZ0etdXC92lwV4RPTwtpr+0A4T/lT06KIQ0T3ft+peg//+8us4e8RQaXmjEDnnJk+Tk0L622Mp1od7olvKY6tKJIyte3SoJhH6dZdFeQuyf351BDsX8djEY3jt0q/j/07cFWcfukPkSKhgT3SviB7tIc07qaObMHMJmgDo1zYZoTp3rmDkFV2rEgnpbQfdvSaZsIw86U2oSspZHmqqghPZWlYmyt5tWjYSPeF6oguR6IV4ort2LglXRFfvL/la26XxRHcjxGurktnz0JqyQydtlm1s1Hp/q5i5YrNyeSptR/JW9xJ0r9QJ9+vqWrKfW1K2NkGn2P+67lBFoutQe6Jn/pftXMLPe8p2Au8Jtu347IWk/UOSUGYj0ZPBkeii8Kxq98aGFum7914rXmMmdcSFqq6giSPv9kETXFJuA9vR/10wOKzoInr0SPSC/JRixk3IO39tzkdeZ6uUd2JR6p0kBhxpkrWMDSGEEFJW3FxOj3y0oMwtIcUkL7Xitttuw+OPP4499tgD559/Ps4//3zsscceeOKJJ3DHHXfE3UZSIoIenVSil5wAUP2r8dbT9sEj5xyEm7+3t3K9y8+P3BEAsP1Wspe0+MAclPj0sZ8elP2sE1LEB3TLsvxCX9JS1rH/9n2ynxs0r2YnLFmAN0nsafrwFjS5oUMlIpsi+te7wpMUpay0xxE/+6OcVdRo7FzymTTwonqzYVh/2W4o4bFzyUWiq8/dGQdvn/1sec69yz7b9cEvjh6OKo2/fr54u6RQOxfVpWTqiT58QMbDW3XuXMHIK3QlExa6COdEZz3htXO58Ihh2naE4X2zpKYq2D5o+626SW+UuIKyKxyKZZl4ontFRPeciZYdgP46EUXfjY2tJfVETyZy10aYQJsPr3++Qrl82pKNBYmkQfcOlYWMBTkSvSUle7u3RrVzaTYX0VWivpjIOeER0YO6xQ6ZZEk7DloD1ptGorsTSjqBOZ0W+04V+S+fH++9duXmJny+dGNgm4pxHajqilJP2CREtsyAfi5GJLo4HjsKYheI/UndkoiUeiqo3JHoTLJLCCGVBW/FHZu8RPSjjjoKX331Fb73ve9h48aN2LhxI0477TRMnz4dTz/9dNxtJBVAUhKgM/8HRXW6dK1J4vg9BkqWKCoOHrYV/v2Lw/DiRSOk5WJUYJAmKdow6CLWpehhy8I39xgkiS5e+weXQWICTjGa1lO2nFg0nmjafBH7w8VU021O2di2T1cAwDd2G9C2b25n1bkUBUbTQ6/WRAbnY1/jpbvi+H93/C7SJI1lyR7KOU90//H9+xeH4eKjdsx+D7O3cct3OXW/bdDfYycTBa+oHVVIMhmPxiJ6WyLMpMJ2xRWT+naTj7UqaUmR6LqoYK+dSyFR6ZkkfrnvNclE4Njcq82Cxr3XuW2sVojoJpHoXnyJRZVvzHTPTniIb4Ssq2spkSd6rm1uP6RtO9AKJE7ueW8u/vyfL/LeP2iSROfDLvott6RtSZhrTfntSYIEClG0DEuAqYpEd8+xZeWOJdee4GjnQBE9JBLdu29zKo07Rs3GpEXrpfXV2Uj0cDsX1USZ9/Soru/v3pux7tB1c3FEdLNlOky9/NO2o/1DbCJ8RfVEz0fMq5w4dDW6iF9aaHRM2stplSdZy9tob/XtpAsJIYSQdkPeCsU222yDm266Ca+88gpeeeUV3HjjjdiwYQMee+yxONtHSsghO24FQP1gK4pPcUbYihywfV8M6t1FWmb6YCTaf2j9liXLkYwY/MHvj84uq9YkFk0kLPz7F4fh+lP2xMHDtlKXZ8n7hlndFJvuCk/wKIy+/CiMv/IY7NQmmIrH1t0jol901I7GAqyITthNxtB3qkj03l2r8a8LDsl+z4jo4iSKPhJ9SN9u2KFfd7zx68PxyZXHSm8s6N98yH3uWpMMFV6DHry8fXriXpmkq+IETxB+72G/5Y6pJ/rubfZGqm3c6/U3x+6M/oKnfTJhSedbJ6InLQvVQltrkvmP48z1LArfCaXw77Lntpnjytq5pLx2LrltxWugp8I6SYV7dl0hTD2BlMgmkXXzEQDA+vqWothYeGnNHnPOTiRlO1oBuhhMX662ejEhaGJXJ3SKx9YcYOeSmwTR1y/ZuYScr0A7F8vKXl+2QSR62gkW0W3HCYyW9tqRPD52Ie59fy6+/8D4bPmA4ImuGQ7i32tVfd7TE3RPLGUkuureGzYJImKaryCo7SYlRBXnohyDS7ncXOau3oJ1dbKHu6otknAuGaTnVy+91ItHMfp2xvLNWCjY+wCVIbTLkehla0Zb/RXQIYQQQkgHJn/PB9Lh2K5vN3z8x2PQu2u1b50uAaFLsX6ziTpDkHgvJh01ETWzvu5iJHJSnVg0aVk4YPu+OGD7vtJyyRPdkoVOEzuXQqipSqA6YaFesF4RUXmiR4kx61KdxODeXbPfxWOr8hzblSftjqUbGnK1GIvo6u2CoklNUUWiA7LIlrAsabKjysAT3U2YKWqxOuFOFNprq5Kh4kTQJeTVfr+992Bs/fNa7DqoJzY2tOLoO8YElu3zRA+xc1Gdw6qEhW/uORDH7zEQgCYSve3psW/3Gjx34SE4/m8fZvcV0YmyiYSFGuGcFBKJXpVMSCO+OiQSfbdBPQHk7h9uPoBcYtFcaX275yLte3WpxpamcNsEp+2QXSFNNfyrkrkI8CYhEr2uOYVGzbUeJ66wm0hYgv+1g5Z08euOg6iR6JZlSRM6zR7/91ZJRAeOv+sD3HvmAdo6xEj0V6csx+ZG/bhQieiinYt7fbkCdtD9wbaDRfu07aA1wGzeK+5+tWqLcn2USHR1fcF2LiL6OuKZ0JEimhWqV5RJK9NIdNvR/xU2qS7qBEKhYp7jOEULmhBZvK4Bx92V+Vux8NZvB7dJuBIkf/TiNI1UEOvqmvGtez4CED5OSk0c/vxx4a2+0t8uIYSQUhFkDRw//GXSkSmv5wSpOLbt01VpBSLZuQg3IFd40iUGLRTxISnotrfzgJ7Yd0gfHLXL1loRXeVjLW5b3RYte/FRO0n76csTPkP2WC+2nUvSsnxitogqwq+QZ+EwhxXJzsWwIt1EQxzP7KpIdMA/kWIaie5tkxSJrnvzQSimpkptFSQiPnj95du7e9ot72xZFg7ZsR/6dKvB0P7dsdPWst+7F++Ehep8ipMBqjF/xQm74v6fHJgdd6rTJyUCFjbw2ru4ArWvDR6LHZ2IvmPI8QKZ8+n1RA96S2L7rbq3tUG2c3HbI94/thJEdBN/dMDMzqUqmbM4Eu1cgDyTBBrijo8WVSR6Olok+sn7boOfHLJ9+IZFIOgNoBaDSPSWlC2NYa84Omd1HX713GRtHaKI/srkpfjls/ptVW9juMKtZeWur5wneoBIHhaJbgf7doeJszkR3fVE19UTHIluYueSLUtr5xLQ0AhICT+Vdi4RItEj2B0VYvUQ9W2UfKL2xb9tpbCQAoDJizcYbys2SUr2W27lkhSdpRsay90ELXIkOu1cCCGEkI4MI9GJEaLgIz4Iv/6rw9GSttGtLZFl3FFL4kNSUNHJhIVXf3FYYFmy5Uhuv+yyts9/PGk3WBbwwJh5gfV67WHEB9zqAiJoTRAT/5WCMIsWVd+GETQJUCiqiSBAFggyQpX8JgKgnoDo43k7Q7JB0RyGHIkeLOACcoTdBUdkLHKuf31Gpo7QfYPp5kk0m7Vz0UxNDexV61vmPa+qSPRT9982+1kcnwM85TW1qpWwZMJj56K5ji4/fld0q0nivCc+Va731u+WFZR4cru+mTcv3K527VRyiUVz24qTAlWJBP5xxv6YtGgDnhi3UFu+LrGoSLVwXTd5RPQNDa2+7eMimbDQmnaUiUXTEe1cTthzIL6x6wD8a8LiorQ1CG+f9u5anfU8V4noFmQx259Y1H9lLV7f4FvmUmfwRkJQ2e6fOzES3RVog0VyJ9BSJO0Ee6J7RR+vIOmuF99O2NzUil5d5PuiFImuEtE933XXt207+uSlMUWipyQRvTA7lyhis25TE+uLqNpcoWJeynaQR/qHyET52aiL+M37UKkwdkhKbksk2gyVWUQvd/2EEEJIRyeSiH7aaacFrt+4cWMhbSEVjNe6xKUqmSiqGCr+GAwTEsMEfNnzuS0SXRNBXRUSlZupTyzbkqLRii1wW1b8vutdqhNacTMsiWxCGh+F2bnEgU5ElyPR5TaoItFfvngEdhvcyzfGLWmshEfUm4joXkSf/9BdQ56ZvB75Q/sHR3LvPKCnb5lXcBevl+u+uyd+cOB2ko2OOD4H9JS92/WR6LKdS63m3tK1JpFNequjKmlJ4lZNVSLwLQm3v93r3RVdq7OR9+pIdCATfb3fkD4hIrr7vz4SPSlEgEcJAj1xz0EYOX2ldv039xiI1VuaMXXJRuX66kQCTbDlxKKuiGs72ihuFRkBuDwvkHuHy8NnH4hfPz8FqzY3a+xc4LFzSUt/c1T76O6RALDW4+kcRKuibHFsiJMYQLDvdiaxaLBdS2vA/t6yvZu6bXDvdZ8t2oB9rn0b9565P76zzzaYu7oOQ7bqKgn1KosT7z1Q54nelEoXPRI9ZavFWJdIiUUjCPs6gas4di55RKILp6hUkehRELtaDFyowKaSIlIqqyFTxEmwcmjYQVVWTi8RQgghHYNIInrv3r1D159zzjkFNYhUJlIkegl/kkl2LgVWq/J8FpNYikKxKIxq7Tokixv5gbPodi4JSyve6gjrvq7VSa1AFLav+DBjKqAV0zdeN7FjecaA2FZX9BX99Xt2qVYK8iZJOMW6otq5AMDe2+but8ftPhAfzVmL4QN6qPcNLjqb/PL5nx+K92atxs8OH9rWRvX2bkJZEe+2okieTFg+H3pxfA7oKUei6yKbveO6ukrdwKAEoWL9ovhbI1ilBOFe1znhsM3WRLi++3bPReC6Hs3h5zezvxuhqxqj1Yq8DH27VSuj0GurElkB+LpT9gwU0ffYpheGtqS1Irp7H3SFzyqPiKtLBKsiYZknF44bsd7uNUnst32f7HjSjTm/nUtunW6yR8fmSJHoChE9+5ZCbtzNXrUFP3poPBoCPPHTIYlD7bBI9FA7l8z/3onPK16ahi5VSVzw1Gc4cpetcdzuuYmtoPpcdJHo9c1prQAcWyS6ImmsXE8UixbzenXlmhThttPUuqRQYdk0YWop0XuiV15bSeHozqrjZP7mvjNjFV6btrykbdK1x6XckeC0cyGEkPLDl4I6NpFE9H/+85/FagepcCRP9AB95AcHbocbXp8hCYCFINm5FFiW2G5XwNUJ5FUacV0qTyrbkh44ix2JmbQs/PhrQ3DnO1/hoB36Gu0TpmsN7NVFaxkRbuei/hxEsScaVMg+9tB4oueiI3XHItvX6CZZcp9rq5KRLVl2G5yLBq9rTmHa1d9Et1p15GaYqNKjNiP6HrpjPxy6Y7/AbQGgq8ZTXiQRMnEiCm79esgiuk6UTVoeO5ekuh0mb3p421ST1Eei3/y9vbOfvefJFbtFe5U+XXOR6EH2LCLu7cEtp6vCS12MRHf52tCt8PaMVb5te9RWoTnVkmljSH/UViUDLVnc/d1txGjyVEQ7F+/kVCkRL4OP/nAMaquS2Xu5+hgsSShvSct2Ls0BUeeFoorud8eI5cl5MXHB+sCybDvYEz1tO0p7lex6r52Lr11yJLrL4N5d8dCHGduzD79agyN3zuVGUUW+e8Ul3URqQ0tKK+yLf2dTaTvvN+HEclRe48USwrTFGlSXE9HN6srPE72w/fMhSkSx2CRb8TbBe7NWoXfXahy4w1baMuifXhri7mZxmNiOgwQsXPjUZ/FWkieyiF6+dmTq5/gmhBBCigkTixIzPNYlOs49bCievfAQPHvhIbFUKwWdaerddaDfekKF2O5ebR7XoiYgrjezc5EnFkr56nMiYeGSo3fC0+cfjCd+dnAsZd575v7YY3AvPHjWgb51Yc+4CcNJFpFi+cYfsH0f7Trv2wiiAOMKRKKdi+7hXlyqGx/+pJZBrfZTnUxkE/cetcvW6N2tWjvxEDbyvHYuLkFNeujsA3HGwbkEkd6+EK8R1bHJ9ieyd7JORLc8Fju6SFUTkdbbVwmFQA0AI3bshzOFRJjeTdz2iG9piO1KGYvoTls5GdFWtOsR6/KWM2In9aSHOKES9lZKl+pE4KSV2y/ZSPSkGIluRxLRE5YVeazHhSgeuAlfqzzHJuP4ItHFMpoiRqJHQdWnop1LFLurVIgn+uam1uwbE8r9PQK7V2TMvpXhadOgXl2wrq4l+12cGFBFonubWKtJylvfnNYnL21b8djYBdjj6lGYtGiDb5vmVBqfLVwfGA2flsT40onouuSgJpHU7ik0TTCaj1gsnqOgMVMugsTKZRsb8bMnPsP3HxhvXAZpn5RbqPaie0OihA1QfSSEEEJIEaCITozwWpfoSCYsHLZTf/T0JBzLF/Fh0SvMHLhDX7x3+VH476VfNypL1KZ6dc28hCFGpqo80zPLdSI6pG1KKaIn2wTgI3beWuv/fd139/R5NwcxfEBPvPmbI3DiXoN867x94O2SfOwb4vZE336rbhixYz/ce+YB2m38yTGFyRLXzsUgEt2yDMaH8LnWYMJA9dz1718chvcuPwp7RXiz45wRO/iW9ayNfj2esOcg3HKaGKEtr0+EXCOiSLxd327SOu+1cuiOmajBs0fsgGpBENaJ6PlEonvb7OIVr7z7uQK1N9Gni3ssYZfA7JVbcPe7X+HGN2YCyNgnealKJHzC1deGqiMqxf3D3G1qq5La680S7FdasolFE3IkeiRP9PgTTJsinkm3/e7kgdI+xZHF7OaULY3NoOjtQlGV7YqeGTsX859nrWk78O/PiXd/hCXrG7Xr/YlFc5+/XLYpa5fgnYgZ3LuL5APfmgr2qPaKutWaY6xvSWmFKFfwvuH1GWhJ2/i/l6f5tvnLf77EDx4cjzvf+UpZBiCPB9WERlze6150x2Xy88H9PWQq0uVzDKJdTski0bXLFfdr4djTUiS6g5Wb9GNcKkPzmZQf9WSnn7BroNR/gWJJchsTfNOCxE1Lysa4eWu1v4MJIaSzQRGdGCFbl5SuXskTHV5xy8KOW/dQRnSqkCLR20R+yetdKF4UC8wsPUobtWUSifvTw4Zi0l+Oy34vxMveq3V4xRQrjzuJTkDJ9/f/8XsMxHM/PxTb9Omq3cbbB6Kw6LZHjI40SSqrS/AqCom1Vcm8jqtbTRV2VPiTexHL3n6rbr712kj0CEPCnxQwuJ+qkwmM++Mx+PiPx4Reo0/+7GC887sj8Z19tpF80HV2DyYWDirROCixqIv3OHOR6IWJ6Oc98SnufndO9ruqT5JJ/2Rcvx7qibCuNUIS1xDBtbZKH4nuOLljXrMlI4gmrdy4Tke0cymXHzogXwfu9eweh+rtB9uR/d5bUnaoP3hcqMQid5FlRYtEr29Oh/pXf/DVGu26ILH0O/8Ym/3sbVO32qTkA9+SDn7A9tajGyr1zamAxKLyCtV2L01aCgB4YMw8o7aozkWxhKiwCPsgotq55BMRGxahXwzEcRDW7+Ja8fiiXLYUGSuTd2esws5/fgvPT1wcum2lnUJpYqbMjau0KH3S/rnpjRk485EJuOIl/6Q1Ie2KCL83CAmCIjoxwpuQsVSIgkZQYkMT5Ej06rZl6mhab9JEFeJiCxaO3X0g+veokZKrxYXo2QyER55m25WHzYoKrzDmFTflNxXMKtJ5zef7J80kas4r9osJKt3zLIrDuiKltxYM3lTQRVSL/PrY4diqew1+cfROodt6CXuVuEeXSOkvlHgPc3DvLtnPumtkmz5dsW3ApIZLbVUSO7fZMomC8DZ9uii3zzcSXTWZ4O2uAb1k/3b3rRqdiG5q5+JF5TtfnbB8Eco68bubIMKHTap1qU4GTjy4+380Z23b90T22kilo4noxQwBHBHi5y9eB+79xT0OXSJPv51LHC0NJ9DOxWM1FUZ9c6qghJvz1tRL33XWIt42ebs0bJx4+1b3N6ChJSCxqGd50OR1t4DcDuJ1pppgKVYUtjaxqEF1tu3Ath386KFgu5Ls9o4T+SFR8oovWSR6bhyE1Smu9ycWzZUTdNwUGSuTnz+d8Tf/47+/yC4rZNKplFSSJzpfryBx8+T4RQCA1z9fUeaWEFIY4q/OCvszQtoZFNGJEZJgXNJIdKFez7oor7sD3kh0v6goCs5VIVYVmfbIAnWP2ip8cuWxeOScgyK1y4QzD9keX914UmibgijkvHl3/dnhwwAgO2FQLg9kEZPXgH1JI4WGVyvsXHSikHjudUKQnFg0EfpcM7h3V3z25+PwfyfuFrKlH/GHwAHb9/WtF48pX7b2JAfdtm9OHI8z+tiNhgb8NjAuruj7q2OGA4AykbFKW+vbLdze6OBhOfuUrbrXYGCbqN6kSTLp+i5HFtFVkeiJhE9Eqk4mlNeuKMKHiei1VQnUBEw6enevSuQ80W0nmp2LiXVRPuy5TS/c/xO9VROg/kFc7SZNVRzD5qYURk5fmf3ekrKN/aYLpVnht+4KQ4mE2USRS31LKjQSPQq6Lqj2tEkU7pOKCSB/ufJ63SHWN6e0bfBFogcMzW41+snDsEj0qOPA9A2Gv72rt5gJI+0A89bU4fOlm8za5Dihgp7jONhQn/O1F8XJOMeUKWH9Lq4XT1tmsVlkuon/fLlwHAc/efQTXPLMpILKaI9EaXWliegotye6QCWPb0IIqRSKfafknbhjQxGdGCGKOKXUS3cemLOy0NksmKKycxERS6tSRCj7thcWu0JqVTJRtEj9mqoEjt9jIADggiN2LEodOrzH9OtjhuPZCw7BP87IiFrltHBwMRHRfRMxijcOxKhxXZmiIKcTDUWhvbYqISW50+2jE+TDEJ/ZDhq6Ff51wSH4wYHbhe5nMlbvO/MAXHD4MJywp+yVL0aYm/ok/vPcr6FHbRUeCBBDF69vyH7WXXuuwPi743bBqN8eiWtO3sO3jSr6v68iR4D3R84hgoi+TZ8u2T5q1ByjK9ZEPXNdFEkVq5OWL7K2tiqBPygmVsR8B2HDprY6ERjZ7B13iUQuwWkqop1L9wDhshDOP3yY8vyJqMQj9xo3OYbmEtq5iDYoLlkR3bIivWlV15wyjho2uVXrNCDvGBLtPrrVJLUJg128Aqnu78bcNXXaiRu/nYv+uIMj0W3lZ5eoOph5ss9o5YqYiOIiaTtc0PvdC1Ox/w3v4JP567L75PYvvZ1LWJ22NhJdJqgc2b+6sh5zF6ytx8dz1+GtL1cGJsYNwntI5TrGuKutqGhvD3LbaOdCCCGVTqX9/Sfti+I87ZIOR7nsXK7+zh7oUVuF7x+wHWav2iKtix6JnvussrcQ15vYuZRjYuH+nxyAeWvqsGub9UWp8EWqJhM4bHj/7HdpSJRJTzcRyYIj0TPjSTzfW/eUo69dxs9bl/2ssysRh2dNVUKKZK6tSoQKTlHw/hD4+vD++DDAAzkK395nML69z2DfcjF58MpNTUZlfWO3Afj8mm8GThY0tPjFRS/uOUokLOw6qCemLdmYXXfEzv1x9K4DMKi33wpG9QaKV305fPjW2fNz9C45aya9nUuckeh+T/TqZAIXHrEjDtyhL6569UvMWpm5Dw7r3z27Tdg9OZNYNEBEV1wX+Xqiu5Y53957MN74Ir5Xf00EPdXvYbef65vDJ3qiRNwXynoh+tfFnT+xLCvS37eG5rSxf3XX6iQaWkK8yzUPFt6J6yYhmn5LUwrPhXgZe0+hhczfDm91D30wX1uGNzo6KFo6SEQXx5M6sWi0h6u07cAwPUveRJ3gcRwnVNB7dWomaez9Y+bh0B37SW8XlCrPiziqItm5SIlF5e2CjruSn5vtGIRi7262k8lzUelYCI7ckyZNKuwkiq0pR9PE6PNK6xtCCKlEeKckhcBIdGKE+Pu7lNYdfbrV4PpT9sK+Q/r41kX3RM9t36NWIaILByYKGGaJRUvTKdXJBHYb1CuviYyCEouG1Gd6/L89bue82xBGmJUAoPDV17xx8PqvDsezFx6Cgb3Untzb98vYjGzbp2vAucgtr6lKSJHM+Uac61AduUl0eFytUPl76wg79pu+tze26l6De87YX7uNVxAWz90ZB2+P89vshrzrVefK++pzTVUCY35/NK745i5SOWeP2AEAcOQuW0vbZxOLRvxrqkosWp1M+ETBZMJCMmHha0O3wk5tSWZrqxKRHpS7VCcC39zx+vq7dQIZu5ooEz6uhca9Z+rPXz6YHK5qE3eyp645fHKmlChF9KwnerQ3reqaU8bRj0HCsotu0sQr7DeGiPFevL7tlmVpE0zry/Daucjfxesi6FhFgVj1tyOqnUspdKuoEa5p2zFul9tv5Y5ED9PtdclEHccxjgaubLuLwm1BvH8byh0Z7WV9fQu+e+9YPPHxgkj7yec75kYViOMUft4Kq1/4XPLaCSGk/VHsWzUnNDs2FNGJEVIkeplCjb21BkVWhqES0cXyRVFO64ku2rm0gyifQk5bFBE9aMtiWiWYCH3iYTiOI03EiKLVXtv2xmE79YeOm7+3N35yyPZ4+ZIRRm3r261GEtEHacT5OLnwyB3Rs7YK5x42tGh1PPbTg3D6QUNw5iHbx1bmoTv2w6S/HIfv7rsNAOC2H+zj28b7doh8vfrLjOIvDWT86S89ZmfJPuTMg7fH/y49HA+ffaC0rSt6R728VBMPyYQV+KPuT9/eHT8dsQNG/fbISFGKfbvVBEY2ey/vlG1n+yxtR/NEd+1cTCb6ukeYfFEJE5P+cpz0XdV3PWNIqlsMgkT0RB6JRU39q1WTN150Irr3b64uT4AOb7R8wsol+TbFK+x6xW4xyr674u+8qi2qvx1RH35K4aUf9UUJ28lHeBcj0Uv/ABjqiS4mPpUik/XbAZnJwHlr6tqi83PLK+0RNw5bEO9elfYcf//7c/H50k249n8zIu5pLlSX8o1ZoAIi0QPGDYUcQgjxU9kT6qTSoYhOjChXYlG5DXorDhNqBAGgXw+/t674o7vawM5Fak/MfXLa/tsCAI7yRL0WQiFNDDvnpqeimA/lZp7oevE1GSEicviAHrjpe3tjcG+1lQsAbGrMiWRb96yVhKn7fnIADhmW8S6PA9Uz0nZ9u2Hy1cfj2u/uqd+xwHF77O4D8dcf7BOYwC8fxGvxRwcNwc3f21ta7732wxIB1wQIkqbPl5ZlYe/tevtESHf/qG+jdFEkew17u2bbPl1x3Sl7YWj/7pEElu36dg2ORPf0Z1OrnYtEj2jn0q1WL9KO+u2ROGynftnvKlstHSpxrZ8n2a3qB3FPRf6LSmBDg0pEz/yfsXMxH0/1Leae6EaR6Jp7aZCdiwne+79lWejbrUAR3ZMgdJSQKNa9JlvTNhasrde2RZlYNA87l2LjOE6khz7bMY9EX7OlGR/NWSP1S6ki0cU2inWqbqniPIwoDjpwAiPaf/vCVBx75wd46bOl7UZUzLf/o1jblANdfpEw4rC6KRbl9kR3tF8IIYSoqLA/jaSdQRGdGCGKj+US0X1WHBHtXLrWJPHshYfg+Z8fqhT9RBFMjALURqJr9o2Dy765C/51wSF44Cx9AsZSEnZ4plE/KtHspL0GKbZUs9sgvRe8iYgu6lLVyYQUnRt1UiaMNVuapbpEdtq6B164aAS+Plwf7R4FnbBSyNsalYT3MIIi0VWTXlHvFfkQ2RNdIWZGGYM+f+mAXS3LksaCN7rfL6Kns9dGVE903Zgb2KsWuw7qiUu/MTy7bO9t+xiXayKaqLYpdSS6Lmmwl0A7l0S0a7euOW2chFDlxe+lWSOOe9s0ZfFGozpdvPfohJV5SyIKXiFefLvptpGzcNmL03z1XfT0JHzjjjF4U/DoDxPRo4p0pUhIm44gigNuIlKzHWat3IKzH5uIkcIkhKnPfqHYGhFdhRgpb3si0YOEzNc/z5z7Bz+YV9Eao9i2/D3RvZHI+benGOT7e1kcj5U2EVLuSPSgJLuljsonhBDC+cyOTsdQWEjRkYOuy/ODzPtqdtTEogBw2E79ceiO/aRl+2zXGwBw3B65JIJVIaKcl7h7pKYqga8P7x9rhO9gRaJFU6I89AT9YD/3sKH4xq5b49bTcpHF9//kAEy56ngcu1um/08/aIhvv3cvOxJXnrQbfnWM3lPdROirSiZwydE74axDt8eQrbpJ5zluoXW1IKIXmwp7nowd7z3He+1LOQyUInpu/QHb95HWxdV1UW9HKluNmmQEexOvd7rmutu37f4mCqBH7CxP3niv2abWtBSJrhNVo/DWb44EINt3nLLfNrj+lD0DJ8dcjERKxYWgTCYbA/+79HDlclN7ki1NKd89yz3EhGUZ/d1xqW82j0Q3yV+g9UQv8B7pbaNlWegTMRLdOw7ENxQe+Uj2WHaP471ZqwEA/xQ8mMVJB9XxRhXF3XZ8uWwTfvP8lEj7mmI70aKT07YTWYidvyYXsV+qxKLiOYxi5yIe20uTlkq2PLpyHABO6fIHR0aaCIgpEr3SXlmPFq+gtnCpvEh0c6uZ4tSf+0w7F0IIUSM+71TaW1qkfVGZZqGk4pBE9DIFNRy72wCcuOegbKRUXKLnf37xdTS2piWf9DB7CC9xR3roBLF8+Od5X8P/pi7Hr4/NP6lnXJH23Wqq8M/zDpaWWZaFvt1r8OhPD0J9S1rpVz98QE8MH9ATH361Rlu2SSQ6APzhxN2yn8UxlM+kTBCrN5dQRM9zv3JNiEXG08yk59oXT12Yncs/zz0Y4+atxSX/mhxrE4MsY1SIEcE/P3JHvPnFCpx72FD87d2vjPb3/vhLJCyfsvDHk3bDDw/cDoA81sXzPrh3F3hvpY2taekeWKhgcdAOfbFVm798L8FepTpp4ZwRQzF1yUbMWrklsAyTW9AO/br7lhXLzmWPbXopl/fuWi29hRLEuvpmyRLKFc2i3m/rIniim5Rt6okeFZUnevRIdM/EQ8Bt3/s3QZyYCLVzifhw5V6PP3xwfN52FaF12E4kEd12ChPQSuWJLgmQIXWK59u77T/emxNaTlRLnFIjti0un/1yCc66avP9vSxPoFTuOSxHfzuetzIIIYQEw3slKQRGohMjRNElbusSU6qSCTwoJPWLy6oimbB8wq0oOJloq3H3SJQoxDC+sesA3HX6fgWJSaU45ZblPw9eRuzUD/tv3wf9FZ72LXm8ei4+zMVt51IsIUVFZ/sh4LWkECdAVBNQ4vXcu1s1Ttp7cPZ7viLTfkP6AAAOGbYVgOjCQG11rs1/+tbu+Oj/voHeEaJyvQ/qquO++Kidsr7h4v1S3HRQ7y6+e3pTq+2bqADUSV5NaBUa26tr7hp3u36YQvx2OfewoRjWvztOO2C70HpO3X9bXHb8LnjuwkOzy4pl56K7R5t4jrtMXLA++3nxuga80WY5oXqb4qS9BmlzZExcsN4osTJgloBZTM4pUug9stWjeOcVie4Z90FCo/dYxWsgLLFodDuXzP/FvO+nbSeSsJ22bfzm+an511ciOxcpWWiYnUtAxO8n89cL6/RliLtV2t/OOITiSvdEz/f3ZNC599WRXxV5U0me6JV1tgkhpDIp9r2ywv70kpihiE6MKGIOzbyJW/QUSYaIcl7ivk9WWoRwJDuXIrajOpnAf37xddzkSTQJmEei64jbzuWKb+4CAPjtcfIbAMUYt/kKwd/ddxsAwK4Dw+00yonYY12rk4Ge6KpJr6AJt3yv3UfOOQh/PGk33PeT4LwFOrHVe8qiivDecx42rMSkkGJV2/Tu6hNtG1vSynH6vbaEx1ERrTPEiTJXrL3wyB3xs68Pwz1n7O/b99rv7on3Lj8qdIINyPT1r4/dGSOE5KWmk4dR3yQAgMd+ehBqkgn87fR9s8sG9TK3zfpgdu7NmiNvfx8TF2ZEQG/X9+lWjQfOOhDD+usnG0xp1AjkJttUFSESfbu++uTMyjI8QnyQ6Or3YBcj0XPr6ppTvn3ztXMpJlE8zgFg0qIN+CDg7a0wShWJLnmiR7BzCfqTH1ROpYnKIpKInudPmo7qiS5ek2HHVOpDFvu8HPYpktVNpXndEEJIBUKrK1IIFNGJEeKP3krJUVPoA30QoogRZ1S4yLUn76FdV2mvGxerD/JFJZwULKLHbOfyy28Mx5grjsZvPDY6Jon9SsUe2/TCJ1cei//9Su3vXCmIArM3NwIgT0yoJqCCJi7y/Q21dc9aXHzUTujfFumt49zDhuKuH+2b9SZ32bZPNPHQi8/OJeTGLEWiw8qK6ifuNcg3UdiUSvuuectS9+OUq47HkK2Cj0W8NsX7dkNLRrzsUp3E1Sfvga/v1M+3b6bu/O8/JpHofzxpN+zQr1vkso/dfSBmXH8Cvrf/dnjz10fgn+d9zaicvbfNjIW5a+qU673n0v0Wh7+7ib99gyaaurrAiUaVqP2jrw3BwF7B15CILjq6SdHmFk99VQkLC9fW44InP8OnC/+fvfMOk5u6v/6ZtrO977q3de8VV1zABgMOJST03iG0xCmUH6GlwBsSEggQUiAmBQIkQAIECGBMNRhsDKbYuBv37u27U/T+MauZK+leSVN2d2yfz/PwMCNdXV2VGe+ce3S+CdfytgPNlrElK7R2hHAV1bSkin2qYoXW7JTfd2aSiY5xIhyJ4r7XVhuevtARz7XTPqMu3cj//XSb9Aeyhux27RomCTLlRM8yUTXVvyfDWRzn0tlPN2TZ6SCEkE6ntjmEixd8iH8v3yJdz69Nkg4U0YkrRLdYtlR6b08nuiG+pp0Ki144rR9+LnFUA+6Kv3UkyRxf0N/+XysywdxNYVE7Mu1E93g86FtZYPm8BNtBRFflM7uha0kucjrgmqWDeAYLg9bzJ35GZcJJpqKfUsED4NRxPTFSENE//vExaX/GzbqI0/eU2Yn+2vyZeOiccfjGqG4W9/7YXmWWSaUcnxcej8fikg4GvI6xDyrhr8HkeHYzMZrsPz9OIvrc4V1w5cz+hnidZNDHPKx7MY4aXK28DlOEgtZ6hEl9s9UBDUhE9Lb3bouW2tHdxeSNSshMd6JR5mwO+n1YYKqToXPj8UMsy1TC4t0vrbQsM/+b4PN6cP2Ty/Halzvwt/c3JfqMatiyr8m4n2Sd6B0ioie3n1rF/TXn3jddbW9XWLSx1X0xWyBW+PPXr32F03+/2LIumkyci0sh9Wf//RKvfblTus4oeGbXz+hMOIrNW3WW4Kw6t8l8hasKrTqdms6Nc+ngncN4jbPslj7k0TQNi9fucV0LhRDSMTywcA0WrtypjLXjdyVJh+xWTkjWUBD04/2bZmPpLXM6eyhxMi16iogaRiaLfJo5a2Iv3HVqQkh/9MIJeG3+TAT9WSaiuzgH1xw1AMeP6Ioj+pZL16frvBWR/dBOtkCdmfaclBHJTVGss+NXp4/GeZP74OXvTs9439mAePvl59g70WXYfVe0eyaeZFlZgbt79a+XyMVFwPoZcLp9xYgqr8eDPhUFOGFktzZhPLHxiaO749YTh1n60ydaXrjWeI8FfF6M6V1qaGPGnIVd1PY0wbT+lYblbj6DgSSFXNn9YthnmwieSpyLDNm/FxP7luPvl06Kv9eLq9ZJYkQA9bXMRL779bMH4tvje6b0fZfuv7lmoVu/71RPUcj+zVCJrB9IHM4h0+SN3+fB1v1NlnYAsHFvo+F9ssKjLu4nk4mfLOFItENiY3RU53pfQyuG3foKTnnwXdd9rVM8dQGY4lyEN7K/O5LJDP9oo/We0DR3RRijUa1THNzihGPqmejG7TrynnFDyoVFk8hE70h21jZ3etFTw8QQ/ZUdysKVO3HWH9/HtP+3sLOHQggR2FPfat+AX5UkDdqn4hY5JOla4j7rtSNIVkxJBkPGsuIPflEIS1Xc8Hg8OGtib+QGvNjbEMLRQ7qk1E9740Zv+cHcwZZld586Eo+8sx5HDqzEJUf2y9h4REfj3y6ZhPsXrla6+t3SUU9YtEecS3VRLn5yyoiM95stiJdGlo0tfl5lfxN1phM9HaYPlBeSBKxCidMj8sbIGyPid9xNxw9BSZvj2ef1xMUBfWJvWPdiXDClDx5bvDHe709PGYleZfk4bYKx+Oe8Ud3w4qfbcPmM/oblb99wFLbub7Y8QeFGpA34PHAR6x3H6ckYXTzP1NMYsuvg83oM97BeXLWuOSTtQx3nkr4TvbIwiF+eNhq76lriednidbYjUxMNOvqpUt26su9KVU63NM7F4kT3Kr9/N+5pAJD4vCWrn2pxEd2vLMyaLqGIhkiqQdkpoDrXb62O3Tcrthxw3Zfdv6/JiKNuM9EBtUAuLpbtLxrVcML9b8Pj8eDFa4909TRiphDPRapPN1ic6B13y7gi1dMpno9seYJgyfq9lqcrOqewqHuX/uHCE0s24a+LN+LRC49o19+vi9pqm6T7NCwhJLM4TShywpGkA0V0ctDSUU50lVYf8Hnx6e3HwoP089m/Obanc6NOJNVCUGdO7I0zJ/bO8GiMP6aOHFiJIwdW2rTODioLg9hd34JjhmXnREk2I8YrFUjiXPyGOBfr9rZZzlnyY9yMU/70gOpCw3vzZ/TyGTWG96K4a/44iz/6CwTntlFE9wrtE9t6PB6UF+TgphOGWsZ43xljcP3sgRhoGmtpfg5KJU+OuIkLiX3XuhcpncRx/TznCE//5AV88Hs9Sqe4HbLvSr/PYxARdTG8ORRFKBK1TPKoTkMm4lyqimL54+Jd360kF5v3yR3aIpn+N1c/VyqBNVcieKuERVkxVHMm+vOfbI1PEJnZ12Cc0Eg+ziX2/4KgD7vdRY4nTWskmlQmum1f4ajjZ0N1DlKZcLbbRBREnYqZRjT3QqrKSe6UX72noRUrt9cBAA40hVw/OZQJMuFoNm+WfU701LYz5uFnaDBp8pfFGyzLOuN0ixMl2TLB0Nnc9MwKAMDdL32J35xpLVpOCDm0cSxA3c5flfwuPrShiE4OWtq3sGjir3y7OJdMOAMPBvpVFXT2EAwM6lLo3MgFHZHfrvP8tdPw9le7cfLY7h22z0MRWWFRoxM9uzLRU8XJWX7WxN7Y3xjC1AGxCSTxO+u9G49GN5PzSpxoMAsQLaHEL3Axq93v9UB/GFIU3Ny6N/w+LwZ1KXLVFnDnUEw2hsTvjeW4q0QX/d4Qvwty/F5UFQVR57IAo9P4zPef+ORSfXPYItJZM9Gt26VCfo4v/vkR/7gf1bNEKqKfO7k3epblx2NYMl18WT8u2SX1eT1S0V4poiuc6OYfMQea5O5/s8M76TiXaMKJLmN8nzIs27QvrR9toUg0Y9nrdc0hVDgURVYJ2qk4iWUFn3UMwrFTYdEk8tM1AFv3N6Fc+Hxp0FwXJ9X76EiMInqKnZi2y7bCosmYMsSRZyLqJtPIjqVznOjy1wSungw60BTCul31GNOrNGvqfhFC0kMmYouf7mz5d4QcnBx8ygIhbXQpsv8BmA6iOJ6qC/tQ4tqjB+CCKX3wj8snd/ZQAADj+5TjoXPG4aXr08sAn9i3HCeM7Iprjx6QoZGp6VaSh9OP6JV1efcHA05xLuKPHtnfRHbiX0f9CdW12N3jxHpm/phepbbt/D4vrp09EOP7lAEwiu7dS/MsPwTF9eaCgc3hxI9MUSwXtxGjPNpLk3Hz4zXZCRGPx2PruA1I4lwCPi+6FKf274ss+sEsrPt9iViReonbXfVvTrrfHVWKfzMn9LHWsQj4YjE9FYIA6fR0RLLo11t2vEG/V7o8GREdAFpcPmJvFoyT/XGlt1c5j7qX5mHusK5J9WmmNZy5TPQ6RdFRkYgiL0U0FoSdMlXasBPeVZnoMkIR9yLzqu11mHr3Qpz8QCK7XdOcBUdxrG7vg5c/24br//ExGluTf3pFRNxf6nEupkz0LBPRkxEpVRMeThE1HfVnu+y+7hQnus0TGtl19TseN/fCsb9+E9986D28rihGTAg5+JB994nLpv2/hahVxCoS4gSd6OSg474zx+DTzQcwZ2j7xWJ0LYllTBcGfR2ah5mt5Of4ccfJ2ZW5fcLIbmn34fV68NA54zMwGtJRyJzoIrI/mnL8NoVFM/gLM+DzWIoZ1rQ9xXHJkTVYt6sBxw63F9Kev+ZI/O39jbj6qOQmdpx+KBYKLmbzRIQsTxowCm0GJ3onujdSiRQJ+n1oDslVF10YDgrifNDvRZci9aTHNTbXRiZYWeJaPLHr0RSK4B8fbsIFU/sa1lufQoi9H9SlEKeN74l31+zG1gPNyjGoqBKcx+IlPHtSb7z51a54RjqQmDQRx57Jp79uO3FY/LVMLM/xeyHbnez8RqOaMo/WjVgs6zf5OJdYe3OEjI7Xk37ufmad6M7nReVEF0XQ1kjU1X1h9/1kcJc7fLeI59dJ4H5nzW4AwKoddcr9yb7LnDLTZVz5t2UAgAFVhbh29kBX28gQ3dYpi+jZHueSRFtx6OJHK1schLIJgU4Zm0NEUTaxeV8jHntvAy6c1k9aPLoz2FHbAgB4+fPtmMPIRUIOCWTfheK/+c2hKJ5c8jUuM8VfZmz/7dIryRYoopODjpPH9MDJY3q0+37Om9yn3fdBCHFG/KHqKKJL/mqyd6Jn7s+cHJ8XoUhMkH7qiil4d81unDGhF4BYTMq9Z4xx7GNgl6KUJqyc4l+Cfh9evO5IaJo1ckLl4hUxFnVLengZw1zc0uNxHo8bJ3owIDrRPahWPDmw8ifHSbO6dUISEdV8bTzwoCjXj111LXjwjbX4zydbDevN/eu3v8fjwT2njcaTH27CDf9aoRyDCtGJLt73uQEfHrt4Ikbf8b943Il+zsRJi0AGJ5QvmpYoNC0TWIN+r1Sgkgm74pMUZmROfxlWJ7qrzYT2sQ1k1x+Iube9aTr5W8OZy0R3475SibjibdAajkJS3kCyjRAnFdUM5gSZw/g/n2zFW8Kkjrg/2XapIjtEUWRPtijn7vqWNMeT/vesebNsjnMR7wXZKA3O/Aycm0wj++7qdCe6aV222YDOf2QJ1u1uwFtf7cYr35vR7vuzi5Iyky33FSEkfZyc6ISkA+NcCCGEZDXiT6BCSWFREVnxxY7KRA8IYu3EfuX43jGD2rV2g4hd7Qad4d1LMKJHiWW5yqUtImZJd5TT7sXrjrQsMzvR3fw8tqt9oIvFojif41fHudgJ6IDciWwZswcoEiaDvt5rzCPPzzGJ6Kb+khEFRAwiuuQSGuJ7dBFdmIAKtFMNCdnTXjmKOBfAKgraZd7WuXxU1ywYJys8xkX0sEJ49nrSrsHRGslknEvI8YkSlRNdXOw2Lke8kubPiHhMetTUdU98bGijSSYpUnFqa5q5sKi1j4hCuHWD05OLd7+0Eo++s165PpyEK1+F+fu5vZ3oy7/ej3P+9D4+33rAVXvxn8RkxmaY3MgStTMbM9HN+8+OM5Vg3e4GANYnRAghJJNI/8bJti9EctBCEZ0QQkhWI/5OLVIU8/3Ft0bhO7P6Y6yQJX7VrP4oCvpxvc3j9ZmNc+m8f1JPPyLmeB/XuzTpbZtdFN4SnasdYWw8eUx3DO9uFfx9XrMT3VlQTi0TPeFEN7vf7ZDFigQkY1bdx4BVRLdgc8h2Gl51EiK6nr8u5rknW9TVLbJug36f8ljM4q5dNEm9yzgXc52AZIVHXdu1c6KnH+eiZSzOpbYp7NiXar14rlQxOmbEz+nGPY2GdeKpVgmQ+lDE85vqd7cxZ9u6XjzuSJvzf+v+Jvz8v19i875G6wYCdpOZa3bW4+E31+LOF75QTmAY9p2pOJd2/sI+5cF38e6aPfjO35e5ai/eC05jU2XEZ4sOIvuO6gzjfyaeYCCZfTKRENK5yJ3oHfgZ59fJIQ3jXAghhGQ14o/SkRInNZAQkUVuOG4IfnDsYMeok0yRjNiaaS6bXoNRPUswqmdp0tvaxWHoiEJlRzjtVLvIMbm6vR7AafR210XPRBcFzpgTPSGiB/1eZda1GZmIKnPPF+Wq//wyx+2Ydblym/yMgM+rdAf3KEvkz8quYW7AeA4AU5xLO93f0kx0n40T3TT2n//3SwAxd3+dKb7F/F6FxYme5C3umInuTb8wbMQm+z1ZaptDSqe5jmq9GCnT4uK7w8zc37yFd288Op6HbBSO5dtENQ2eqLGwaOqFN439WvYljEGfMLj0sY/wxbZavPbFDiz8wSxl3+K/NZ9u3o9eZfkoayvOK167lnBU+lSLXYFIt5hFgmQjaVJlf6O7pz7Ej7XTPSiOPZKFTnTZU0GdMTbxNJp3n21xLh1NRxWZJYRkGTIjenb800EOAehEJ4QQktWsFh77HdqtOKltnQT0TP5Bla7TNB18Xg+m9q+0FA11g7kYqhMd8UeoahfmeBw30SZBmwiWeCa6IHDGnOgJ13Yy11UW52EWnz0eY7SKGScn+tFDqnHu5N74xbdGWdbZTRh0L0mI6LLzWyCI97LCou0lostEDrs4F7NQ/eoXOwAYi+fqODnR9eMMRzTsbWjFrrpYpnUkSeXRKRPdmwEnOqAuApwsDS0RRxF6wbvrpREd4jG6jXMx7+vv728EEBOa7331K2U7naimIWS6JqmKlSpxujkUwbJN+wz70ffxxbZaAIkoChX6vzfvr9uDkx54FzPveSO+Tqy70KR4+icThUXNH+6OKixabfOdJuJNwokuTghEMjDBkGlk5VY6ZWyaeoIhO84UIYR0LDLXuWWSkZNsJEUoohNCCMlq5gztAgAY06s0Y67yyTXlAIBzJvfOSH9AwtV8qNOrPL/d96ESIsyRIm7+AA7aOtGtcS5Bv9cgcrspvKpjFvoA60SO1+OxFZzyLJnopu29Hvz0lJHSpy/MrneR7qUJEV2mrIhFexOZ6EKcSzvd30onuuKyhRTCbZ5kssSpsKieU94aiWLcT17FET97Dc2hCBpbkhOr4050xdh8Xo/lKYpUSOZetOOpj77Gul32gnBtcxjz7n/HsjxkcKLHjtdJODR/LpZu3AcAuOKvSw3LVcK4plkn+1IV0UXdVuzjuic+xqkPvYffv7k2vszJKW1G/7y81jaxU6uYxGlolS9XFdJMBvNW7R3nolOtqCNhRvwUOMe5yNs6H1LH/FssixPrDNE6y2rHZhWHk0j21Y46zLv/7fj3DyGHM50dic6v5UMbxrkQQgjJakb3KsWiH8xC15Jc58YuWXDRRHy1o04ZD5MKnZmJ3t70EATYq2b2x/7GVhw3vGu77U+lH5nP8Q+OHYyf/fdLnDXRKijriA5QM7oTOcfkuBad6fk5ftvilSIyV7+ssGh1sfpeNjvRkxGvzZnxIuLnR+bQkYroYpyLTd/pIBPRKwpzlPnSf3x7vXS5LB7DUUQP+FDXEsa/l2+NLzvQFHLczkxUi+WVq8Qsr8eTke8HlYM5Wbbsb8KJD1gFchmt4ahhkknMRG8JRXHlX5diza56/Pe66Uq3vfkJDV0QNT8poC5mqlkmT1IRDmNivzw/+n9twtNTH21OjMfFUzriBIJeWNQpa111HcUHGVI1NJu3a894EfHJiOoi43eam92aaxFYECc8RBE9S1RjaSZ6J4xN0+T3NGl/skmkv/rvy7B6Zz0u/ctH2HD3PMv6A00hfLblAKbUVDgWQSbkYEcqovMLkmSIQ/cXPyGEkEOGvpUFUpEsVXIDPozqWeqqMKVbjhkWc8xXFKgzqw9W/nrJxPjrvBwf7jx5BKYOqGy3/amK//SuMLrgL53eD6/Nn4mfnTJS2ZddxIkuEov3lt7+4XPH4brZAzGlf4XrcbdKMqJlhUXtnOi6iH7v6aNRWRjEA2ePc71/O51WFHFlvyMKBPFed2iLn4/2c6Jblw3uWqT8bD4sOIVFzA5+wL7oKJA4ThFNcxbfzUQ1TRnlAmQuziVTTvRkWLe73vBenChqjUTx8ufbsWZnPT7asFfZx/6mVmMfbUKjWeBVCZBRzRrjk4o4rMFdIdNk9iGeD33iR/b9JYroDUoRPSq8zkwmupuJgFTZWdsSf+02SszwJICDhq5y5neWhv7XxRtwyYIP45MHsgnAziksKr6mSJQyB/mp2+dQl+DUh97FOX/6AH9fsqmDRkRI5yH7LuzIjzinqQ5tKKITQgghGeA7swbgl6eNxgvXHdnZQ0kKPbpElTc/b2Q31FQVduSQlOLKDXOH4KTR3bHgoiMAxETeAdWFtq4qOye6LiwXBIVM9Dax87gR3TD/mEEGF74TR/QttyyzxrlYXZsiemHRU8f1xIf/NxtjepW63r/SvX3+BMN7aSa6IILp4rK3A0R0mVg+pGuRVFy3I8fnRa/y2LXSJyLqW+xFhVzJvdEciiifPPB7PehZZr0fIlF1UVEgNrmRCSe6XSZ6e8VJrdpeZ3gfFjPRhfGoJj3++v5GPLNsi7QPt9EjUc1aVDUVkTlselrAqQs3cS7idY870SXbieNtVMS5GIqsppz5bnzfnqLq3sbE5Ijb/YjtnJzoYo/iueksN+GP//05Xl+5E09++DUAlYjeCU50xWvirm7KoYLTvbe2LcLr+U+22rYj5FClI7+e+V18aMM4F0IIISQD5Pi9+Pb4np09jKR54rLJePTd9fjOrP6G5UO7FePLbbXS7O32RuVEL8kP4P6zxibVl50TXRc28yROdJ1rjh6AbQeacOKo7o77umBqXxQE/RjZowTf+G0sLsN8JB54XBcWTfZJCdVkgv6UhI5MhCqUxLmI3bVfnIt12dQBldhb32pdYcOKLQfw0S1zsK+xFVf+bRk++Xo/9jXYi+hibI/O/ib1No9fNhlH9C3D3N+8ha92JBzakag1bkTE6/HY3odusYsV8nu9CEUy71Tfsr/J8F503IvZ3qp6FT9+7jPLMt0dbb4NVcJxNGp1+qfyYzgUiRq/Wxz6cCPUi+K+TxLnsrehFR9t2IuKQqHOgqqwaAaE4o7MRBevidvdiMflnIkub+u8r/aVL/QnVWRfz51TV7TzJxgOBQ72M+f6s36wHyghLpDf5rz5SWagiE4IIYQcxgyoLsTPv2mNQ/nnlVOwcU8jhnWXO9Tbk0zqPmah9M8XHYHH3tuAz7YcwIxBsUga3f0NADl+ozJSGPTjvjPdCfcBnxdnTextENYiJrelxwNUFuagprIA63ZbizvKYklUPHrhBFy84KP4e7eFd+VOdOtEQkc40cV93HbiMJw6tieKcwM44PBoupmCoA+5AR+6leTFi8ku/3q/7TaypxT2NarF+4DPI53YiMW5qG9ar9eDgD/982fnRDcX3c0UzSHj/Ssep5hpnswcgV5o1DxZpnaiW53+qYjDoXDU8JSLzLnp93riYrabKBTxs653Jx7XmX9YjK921BsmslRxLkbR2HHXUswiaqqOdjeIE0dm971qIlQcjtM1VLV1ctx2lI4sczl3ihM9iacrDjsOHyO663tP9dkk5FBCnone8eMghyZZEefy4IMPom/fvsjNzcWkSZOwZMkSZdsFCxbA4/EY/svNzVyxOUIIIYTE4j06Q0AHMisIilnUfzhvPI4aXI0/X3gEFt80Oy6e50sE5HQQozXMkRAexBzmz35nmnRbUdB34ughXXDymIRDXhXnYkb2Q0Lcr8yh3V6Fc0URvTDoR0l+ILY8iXugZ1ke7hcmOnTB2uyiNpMrOc59DXYieltWvEmZiUic0iK+TBUWtRPR22mSwyzcixEctYKInsxTE0onum1hUc2yLFlCUc0gIMl6EM+jm33IJszEw9CfWHi1rXApADQp4lzMTzekgiXOpR1V1RaDE91tnEvitfJ6R/X7Qy6cO4rorkaSOvq4pIVFOyUTnYEunUU2iXLZNBZCOp/OzUTnU0GHNp0uoj/55JOYP38+brvtNixbtgyjR4/G3LlzsXPnTuU2xcXF2LZtW/y/jRs3duCICSGEENIe3DJvKLqX5OKm44dmrE+xeKQu2HpMoqYYoZIJsVMUFM0ilj4GVVZ7XpIFdGcPjTlci4J+abyADPdxLokOZeflypn9LcuSRRyzeN4qC3NcH89fLp6ISTWJArBuJ0Jk12CvjYiuEqqjmmabie71pHdf6fewKgYkNrb2+ZPevE/RnS0WYI1GY7nla3YaC5HKiGeiu8zvlp3flET0SFRaWFT8PIixRW4y0VuEYsL6EJ1+PMtieV75fDv+uXRz/L2qj5ZwBBskT7CoaNc4F3ECIaVMdPk2o+/4Hxau3GHKRE+8dtpVR4kXsom+znD5iqeRuo2RZKYWD3bRqz0/64QcbNCJTtqTThfR7733Xlx22WW46KKLMGzYMDz88MPIz8/Ho48+qtzG4/Gga9eu8f+6dOmibEsIIYSQg4NLp9fgvZtmo3dFfsb6FJ3oqljv/IBVQM4UFqGo7Ve9Suh1G8mic+Koblhw0RF4/QczMbx7CYBYYU475HEu1nNQ2uYKV41rZI8S24KWXYrV2e86olAu7iLo90mLeMowC9RuBWupE90mzkW/ZmZxPxbnYiOie50z0e3OY1xED6n30V5xLmb3uyhm1zUnIndCEQ2XPPYh5tz7Jv73+XbbPkO609hlnIumwVJYNBW9SNOMn0f9ZYvQtzhRYo5ikiFu++SHm/C9J5cblsmQieh/e99oCJKJ0jvrmjH7V29i1i8X4a2vdkn77sjComK0j1vHuyoTXbx761rCuHjBR6Z4G/eu947SErMmEx3We5ocfriOc+nEe2RXXQveX7fnoJ+wINmP7PPAKCOSKTpVRG9tbcXSpUsxZ86c+DKv14s5c+Zg8eLFyu3q6+vRp08f9OrVCyeffDI+//zzjhguIYQQQg4yxGgSVeSEmEOeaUevWRgszo2J1V6vxyKcphIl4/F4MGtwNaqLcvGzb47AE5dNxgvXHmm7jez3a4FwDnQRvXtpHu46dSQeOFueCe/xAK/Pn4X+VQXS9f+8cqrj+EW3u/ny9K2Q92smaJr4cDsRkitxoj/4xlpl+4oC+aRAfXMY/1m+VbmdmziX8oIc5brctqcTNu1RO5A7LM5FEE7fWb07/joS1fB22/sF722w7VPlRLeNc8lAJjpgEuPbBlDXLC+Q6iaXXJxU2HqgGc9+vAXPLNtiu02jJM7F/N0kO74l6/di875YRJHK8W+dmLAdSlqkUljUTZxLvK0wdnFSwWlf7R/nEvu/NBO9E1Rs8XNkFiepVbrnYDpVr3+5A8f95i18tuVAfNnBcK2n3b0QZ/7hfSxaJZ8EJCRTyD4OHfkZOQg+jiQNOlVE3717NyKRiMVJ3qVLF2zfLnexDB48GI8++ij+/e9/429/+xui0SimTp2KzZs3S9u3tLSgtrbW8B8hhBBCDg9EbapSIYKKRTUzLYLoztc7Tx6OU8f2wDHDusbXiQJ/eUEOlv54jmX7ZCjKDWBK/wrHiQCZG8fgRBe2P2tib3xjVHdLeyDmHO9dkY9XvzcT/7pqisUN3as8H9+ZZR/5YhDRTaJUTWVCRL/rVGvxWx2zQJ2jiOoxk5tkdE5xnjyv/sf//hwPLVKL71HN3mkOAGX5ahFdPz5VQUrAGEOSSewy0TfsaYy/DgnL7QqgxvrQnehG7AqLmkX0VJ2MYvzKJ5sP4Mw/LMaehpb4MruiwDLMDnk3yJzo5ttD04ClG/dhwbvr48cqTmA0tMhz1S0TE+2oGrS2U5yLjirOxbmwaMfIF7L9dIYTXByHho47/oOBZGo1HExc8thHWLm9Drf++7P4svb8rGcKfdLxTcWTNIS0J9n/CSEHC50e55IsU6ZMwfnnn48xY8Zg5syZeOaZZ1BVVYXf//730vZ33XUXSkpK4v/16tWrg0dMCCGEkM5ick0F8gI+XHpkP4zoIS+UKsZ6uMlBToZIm/B1/pS+uPeMMQanq+iY7lmWh6LcgGX7VJnTlpU+skeJZZ1MGxzRoyQenzKmV6mrfRTnJYqAju9TLhWlvQ4ihqi7m5sOF8besywPlYXySRCz81x8P6iLOtrG7GB3Qhdk+iQZNxSKRBFw2JedU31XXYtynU6mnOhmR7w5zkWM8BCJCMud4kx0MdgshqpEoKikcKve9rjhXWWbKDH38/66vXjk7fXx983C2N1loqcgordIRHSv1Yn+rd+9h9uf/wL/aytKKk4y1CuKk5pH3J7OaNGF71a4FYeTjBguHke2iMSySZ/2jM9RYT6n4hAOUQ2ZtCFOmLt9Oic7Pj2EtC9Z8s8EOUTpVBG9srISPp8PO3bsMCzfsWMHunZ190dxIBDA2LFjsWbNGun6m266CQcOHIj/9/XXX6c9bkIIIYQcHEzsV44Vtx+LW74xTOlKEwvEhTOcf2AnxIkibqYzrX912mjccdJw/PmiIyzrZCMqL8jBmz88Cp/dMRdHDam27buyMIgLp/bFFKGYJwBce/QAS1unw7JzCp4wslv8dTiqIS9H/merXSZ6dZE6lz1ZJ7rOT04ZgZNGd0dZvrtJj3BUc4zqEc/TzEFVuGBKn/h7u6Klie0zc/+Umo7JXFhUlf0eVjjRZcPS21ryu23iXMxitb47p8kJM7JJgK0HmuKvjU5051/hqTjRZdfTfP3ECYW1u+ot41E70d3lzGcC8V6IRDVHcXvTnkY8/GbiiY2wYkJGR+wumTiXjhKyZePoDIHfsEvNePyHu5B0KM4hiJ878Wktt2TDJFQ2jIEc2sjjXDruvuMtfmjTqSJ6Tk4Oxo8fj9dffz2+LBqN4vXXX8eUKVNc9RGJRLBixQp069ZNuj4YDKK4uNjwHyGEEEIOH5LJOc+UE/3sSb0BAFfNqlG2ER3Tmc5iL8kP4IKpfZXubRk+rweFQXlkicg5k3rj9pOGWwTwy2fU4OJp/QzLkjmb5v4Kg3789qyxOGtib0wfUCktBApYo1LE86q75WUk60TXqS7Kxf1njcVk0ySCinAk6piJLh57cV4APhfxLGJUTrIFaVWU5AXQqzxR0HXZpv34YN0eADGhdPHaPdLtxM+NKHiLMTO3nTgMQEzIlv2YVc0VxOJcNNOy2Ps8Sa69HbJJgD318mKyKgH67dW7cP6jS/D13saURHTZGMwiunh+9Jgj8RzXN7tzordnxIM5E92YzW1tf/rvjfWuHDPRFUVInR3stqszhmwcnRHnYhDNQafxoc7XexMxWnaTxNkM71HS3nCihrQnnR7nMn/+fPzxj3/EY489hi+//BJXXXUVGhoacNFFFwEAzj//fNx0003x9nfeeSf+97//Yd26dVi2bBnOPfdcbNy4EZdeemlnHQIhhBBCDhFUTttk+fk3R2LlT47DgGp3cSJOmdmZJN0fFyrTs8fjwZBuxuNN1xV64ujuuOvUkfD7vIYCsOb9ioiCdYmdiJ6iE13H61K4Dkc15fUd27sUv/j2KIMT3etxF8PQrSQ3/jpTInppXgCvfm8m/nj+hPiyM/7wPg40hvCHt9ZhpyJaJiyJc3lm2WaD61ocr0xAjWhycV1WWFS/r/Jz/JjQp0w6JlmBWamI3iAX0VUTauc9sgRvfbULN/zrU7RG7PPfZRM1Mge2ec5E9jUkZrTXSyJhAPfu/kwgTiBEo5rjZ317bbPhfdghc96Yie7eid7e2oneveweltWbaG8MsTemOBfinoPlvJk/RwcjB8u5JgcvsnuM9x3JFM52o3bmjDPOwK5du3Drrbdi+/btGDNmDF5++eV4sdFNmzbBK/xluW/fPlx22WXYvn07ysrKMH78eLz33nsYNmxYZx0CIYQQQg4RMhl/4BQXYnCit1NhyPbALjqkS3Gu4X0yP1qcZGCVE92MeF5tRfQknOhdTccFAD6XESohGyf6s9+ZBgB46sNE3KDXYyyx+n8nDMXP/vul7T4yVUCvJC+A3IDPIHgDwOg7/2d7LkVxuqUtzmX+U58Y2oj3jUygjkSjcmFS0yyOb7HdvFHd8NHGfZbtyvNzLIKTLM5lT718YsDpu2B7bbOjEz034LNE0cjEY7s4F12YDbuIczF7PNu1sGjEJN4mub3ZQW233nA+nJzoDiMJRaJ49YsdOKJvOarScPLKzq3qltHH3B6FLg1pLlrn5LJnK4diJrz49dEZTz4QcjAg+3fAvKw9Cw93xoQq6Tiy4hfbNddcg40bN6KlpQUffPABJk2aFF+3aNEiLFiwIP7+17/+dbzt9u3b8eKLL2Ls2LGdMGpCCCGEHGp05OPRQUEU7kgn+i3zYsaDK2aqo2bssBvpjIGVuG72QPzunHEAko1zsV+fq3CimxHF8eJctV/EbSb6vFHd8PfLJlmWu3V/hyOaQdiXXWtxvXntuZP7SIuZiuc2U5H6enFb2bmxE+ciijgXEXFrmSM8EpWL67E4F2N7fSgej7UYqk6ZZLlsvyohyklE98C5sKjsWrfKnOimm1/mIDdkoqsKi3agE92QiZ6CeOuUiS7eMMbCovabOR3yn95ej+/8fRlOfuAdhxHao3pqQtbugj9/iFMeeq9drofTZISZd1bvxlc76jKy79e+2IH/e3ZFSrFGhxodFR8RMT15kCzZIO1RYCSdwcE2v9jQEsbFCz40mCxIdtDpTnRCCCGEkM7mkQsm4MVPt+Hymf2dG2eI3EDnONGPHFiJz++YiwIX+ecy7MRuj8eD+ccMir9P5ke+x8GLnuvSOS4KlyU2xT/tnOg3nzAEO2tbcO7kPuirKN7mtphnKBo1FBaNbWc8LzMGVeE9PW/cFOfi8Ti75jNVWFS/J2XROUG/FyrpTXR8K0V04ZBlAmpU0xQiumYpxqkLSR54kKeYDJEVfg0lIfY5iehejwctIfv+ZM53WfFi84SM7HMjjqdeVVjUZptMI55LLYUYkfbKRHfS5/73xXYAwNYDqcVi6LuXPzVhbR+Oanjrq10AgA17GlBTVZjSfp3GAzjHuazeUYdzH/kgNpa756W970v/8hEAoG9FAS6bkdqkbHuSzLdiKp8U8WtX0zrG+R41iOjJb58NQmI2jIEc2nR2nIvT37NuePSd9Vi4cicWrtyJ04/olYFRkUyRFU50QgghhJDOZPbQLrj3jDGuCmtmijG9SuOv/R3oRAeQsoAOJPcIbFJxLg7dqjLRzYjRKcW5ahHdzoleU1mIW74xTCmgA+7d35GohoDoNJdsd+q4HvHXH2/abzjHXo9Hmu8t4jZaxgl9PzJh2s55/5vXVjv23b00N37sIUmkSWs4KhWYo1FYxGpdSPJ41PeFbOJBJmqrcCoyvHpnPV75fLttm5awNbtcNYEgIhNoXRUWNW2WxOEmjTixEYmmIKI7xrIIbZPJRHeQQzM14STLrZc5zcXrfdMzK3CgMZSR/cf3achzsZ9k+GpHfUb3rbNhT0O79Hsw4WbCuDkUwaeb96flWhe3zabiibLvbkI6C6mI3pH7z8De9jdl9t8KkjkoohNCCCGEdAKzh3aJv+6fYXdie5KMBmX+kT9amDiw9OvQV36OO+FfFNxSzUQvtImB0UkmzkV0x8scStVFuZg3shsAxP+v4/XA4GSXkSkHZI4vJkjLRPRknFVmQWX+MYMwqmcpAm1PXMiE5MbWsFTkjmoatuxvMi6L6mMC8iUi+rBuxdIcerOj3Y6IQ+FLANIsdhGZK182gaAqnGocj3MmuvmHe6bjQ2qbQ/jNa19h3a56w5i/3FaL2ubED343e03KiZ5EhIWTrpiJ6KMXPt2Kfy3bbFkuOyTxen+wfi/uftm+vkGyiNdc66SgjGaHJzIOBtIVpN181C5e8CFOeuBdPL5kU+r7MWSiZ4eI/vbqXRh668t48kN3x5UdoyaHMqporYOJg2y4hxUU0QkhhBBCOoExPUtxzqTeuGBKH1w1q+NiZNIlGTFV/BHw3NXT8IQkWzzer0O3o3qWuNqnKJTaieF2TnQ3TyTIHPl9JdnloUg0Lh7HtpP399uzxuKvl0zElbP6G86w1+Mx5OfriOc2U+5a3Ykum2A4kIQram9jq+H9BVP7Akg8cSET0RtaI1JhNappWL+7wbIMiJ1L2XX8zzXTDO/1SQjHHG6BTBgrZT+CZbnsrWGT+K2IBdFpaI3EBfIt+5tw5/Nf4Ou9jRInemZ/hf/k+S/wm9dWY9797xjGvK8xhFn3LEqqL6drIYqFEUMmunvxXUYqxeQM7l9ouObxj13v23ycm/Y2Jr1/+7EJ+492jlAke+IiG2jPwoFm3AjaemTXXxdvTHk/xgmllLvJKJf/ZSlCEQ03/GuFq/YUB0l7I7vFDrbbjrUDsheK6IQQQgghnYDX68HPvjkSd5w8wnWRy2wgGV1C/JE/pleprZvczqUOAEcOqHS1z5Ag7tllzds50d3E3Zjd1mdM6IWxvcss7fJyfPC6sL96vR5MH1gVE/BNmeiOcS4ZqiyqnxOv14MRPYoN65pC7oWyXXUt0n79beOUubEbWsLKwp+6iK4XEdVFVY/HI72n/D4v1gnCe6/yvNh+M+xETwWZeGzJfJcU0jSPp7lNuPzuPz7Go++ux5l/eN8iTt390kpc+tiH0nGkIra+vz4mAjaFIpZzmcz9ASQn8CeTA+3UayoflXSy5ds75sIo8HeOsJpNTvTOcpsms9t0hqgZPgvJd5Qdslx2jIIcwrhQ0dtzio0TRYc2FNEJIYQQQohrkhGh3DhpPrn1WLz9o6PQrSTPtl2vcqvLW0ZrJCHm2YnL6TrRzVEdx4/sit7CGO/59iiM7FGCG48fYmjnNpZGx6PIRBfFFKeJjbd/dBSeumJK/P0t84ZKj1Hcz+/PmyDt646ThuP2E4fZ7s8soutOcH/b/2UFORtawtIc8n0NrdjbEHO217Rl1OuueA/k0TMAsGZnIv9Zj3ZJRkR3ykRPFbkTPeLYxjwePfrmk80HAMQc6bLP22tf7rT098yyzRj3k1fx4Ya9SY1d1PFbbYq0uhEy26uwqHOcS/LSidtbQTa2ZCKEUkEcm6ZpBqGoo5yM2eREF89HR1YaSUbQTieGxXi9U+iA6h45DJB995mXZfsngR/V7IUiOiGEEEIIcU2qcS4qSvIDrgXyK2bWOLYRc7X97SiiN5uctwGfF1fO7I+zJ/XGYxdPxGkTeuH5a4+0TA5ccmQ/jOtditschGgRs4j+mzPGGN47CYO9yvMxsV95/P2QrsX45LZjbfejymGfPbQaF07rZ7s/s4iuO/H16yFzLde3RKTubz3+oqooiPy26yJOYKgKi47tXQoAGN+nLB4jk0xh0UznievIxHnzuKSOfNN2usO5os2dD6g/b5GohmhUw89e/AIvfLoV85/6BPsaQ7j8Lx8lNXZR/EtmQkKG0ySFOaYkMQb7fp2d6KmI6NYnA5za6SQTIZQKZpe+nXDeXukmskmxziKdpwbSIZm9pjNC4714cKpsB+mwyUEO7zuSKSiiE0IIIYQQ1yQX55LZXy03zB2Ce749CpWFQWWb40d0BRDLJ7eLUckNqP8Mtlun02xy4gZ8XuTl+PDzb47EzEFVyu0Kgj48851puMhGiDZPVIjRM69+bwZOGdvDsN7t0wHVRbHzNqpXidSlL+5HFSFTlKsu1qqzq75FulwX0U97+D3Lulici/V+2XagGQDQtTjXepwetRP9vjPG4juz+uP3542Px/pkhRNd4uA2u7pl58E8Hl0srCjMsbSVbbtw5U788e31hjzv2mZ5gVIVokCZrsM64rC9+N0RFlR0TdNsxUMnYTEVIdnt95g8y759BOa65hCWrN9rmlzRTE5s9cFmcpKoOauc6KkdV7pno6Oc6ManMlLuJqMk+5nKZjGzoSWMZz/ejAON7muAkOxDdo+ZF7VrnEs79k06n+SeJyWEEEIIIYcl80Z2w8KVO/FNk4BrR6Z/LHu9Hpw2oRdG9izBab9bjKuPHmBpM7Z3GV7//kx0K8lFnY1IKCvWCQCf3Hasq4J0LRYnurufZG6cqebdi+K2ntc+vk/CWe42E/2tHx2F1kgUxQohXNyPKjNed+lfPK0fHn13vbTNvf/7Srp8f1sMi0z8aWgJS8/NtgNNAIAuxUGLy9QDedQNAPSuyMePjotF6cSz2JMQfh9atBYXTutrWJYJ4THU5gpvDkfi0T7mccmiUszHHmp7X16QmFBSiXPhSDR+7u36dEJsbhfn4gZHJ7rwWrwtolHN9nulM+NcZAJ+Mk8/JMO3f7cYq3bUIV94EkPTrEVQVUQ0Dd4MyUjZ5EQ3XIIOzHPRkjkFNrfEgaYQPly/FzMGVSlivBKvMz1JTYAbn1mB5z/Ziik1FXji8smdPRySIrLPxsH2cTlYnzQ5HKATnRBCCCGEOPLA2WPx6e3HosLGBW6mvTJ5h3QtxvLbjsWVM/tL1/evKkR+jt9WQ1GJxCV5zk5rwJqJHlDEn5gpK3DXv5HEkegi+sieJXj6yil478ajXbumcwM+pYAOOMe5lOYH4oL9LfOG4qTR3aX9qMZjV3yyoTUsde3qTvQuxbkWAdStHurzJh/nAgC/eW214b2sIGqy7KprwdG/WoRht76C3W2OfbMgLXN5m8+pHudSnp+4nnvasuNl27p5usIJ8Ud9unEuTtfCUEDR5L61Ew81aFi1vQ5n/eF9LFlvzXxvz8Kistsj3fOkYtWOOgBAY2viMxXV7B2Q4mnLZOxJKpno5sinTJGysJzm6Uhmv3Ytz3/kA1z6l49w/+urpeuTKbKb7L47io7K6k+F5z/ZCgBYvG5PJ4+EpIPsDsvm+07GwTXawwuK6IQQQgghxBGPx+NaKNZpz8fN3biv7XYvZqLfePwQFAb9+OHcwa73n6yIft+ZY3DWxF44cZRceBYxH1lYEOIKBOfpEX3L0b00L154M11Ed74sCmdAVaFhfU1VgWOfbjK7AaA5FLWcUwDYLojoZtHcrR6aSmFRANjRtm+dTAmPG/bEct5f/WIHgIRoPqFPGQB55EvEkptuHcvO2mbLMiD29IPqyYtkiBhEdDsh25mww7VQCb5RTbP9XtE04OIFH2Lxuj04/feLLetTcaK7dQTKhFTzeUqmpkSyaDC69JPNb08V2efWiSN+9hqe/ujrjI1BRzyu9jzXQOqucLv7SS8U/OzHW6Trxc9CKk5VmlvJ4YA0zqUj7/0M7Iuf1eyFcS6EEEIIIaRdyObHUUXX9bBuxVh+6zHwJzFJkGycy8ljeuDkMe6icMw6n+hClo1xd4ZcnapoFJ2BXQoN7+0KtwJAeUEOFv1wluv9H5BEjuhxLl2Lc7EibSe6XOwrCvpR12KN/nl95U7D+0xHc/jaDkB3outFUkUnui6aWZzobbbnkLC8oUXuCA5Ho9KirckiOsLtJiTcfOydJjSMjltROLQXLKMasGV/k3K9XZ0Euz7j+7dpJ1vnNFmQSSxxLjaDzaQT3Vxk2S0/ffFLnDahV8bGAcifBmgvRGdrMqcznVN/sMW5PPfxFoQiUcN1PgiGLWXhyh3web22NU9IdiB3ohOSGSiiE0IIIYSQdqGzfyxXFwUxsV+5NNZBdLJ7PZ6kBHQg9TiXVHAq4ri7PuFEP6JvGbweDz6QHLMTsggXkQHVRYb3TudsYHWhq0KkOjIRXReuKwpzLL5St05Tp0z04ryAVEQ3k0pshR26oKtfX90tLo5TF8/NgpmeHy861FX3STiipeQWNuM2E93N5FkoiUz0sMmJbp+Jbt+vqKFrmuaq/oFb969M1GyvArWq/Yt7s81ET3Nc4oRKc4qZ6KkUeXUi9cKiyW9nmFxJKs4l9XOfbpxLR9IciuC7Ty4HAMwZ2iW+PMuHLaW2OYSLF3wEAFj10+My8mQPaUc63YqePgdb/MzhBONcCCGEEEJIu9DZv1k8Hg+evHwyzp/Sx6Fd8n2b3ZeZFNHN4rAs3kNEFE+fumIK/nH5ZFfZz5YCpg652b3L8w3vnZzoYmSOG370z0+V6/JyfJYoDreCmd+XWia6mUfeWZ/W9mb21LfgqF8uwv7G2OSBnlveGk6MU3eQW53oukM9ce1VBR7D0UyJ6Ikx2PXnRtxzuqcNOehJZKI7Id5DboVkTeGKt7azLnOaAMs0dnEu4scnXRE9YrgXUptcao+wFUOci8MO0n1SSktR0HazW9XYxf2k8jnoSGFO/I4Q62F09t8FqdAgTLKmW1SZtD9unOhO3w/vrd2Nmfe8gXdW787UsJLiYPycHC5QRCeEEEIIIe1CNjxu7vF4UOlQDLW6yH2xVJ2jhlQb3jvFuSSD+cddMlneHo8n/p8TZhHcyYlemm90lctE9O/MShR7VRVvTYXcgM9yXnQx4/9OGGq7rd9rn4nuVuj8/ZvrXLVzy2PvbcD63Q3x93kBa5yLLpab41g+2rAXmqYZJgZUYmY4GlUWckxGAI0Y3Mfq7dx87p0c2oZMdJOInayQHVuuIRLVDCK620kVcah2t4p5XOt3N+CjDck/EZIq5qgb83jE95E0v5sj0dQEZJFU8umdMLrD7du6zY9X7ku4F5LLRE9+XzridZP14/UAu+tb8NmWAxnftwo3V7E9njroSMSJ7Wx/AoBY7/NwJJr0vX/2Hz/Axj2NOPeRDzI3sCTgbZa9UEQnhBBCCCHtQrb8CFCJ6I9eOAF3nzoSA7sUSdfbcduJwzBIyAhPNg4mGdxGQgyoTozHjWZhLs7qlIlemmcS0U3H/MO5g3Hs8K7x98k60YFY9rmMXL/Via6LzZfNqLEI/MZx2se5tIQiePyySUmPNV0aWo1CtH6+RJe27sIOmwTfn774Jfrd9F+8+dWu+DKVQ/KpDzfjnldWSdfJInRUiCKEnYieiUx0sQtRsL3nlVVY8O4Gm+3kOz/vkSU45t43Dc59t5MnZhFfhfljetQvF+HBN9a62odb7CY9rHEuRgzCd5pm2kzE1LiZ6EsWYya8/RjTneS1m7CwIx0HvNNTET6vBxN++hq+8dt38PlWuZDeYagmtLLmLwP3iLdqNtd6ITHEe+xX/1uFYbe+gpXb6zpxRORQgiI6IYQQQghpF4pys6P8TkVhjnT50UO64MyJvVPqsyg3gAum9o2/d3JxJ4NZWppcUxHbh0LkfvzSSZhSU4E/nj8h0YcLfSrgNfbnlPNaYhbRBRH+sYsn4uqjBqBfZUF82Wc2Is78Ywbh0iP7oaooiO/NGRRfvuDiI6TtcwNeSwSFKMRGbUQ9Xzx7XN6mJRxFlcPTCjLmHzMIf780dfHdLESXtU0EyJ3ozsKNKmLl0XfVMTR3Pv8FfvHySgCxc3jDPz/FY+9tkLYVRTvzBICIWWRas7Pe0iapwqKmY//Vq1/ZbCdf/s6a3Vi3uwGfb611PQbZ/u3uMzdCajq68aY9jRh+6yu48V/y2COnwqIZdaKbPksqYVHTNPz0hS+wQHIP6l8fO2qbbc9rUuNKQthOd5eGCYtk4lxctJHdJ9Gohi+3Je5f2fjFicb313XcUxAylOf/INegO7LOAUkN8db77cI1aI1E8cSSTR23/wzc5JyryV6y45cNIYQQQgg55Lju6IH4fGstvj2uZ6eOY1zvsnbpVxQsMhnnYlZQLpteg7L8HEwfWCltPnVAJaYOMK6LPX5u/yvM50vOiV5s40TvURpzkJfkBfDNsT3w7Mdb8M0xPQztbztxGO57fTX+evEkjOhRDI/Hg5tPGIpQNIrttc2YXFMej14xkxvwWZzDomg8fWAVXlyxDV2KrWJ4vLCoQmRuCUdT5cqJywABAABJREFUcs1XFwVRli+foHGDWfQOBqyFRXXx3I1wk0o29QufbgMAXHJkP3y+tRZPfvQ1ABgmiOJjcfmr3jzUOfe+aWkjOuudasAlI1qJYq4+eSJOQIiv3YroqmgZa0OXg0yRP72zDuGohn98+LVy9+bhicVTxcNNV7QOm6zs4agm/Q5c/vV+/ElRS8DjAV77Ygcu/ctHOGl0d9x/1ti0xgSYM8Pt26YrdKXuRE9tf3e99CWeWJK49rJ9tkdEjhOqJwoM+fTtkoDfcRgmoCiiZz1uPmPZL1Jn/QAPWyiiE0IIIYSQdqGsIAdPXTGls4eBqqIg3vjBLOTn+NDYGok7ftNFlAXM0SiZJMfvxdmTknPMu9FSzJnmThnmZqFZFG27CDEsvzptNM44ohfG9Co1tL9oWj9cOLWvQXTxej0Ien2469SRAGDICDfv2xxXIr7/+TdHYkSPEpw4uptlW13sX7fb6ojWcSqqKiPg88ajYtLl2qMHxAUw8bh0AdmNSJdO8dC65jAabdzl0ajmWnRwI046RamkLFAKr/WnQ0ISZz8AhIQCrs2hCJZt2ocj+pZbigQ75VDLxtkekQ9OT7tENes10rTEd4EonKfrppU9FSIrrrzfJi7I6/HggTfWAAD+88nWpEX0xz/YhAHVhZjYrzy+TDxGpyNM9xKJ2ydzOl09sSARnf/4tnEyQr/HxGMWv9Jl92B7CIeqe131VMDBKA2qJuJIduLmCmVDzR47snx4hzWMcyGEEEIIIYc8/SoL0KU4F/0qC1CahntYRHT9ZTLfNxM9uRPRjT8Fko2k2VPfGn9dlJuYmPB6PZhcUyF1dzudJ59ifW7Aayuil+QHcNWs/uhZlm/ZVp8saA7JhdsbjhtiGGtBjjtXesDvdXR+Orn7AeD8KX3wvTmD4gKYKDBHIhq+3tuI2uawYz+qTHQ37GtsNTiJza72ZAT6qBYbS9hGKDdnvJsR1yYjWolt9XMfVghg4nm++ZkVOPuPH+D/vbTS0qdbF6re7o2VO/Gb11a7HrNbAk73kmadwFBFuKQrBJpFeHFCwtDO5jp7PZ6UBdX31+3Bzc+uwOm/X2xYbhS2He4xTf7aLZmY6FHh5vtbfxhAvK5eh8ncjtTlVNfiYMwUNzyNkuJnJxLV0Njq/D1O0qez77GD8BYnSUARnRBCCCGEkFTI4ifU3TzWP3VAhXGbJN30extanRsliTliRifo96HFJMq6LQypioipKMjB69+fiStn1iBXyIM/bUIvV/0GvB5n974LEf3iaf3g9XriTzOIzumvdtZh+i/ewCdf73fs54P1qWcg3/XflXj24y3x940tRhHdrpComebWCKbe/Tq+/fBiZcSMY2HRFAXfemGyIS6iC/sKKV4/03bssugRTSFEm9GHedGCD3Hf63IR/e3Vu/HO6t12h6BE5vQW0aBZHNGqyYh0XZjma6L6LEbSrWCqYIPiiZVkxNr0C4smXicj2okO8h8+/QkeUcTdOO8/1k/E4ETPnn+UxHEZJiw6YN/hSBSb9jRmrL9IBp7imHf/2xh26ytJFXPubP76/kbMu/9t7Kpr6eyhZJxsF7qzfXyHMxTRCSGEEEIISYHskSusuBnb7ScNx9T+Fcr1jzsUzbxwal8E/V6c4VJ0doPMiR7wxQTmFpOQ6zbTWhW5EtU09K8qhMfjMbiw3fYb8HlR6VCQ1CnmJzfgRa/ymHteFufy8ab9rsYCpDepsWTD3ng+OgDUtxgdk01JiOh1LWHsrm/F8q/3o6FFIaILQpRM9wtFRMHX9a5RJ4joeretCuHcrXNfvB3sxFK3Quq5j3zgqp0ZpwmbqGYdg8pBm2knukpED9k50b3IuFJkiBBxuLzJiOh/fX8j/r18i2GZ0Ynuuqv4Ib+3dg+eXroZP3nhC/cbC6zeWY97X/0K+xsTomw7poopUT1dlMlJm2S5/K9LMeOeN/DSim3OjV1gfBoltYmhldvrAMSeojhY+PFzn+HzrbW499VVnT2UpHCViX5QBguRbICZ6IQQQgghhKSAk4CaKpkwE7qJlynODeCvl0zChX9egiFdiyzrpw6oxMXT+uHRd+VOyb6VBfjktmMdxb1kkJnGdZe4WahzK4KqhGwxakI8X2YR3eOR/yj3+zzIc4h+8Slc8DqDuxbHx6dHyphd4J2BOR89GRFdZH+jXNi3i3qxtE1CtBLvEf21eJ3FSB+3kyXu41xcDzMlnIoXa5rV5asae7oiullIVBXttduPB6nHuai20zT3wq1meK1uu3lfI3783GcAgJOFYsmqfe1vbMXjSzbh5DE90KM0T7nf+ha1I9nNPwHrdzfg/tdXY83Ouvgy8btOXrA3/Zv0ndW70dAaxtzhXW3bqYoDd4SevnDlTgDAI++sx/EjrXUykkW8vunWEzgYabKpl5GNuBHI2/MyZqJrivzZC53ohBBCCCGEpMCswVW4cGpf/PK00RntV1ZULlmuPmoAAODkMd1t2/m8Hvz1kkn4v3nDpOvnHzsI848ZhNfmz5Cuzw34MpoHL4teCbaJyy2h1ET0gEpEV/yKDkU0XDmzPwBgcJciLL3lGKkQ7yZD3ly81cywbsXx13k5sf7qWtS5uW4y1jOB2YmeTJyLyL5GuVAoCthOoppKQ88NeDHaVLxWRL8/VLncouBul4NvFJ9txtnO6qCbOBc7kTTVDG8Zlkx0xYmxExy9psmpu/77pev9q4ZviFhx6sPl3IwYvyEW8RTvS/H1D//5KX7x8iqc+QdjXnt8v22Dz9Tt8uGGfcK79rein/vIB7jir0uxo7bZtp0qg78jpcFMfSbF29upngPpfFw50bP8Mmb7+A5n6EQnhBBCCCEkBTweD24/aXjG+53YrzztPq6cWYOZg6owqEthWv0UBv24bvbAtMfjFlmcS24gJh6anehui12q3OCqfOvWSBTzjxmEI/qWYWK/chTlBvD0lVNw6kPvGdr5XYjoTnEuNZUF8dd5kkKsZoI+a4HV9sBcAE8moufn+CyOdTP7FBEzesyHG2es6jp5PR6lAxpIiOhuokaK8wJoaLXmwP9r2WZUF+XGl9nHuShXZQSn+21HbQseWrTWsEwV55Kum9YsJKrOsd0TB+bJt9+/tQ7nT+2Lolw/ioVCxcmQTCa66PS0aypOakY0Dd6296pJibe+2gUA+Hpvk2KMtsMCELt3v9xWiyFdixwnKVVu//ZwsoqTCDtrW9ClOFcp2xvut4j765JJMrWnTE5AkfbHzTXKdqd3do/u8IYiOiGEEEIIIVnElP4V+Nslk9CvqsC5sQKPx4Nh3YudG2YZssKiuXEnulHkdF1YVBGDoRKfQuEocvxezB7aJb5sXO8yLL/1GBz3m7exvc2B6RSvATiL6NXFiUigXBciuqrwaqZpMGeit1rPtRsRfa8iziUUieKSBR8iP+j8c1R1nbwej23USziqIRrVlG1EAb4o149tB4zrf/nKKkuhUfvCou5lj3Ak6moSRsRJfLxfUszUEOdiyAtPN87FJKIrJjNCdnEuHquQNe3uhfB6gHV3zbPdv0oAM7jDnZ5wUKzXNM0gXIsadiSqQf+YGguLQtpe1T9gL5JtPdCM4+97G785YwxOGdvDpqW5gGf7Sm/i/dQasf/sp1ocOJNkareZnICiBt/+uDnF7XkdOnKiiHQ8jHMhhBBCCCEkyzhyYKU0T/dQJxknultHtipSRSmiK8T50vwc/POqKfH3TvEaqn2feUSiEGtVUUJEd+NE97qMznn3xqMxc1CVq7YyzAVBZZnoTnnwgDoT/eu9jXh95U48/8nW1AaImFjpFK3QGokiFJa3qW8JY8v+mFtYdD7r+b+vfrnDso2dGKhp7sXC/U3qPGwVqcRIiFtEo5kTNa1xLvL+IjYTXV6PRypkuRmaOs4liUx00bXe9v+9Da2YdvdC/FyIlhE/cmGFYC2K+k5xXPpm4vBUotvjH2yy7QswXtf2lO40TTNMxLQqPlc6Yttk6hpkkkyJmW7rIii3Pwxz1DsVV98h2X1Nsnx4hzUU0QkhhBBCCCFZgUwjjhcWDacmoju5wc2oBEHAKJzrr48cUKlsf6AphJtPGIIepXl48boj8fw1R+LIgYn21aKI7kKUdnskPUrz4pMPqdAgxLl878nluOwvH1naFOQ4u8j3Nqgy0dNXCLweD0IO4lxrJKps890nl2Pa3Qvx5bZaw1MAunteJsLaCRtRTXNdrNQcc/PZlgNYvHaP7TapxEgYxb/EcjtHvRvMQmIqmegeZF4oMoroTm0Tr/XN/rp4I7YeaMYf3loXX2eIc4mI/bvfl4isqdIV70INFM9xWFJrwCD2p3G+Nc3o9Hd6EkhVyLYjtcFM3V/i9UllMivdzxvJPLwkJFUoohNCCCGEEEKyApm7Wxc4zULTT08ZkXKfdtiJQ6Igr8fE3H/WWEwXhHGxWOiehlZcPqM/3r3xaAzvXoKRPUsMP96rhLxtN050MWbC6UkFWZFWt9Q2hbByey0aWsJ49uMt0jZuRH9VJrrM2Z4sXg+ULnOd1nDUUfT6zydbDSJwfXNsAkGmvdu5UJMR0feYzss3fvsOzvrj+9h2QJ6jDaQWI2HIRDfEuSTdlWkspgmtlAqLph5NpHLYG4Vx2SSIwkne9lo2UWF0oieOU9yXuJ37OJfMOLWdXNKZEgs1wOREtx+zuF/DfdCB4mWmcq9VEwKpbE/aHzdnu7MvybtrduOiPy+JPw1lJtsz2w9nKKITQgghhBBCsgKf14NXvzcDZ0/qHV8mc1R/dsdcHDu8q6s+65qTi86wK2oYEIRpXSsrL8jB944ZlGjjkFteK4ynODfh5naTiS4KdPkOIrYqC94Nv/zfVzjuN2/j/728UtnGaf8AsE8R55IJnDLRgZjQ5yRsB3xeg8ilF1WVCar2mejuHfb7GxP3gCjmbt6nFtFTEuJEoVcUAjPsRFdmotsIrbFM9BRRZb3buK531jZj6t0Lce+rX8XWi921/d8s7LeGo/jn0s2JfUVV+3J/JFInuuI0uelWHEdI4vg2jDMNYS6qacZ9pexE7zhxMFMpMprifnOL8fNCcbS9cfN5dHsfpjHXZ8s5f/oAb6zahR/98xN5A94mWQtFdEIIIYQQQkjWMLBLESb0KYu/D0rE5UIXBSl1vt6rFiZFHj53PCb2K8ftJw1TthGFadEVrkfOADFR1s5VLo5d7MONs1tMpinMtT8Hsnz5ZPnL4o3Kdfku4lzaU0T3eDyOorUbET3H5zGIj3omujzORb0/TdMMcRp2tIQjwmtrBIeMlLKY26mwqDUTXX7cLbYiukd5PpMRpX/96lcJd7dNJvqDb6zBtgPN8QKssvx08aEVTdPw+zfXGqJdjJnoibbi6XD61Mky0VWTQW7OgqHopeQ6ZMpxG4tzcS+ii+c33WKcqZJKBJIM44RA8sr8wR7ncrCNPp26CmbSeWLGDdsPNEuXH2zn/HDC/V+fhBBCCCGEENIBiAJtRUFOWn31KndXoPW4EV1x3Ah7d7sY5yL+tC4SBO2Az4uqoiA27W2U9nHCyG74YP1eTO1fYVjuKs5F2KtTJrlZuOpVnud6QsENbpzoexVxLpnA65GLhiKtkaij0B6JGvtpjIvosrZ2IrpzTrROsxBnI25jJ/qlFOcivBYF0HRFzYjpnKoEVbvz4fUAqksTiWqun6R4aNFaTO1fiSMHViojVmJjMb6XieDipFZrJIqFq3ZaxhXfXji74rn1OIhu+riM10be1s1kQlRyHLFtjfsTl6VCVNMMxx+fIFEcrjFmxt1EUbZiKJKaSiZ6KkWBNU16L+2sa0ZVYdDxPjucceMydztRl8pZPghvcZIEdKITQgghhBBCsgpRoO1aEssNn1ITE51nD6lOqq+Lj+yH644ekJFx5QhW1eriRFFQo7vcWDDUTMDnxc+/ORLfGNXdsDzZOBcn57pZ2HzgrHH42TdHYFzvUlx6ZD/HfTlR5OCEB4yxJZnG69KJ7iS01zWHjHEubQK3TGSxE5+jmmYR2Erz5dFAutsdAFpCifHZCXSpOGBVbmA3rnZN07BxT4Or86DKprfLzbbTAJ1EfvPaHbUxN6cxbsW0jek4ZHEsouu0qTVi6UMcl3g5UiksqhmuTYZyR2T7y6ATXRST7Z4yAIyFbMX7uiNF9Ew50Q21BVLJRE+yCO21T3yMEx94x/Ld9dh7GzDxZ6/j16+tTnoMhxNuLrvbq9jeTnQVyTyNQzoWOtEJIYQQQgghWUVBUBDRi2Mi+kPnjMPLn2/HCSO7JdVXfo4f848djIffXOfaKazC6/Xgg5tnIxzVDG55MVqlJRzFvJHd8NHGfShKInbG5/Ugx++1FR5FUajAUUQ3/ggP+Lw4Z1IfnDOpDwDggql98cH6vfjB04pMVgdK8tTZ8Tp72zUTHQg5iI8t4aghqkVGXXNYXlhUspldhEUsE924vkdpHkb3LMWbX+0yLG8WrrEY7dJsU3A1lVtXJf7ZiYtb9zfhtwvXoL4ljOc/2Yr5xwzCdbMHmsZi3L5FGeeiPh5NU4tddiL6/sZWPLBwjWGZfjyG4zJ1YT5moxM99kacqGhsjViErLDiqQGxnXOci3WsKmE2HRlNd+NmKoNcg2Y4Zy0OxYFVx9ehmegZ2pUhMifNwqJuRPjnP9kKAFi6cR8m1SSeWLrtP58DAO5/fTXmC3U4SPK41ajdauiZFr3F3lRPJZDOgU50QgghhBBCSFaRF0iIz7oTvawgB2dN7O1KvJURlBQoTYUuxbnoUWqMiAkIDvXmUAQXTO2Le749Cv+9fnpSfcsiXf5w3vj460bBwZznEOdiFnRz/MYf4b3K83HCSHfFWXXESYHSPOeYnXR0hT4V+ZgztItyfSxT276P5lDEtrglANS1GJ3oNz+7Ass27ZOKXXbOd03TLJM0fp/8nhOd6OKkyWtf7lDmlafiRDdEjphEzX8v34JfvrLKErlzzePL8MSSTXEhTy/EKWJ2TqvOsZ1bORLVlIKqXfzF95/6BDvrWgzLEu7uxDKzaC6e1jU76/C7N9cm1rUNszkkiuhhSx9uMtGd0LcT3dmqzOx0Pj+JOBdhWerdIaoZBWB9wkcl7anigzrSYKsSNmubQ9hZK8+ilqEqXut6e5cTWGY6KUr+oCfZgrx2uHWiG+bvMnDdUv1+Ie0PRXRCCCGEEEJIViHGuXQrcZdp7kTQ7xyXkgmaQxH4vB6cNqEXepXnJ7WtTEQXnYjdhXOhyiR/+NyY6G4WOgMSQTc3yXNSWpCYwEh1MsMtPq8HVUIszqAuhThueEL099r8ku1ZFjtP9S1hx6iMmBPdqFLc+K9PcaDJGkUjTkyYr1VUs8axBLxyAWZnXQu+2lEHwCg0/+PDr3HGHxZjV5tIHIlq+M8nW/HSim2pZaLbONFvfmYFHnhjDS7/y0eGbT7bWuvYr3mCQZmJbiOi2x3P++v3KNe9vnKnZZnM3W0V0RPv59z7Fh7/YJNlnfgkQGNrxJJVrhJDnQQ5UczVX4nHn0rOthO6iG03tlDEOe5IRzNlousTDqreI6ZJm/i42l7e/p/PcfrDix0LlKaDamyjbv8fJv78dex3+aSMIXonlUz0dr7WJHncXgXXTvSUR+LcH6NdsguK6IQQQgghhJCsQnSN63Eu6TKpX3lG+nHCKSvYDlnOeXGuH8t+fAzeu/Fo5AsxNzIRfWzv0nhxVFmcixmvQuRVUZ6fcJ8X57VvMmjA6zWI6JfP6I/+1QXx9x6FBzbH50W/yli7l1ZswyPvrLfdT21TyCK0f7WjXtpWFIX/fNERhnUrthzAb14zurZVxTGfWLIJx/76LXy25YDlfvlwwz5c1iZsL3hvA6574mNc9fdlWK0Ykx0qZ3ZzKIKGNjf81v3GYrNu7ohnP95ieK8SQu0+C9GopnRsXvHXpfhsywEXI2nrS+K6tmaiq7fX14njbWyN2DrRlSK65AQaC35qtn0ZxqUesiO/evUrnPjbdwyZ+wYxX9Nw/iNLMPHnr6Ou2bl2QVSz3kNO7XVCkhicBe9twJINe/H26l3mTTOGk/b45bY6V/2It7d5AunTzftx9ePLsGmPvJC0eRunOBdjkVpXwyMm3LjM3erS7p3o7Sd004meXVBEJ4QQQgghhGQV3UrycMmR/fC9OYMcC2i65WffHIErZ/bPSF92iJEQySIrLurxeFBekIPupXmGH9PieelfFRONJwuudbPDVCaiJ0t5QUJEL8p170T/3Tnjkt6X2Ynu9QD9KgsN72X0KMtDcdvYnlu+VSmI69Q1h10XCxTFwBy/9Xy+scooCDqd89e/3CnNll7+9X4AwIbdDfFlm/epRToVqlzqPUKEi+w47NhV14K3V+82LGtVuGudnOh2Z/3DDXtdjymeiR41isQidtdY5kRvao1YhDYxUscojNuPT+bEjgj3ktKVn6Ywt2LLAbz1lVykfnv1bixetwd7G1px+u/fxxm/X2x7jjRNMxxzs03ePWC8FnZxJk7FgdPBSdh0m8+uKtALACc98C5e/HQbrvr7UuX2EZvtzaTyxAkx4qqwqMvPVqc50Q1Pr/CeyCYoohNCCCGEEEKyjh9/YxiunzPQuaFLSvNzcOPxQ/DCtUdiTK9SPHHZ5Iz1LeJUcM+OakE0rizMwSvfnWFYny+I7AVCJvqTV0zBnScPx1WzEpMEZnEqJwMi+tBuxfHXRbnuneipRL8EfB70rUjE4Xg9HgzpWhR/r2flm+lRmmcoTOtEbXPItZAntnNzPv0OTn8N1hx1neZQxFCYdb8kXsYJQ+62sJs99Yl+9zS0Yp8gqjuJRo2tYcsylVhuV1g0EtVshSy3ExuA6ES3xqYk2rgQ0YXjaJBlogvnU1M40WWnT7bvcBLu5HRQ5a2/uyYxEfLltlp8sH4vPt60L77MfG00zZyJHjtXqtvF4L62KaLankkVjqfV5b6NEwLye/3zrbV4+bNt0qcyVOdCui9Gd6SNG9HZ7VlOJRM9ExjjXDLbN0kPiuiEEEIIIYSQw4YRPUrw3NXTMKV/hXPjFHByaNpx5IDK+OurjxqAwYJoDMTc9AOqC3Hv6aMNTvTKwiDOn9I37sAGrBEbAX/62QCjepbGXyfjRBfb3nj8EFfb+LweDKhOOM/DUc3wfmSPEul2lYU5KAy6H9v+xpDrXGa9ndcTG58TPrvgdsTEkRbFkwsb9jQYxO39jcmL6K2SGA0AhmKidc1hjP3Jq3FxXBWToyM7VyoR3d6Jbn/OkxGOEpnoiWVmMdKuP307Sya6jZtdFR3jkYhuMmHUTcRHRrQzTfpSGrUTMTj5jeuimmY4jibHOBf58VlPRXvGYFj7Njyt4LIft07yK/+2DA+9sRb1LWHDvWS41g7fNR1RhHXNznqc/MA7eP3LHa7aH2wibuc40dtPRReHumV/E7735PKk4q5IZqGITgghhBBCCCEZIp14gtlDq+OvvzW+p2V9TVUhXps/E6eO64mjBsfa6lEu1nFkPs6lLD8hTifjRBfz0/Win074fV5DHv6mvY3IDfgwc1AVioJ+XDC1r3S7gqAfhUmMLRzV0NjqbuJj24FmADEB3cllHsMhTkLTlLnh63c1GMTuVBBFbFWci86G3bG4GJloJG4rG29rRH7+7D4LEYc4l2QcuTtrW3DCfW/jL4s3JLY3DdOuP32NeGyyOBdXmegSZCK5Gyd6JsRLwziF/cgmQ8TdWSYhYLoPkhDRQxH1sbanQCvrO5nrlmiXeO301MAzH2/GiNtewbifvAog9uTGvPvfjq93imtpz6cSdK55fBk+2XwAlzz2kXPjgxA3ZzDzmeju+ksF8Zvyuic+xrMfb8E3fvtO++2Q2NK+1WAIIYQQQggh5DCgpqoA63Y1YGLf1AuY1lQV4vFLJ6E0P8fgKpdRVRTE8luPUWbGj+5VirW7EpnaTqLvBVP64JhhXXHuIx9I188YVGUQeOxE9NPG98TTSzcLbRPH4jZWxu/1GFy9RcHY/v5w/ng0t0ZRki8/PwVBf7ytEzl+r61bWoXP63FVlLUlHLUVYaKaNfJk2oAKvLtmD+pawim5z0VEJ7ropt1T32Jpqz9BIRttbVMIZW15+DIRXVUHwM7hH45qtmqXTEuMKgTGB95YI9les30vortSzU508xYqJ7roapVdbtmwwzbCcrzfDLhbVY75sGSCQzxF5rVmJ3o8zkVxfxuFZ+MTEca85/ZD5jYWPwduhU9VvruMjW0FRhtbI4hGNTz78ZakRPiOENH1ycBDFTcuc7enOcna2xlD/OyLY129w10xXNJ+0IlOCCGEEEIIIWny10sm4ZqjBuC3Z49Nq5+pAyoxrHuxc0PEct6DfrmIfts3hhsyxFVi1z8un4wLp/bFDccPwYS+ZdI2//veDPzhvPGGPlT7ff+m2egiOMgDPg9yA4mfneZCliqN2d8mtj95+WRcNK0vzp7UO75flYAOAPk5Pkcn+uwh1Th3cm90V+Sq6xQoJih8HndO9GYHt+4Db6zBy59tNywrzY+J1et3N2B7bXpilyhii0KgzOFe25a5LrtP9gnZ7OKkww+OHQTAepyapuGHT3+CldvVgk8yTvR/LNmEo3+1CF9sq7XZwog1jsR5X6K7OhSJWjPRFYVLnQQ5s/gfjWoGYVnlTs6Eu/XmZ1ck9ptEgUvLpINmdPc7xfEY42qM/XaEUAzIBXpx2K7jXKLuz5thO02zTFY4ZaJHFPdYJnH6XjrYcXOJ3E5Qqf7dtO6z/e7p9roPSGpQRCeEEEIIIYSQNOlRmocfzB1sEJA7k5L8AG47cbhju8k1Fbj9pOHIz/Ej6Jf/PBzUpQi5AR+O6FuGSf3Kce7k3sr+upbkGoTygqDf4MYWnejzRnbDqp8cjxmDqgAAw4TCpbpIPammAredOBwFLt3lBTl+FNq0zQv48MiFR+Cnp4xEeZu7WiffJJo3KGJevF6Pq8f8VVEtIv/7wphLrBeP/d2itY7bOiEK3qL4JxYW1fnFy6uw4N31Uif6mp318df6MQ3rVoyKwlghXLMTffO+JsOTCDKchFRReL7xmRVYt6sB859abruNiFkksxOidGFVPA6ZiG52VMtei+fvvEc+wGdbDlj70TRLNExHCGXiLmQiuHjOZJMQogCsb64atzETXTxvHZP7bR5DfCxa8iK1Kt/diUhUszyxEnGI+0qmCGmquPleOphxI2i7j3Nx1y7jhUUVT5CQzociOiGEEEIIIYQcgkzqV455o7rhutkDXbV3ct35fV48ecUU/PSUkbbt8gIJMbpnWZ6xCKfwMhLVkOP34pELJmDZj48xOOHdZY5byQ/aO9FFMb68IGhY93/zhhreD1c8EeD3euD3pe9EdxpfuogiuihKt0piVr7YVovbn/8CIYm4uvzr/ZY+c/ze+BMG5kiahrYipXaEo/bCsUxA3C0R/1WYhSdXTvSw6ETXLLnqRuFb3rf4GXp79W5c+bellmOJRI1u7HBEk44v08KcwYkuE3MNETXWbZMReFWud3MsTHsi243B6e2yn2Qc/B7T95v5a8xp+7CLJxSIParYJxG3EyiuM9FdtXKPYXiK7xfSOVBEJ4QQQgghhJBDEK/XgwfPHof5xwxKetvStsgUu9/sY3qVAgCKTaJ1/+pEsdP+VYXwiZ0YxMfYm4DPi/KCnHiUCQBXIrWMghz7TPTCYELgryzMMa1LbBf0e/HwueOlffi8HuMxKWgJR5MWV8xueDOXz6hx3ZcqE90OmUtVF9Gf/2QrnlkWc5gH/V7ktkX6mCcLWhQZ6SKRqFw41pEJYY0uxPn49hLhWoXeVHSihyNRi9AWMYnBie3Vfe+sa7E4/0ORqKXYZkdEnIj7sMurbw5F8K9lxicJNBiPUxd4ZaJeSziC2qbEtTKcN9OxZiL3XYVjrr7LXRsmTByukyi6hqOaRYR1mkAQJ27ciMHZSnMogh/98xNLXFVH4Oarzu2ZdV9YNLPXypiJfvDeB4ciFNEJIYQQQgghhABIuMgfv3Qyfjh3MJ79zjRl22eumoqVPzkOVUVGR/eI7iXx191L8wyRBgqDHQCgXMg693tT+6man+PDICEL3ozo9O5Vnm/a1m9o16M0T9qHz+tBwEWB1GSd6M98Z6pBRA/4PPjj+RMMbQZ3KcK/rprqqj/Rie5WpJXpNV/va0RLOIJrn/gYL7WJYjEnui6iGwXZRkUMjkgkqiFsI+QmU8BUhsxJrUJfJ046/Omd9dhqKsAourcNRThFp6ip79ZwFMff97ZlmRhxElG4szMtnRkKi0ruB33RPa+swi3PfWZcZxK/7QTeqXctxG3/+Vy6r6imLtCaaRwLi7o8wylnokc1y2Tb4eJE/8viDXjqo8248m9LO3zfbiYMU9WlNU3DgSZrwef2vFIH711waEIRnRBCCCGEEEIIAODtG47Cf6+bjmHdi3H1UQPibnMZXq8HuQEfckxFRquFXPjSPGMRUKP4aJQHyoSM8lTjXAqCfhTnBvD4ZZMwbUAFHjpnHGYNrkqsF4Ty86f0MWwrCtj5OT5LnrGOz+NBWUEOThzdHX0q8qVtgORE3ytm1GBc7zKDkN+3ogBFJpe/3+euqClginNJw824p74Vza3GYwn6fQi2xbmYJwsaWtw5xltt8qHTzW0231t2h6+fm5DDPlVFHw2Z6C4uTVMoYhBII9GodJJD0zTUNlsFu1QRxyxzousC7n9XbJNuL4qTqvECwB5T4VpxssTsum/PLHhZz+K1embZFksUkQxVvrtT23A0arkfHGsBpJi/3p4kO4qFK3dg8do97TIWN0ijiky4/T40z+Xe/OwKjL7jf/hww17D8lRvY1U8izETPTvuAxKDIjohhBBCCCGEEABAZWEQwxRZ4CpyJAVJf3LycMwYVIVzJhuFauNj6sZtylKMcykwid8AMLV/Jf5+6WScMLIbfnrKiERbIc6lKDeAmspE9IxYWNUuVkUX13971lg8ePY4ZbtknOj6ORT32700zyD6A21RMi5F9FAkeSe6jMbWCPY1GoXRoOhENwmR9S5F9Bab85NKnryINRPdOc5Flgcv4iYT3Q3NoYjhevzfs5/h38u3Wtqt3F6HUbf/D59vPZDcDhQ4ZaLrY5JFWEQ1Y0a83lZsqjoP5kx0o4juaugpIbvm4jG88Ok2PLhwjXM/STjRDeJnFJbPqpPAa5xcyQ7xNJmJjo827MXFCz7CG6t2teOI1ESimrTmgxm7IxKvt/mz8MSSrwEAv371K/cdpoDhiS3xSRdGonc6FNEJIYQQQgghhKRMjkTwPm9KX/zl4omGnHHA3mFXLjjRfS7iXH512mh0KQ7iwXMSQrasMKeYtW4WtUSnd9AvivHqXHXRCW6XmXvi6O7KdWZ0AT9fGH/30lzkmcR8n8e9iH6gKYR7X/0Kq7bXWYpkJsuW/U2W8SYy0Y1ivSzuQIad2zxdJ7r53nIT5xJyEDhFF3JU4US3BrpYaQ5FDWLptgPNuPnZFcr2v1u01rFPNxhjSaznVz9+2Ucvqplc0jKBWtOkMS9Rs4ieRKHOdJAWFjUtvH/hGqzYbD9JId4WyQjb4Wg06Uz0SAeK6Hph4EwiFiFOhVe/2IFLFnyIPfUtKW3f6vJ7w+4yhG1EdB3zJF+q2f6qCYqo4mkNmtI7n8yV/iaEEEIIIYQQctghc6KrqCxM5KebNaJSQya6sxj5rfE98a3xPbFbEFz0THcR0alujhApFEX0QHJOdEDuDLzmqAHoW1mA40d0dZ0JPLxHiWWs3UvyLONIxol+10srAQCPf7ARfSoKHFrbYxbRY5noxjgXTdPw7Yffw8eb9rvq084xKvaZCtZMdHXbqGbN/JYRVjiokx1jUyhimwdvxq2z3wlxmDLxWj9+WdFczSR+y8TySFST2jTtMtHN8Sgt4YhhMisdZNdFNu4TH3gHG+6ep+wnmqKwHYlqlu8H2eSFeZtU9pUKuZLvynRJd8yX/eUjAMDdL63EPaeNTnp7t0+w2H1mDfFMijZNpqguc3eaphmiWvY2tKIg6HN9bxsKF7vagnQUdKITQgghhBBCCEmZHBdFNv90/gTcefJwQ1SMWcgQnehuHYWAUXCQid+imGEWL0WnvBjn0kXIdTcjiowyp+Lc4V3x7fE9pa54MwOqC3Hh1L44anA1ABic590kcS5+n3sRXWd3fautuFVRkINe5fIiqvqkx1aZE71NhGtpE5RawlHXAroTuhM9VUe6xYluc/waNMcoFyAhENa3hA2TCmLXrjLRWyNJObDrmzMjojvFuegCr6wWgD7RkGgry0SXi6jivsyTFWI/tzy3AoNveRlrdtY5HYorpE70FETeVHPKw5KJGaftO1REz9BkhUimnizYlaIT3e33hVsnuurzbHWiW/vX/33bWduMcT95FTN/scjSz9pdDdInd4xxUZTRswmK6IQQQgghhBBCUqYoN+DYZs6wLjh/Sl/DsiP6lhveiy7yuhb3BRXFGA67GJZYW7OInhi76BLsVSYXlQHAL0wayPTsZDLlX5s/E7efNDz+XhTNZXEuXo9H6hR2wi5mwef1oKIgKF3XrSQ2mbB04z7D8hxBRG+NxApjui0o6ga94GOq2ehm3SlkI+5Fo85RLkCizU3PGKNX3DhXRZpMmehOZMqJru8yFIliR22zZb0udssjLDRHgTeqaVIR1S4TXRTY//b+JgDAQ29kJr5GJj7KYmicSDV+Jhq1ng+nTHTVBEOmECcn7eJcUn0CxE1RTzekqhu7dqLb+LvFa6CKc2lqNYnopgF/6+H3cMbv34emaXh37W4AwHbJZw4AvvnQu5ZlBic6NfSsgnEuhBBCCCGEEEJS5qYThuCzLQdw3pQ+rtq/8YNZeOurXThzYi/DctExXpeE+7Z7SS6OG94VBUG/RXQ2YxZLjZnoCVGph42ILm4jjnnO0GpcfGQ/107xK2bWWJaJTvoepXnI8Xvh93rigprf603aie6E1+NRCmq9y/OxYssBvL16t2F50O8zbNMSjqCxNb1ioCJ6znpTiiK6WUAN2ThUNU1zFa+iR488/4mxCGgqhUWTcqJnSESPaBp+9M9P8NRHm+XrbeJcYpno4nu54/ye/62UbGuMc4mmKEoni6xnuycSlP2knIluzYh3EvENExXtoJ6K4q8qzqWuOYRPHXLiVbTHmJPBrRPd7jK6EdHNxZTN3elP5NQ2hx1F8HW7GizLVLVDWFi086GITgghhBBCCCEkZbqV5GHhD2a5bt+vsgD9Ku0zupMR0T0eDx4+b7xtm/wcHxpbI5jSv8KwXBTExWz3aps4lyIhpkXUsy+a1g9T+1e6HTZuOn6oZZkokHdtc4Hn5fji5yOZTHQZNVUFFtHG61ELat87ZiBeXLHNslyDZoiDaA5FMyb2AqITPdU4F+N78xMI5rZ2+ew6KsFXS1Lkau4kJ3prOKoU0IHE8cnjXDRH8fvppV/H3eQi4rmPmNzZ5kz0dNEMgr3MLZ98n6m6w83Hau7LaV+RJHLz3dIiiL8qgfi0hxdj5fbUInWSyfpvD9xnoqvXuflsWp3o8nZeT2pOcnEMdKJnF4xzIYQQQgghhBCSVdQ1u49zccNL10/HLfOG4rqjBxqWz2rLIvd4jE50uzgXUXgXhSi7YqRu6VdZgDMm9MI1Rw2Ix8uIES/piui9y/Pjr/VJg2OHd5UWZAWAPhUF6FORb1neEorC6/XE8/CbQxE0tmZORI870VN0t5vjFezEz6imuYqhUIlrybqbm1ojtqK+mUxlojuhZ6LLShw89eFm/OzFL+Pv9WMW78T1u62OWsAsCKoz0TOB0S1vPxa32BVCtSMc1SxCvtPxGiYY2kE8jThMMgCwCOjJDCNTOe6p9uI6E91lnIvqHJn3o+ovGk3tWBjnkr1QRCeEEEIIIYQQklVkuqhen4oCXDq9xhL3Mr5PGZ6+cgoW3zgbfp8XP5w7GFfN6o8B1UUAgGuPHgAAmDYg4WAvVIro1ge9vzvHKNr/+ozRCPq9uOfbo6Tj9Hg8+H/fHoUfzB0s9JsYs8/rUTpIdeyyjruXJiYH/nPNNNx16kjccNwQpRPd7/WgJM+aea+LSMFAQkSvb4kJ3kO7FaO6SJ6xDkBZxFSkqTWC1TvqcPvznzu2lWEWv+wK1UY1e6e6jkoA/XpfE578cBNaw1F4XKSiN4ejSceCdAT6RIIszuXRd9cbiqnqYqw4MpXgaBSGNYg6dCTDSrFhDJKuUynSKE7IJJP5HYlaJ2ecjlcUuTPt0tfHJHvdHv2L/HPpZjz7sfopiEzREs6AE10UsF3uV9VfRNNSypc33sZU0bMJxrkQQgghhBBCCMkKfnPGGPz0xS/wq9NHd9g+xQKnVx81wLDuu3MGYfbQLuhanIvJd70OwFiMVNQbZU708X3K8dL103H8fW8DAI4e0gWf3dEdAZndV4Eo/Pu9HvgdnOi5AZ8yBqUsPzH23uX5GNK1uG0b+Xg8HpWIHonvq645jOZQFI1tsSMFOT7USo7vlnlDsWLLAcwaXIXvPfmJ7THUt4Rx6kPvoS7FKBOz7BS2ESQ1TXNVWDQmilr7eeSd9QCA3fWtrsbW1JpcJnpHoQugHheZNLoYLIp9KoE5anD2Gq+F6jxs2d+E4ly/q6LFhn0ZBFBr3yk50VPMcI9InOiOmeiR1PblFsMERjtYnGVjPtAYwg+ejn3ejxvezbFuBZB6YVO38U92/YvXwO2ki6qZ3feOHeY6AjqMRO98KKITQgghhBBCCMkKThnbAyeP6e5KyOsIfF4PxvQqRa0QLyPGvojiiSrOpVDIUA/4PEkJ6OZ+fV6PwQkvIy/gw37I43B83sS+xUzzoF8tbBVLRHRdrCrK9WNXXQv2NbbGs7sLgvLxXTo9VkjVTVRPfUs4LaesOWLFTiSPapo7J3pEw75G9djfXbPbleiWbCZ6RxFqE/zcHIMuwEYNzmn5diFRRDcJy7Jtttc2Y9rdC1GWH8DHtx7rbvBtGAsyqsdt3c4u3iPxOpkYnnA0mnwmumY8V5lGFHXbu3+dxlBiIqw1EkUe0o+9UuHaiW6zzuBEd3mK2jfOJfu+Kw5nGOdCCCGEEEIIISRryBYBXSRHEL7FAqSiaKMSj0UNxCmKRUa+KRPdSYS3c3qKUR1iAUlVnAsAlEpE9BE9Yg72mspCAMCanfVobMsvLwz6bQtsFuUGcMdJw1FZqI58SVdkrm0OY+Y9b2B3fQuAzMS5RKJR7GmI9VdRkINzJ/c2rPd6PK5F9Kx0ordNNLS4cPPq18fgRFcckxhL0hqJGiY0QhLR9eNN+wEA+xpDSReqdMqzVgnHdlna6cS5mO9jp+vennnxgGnSox3EWdnlMkxstPN979aJbjcM8X5N14meapyLeJtl4VfFYQ1FdEIIIYQQQgghxAbRfS6+Fot8istFKgpz4q+TdaEDRie6U5QLYC9UqTa3y1EX41zG9S7FzScMwQVT+wIABlQnRHTdiZ6f47MV0QHggql98dEtc+wbpcnGPY149J310DQNDTZFT6Mu41xWbDmAfQ0xJ3ppfgA5PuPEg9frcSX+N4UiSYvDHUFdSxgPvrEGq3bUObbVj9NNxrYoPNc1h/HMskQ2tiwjvDgvMWm0o64FexvcxeQAzgUZVWM0F7DVFG75ZOI5ZCK6k4hsmARIUz3duKcBi9fuMfUvjiWt7qXI7mvxmGSTJpnEfSa6uycPzM3E71+xD1VvqWb+G8dHFT2bYJwLIYQQQgghhBBig+iOF8XymqpCXDGjBlVFQaWDviDox8Lvz0TA5zWI7m7JE1ziunu8prIA63Y3AABOGt0dl8+owTd++w4AYGddi7Ivr2L/ohPd64m5H/XioKKIPqpnKS6f0T/+XhfRv9hWi6K2mJmCoN9Vgc2OIKJpaGyN2MYyaC6d6Ms27cdbq3cBiEXc5JgmTbwedw76plA0KUdzR/Hip9sMxUPtkMW5qARm8zlZvC4h7Mrc1uKExo+f+wwLV+7EfWeOwcljejiOy+n0q9a/sGKb4b2mJeodiNvsrm/FAwtX45SxPdCzLN92X5GoZjk+JxE+k070mfcsAgC8dP10DO1WbOm/owqLisfx9d5G+L1elBfkWNqJpGqSd52JbrMubONE9/s88eOJaoCv7R5RifIxJ7qwX01z9aSVKhM907z2xQ6U5AcMdUGIPRTRCSGEEEIIIYQQl5jF05tOGOq4TU1VYcr7qypOxJ50Lc4FADx83nh85+/LcP3sgThxdHdD+wHVhfh08wFLP/+8cgq+2lEv3UeB4Haf2K8cPz1lJCrahC5RRDfHvozsUQIAWLpxH5Zu3BfrK+jsRE+HU8f2wDMfb3HVNhTWcNID79i20TTNtai94N0NAGKRNOb7wOfxuBK8mkORpLK1Owo9qsYNmhZzSosCodKJblq+X8iVl23THEq4iReu3AkAmP/UJzh5TA9HEVLm3m4ORXDKg+9iUr9yHDu8q3S7Hz/3mbEfTYO3bSJIFDTX727AL//3Ff72/ia8d+PRynEAseM2j8cxE10UuZNQkjfsboDX40HvCquw//nWWrmI7j7w25GPNuzF3z/YhOeWWz+Xojv9W79bHBvv3fPc7TtJ3DrR7Y4pauNEFycHw9EofF6ftJ1OJGpMS49ENfh9bp4mko8hU1Fn+xpa8f2nP4l/vtrrehyKMM6FEEIIIYQQQghxSZc2IbujuGJGf/z8myPx4f/NieeuD+pShNfmz7QI6ABwxhG9cMu8oXjp+ulxN/k93x6FCX3L8a3xPXDssC74ySkjDNuIx1RREMSA6kKUtYnoFUJ2uTmyZnDXIlw5s79hWcyJ7o4Zg6pctkxw9dEDDO+vNb0X+c8nW7B2V4Ntf42hCBrb4l7MwriZpjaBtyjXbzkXbuNcmkMRtGahiO7WxasT0TSTE12ViW5cXteciNaRbdMUsgqhkaiGL7fVYsydr+KPb61TjsnsHI5GNbz82Xas3F6HxxZvdO2+jjpMDmyvbXYUoeVO9CREdJcTO82hCGb9chFm3POGNPtffPgklcKlbnLB5z/1CZ79eItUTLbLm1ehKtTphPtMdHX/dk50w7l0cf6iJie624kLoxM981b0O1/4Ii6gAyxemgwU0QkhhBBCCCGEEAfuPX00Lp9Rg5kpCL/pUF6Qg7Mn9UZVkboQp0iu34dLp9dgaLdivHDdkXjkggn41rieAICg34c/nD8B503uY9ime2le/HVZgbGQ6NBuRbb7u/H4IfjLxRNR3BbnorvT3fDoBROw+CZ7R6+ZPJMb/vvHDkZloTwewo2opmnA1/tiMSb5iqKs/SoLDO+Lc/2GYrNAzInuRiRrao3YFjo9WIhEjSK6SlS0ExsjkngT1Sn8/lOf4EBTCD/775f4SpHbbt5VKBpNyX2tH1dzKBLP+jfjJKK+sXInPt9qfCLESbgWRXa3cS4HmhLO/gbJWMUIqXTOhR2b9jYq18nc4e0l2rrPRFevs8vVF53g4vVR9ReOaIYJAbeH7ZTtny7mz4+bmhAkBuNcCCGEEEIIIYQQB05tE6KznYJgQgiuLsrF7KHOzvmeZQkRvSDHKBP0EAT2DXvkru4Zg6rwwc1z0NAaRmWhOh/ejN/nRbeSRP/fP2YQlmzYi7dX7za0EzPgzSI6APi9cn+gW8f3T174AgCQH/BhP0KW9dVFQazfnTh2WZyL1+s2Ez0741zM/O2SSbj52RVKgTSqaQbRWhWJY5cDHs+XdnHe1uxKRBEd++u3pBEUZsG3udW4b7fua72baXcvxB5FYVMnkfvppZsty1RiZTgShd/nNTrFXaqn4idNv69EkVr8LKaSiZ5uJrdsIkvME5eRqnDc4joT3caJHlFfA3HI4pMCqv4sTvQUznl7ONHNhCJRx6dwSAyeJUIIIYQQQggh5CBn/jGDcPSQaswZ2iXpbcXcc3P8gijC9akwOrJF8nJ8qGyLfkk1uTeiaSgMGkX8566ehiJhfHkSt7iqYGuyju9chRO9wDSmoqDfIjp5PPI4l2rTEwSxTPTsd36O7FmCPpKMbR3zMThlopvjb4CEYOnGdW2+lrL9mQXHhtawIZ/frYgZ0TREoppSQAeMWd9ukU3q/N+zKzD+p69hZ20zIsJ6t070kNBOF6zF41RFkLifULBv19Rq7/6WucPbaxLJ7nqJ2B26cSLDuE5868aJbs5EdyuId4RwLnIoPBnTUVBEJ4QQQgghhBBCDnKumz0Qj154BPy+5H/mi0K5TKR+9XszcP3sgbhsej9X/Q3skloh1WhUMxSeBGJFT/MC3rZxysXYgItifW5QxbnkBoz7lGWim0dw6ZH9sOzHx+DoIdWG5c2haFZmopsJ+r3Sc61jFpFVjnNduBUnaszr7NzqKkKRKEKRKL77j4/xjyWbAAA3P7PC0KbRJKK7Pe9RTXMUet2K3CIyQfnvH2zCgaYQFry3AeK8hFuROyQIoM1t/YvHec3jH+OnL3wBzZRh73b4Tu32NtoL1zInutNkRioasqZpeH/dHpdt1esiBnFcPVFkaKfoKxzVDDtze5uLu20PPd3c58HwfZQtUEQnhBBCCCGEEEIOc35y8nCM6VWKS4+0CuUDuxThe8cMQlGuVQiV8dNTRuK08cnH30Q0DccMMzrpc/zeeIRLXsAnjYpROdGTRSVyeeAxCOyyOBez1pUf9KO8IAc3HT8UswZXxQugHixxLjk+L4J++aQCYHWiqwpI2onounieiiDdGoniuY+34LnlW3HjMysQikTxxqpdhjYNLRF4hOkNJ9e0jhZ1Lojp1tVuGLNNny3hqCEj3rUTXbiXmtuKsobCxm3/9M56bD3QnJFM9B/98xN8+3fvxSdR9jm4v2UTB6lcbyfW7mrArroWBP1e6b1mxC6nXy2Oi+dMnPhRufXN5y5bCouaoRPdPRTRCSGEEEIIIYSQw5zzpvTFc1dPQ0WhuwKmdlQVBXHPaaNRlJtcGbZwVMNF0/oaIl1y/N64O14X0yf2KwcATBtQAUCdiZ4sKkemBg35QlZ8kaSwaMQkKuuTCCX5ASy4aCJOG98LAFDfEm4Xd2kmOfOIXvB6PbY5yeaJAF3ANbOrrgUAUGznRE8h3iYUjmKf4IKub7YW1WxsjRic6M0uxcL2c6LbiegRiLt0K9KLkxm667slYr0WLaGIY5yLTAwWm2mahqc+2oyPNu7D8q/3AwD2Oojodk7099bsxtb9TdZx2IjcKr5uy+7vX1Xo+GSKbZxLVC1gR5N0oseK74r7TV5Eb4+vCnOfdKK7hyI6IYQQQgghhBBCMk+SCpDP44HH40G/ykT2eo7Pi7xATMDWxfSHzx2P208chgfOGgcgc25NlSNT04wFW2VO9GbBcbvk5tnoVW7ME8/N8Vr28cO5g9Mec6a59/TRuPtbowDIo3N0zCKzSiDWl/u8HkvefTitOBdj0cb6FpmIHjaInc0unehuRHTzpIkd50zqDcDe8dtqcqK7F9ET2zTpTnTJ2CJRzdGJLpsYEIV18RrrsVH7HONcZE70KN5ftwdn/+kDTL17oe32btnfFBtHeUGOYwSNXc673USD0YnunIkejWqGdm4jesSPQ0cVFiXuoIhOCCGEEEIIIYSQjONWAJp/zCD0qyzApdNrAAB+wUkac6LHpAvdiV5ekIMLp/VDWUEOAOBAUyJH/ZELJiQ1xlPH9oi/lkVP6Oj7BoDSfImILoiFMte1uL3OZdNrUN52DG54/LJJrtumijhOOye6WXCVOY5FvB5YnkxIy4keiRrmaOokTvSG1ohUZHYiqjlHXCQj/OtRQHZO9OZQNEUnujXORTb2UEQziMCaZhWTZddBHIc4UaE/ieHkRJcdczjiPr/cLfsaYt8BpfkB58x1m3Uqh7mmGSdt7LLTdcJRzTgxkooTXdgkM6FV1n4Y5+IeiuiEEEIIIYQQQgjJOG4TL66bPRBv/GBWXFAOCPEshkx0ReFP0Q07e2gX/PF890L6L08bHX+tEoI1zejKrioKWlza4rayjPZciYge8HlcC1hFuX5M7lfh2G7F7cfix98Y5qpPGeI47Zzo5nG3OAjUXo/Hcv100TalfPFI1NmJ3hI2jNOtiK5pGrbub7Ztk8yY9SigSFSzFGTVicW5GJ3omqY5Zo4b41x0J7o8QsXirI5q+GzLAZz3yAdYs7MeIcnEgCjoNgjnWF/udE5lTvTYsam3ScV8vb/tO6AsP8dx8i7aNoEgO7cqh7n5eosTDso4F00zTIy4L+bqLNCngyXOhSK6ayiiE0IIIYQQQgghJOOkkm0MAAG/4ET3GQuLyjDHV1QWGt3dj144Aarao15hRYNEiNURRfKy/Bzk+Hym9Qmx0CcpfhrweeEX9hXwxaJr3ApYPq8Hkm4N9K8qQFFuAD3L8hz7++1ZY6XLc1N1otu4+IGYiJ5rKlSqu7nt4iRUYwhFogaxsb4lZGnT2BpBq3BvuC0s+o3fvoOz/vi+bRsn572IWJRWlT/dGo4azmk4quEnL3yJiT9/DS9/tl3Zt3juWtrGJLunwtGo5ZpFNA3f+O07eHv1btz0zKdSJ3pUMVGh99XicB6kTvS2CYJMsq8x4UR3ik3RNA33vLIKY3/yKv67Ypthnaqop9lFHlGI7Ya+TE5013EuYg69oo2maXjknfVYunGvqz7tYCa6e7JCRH/wwQfRt29f5ObmYtKkSViyZImr7f7xj3/A4/HglFNOad8BEkIIIYQQQgghpEMQC4V6PB7k5tg70c2IsSFFQT+OGlztygUajmp44GyruKxBQ0NrQkD0SYpuimKhV6HYi5MAgbY4DLcClp4Xb4d+fvyqGQMBMeNdJDeQOK6gX32+zY5qWQ63iMdj7BtICLF2ru4KIe5mSNei+ARJKGzc5uIFH1m2bWwNS+NOnNjZVgzVjjqJaK/CIKLbZMdHTALuo++uRyii4cq/LTW0FQVoMVZGn8iQ3VPhqGZxaIsTAfsaQ1KXvLgvsXir3tbp/pU70aO2n8dU5HX9aZTSfOdMdAB4aNFaAMDt//ncsFycSDAI6qbDFM+7arIwFueSeO/26QXNsF/5Nq99uRM/eeELfOt3i131qeofoBM9GTpdRH/yyScxf/583HbbbVi2bBlGjx6NuXPnYufOnbbbbdiwAT/4wQ8wffr0DhopIYQQQgghhBBC3PLnCyeiJC+A+84ck9R2AZ9RBC5qK0hZYCpMaUYXtvtVFsaX1bWEHcXnmYOqAAAnjOyKb4zqLm3TaHIxW0T0NrFQFuWiE3Tp8pbhdAwAkN9WgLVbibMT3eeV71+cqLAbY7LuVU2zToLogqWdAF8hPFUQ9Hvj0SixOBd7UfL9dXtx90sr4+/dxrm4oaHFfV85fm/8SQhxskUUVVvDUUOx0nBUQ01VosCuiHE7SZyLIofcLOJ+sbU2/rqyMAchiWArLhInkvRr5uRE//O7GyzLQhEt5adUVOh1EcryAy7iXBLrzRNO5tx42XIgRSe65s6Bb4iUEZaLXwGb9zU69uMWpwkwkqDTRfR7770Xl112GS666CIMGzYMDz/8MPLz8/Hoo48qt4lEIjjnnHNwxx13oKampgNHSwghhBBCCCGEEDdM6V+B5bceg5PH9HBuLOA3CbzHjeiGb4/viUuO7Ge7XXl+THD1eT341rieAOC4DQDcf9ZY3PPtUbj7W6Ok6zXNGvViKSzaJlzKolx09AKpQMKJ7hY37nLdsT+ka5Ftu9L8AAKK/sTIlRybMSZbDLQpFLHEueiir51Dt7IwmHjj8cQnWEKmTHQZ76zZLd1fJpDFx6jweb1xV7/o+jW45MMRg1AbiUYNtQFExNNlKJzaqnaHR6JWEb2uOXEMu+tbpU50Y2RO4vzpTmy7YrwqnDLRU9HX9wmZ6E73hbjeZ5qwU4njlkx0FyJ6RDMWc61vCbvKexcnJlQTAoXChGK60Th0orunU0X01tZWLF26FHPmzIkv83q9mDNnDhYvVj+ScOedd6K6uhqXXHJJRwyTEEIIIYQQQgghKeDGQW3GbxK2qoqC+OVpozGud5m0fY/SmPP6xNHd4svuOnUkHjh7LOYfMwgAcNJoucMcAEryAjhtQi8U5waUbcw6lVlg3ttWpNDOiS7GudgJ1DIKc+1d+ACQ39a/OU5m+sBKw/tnrpqKclNufHyMglvcTre3yzGX0dgasRRXrWuLB5EVtNQpF+JcoGnxyYdYJnpSQ8Arn+9IbgMb6pNwovu8iUkXUXQ255mLQm0kqikF6qhDnIvs2oSjUYsQLDrztx9oNjjTE/tKvG6QZaKnIMCGo1Yfuhsh+IdPf4ILHl2C9bsbcPID7+DFTxN55vsaYhMCJfkBi2vcjLhanLD79atf4a+LN8bfR21iVQxiu0L1j0Q1g9h+0gPvYle9fVSQpmnGJyYUhyKK6OanZJIl2c/y4Yzzt3A7snv3bkQiEXTp0sWwvEuXLli5cqV0m3feeQePPPIIli9f7mofLS0taGlJ3KS1tdYvBUIIIYQQQgghhGQHybq0n7pyChau3InTxveML8vxew3RLL88bTT+88nWlMZz5az+OHNiL1zz+Mdxt7oq6mRyTbmyH2MmenKTC4UOUTaAUQB/4wez8OzHW3Dc8K4Y1KUQA/7vJQDARdP6oqaqMB5/AcQyu3UhTuzDTopMXkQPI2jKRNcLVdo50UvyEhMbGhLnPRSJZjwSxI6Az2OIvahtStaJrovoifMmuvlbI1YRXVW8VBR3Q5I4F2lh0YhmEZdFZ359SxhX/X2ZZTt1JnpseSoRObFMdONYxLcaNESjGj7fWouh3Yrg93kRjWp4eulmAMA5f3wfWw804+rHl2HeqHkAgP2CEz2ZOBd90mvV9jrc9/pqZbtU4lwiUc0ivr/yubpILGCdlFB9NMTvn/1NIceoKzvoRHdPp8e5JENdXR3OO+88/PGPf0RlZaXzBgDuuusulJSUxP/r1atXO4+SEEIIIYQQQgghqeImukSkR2kezpvcx+J0Fsnxe9FfkTFtZmSPEgDAzScMwae3H4txvctw9JAuWHH73LijPagQ0W88fqiy31xJYVG36EU5DfEmAN784az4a1EA71dZgPnHDMKw7sXwC/vSBb9iwdk+c1AVLp9Rg+/M6m/rxhdJNke5ocXqRK9vCSMa1WwF+YIcMbZCKMgatt/OjkqFC1/Ff66ZZnnCQBdt3eAXCtGKIqnowG9sjRhE9ZZwVOlEN2SiGwqn6nEu1msTlgi6bhzM4r7qW4yFRR98Y43BDe6WUMQY5xKNWgX++15fjRMfeAc//vdnAIznbV+jcQKjNRxFQ9uxlOUHnONchNf6d80+yfUU29k50VWYneiA/HMj9t1kuiYaNPzp7XX409vrlPs/0Oh+Qgewiv4tdKK7plOd6JWVlfD5fNixw/hIzY4dO9C1a1dL+7Vr12LDhg048cQT48uibV86fr8fq1atQv/+/Q3b3HTTTZg/f378fW1tLYV0QgghhBBCCCEkS/EnKTC7xVzYUsXfLp2ET77ej2kDKg3iqfhaFsdy/eyBGGyTR963ogAfrN8LICEGD+9ejM+31mJKTQUWr9uj3DanLVP78hn98PP/xp7c//k3R6JPRQFK8gI40BTC3OFWHcWMLr6JMTs761rwu3PHW9raiZHXPvGx475EGlvDlkx0IFas0ixIdivJxbYDzQCMkxUaNEMmeqoO2mSidCb0KcOonqUIeL1oRmJ/X+9tct2H1+OJH4cxEz1x3A0tYYMg3tASVjvRhcVhQySM2okuy0R34yJXxbmEohrueWWV4/YyYpnoxiKq4lMFmoa4K/yJJV/jrlNHGVzzeTk+w9j3N8UEcK8H7iaBxEz0ts+0WSTXx9HYGo5F7Zg+DG4y0aOa9ZxLr42mwYvYOMzXZF9jCD998UsAxidZDCJ6Ek9FyJAVoiVyOtWJnpOTg/Hjx+P111+PL4tGo3j99dcxZcoUS/shQ4ZgxYoVWL58efy/k046CUcddRSWL18uFceDwSCKi4sN/xFCCCGEEEIIISQ7+c6s/sgNeHHh1L4Z7VcUoQpyfLjjpOHSdiV5AcwYVGWbb+71enDd0QMwbUBFfFllUVDZHgDG9SmNvw60iaqPXngEbjhuCO4/a6zttroIe+mRNfjFt0bhjR/MwtmTegMAFn5/Jv555RTMHFRl2wcgL1S4o7ZZ2taN17yqKIgbjx/i2C6WiW6VoOqaw5Yipf0qE08MiIUfRSd6OiJ6MpM0unhqzunfsKfB/f68nvgkyK3//gwfbohNpIgCeGNrxOBCbmiJxDPOzRjiXIRzpwuwMof+n99djz+8ZXQzN7twomuahvW7G/DDpz/BZ1sPxJfLipC6JWwqLBqORmGOxTc/jSKeCzEKKRLVsL/NiV2SF7DUA5Ahnj99Pypj+dg7X8XYn7war3mQ2G9iwKpYobBk4uL/vWyNrhbbmEX0ptawtJ0o6icropvLVMgK0RI5nepEB4D58+fjggsuwIQJEzBx4kT85je/QUNDAy666CIAwPnnn48ePXrgrrvuQm5uLkaMGGHYvrS0FAAsywkhhBBCCCGEEHLw0as8H5/eNleZO54qA6oL8eGGfQCAT2+fayuSu2H+sYOxYXcDZv1yEQCgyiEmRCyMGmjbd5fiXFw1q79jYUVdRPd6PTj9CKOBsKIwiIpCewFfR9T0pvavwHtr9+DUsT2kbd0Ue6wsDOLKmf1x+oReGPeTV23byuJ26lvClsiLfpUFeG9tzJWfY4qiyRFF9CQjZXSSyaPXHcpm4X39bvcius+bcKJ/taMeV/51KZb++BhLtMdeIVJkf1OrOmtbFNEFMVefVJBNLny0cZ9lmRsneiSq4du/ew97TCKy+ZolwwWPLjG8D0U0iB912VhFV7646z0NLdjXNrbSfHcxPeLI9e+AsKK4rR4j8+nmA4blrpzoEhFdOh6hiTnORdyPOEajE70Vv/rfKjS1RnDLN4YltT+ATvRk6HQR/YwzzsCuXbtw6623Yvv27RgzZgxefvnleLHRTZs2wes9qKLbCSGEEEIIIYQQkgaZFtAB4MbjhqKxNYJTx/VMW0DXCQjjNOeVmxEd1vtN7lExXiXo91oKDAYlUSipIMZW/OH8Cfhg3R5MHyh3sFcX5zr2p+eLlxc4C5hqJ7rxWHuX58dfi45kDQkn+g3/WuG4PxU5wrk8d3Jv7KprwSuf75C21QXrgOl+MV8fO3xej0G41wVps2N8SVvUD2AUjWuqCrBuV0K0VxUW1R3FbrPi3WSir95ZL11ufnogHcKRKDwe+8+7GOeyq67F8FrPSC/Nd5fnL04y+L3WrHoZ5ic4dta2oL4ljMKgX/nEhixCR9pO6Nucgy+OVexK7HdPQyt+u3ANAOCCqX3RS/j8uIFOdPd0uogOANdccw2uueYa6bpFixbZbrtgwYLMD4gQQgghhBBCCCGHFCX5Adx3pn1sSrKI4qiTiC66mXccsEaovPGDWWgJR3DFX5di455Gw7pMTSqIYmBh0I/ZQ7so284b2Q3LNu7Dii0HsFTiDgbciec6Mif6olU7MaiLMUe+R1le/LXP4ETXDJMWqZIjXLN5I7tj094GtYjepi+mk9Pv83qwUxB+c3xeaJrmSoj2eID+VYVGET2q/1/D/77YHl+uC8FuRVE3TnQVKud2KkSiGvwO3TUrxrqzriVe5LXMpRO9QYhI0SfTVP3rmLXwO1/4As8t34L/XHOk8okNWZyLDEOcS6vxRNzx/BfKvnXEOJdvP/weXrxuuu13kTl+hiK6e2jxJoQQQgghhBBCCEkBr+AgL3eIcxGpE4o06vSrLMCQrsXoKnGAB9MUjyf2KwcAnDnRWktOhc/rwe0nDcfJY7rbtlExqW2fOrLCor9duMaSN92jNCGimx3gyUSxqAgIgrjf54HPJv1AU2SiO1EgFLH1eT2GSZHWSBT1pkKiKoJ+r+Xa687lf3+yBZ9vrU30axPnIqNFUbjUDeYomnQIRTVL4U4zqiKrtU2h+FMdbp3ojS0JwVyfVDLHqJj5WDKJ9OnmAzjQFFLmqUc15+MCjE+HuJ3YECdgxOu4o7YFv2jLXX/x02247d+fOQr5qdYWOByhiE4IIYQQQgghhBCSApWFQZwzqTcuPbIfinPdiXhO/PK00ZjYrxyPXjghvixdEf3vl07C4puOxvg+5c6NTdhG39joc09eMcUgJgclcS4A8PVeo+u+R1levPjhpJpE4da+FQWGjPRUEUV0r8djKWIpoguQgSRihu84aThmDamOv5f1v6O2xVVxzqDfZzlmXXR97cudhuW6GOo+zsU6keOWSAad6OFI1FHoVRVZve+11bj7pZhoXJLn7vMnOtH1c+XkRH/m4y3S5Wt21kP1IYhE3WXHi0K7axFdOP/mse+pj01KXf34Mjy2eCP+vVw+dh2K6O7JijgXQgghhBBCCCGEkIORn31zpOu24/uUYenGfbaieK/yfDx1xRTDsmKXAqGKgM+LbiV5zg1l2woCcmHQj/oWaxyGmYfPHQ8AmNK/Eq99uQMFOT5DnEtVUTCebb1DiDoBgMqCID76vznY09CKfpUF+OeVU/C39zfi5nlD8dvX16R0DCJiNI7f67GdJIik4ERvbI0YhO/cgA8T+5UbMs9veW4F3l+3V7a5gdyA1xLlo7unC3OMkp7ubG9ocSfEphPnklEnekRzLGLbohjrOqHAqywuSIboOtfPWVOKrvy1O+tRU1UgXRfVNIPLXIUYsdTsIqceMArf5uvoNd3Pu0yfL/OppojuHjrRCSGEEEIIIYQQQjqAB88eh1PH9rCI5CpuPH4IxvYuxQVT+7bvwGwQReYKU2TN1UcNiL8+dVwPAMCPjhuM40Z0BQD84tujcMXMGvz7mmkQpb2/XjIx/nrb/qb46wUXHQGv14OKwmA8K31C33L85syxqC7KNbjIk6FSGLcYCePzGp3oD587Du/fNDv+Xhcck8lEr2s2Fo3tWpKLB88eh7tPHYmxvUsBwJWADrQ50c1xLm3CbH7QKBrrYujOOmvevoxUhWMgs4VF1+9uQIODeOymkKvbe0N0h+uFWVOdUFi7q175MEY4orlyooumfrfjECcxzE5085MPHof5HzdjJDHoRCeEEEIIIYQQQgjpALqW5OLeM8a4bn/lzP64cmb/9huQC8R4kPKCnHi+9z8un4y+lQkX7i++NQqXHlmDod2KDO1vOn4oAODjTfvjywd3KcI3RnXDC59uw0dtedNnT+qNWYMTMSgyAv7UMtGDQh67KLb6vB4M7FIYf3/ciG6G7XTBOpiUiB425LyX5+fA6/XgzIm9sWT9XsN5kBHweeIiaW7Aa4lz2bq/GTVVhcgLyEX0HbVG57GKpjTiXDJZWPSyv3yEqiL7orxOcStAapFHrS7jXFTUNocszm6diEsneipxLuJn0jwZYhu/BGv4jNv4H0InOiGEEEIIIYQQQghRIMa3FAm57+ZCjn6fF8O6F8OjsL4eO6wrqouCOG18T3g8HlQWGoXTagchFYBtJvogQQwXqSoK4tLp/RJ9+I0i+oDqIiy46Ai8eN2Rlm11ET2ZOJ36ljD2NCSEbDFeo0eZc6ROWX7CNS9zop/7yAd4Y9VOPLRorWG5Lgi7d6J3TJzLL749SlosV8QcOSKiaZqlsKiswGwqRWf1iYdURfSWcFQZRRONunWiq13lKsQ8fXMEjJOIboYiunsoohNCCCGEEEIIIYQQKXXNooieCDQoyEku3KAkP4D3b5qNe04bDQDIzzE6qauL7IVWwD6yQ7b9lJoKfHDTbPQsy5f24W0T/GcNrsbw7iWW7QvaIlPEopVOxU37VRZgd12rdF33UmcRvbwgIaLLMtEB4KI/f2hZ1tom6Lp3oqcuniZTWHRsr1K8f/NsnDKme0r7aglHLeLy+VP6WtrJrsuQrkWWZSLxTHSXWeSysSnjXKKaY8FUAIY2bic2WsU4l7C9iO6BvaieyXz7Qx2K6IQQQgghhBBCCCFEilj4cGr/ivhrUVB3i+jKbjQJl26c6HYiusyBO6pXCbxej2GsjUKMiTk/Wuehc8ahpqoAvzljLADjsdq50rsUB3HZ9Bq0hOViaA8XIno/ISInN2B1oquobwnjxn+tcF0oMp04F7Pwet+ZY5Rt9YKfPm9qEmRLKGpxopufYgCAgOQ83XTC0PjrU8f2sKwPxQuLpiaixyYu5OuimjsRXfx8tbjMqf/7+xvjrz/dfMDap7Bf84MhUdOA6UR3D0V0QgghhBBCCCGEECLlgql9UVNZgO8fMwjHDOsSX14YTK/M3vlT+hjeVxc7i+i9y2OO8tL8AH78jWGGdWdN7AUAGNWzBH++6AicPqEnrp89EADiBT1jfSREalX0xQkju2Hh92dhWPdiAMZjLclTH/dNxw9FXo4P9505FkW5fvz2rLGG9bI4lzlDq3HRtL7x91fN6g99WAOrixyd7yJPfvS167a6cFyUwnUUndt/On8CTh7TA5/fMReT+pVb2upPHKgmLNyM0+y2Lsu3TmTIJljEiBdZzFAiziU1Ibk1HIWm8KJHXDrRDSK6ywmQuhb1BEhLOIqQzZMC5jFRRHcPC4sSQgghhBBCCCGEECmVhUEs/MGs+PtXvjsDPm8sAz0daqoK8dL103H8fW8DALo45GYDwAkju+KV785An4p85AZ8+MkLX8TXzR3eFS9/dzr6VhQgN+DDUUKR0qDfh3duOAr/Xr4Vx4/oioffjOWJe10KuwUGEV3tRNeLW84YVIVPbzvWItzKnOh+rxc+od3QbsV4/LLJ2NfQimOGdcHjSzbZjm3+MYNw76tfuToOEV1LzQ/6bEXZ/zthKH723y8NyxoF5/bUAbGnEwqC/ng8joh+vnwpZJYDbSK6ySmeZ4oCAuSFRXMM0T3WvkNpFhZtCUeslTrbaGgJY8mGvY59iBp2JgTt5taIbURL2LTO/J6ooYhOCCGEEEIIIYQQQlwx2CFnOhn6ViRc4RVCFrgKj8ej3L/H48GQrsXKbXuW5ePqowZgf2Mir9ytrKtnowMOInogIdrKnM96tInIrvoW9K5IZLb7vR5MrknE5jg50Z2KdjrhFBdjLiALGJ3oogNc5uzXJ1sCqTrRWyMWp3i+JI9f7kS3H1sooiEa1dKLc1Gs+8eH7p4KEJ3hbqN47GgOR9BiczxmJ/qW/U1obA1LzykxwjgXQgghhBBCCCGEENLh5OX48O6NR2PxTUen7Wx3iyimuvXgikVURRE9zySKB/1WkdyMWRRftmmfIbfaLL6LIrfMyV5sEy/jBqcxl+VbJzdE57YY02Ln7E81E72hNWwRhc1FaQFnEV02qQEAH6zfi6Ub96U0thabTHS3iHEumXCi1zaFMfOeRfH3ZtE8bHq/7UAzpt69MO39Hg5QRCeEEEIIIYQQQgghnUKP0jx0K3EuuJkpVGKqHWKcS6FQZPTmeUMN7ewKn+qI+ecAMLhLkTQGRUcU0d+98WhL7rjMQeymSGu8f4cxlxVInOhtonbA5zGcTzuzuT/FOJet+5ssmeiyOBeZoz7HLx9bufDUw1l/fD+lcQHA51trseC99SlvDxhF9Ew40VdsOYB6IZ7HLMxHJHnp+xtDae/3cIAiOiGEEEIIIYQQQgg56JgzNFbo9IwJvVxvI+aPu40YESNNygVndv+qAiy9ZU6iPxdC8fePHYy7Tx2Jf189DedM6o37zxprKz5XFhoF8QJTIVCZoNyrPN+yTEVFoX2MTkmedX1jW5yL3+Qu99lMBqiKuDqxcU+jJM5F5kQ39u/xqONcZIVJU+W1L3emtb0hzqUdinya89HNTnTiHgbeEEIIIYQQQgghhJCDjvvOHIN31+zGjEFVrrfJy/Hhipk1aA1HUe0yT3xi33LMHlKNXuX5mNA34QQP+r2GGBo3TvQcvxdnTuwNABjdqxSAvfg8qV85LpveDzVVhQAkIrokZ318nzJXESU5Pi/6VOTj7dXqNrkB6zE1C050Ebs4l1Qz0TftbTTEx/zqtNGGeB0dc2FRr8djuB6i278oN4CAz2NbgLOjyHScixmrE73zj/lghSI6IYQQQgghhBBCCDnoKAj6cezwrklvd9PxQ50bCXi9Hjxy4REAjEU1I1Gjw9mNiC6jf3Whcp3H48H/zRsWf28Wi80xJjMGVaEw6E7u619daHGT63QtzsXTV06Rxr00toal+zbr5GJeujkTPT/HF3e027FpbyPCbULw788bj7nDu0rFZvO595lEdHGeoijXj9yAD6FIGJ2NeCjpxLmoJgXM50rlRNc0LaWoo8MJxrkQQgghhBBCCCGEEOKCvBwfbjhuCOYO74KxvUsNwnGqud8njuqOHx03GE9ePtmxrVmoNjuL/3j+eNfFLgd3MYr3/7lmWvz1wC6F6FWeL80ab1LFuZgGN2twdfy1eG5umTcUswa7e3pgy76meJxLbpvr3qmIKBATzcUJAPGcFAb9Ugd/ZyBeP10EdxMLZEY1cWIW1qMKEZ0OdWfoRCeEEEIIIYQQQgghxCVXzeoffx0WREqnIp0qvF4PvjNrgLu2glu4IMeHfpUF8fezBlch6PdBgztBdFDXIuyqa4m/H9qtOP566/4mAHLBWhe1A35TnIswthE9ivHL00bF3xcLBVkDPq/Fma6itimEYFukTK4g6E/oU4aPhMgaqyveYxifKPAH/V5prno6nDe5Dxpawnjm4y1JbadJCosG/cm75AuCfuyTFAgVneiapimd6KGIBn92zCtkLXSiE0IIIYQQQgghhBCSArkBL2YOqsLkmnL0LMtr9/2JueMf3XJM3J0NJATv4lx14UxRbB7cpciwThTM1+5qsCzT0QtgBrxW4VrnR3OHoFQowioWSM3xe11npNe1hNHYEnO+i8f68HnjDdE25gkMr6mwaE1VYrLh080HDH25pXtJLu48eTheuPZIy7q+lQW494wx+Nslk5LqMyLJRDdH9rihojAIWRqLKKLbmc1D0cznsR9qUEQnhBBCCCGEEEIIISQFPB4PHrt4Ip64bHKHZEqLRUjzTG5qXSA/e1JvzB5SbYl+AYDxvcvirweZRPTYMmPEi120iCWHXNih13QuKgQRPeDzJhV9s7s+5pYXhe/KwiDuPHm4cixer8eQyR6NapjavwIAcPKYHpZz58T0gZX47/XTcf6UvuhTkW9ZX1EQmzA4cmAlLp7Wz3W/YoxKSzh1ET3X70U3SaFcMc4lbCOUh9LIYz9coIhOCCGEEEIIIYQQQkgadFRRRpkwPrZ3KQDgrCN6A4iJzY9ceAROn9Ar3uaFa4/EdbMH4qYThsSX9SjNs8SaPHLBEZjYrxyPXDABgPG4iky522YhXBTOzWktlYUJV3rA50GBi+Knem65HkFiFpfFSBhZnIs49nBUw6MXHoEHzx6Hy2fUJB3ncuywLnFnvSwnvrwgcXz9qwss61VEZU70FFzyGoDeEnFfdKLbFS6VFSUlRpiJTgghhBBCCCGEEELIQUC+RHx+4rLJ2LK/Cf2rjC7yUT1L8Y8PvwYAjOhRghE9SgAAd586Ej3K8uD1enDZ9BosWrULJ4/pDgDoVZ6Pp66YIt330G7FWLJhb/y9xf3tEV+rnegA0KvMKvgCwBUza/D7N9cBAEryAmgKReLrzBEs4h7MjnlzkdNwRENuwId5o7oBQNKFRfNyEuddln0viuiqIp8y/vzuBtz+ny8wa3AVdrbl0wdNkTurdtQ5d6TFImXeX7fXsFgU0TfuaVRuLrYjciiiE0IIIYQQQgghhBByEHD59Bq8sXInTh7TI74sN+CzCOgAcPqEnohqGibXlBuWnzmxd/x1aX4OXrxuuqt9D+lWZBDRzfEgonBtFrHFwqK1TSH0LreK6AOqC3HssC5xEb04z4/ttYn1uQF1oIbViW5c32oSif0uC5vqiKK77KmDkrxEDn0yIvrbq3cDAP6yeGN8mSii57p0zGvQpE70/32xA82hCHIDPqzeqRbjKaI7wzgXQgghhBBCCCGEEEIOAsoKcvDyd2fgqln9Hdv6fV6cO7kPBlRbs89TYVi3YsP7z7bUGt6L4rJZxBbXHWgKoZdERK9vDmNc7zJ8d85A/OaMMQZhGrA60UXMRU7NQnfYFFciS985ZUx3pQCel2MvoYrxNMm63M0E/Ynt8132pWlAn3JrjIymAXe/tBIA8NWOeuX2jHNxhiI6IYQQQgghhBBCCCFEyg/nDsbsIdU4dVxP23ZiyonMra074k8a3QO9yvMs6/c2tsLj8eC7cwbhlLE9UJxrFNHtCm56veZ8duN6s2tek2jGvz5jDD66ZQ76V1nF6LyAvbu8IJgQu80u/GQRXfVuC6BGNU1a8BQAFry3AQDwxdZa6XqATnQ3MM6FEEIIIYQQQgghhBAi5eqjBkiXP3zueMN7nyCc+yQi+t8vnYy65lC8QOcRfcuweV8Tth1oBmAtfCk60YN+b1LFW837d+O09ng8yA340Ls8H2t3NRjWOYnZons8lcKgxr6S9zxrgFJEB2Ln9sO2KJ4Xrj0S1UVBTPz564n1FNEdoROdEEIIIYQQQgghhBDimpK8AI4b0dWwrEhwjpsLiwIxh7YuoAPAk5dPwaIfzlLuo1gQ0e2iXGRY41xMTnSoRfU+FVYner5LRzgAjOpRglmDq3DOpN7OjSWIOei72oqNOqFpxvNv5tPN+9HYGkF5QQ6GdStGdXGuYb057oZYoYhOCCGEEEIIIYQQQghxjTkfHYChWKgb07jX60HQ70PAJ29sFNGtEubALtZiqjrmSJVw1CgSm+NcJvVLFF+V5bWbc87fueEo5b69Xg8WXDQRP/vmSGUbO8QisTvrml1to7UdkFjAVWTjnkYAwPDuxZboG4BxLm6giE4IIYQQQgghhBBCCHFkwUVH4MgBlfjFt0dZ1oniczK54GWCO11EFIRlTvRRPUvx0Dnj8Pw1R1rW6bsfWB0TpE8c3U25/39dNQV/vuiI+Ps+MhHd5ETvWaaOTkkHn9eD4d0TExSXTa9xtZ0+J/D692dh/jGDLOv3NbYCME5MiDDOxRlmohNCCCGEEEIIIYQQQhyZNbgaswZXS9eJmdyywp0qygtysFMSWyJmouf65XEqJ4yUi+O62/r5a4/EjtpmS0SLOLzxfcoN68b1KbP0Z3aiu2XW4CosWrULU/tX4L21exzbez3AkK5F8ffHDuuKz7fW4tmPt1ja9ijNw5b9TQAS57uqKIjrZg9E99I8/ODpT+JtN+yJZbyrnOqhMEV0J+hEJ4QQQgghhBBCCCGEpEUXIWe7sTXsejtdCK8sNDrSRdd0icJBbea2E4ch6Pfinm+PBhBzsMsyzi+a1hdATOQ2U16Qg/5Vxm2SzWTXue+Msfj5N0fioXPGoaJA7rgXCUU09BJc7uWFOdLoHAC46YQh8dfmjPep/SsM799dExPwixW56ea4G2KFTnRCCCGEEEIIIYQQQkha+LweXD97IL7YVouxva1ubhVXzeqPrsW5mDaw0rBcFHyrioOu+rpoWj+cN7kP/D573/DU/pV478ajUV0k7/f5a4/EvPvfwfrdMQd3MvE0IiX5AZzdVmB0cNciqRs94PMgJBT29Ho9+ODm2WgJRVEY9CM/KBfwRd07ajKSdy/Nw5KbZ+PXr63GE0s2xY+jSOVEZ5yLI3SiE0IIIYQQQgghhBBC0uZ7xwzCH8+fkJToHPB5cfoRvdCjNM+wXHSfq8RuGU4Cuk730jxl2/wcP+YO7+p6n264+YSh0uWigK7TpTgXvdvicQpyEsL3OW2CPJAoJgoAMh95dXEuTp/Q07CsSJiY6Co8OdDKOBdHKKITQgghhBBCCCGEEEKyiuK8hHhcXZRr07J9yLQ7e0SPEiy9ZY503Umju8PjAS6fYS0kmi8UNf3ZN0fGX0cEK7qmCKEf27sM84TceNGJ/tr3Z6JfZSy2RibkEyMU0QkhhBBCCCGEEEIIIVlFSQqZ6Jkk7CCiTxsQyx0/dlgX131WFAZx+4nDcMNxQwzL7ztzDD6/Y67UrV4QlEewiHEudoVcxUKpYkROYdAfz1sPm/NgiAVmohNCCCGEEEIIIYQQQrIKMcYkN9DxPuBWBxH9gbPG4eXPt2PeqG627cxcOK0fAOD/vbwyvszj8SA/Ry7TqgqLRg1xLmoVvXd5olCpORPd74vF7jDOxRmK6IQQQgghhBBCCCGEkKzCK+Sq968q7PD9XzytH55Y8jVOGt1dur6sIAdnTewtXecGc0FRFWUFOXj3xqORFzAWGDVkott0YxTRjY7+QFsmPONcnKGITgghhBBCCCGEEEIIyToev3QSvt7XiNG9Sjt83wO7FOGzO+aiIMfn3DgF8gI+hCJhV23NRVcBoHd5Qfy1nQQuiuhBk6M/IaLTie4ERXRCCCGEEEIIIYQQQkjWMXVAZafuv1CRR54JBnUpwkcb9yW93T+vnILVO+sxpX9FfFnUxoqel+PD5TNqsKO2GTWVBYZ1OW1xLk7574QiOiGEEEIIIYQQQgghhHQovz5jDG557jNcMaMmqe0m9C3HhL7lhmVOcTeygqUA4G9zorcyzsURiuiEEEIIIYQQQgghhBDSgfQqz8djF09Mq4/nrp6Gv7+/ET86bkhK2zPOxT0U0QkhhBBCCCGEEEIIIeQgY0yvUoxJIy9+Ur9yRDUNE03OdmKFIjohhBBCCCGEEEIIIYQcZhw1pBpHDanu7GEcFHidmxBCCCGEEEIIIYQQQgghhycU0QkhhBBCCCGEEEIIIYQQBRTRCSGEEEIIIYQQQgghhBAFFNEJIYQQQgghhBBCCCGEEAUU0QkhhBBCCCGEEEIIIYQQBRTRCSGEEEIIIYQQQgghhBAFFNEJIYQQQgghhBBCCCGEEAUU0QkhhBBCCCGEEEIIIYQQBRTRCSGEEEIIIYQQQgghhBAFFNEJIYQQQgghhBBCCCGEEAUU0QkhhBBCCCGEEEIIIYQQBRTRCSGEEEIIIYQQQgghhBAFFNEJIYQQQgghhBBCCCGEEAUU0QkhhBBCCCGEEEIIIYQQBRTRCSGEEEIIIYQQQgghhBAF/s4eQEejaRoAoLa2tpNHQgghhBBCCCGEEEIIIaSz0DViXTNWcdiJ6HV1dQCAXr16dfJICCGEEEIIIYQQQgghhHQ2dXV1KCkpUa73aE4y+yFGNBrF1q1bUVRUBI/H09nD6RRqa2vRq1cvfP311yguLu7s4ZDDEN6DpLPhPUiyAd6HpLPhPUg6G96DJBvgfUg6G96DpLM53O9BTdNQV1eH7t27w+tVJ58fdk50r9eLnj17dvYwsoLi4uLD8sNBsgfeg6Sz4T1IsgHeh6Sz4T1IOhvegyQb4H1IOhveg6SzOZzvQTsHug4LixJCCCGEEEIIIYQQQgghCiiiE0IIIYQQQgghhBBCCCEKKKIfhgSDQdx2220IBoOdPRRymMJ7kHQ2vAdJNsD7kHQ2vAdJZ8N7kGQDvA9JZ8N7kHQ2vAfdcdgVFiWEEEIIIYQQQgghhBBC3EInOiGEEEIIIYQQQgghhBCigCI6IYQQQgghhBBCCCGEEKKAIjohhBBCCCGEEEIIIYQQooAiOiGEEEIIIYQQQgghhBCigCL6YcaDDz6Ivn37Ijc3F5MmTcKSJUs6e0jkEOGuu+7CEUccgaKiIlRXV+OUU07BqlWrDG1mzZoFj8dj+O/KK680tNm0aRPmzZuH/Px8VFdX44c//CHC4XBHHgo5SLn99tst99eQIUPi65ubm3H11VejoqIChYWF+Na3voUdO3YY+uD9R9Klb9++lvvQ4/Hg6quvBsDvQZJ53nrrLZx44ono3r07PB4PnnvuOcN6TdNw6623olu3bsjLy8OcOXOwevVqQ5u9e/finHPOQXFxMUpLS3HJJZegvr7e0ObTTz/F9OnTkZubi169euEXv/hFex8aOUiwuwdDoRBuuOEGjBw5EgUFBejevTvOP/98bN261dCH7Lvz7rvvNrThPUjscPouvPDCCy332HHHHWdow+9Ckg5O96Ds70OPx4N77rkn3obfhSQd3GgymfpNvGjRIowbNw7BYBADBgzAggUL2vvwsgKK6IcRTz75JObPn4/bbrsNy5Ytw+jRozF37lzs3Lmzs4dGDgHefPNNXH311Xj//ffx6quvIhQK4dhjj0VDQ4Oh3WWXXYZt27bF/xP/0Y9EIpg3bx5aW1vx3nvv4bHHHsOCBQtw6623dvThkIOU4cOHG+6vd955J77ue9/7Hp5//nk8/fTTePPNN7F161aceuqp8fW8/0gm+PDDDw334KuvvgoAOO200+Jt+D1IMklDQwNGjx6NBx98ULr+F7/4Be6//348/PDD+OCDD1BQUIC5c+eiubk53uacc87B559/jldffRUvvPAC3nrrLVx++eXx9bW1tTj22GPRp08fLF26FPfccw9uv/12/OEPf2j34yPZj9092NjYiGXLluHHP/4xli1bhmeeeQarVq3CSSedZGl75513Gr4br7322vg63oPECafvQgA47rjjDPfYE088YVjP70KSDk73oHjvbdu2DY8++ig8Hg++9a1vGdrxu5CkihtNJhO/idevX4958+bhqKOOwvLly/Hd734Xl156KV555ZUOPd5OQSOHDRMnTtSuvvrq+PtIJKJ1795du+uuuzpxVORQZefOnRoA7c0334wvmzlzpnb99dcrt/nvf/+reb1ebfv27fFlv/vd77Ti4mKtpaWlPYdLDgFuu+02bfTo0dJ1+/fv1wKBgPb000/Hl3355ZcaAG3x4sWapvH+I+3D9ddfr/Xv31+LRqOapvF7kLQvALRnn302/j4ajWpdu3bV7rnnnviy/fv3a8FgUHviiSc0TdO0L774QgOgffjhh/E2L730kubxeLQtW7ZomqZpDz30kFZWVma4B2+44QZt8ODB7XxE5GDDfA/KWLJkiQZA27hxY3xZnz59tF//+tfKbXgPkmSQ3YcXXHCBdvLJJyu34XchySRuvgtPPvlk7eijjzYs43chySRmTSZTv4l/9KMfacOHDzfs64wzztDmzp3b3ofU6dCJfpjQ2tqKpUuXYs6cOfFlXq8Xc+bMweLFiztxZORQ5cCBAwCA8vJyw/K///3vqKysxIgRI3DTTTehsbExvm7x4sUYOXIkunTpEl82d+5c1NbW4vPPP++YgZODmtWrV6N79+6oqanBOeecg02bNgEAli5dilAoZPgOHDJkCHr37h3/DuT9RzJNa2sr/va3v+Hiiy+Gx+OJL+f3IOko1q9fj+3btxu++0pKSjBp0iTDd19paSkmTJgQbzNnzhx4vV588MEH8TYzZsxATk5OvM3cuXOxatUq7Nu3r4OOhhwqHDhwAB6PB6WlpYbld999NyoqKjB27Fjcc889hkfHeQ+STLBo0SJUV1dj8ODBuOqqq7Bnz574On4Xko5kx44dePHFF3HJJZdY1vG7kGQKsyaTqd/EixcvNvShtzkctEV/Zw+AdAy7d+9GJBIxfBAAoEuXLli5cmUnjYocqkSjUXz3u9/FtGnTMGLEiPjys88+G3369EH37t3x6aef4oYbbsCqVavwzDPPAAC2b98uvUf1dYTYMWnSJCxYsACDBw/Gtm3bcMcdd2D69On47LPPsH37duTk5Fh+sHfp0iV+b/H+I5nmueeew/79+3HhhRfGl/F7kHQk+j0ju6fE777q6mrDer/fj/LyckObfv36WfrQ15WVlbXL+MmhR3NzM2644QacddZZKC4uji+/7rrrMG7cOJSXl+O9997DTTfdhG3btuHee+8FwHuQpM9xxx2HU089Ff369cPatWtx88034/jjj8fixYvh8/n4XUg6lMceewxFRUWGGA2A34Ukc8g0mUz9Jla1qa2tRVNTE/Ly8trjkLICiuiEkIxz9dVX47PPPjPkUQMwZAqOHDkS3bp1w+zZs7F27Vr079+/o4dJDjGOP/74+OtRo0Zh0qRJ6NOnD5566qlD+h9ykr088sgjOP7449G9e/f4Mn4PEkIOV0KhEE4//XRomobf/e53hnXz58+Pvx41ahRycnJwxRVX4K677kIwGOzooZJDkDPPPDP+euTIkRg1ahT69++PRYsWYfbs2Z04MnI48uijj+Kcc85Bbm6uYTm/C0mmUGkyJD0Y53KYUFlZCZ/PZ6m6u2PHDnTt2rWTRkUORa655hq88MILeOONN9CzZ0/btpMmTQIArFmzBgDQtWtX6T2qryMkGUpLSzFo0CCsWbMGXbt2RWtrK/bv329oI34H8v4jmWTjxo147bXXcOmll9q24/cgaU/0e8bu77+uXbtaisyHw2Hs3buX348kY+gC+saNG/Hqq68aXOgyJk2ahHA4jA0bNgDgPUgyT01NDSorKw3//vK7kHQEb7/9NlatWuX4NyLA70KSGipNJlO/iVVtiouLD3nzGkX0w4ScnByMHz8er7/+enxZNBrF66+/jilTpnTiyMihgqZpuOaaa/Dss89i4cKFlsfMZCxfvhwA0K1bNwDAlClTsGLFCsMfsPoPrWHDhrXLuMmhS319PdauXYtu3bph/PjxCAQChu/AVatWYdOmTfHvQN5/JJP8+c9/RnV1NebNm2fbjt+DpD3p168funbtavjuq62txQcffGD47tu/fz+WLl0ab7Nw4UJEo9H4JM+UKVPw1ltvIRQKxdu8+uqrGDx4MB8dJ47oAvrq1avx2muvoaKiwnGb5cuXw+v1xuM1eA+STLN582bs2bPH8O8vvwtJR/DII49g/PjxGD16tGNbfheSZHDSZDL1m3jKlCmGPvQ2h4W22MmFTUkH8o9//EMLBoPaggULtC+++EK7/PLLtdLSUkPVXUJS5aqrrtJKSkq0RYsWadu2bYv/19jYqGmapq1Zs0a78847tY8++khbv3699u9//1urqanRZsyYEe8jHA5rI0aM0I499lht+fLl2ssvv6xVVVVpN910U2cdFjmI+P73v68tWrRIW79+vfbuu+9qc+bM0SorK7WdO3dqmqZp/5+9+wyPsszfPv6dnjKTSgigCChdQXrRxcoKdnHXgggCggVZF11W/quLgo/iunZdwbIqiri4upZdsaNYaALSFARFioUESJ+Uqffz4k6GDMkgIMmknJ/jmAO5Zib3lYiSOfOb87ruuuuMY445xvjoo4+MVatWGYMHDzYGDx4ceb7+/MmREgqFjGOOOcaYNm1a1Lr+Pyh1oaSkxFizZo2xZs0aAzAefPBBY82aNcaOHTsMwzCMv/3tb0ZaWprx5ptvGuvXrzcuvPBCo0OHDkZ5eXnkYwwfPtzo3bu3sWLFCuPzzz83OnXqZIwcOTJyf2FhoZGdnW2MHj3a+Oqrr4wFCxYYSUlJxpNPPlnvn680PAf6M+j3+40LLrjAOProo421a9dGfY/o8/kMwzCMpUuXGg899JCxdu1aY+vWrcaLL75oZGVlGWPGjIlcQ38G5Zcc6M9hSUmJMXXqVGPZsmXGtm3bjA8//NDo06eP0alTJ6OioiLyMfT/Qvk1funvY8MwjKKiIiMpKcmYM2dOjefr/4Xya/1SJmMYR+Y18ffff28kJSUZf/7zn41NmzYZjz/+uGGz2Yx33323Xj/feFCI3sw89thjxjHHHGM4nU5jwIABxvLly+O9JWkigFpvzz33nGEYhrFz507jlFNOMTIyMgyXy2V07NjR+POf/2wUFRVFfZzt27cbZ599tpGYmGi0aNHC+NOf/mQEAoE4fEbS2Fx22WVG69atDafTaRx11FHGZZddZnz33XeR+8vLy41JkyYZ6enpRlJSkjFixAhj165dUR9Df/7kSHjvvfcMwNi8eXPUuv4/KHXh448/rvXv36uuusowDMMIh8PG9OnTjezsbMPlchlnnnlmjT+beXl5xsiRIw23222kpKQY48aNM0pKSqIes27dOuM3v/mN4XK5jKOOOsr429/+Vl+fojRwB/ozuG3btpjfI3788ceGYRjG6tWrjYEDBxqpqalGQkKC0a1bN2PWrFlR4aZh6M+gHNiB/hyWlZUZZ511lpGVlWU4HA6jXbt2xsSJE2sMk+n/hfJr/NLfx4ZhGE8++aSRmJhoFBYW1ni+/l8ov9YvZTKGceReE3/88cdGr169DKfTaRx77LFR12jKLIZhGHU05C4iIiIiIiIiIiIi0qipE11EREREREREREREJAaF6CIiIiIiIiIiIiIiMShEFxERERERERERERGJQSG6iIiIiIiIiIiIiEgMCtFFRERERERERERERGJQiC4iIiIiIiIiIiIiEoNCdBERERERERERERGRGBSii4iIiIhIRPv27Xn44YfjvQ0RERERkQZDIbqIiIiISJyMHTuWiy66CIDTTjuNKVOm1Nu1586dS1paWo31lStXcs0119TbPkREREREGjp7vDcgIiIiIiJHjt/vx+l0Hvbzs7KyjuBuREREREQaP02ii4iIiIjE2dixY/nkk0945JFHsFgsWCwWtm/fDsBXX33F2WefjdvtJjs7m9GjR7N3797Ic0877TQmT57MlClTaNGiBcOGDQPgwQcfpEePHiQnJ9O2bVsmTZqE1+sFYPHixYwbN46ioqLI9WbMmAHUrHPZuXMnF154IW63m5SUFC699FJyc3Mj98+YMYNevXoxb9482rdvT2pqKpdffjklJSWRx7z66qv06NGDxMREMjMzGTp0KKWlpXX01RQRERERObIUoouIiIiIxNkjjzzC4MGDmThxIrt27WLXrl20bduWwsJCzjjjDHr37s2qVat49913yc3N5dJLL416/vPPP4/T6WTJkiU88cQTAFitVh599FG+/vprnn/+eT766CNuueUWAE466SQefvhhUlJSItebOnVqjX2Fw2EuvPBC8vPz+eSTT/jggw/4/vvvueyyy6Iet3XrVt544w3eeust3nrrLT755BP+9re/AbBr1y5GjhzJ+PHj2bRpE4sXL+biiy/GMIy6+FKKiIiIiBxxqnMREREREYmz1NRUnE4nSUlJtGrVKrL+j3/8g969ezNr1qzI2rPPPkvbtm3ZsmULnTt3BqBTp078/e9/j/qY1fvV27dvz1133cV1113H7NmzcTqdpKamYrFYoq63v0WLFrFhwwa2bdtG27ZtAXjhhRc4/vjjWblyJf379wfMsH3u3Ll4PB4ARo8ezaJFi7j77rvZtWsXwWCQiy++mHbt2gHQo0ePX/HVEhERERGpX5pEFxERERFpoNatW8fHH3+M2+2O3Lp27QqY099V+vbtW+O5H374IWeeeSZHHXUUHo+H0aNHk5eXR1lZ2UFff9OmTbRt2zYSoAN0796dtLQ0Nm3aFFlr3759JEAHaN26Nbt37wbgxBNP5Mwzz6RHjx5ccsklPP300xQUFBz8F0FEREREJM4UoouIiIiINFBer5fzzz+ftWvXRt2+/fZbTjnllMjjkpOTo563fft2zjvvPHr27Ml//vMfVq9ezeOPPw6YB48eaQ6HI+r3FouFcDgMgM1m44MPPuCdd96he/fuPPbYY3Tp0oVt27Yd8X2IiIiIiNQFhegiIiIiIg2A0+kkFApFrfXp04evv/6a9u3b07Fjx6jb/sF5datXryYcDvPAAw8waNAgOnfuzM8///yL19tft27d+OGHH/jhhx8iaxs3bqSwsJDu3bsf9OdmsVg4+eSTmTlzJmvWrMHpdPL6668f9PNFREREROJJIbqIiIiISAPQvn17VqxYwfbt29m7dy/hcJgbbriB/Px8Ro4cycqVK9m6dSvvvfce48aNO2AA3rFjRwKBAI899hjff/898+bNixw4Wv16Xq+XRYsWsXfv3lprXoYOHUqPHj0YNWoUX375JV988QVjxozh1FNPpV+/fgf1ea1YsYJZs2axatUqdu7cyWuvvcaePXvo1q3boX2BRERERETiRCG6iIiIiEgDMHXqVGw2G927dycrK4udO3fSpk0blixZQigU4qyzzqJHjx5MmTKFtLQ0rNbY38qfeOKJPPjgg9x7772ccMIJzJ8/n3vuuSfqMSeddBLXXXcdl112GVlZWTUOJgVzgvzNN98kPT2dU045haFDh3Lsscfy8ssvH/TnlZKSwqeffso555xD586d+etf/8oDDzzA2WefffBfHBERERGROLIYhmHEexMiIiIiIiIiIiIiIg2RJtFFRERERERERERERGJQiC4iIiIiIiIiIiIiEoNCdBERERERERERERGRGBSii4iIiIiIiIiIiIjEoBBdRERERERERERERCQGhegiIiIiIiIiIiIiIjEoRBcRERERERERERERiUEhuoiIiIiIiIiIiIhIDArRRURERERERERERERiUIguIiIiIiIiIiIiIhKDQnQRERERERERERERkRgUoouIiIiIiIiIiIiIxKAQXUREREREREREREQkBoXoIiIiIiIiIiIiIiIxKEQXEREREREREREREYlBIbqIiIiIiIiIiIiISAwK0UVEREREREREREREYlCILiIiIiLSCGzfvh2LxcLcuXMjazNmzMBisRzU8y0WCzNmzDiiezrttNM47bTTjujHFBERERFpaBSii4iIiIjUgQsuuICkpCRKSkpiPmbUqFE4nU7y8vLqcWeHZuPGjcyYMYPt27fHeysiIiIiInGhEF1EREREpA6MGjWK8vJyXn/99VrvLysr480332T48OFkZmYe1jX++te/Ul5e/mu2+Ys2btzIzJkzaw3R33//fd5///06vb6IiIiISLwpRBcRERERqQMXXHABHo+Hl156qdb733zzTUpLSxk1atRhX8Nut5OQkHDYz/+1nE4nTqczbtcXEREREakPCtFFREREROpAYmIiF198MYsWLWL37t017n/ppZfweDz85je/YerUqfTo0QO3201KSgpnn30269at+8Vr1NaJ7vP5uOmmm8jKysLj8XDBBRfw448/1njujh07mDRpEl26dCExMZHMzEwuueSSqInzuXPncskllwBw+umnY7FYsFgsLF68GKi9E3337t1cffXVZGdnk5CQwIknnsjzzz8f9Ziqfvf777+fp556iuOOOw6Xy0X//v1ZuXLlL37eIiIiIiL1yR7vDYiIiIiINFWjRo3i+eef59///jeTJ0+OrOfn5/Pee+8xcuRIdu3axRtvvMEll1xChw4dyM3N5cknn+TUU09l48aNtGnT5pCuOWHCBF588UWuuOIKTjrpJD766CPOPffcGo9buXIlS5cu5fLLL+foo49m+/btzJkzh9NOO42NGzeSlJTEKaecwo033sijjz7KrbfeSrdu3QAiv+6vvLyc0047je+++47JkyfToUMHXnnlFcaOHUthYSF//OMfox7/0ksvUVJSwrXXXovFYuHvf/87F198Md9//z0Oh+OQPm8RERERkbqiEF1EREREpI6cccYZtG7dmpdeeikqRH/llVcIBAKMGjWKHj16sGXLFqzWfW8SHT16NF27duWZZ55h+vTpB329devW8eKLLzJp0iQef/xxAG644QZGjRrF+vXrox577rnn8vvf/z5q7fzzz2fw4MH85z//YfTo0Rx77LEMGTKERx99lN/+9rc1ps7399RTT7Fp0yZefPHFSE3Nddddx6mnnspf//pXxo8fj8fjiTx+586dfPvtt6SnpwPQpUsXLrzwQt577z3OO++8g/68RURERETqkupcRERERETqiM1m4/LLL2fZsmVRNSkvvfQS2dnZnHnmmbhcrkiAHgqFyMvLw+1206VLF7788stDut7bb78NwI033hi1PmXKlBqPTUxMjPxzIBAgLy+Pjh07kpaWdsjXrX79Vq1aMXLkyMiaw+HgxhtvxOv18sknn0Q9/rLLLosE6ABDhgwB4Pvvvz+s64uIiIiI1AWF6CIiIiIidahqIrvqgNEff/yRzz77jMsvvxybzUY4HOahhx6iU6dOuFwuWrRoQVZWFuvXr6eoqOiQrrVjxw6sVivHHXdc1HqXLl1qPLa8vJzbb7+dtm3bRl23sLDwkK9b/fqdOnWKmqqHffUvO3bsiFo/5phjon5fFagXFBQc1vVFREREROqCQnQRERERkTrUt29funbtyr/+9S8A/vWvf2EYRiRcnzVrFjfffDOnnHIKL774Iu+99x4ffPABxx9/POFwuM729Yc//IG7776bSy+9lH//+9+8//77fPDBB2RmZtbpdauz2Wy1rhuGUS/XFxERERE5GOpEFxERERGpY6NGjWL69OmsX7+el156iU6dOtG/f38AXn31VU4//XSeeeaZqOcUFhbSokWLQ7pOu3btCIfDbN26NWr6fPPmzTUe++qrr3LVVVfxwAMPRNYqKiooLCyMepzFYjmk669fv55wOBw1jf7NN99E7hcRERERaWw0iS4iIiIiUseqps5vv/121q5dG/k9mNPY+09ev/LKK/z000+HfJ2zzz4bgEcffTRq/eGHH67x2Nqu+9hjjxEKhaLWkpOTAWqE67U555xzyMnJ4eWXX46sBYNBHnvsMdxuN6eeeurBfBoiIiIiIg2KJtFFREREROpYhw4dOOmkk3jzzTcBokL08847jzvvvJNx48Zx0kknsWHDBubPn8+xxx57yNfp1asXI0eOZPbs2RQVFXHSSSexaNEivvvuuxqPPe+885g3bx6pqal0796dZcuW8eGHH5KZmVnjY9psNu69916KiopwuVycccYZtGzZssbHvOaaa3jyyScZO3Ysq1evpn379rz66qssWbKEhx9+GI/Hc8ifk4iIiIhIvClEFxERERGpB6NGjWLp0qUMGDCAjh07RtZvvfVWSktLeemll3j55Zfp06cPCxcu5P/+7/8O6zrPPvssWVlZzJ8/nzfeeIMzzjiDhQsX0rZt26jHPfLII9hsNubPn09FRQUnn3wyH374IcOGDYt6XKtWrXjiiSe45557uPrqqwmFQnz88ce1huiJiYksXryY//u//+P555+nuLiYLl268NxzzzF27NjD+nxEREREROLNYujUHhERERERERERERGRWqkTXUREREREREREREQkBoXoIiIiIiIiIiIiIiIxKEQXEREREREREREREYlBIbqIiIiIiIiIiIiISAwK0UVEREREREREREREYlCILiIiIiIiIiIiIiISg0J0EREREREREREREZEY7PHeQH0Lh8P8/PPPeDweLBZLvLcjIiIiIiIiIiIiInFgGAYlJSW0adMGqzX2vHmzC9F//vln2rZtG+9tiIiIiIiIiIiIiEgD8MMPP3D00UfHvL/ZhegejwcwvzApKSlx3o2IiIiIiIiIiIiIxENxcTFt27aNZMaxNLsQvarCJSUlRSG6iIiIiIiIiIiISDP3S7XfOlhURERERERERERERCQGhegiIiIiIiIiIiIiIjEoRBcRERERERERERERiUEhuoiIiIiIiIiIiIhIDArRRURERERERERERERiUIguIiIiIiIiIiIiIhKDQnQRERERERERERERkRgUoouIiIiIiIiIiIiIxKAQXUREREREREREREQkBoXoIiIiIiIiIiIiIiIxKEQXEREREREREREREYlBIbqIiIiIiIiIiIiISAwK0UVEREREREREREREYlCILiIiIiIiIiIiIiISg0L05iboh9evg+8Xg2HEezciIiIiIiIiIiIiDZpC9OZm45uw7l/wwoUw52T4ch4EKuK9KxEREREREREREZEGSSF6c3N0X+g/ERxJsPtr+O9kePgE+Pge8O6O9+5EREREREREREREGhSLYTSvTo/i4mJSU1MpKioiJSUl3tuJn/IC+PIFWPEUFP9ortmc0OMSGHQ9tOoR3/2JiIiIiIiIiIiI1KGDzYoVojd3oQBs+h8snw0/rty33n4IDL4BOg0Dq96wICIiIiIiIiIiIk2LQvQYFKIfwA8rzTB945tghMy1jGNh4PXQ6wpwueO7PxEREREREREREZEjRCF6DArRD0LhD7DyaVg9FyqKzDVXKvQdAwOuhbS2cd2eiIiIiIiIiIiIyK+lED0GheiHwOeFdf+C5XMgf6u5ZrFBt/PNqpe2A+K7PxEREREREREREZHDpBA9BoXohyEchu8+gGWPw7ZP9q0f1c88hLT7hWBzxG9/IiIiIiIiIiIiIodIIXoMCtF/pZyvYMUcWP8KhHzmWspRMGAi9B0Lielx3Z6IiIiIiIiIiIjIwVCIHoNC9CPEuwdWPWt2p5fuMdccSeYBpAOvgxad4rs/ERERERERERERkQNQiB6DQvQjLOiDDa/C8tmQ+9W+9U7DzKqXY08DiyVu2xMRERERERERERGpjUL0GBSi1xHDgO2fwbLZsOVdoPKPVcvjzTC9xyXgSIjrFkVERERERERERESqKESPQSF6PcjbCiuegDXzIVBqriW1gP5XQ7+rwZMd3/2JiIiIiIiIiIhIs6cQPQaF6PWovBC+fAG+eAqKfjDXbE444ffmdHrrnnHdnoiIiIiIiIiIiDRfCtFjUIgeB6EgfPM/s+rlxy/2rbcfYobpnYeD1Ra//YmIiIiIiIiIiEizoxA9BoXocfbjKvMQ0q/fACNkrqV3MMP0XleAyxPX7YmIiIiIiIiIiEjzoBA9BoXoDUTRj/DF07B6LlQUmmuuVOgzGgZcA+nt4rk7ERERERERERERaeIUosegEL2B8ZfCun/B8jmQ9525ZrFCt/Nh0A3QdgBYLPHdo4iIiIiIiIiIiDQ5CtFjUIjeQIXD8N0HZtXL94v3rbfpA4NvgO4Xgs0Rt+2JiIiIiIiIiIhI06IQPQaF6I1A7tfmZPr6f0PIZ6552sCAidB3LCRlxHV7IiIiIiIiIiIi0vgpRI9BIXoj4t0Dq56Flf+E0t3mmj0Reo2EgddDVuf47k9EREREREREREQaLYXoMShEb4SCPvjqP2bVS86GfesdfwuDJ8Gxp6s3XURERERERERERA6JQvQYFKI3YoYB2z83q142vw1U/tHN6gaDroeel4IjMa5bFBERERERERERkcZBIXoMCtGbiLyt8MVTsOZF8HvNtaRM6Dce+k8AT6v47k9EREREREREREQaNIXoMShEb2LKC2HNPFjxFBTtNNesDujxe3M6vfWJcd2eiIiIiIg0fWW+ELmFPnIKfeSXBEh22cjwOMn0OMjwOEh02uK9RREREamFQvQYFKI3UaEgfPOWWfXyw/J96+1+Y4bpXc4Gq75xFRERERGRXy8cNsgrCZBT6CO30EdJeeiAj09yWcn0OMlwO8j0OEhJsmPRuU4iIiJxpxA9BoXozcCPq81DSDe+AeGguZbeAQZeB71HgcsT1+2JiIiIiEjjU+4PkVvoJ7fQx+4iP8HQvpfSFiDD4yA7zUWLFAdlvhD5JQHySgIUlQVrfCy7zRIJ1DM8DjLcDuw2az1+NiIiIgIK0WNSiN6MFP0EK5+GVc9BRaG55kqBPmNgwDWQ3i6u2xMRERERkYYrbBjklwQiwfn+YbjLYSU71Ul2uouWqU6c9tpD8EAwTIHXDNTzveategBfJTXJTqbHYU6sexwkufROWhERkbqmED0GhejNkL8U1i0wq17yvjXXLFboeh4MmgTHDAK9lVJEREREpNnzBcKRbvPdhX4C+4Xd6W4HrdKcZKe5SEs+vEoWwzAoKgtGJtXzS/yU+cM1HpfotJJRGapXVcBY9bpFRETkiFKIHoNC9GYsHIati2DZ4/D9x/vW2/Q2w/TuF4HdGbftiYiIiIhI/TIMgwJvkNzKbvOC0uhpc4fdQnaqi1bpTlqmunA56qZypdwXIs9rBup5JQGKSoPs/0LdZjUrYMxg3ayAccSYfhcREZGDoxA9BoXoAkDuRlgxB9a9DCGfueZpDQMmQt9xkJQR3/2JiIiIiEid8AXC7C7yRWpa/MHol8RpyXay01xkp5kHgcbjANBgyKisgPGTX1kDs/9UPEBKZQWM2a/uJMll1YGlIiIih0AhegwK0SVK6V6zM33l0+DNNdfsiXDi5TDoesjqEt/9iYiIiIjIr1JVn5Jb6COnwE++NxB1v8NmoWWqMxKcJzgbXhe5YRiUlIfIq5xUzy8JUOoL1XhcgsO6b1Ld4yQtyY7VqlBdREQkFoXoMShEl1oFffD162bVS876fesdh5pVL8edod50EREREZFGIhAMs7vIT06hOXHuC0R3jqck2clOc9IqzUWG29Eog+YKfyhyWGleSYDC0gD7v7q3WSE92QzUzWDdEfMAVBERkeZIIXoMCtHlgAwDdiwxDyH9ZiFUNRFmdTUn03teBo7EuG5RRERERESiVU1q5xT6yC3wkeeNDpRt1qppc3PiPMnV8KbNf61QuKoCprJb3RsgEKz5ct+TaCPT44xMrCe7bKqAERGRZkshegwK0eWg5X8PK56ENS+C32uuJWZAv/Fmd7qnVXz3JyIiIiLSjAVD5rR5Vbd5uT962tydYKNVulnRkulxYmuE0+a/hmEYlFSEIoeV5pcE8FbUrIBxOayVnermpHpasqPZfa1ERKT5Uogeg0J0OWQVRfDlPDNQL9pprlkdcMLFZtVLm15x3Z6IiIiISHNgGAbeipDZbV7oJ6/YTzhq2hxapJgVLdlpLpITmt60+a/lC4Qjh5VWVcCE90sErBZIdzuqBetOXA5VwIiISNOkED0Ghehy2EJB2LwQls2GH5bvW293sln10uUcsOobdRERERGRIyUYMthbbE6a5xb6axymmeyykZ1uBuctUprftPmvFQobFJYGIpPqeSV+/LVUwLgTbJFAPdPjwJ2gChgREWkaFKLHoBBdjoifVpu96V+/DuGguZbWDgZeB72vhAT92RIRERERORylFUFyKita9hRFT5tbLea0eVW3ucLcI6tq2j+/JECe1+xWLymvWQHjtFvMTnW32a2e7lYFjIiINE4K0WNQiC5HVPHP8MXTsPo5KC8w15we6DMGBl4D6e3juj0RERERkYYuFDbIK/ZHgvP9e7sTnVazoiXdRVaKA7tN1SL1yRcIk18ZqOeVBCjw1qyAsVggLdmsf8n0mFUwCU69S1dERBo+hegxKESXOuEvg/ULzOn0vVvMNYsVup5r9qYfM9j8zlJERERERCjz7es231PkJ1QtlbVYINPjIDvNRas0F55ETZs3JOGwQWFpkDzvvm51XyBc43HJLps5re5xkOlx6t+jiIg0SArRY1CILnUqHIatH8Hyx81fq7TuBYNvgO4Xgd0Zr92JiIiIiMRFOGyQVxKIdJsXlwej7k9wWM2KlnQXLVOcOOyaNm8sDMOgzBcirzJQz/cGKC4L1nicw2aJhOoZbifpbgd2m0J1ERGJL4XoMShEl3qze5M5mb7+ZQhWmGue1tB/AvQdB8mZ8d2fiIiIiEgdKveHyK2saNld5CcYin7pmeFxmDUtaU5Sk+yaUm5C/MEwBd5AZbDup8AbILTfsLrFAmlJ9shhpRkeB4mqgBERkXqmED0GhehS70rzYPWz8MU/wZtjrtkT4MTLYeD10LJrfPcnIiIiInIEhA2DAm+A3EI/OQU+ivabRnbaLZGKlpZpTpyaNm82wmGDorJgZf2LnzxvgAp/zQqYJJeVTI+TDLc5sZ6iH66IiEgdU4geg0J0iZugH75+3ax62bVu3/pxZ5q96R3PVG+6iIiIiDQqvkC4sqLFrGkJ7Ddtnp5sJzvdDM7TkhWIiskwDMr9YfJK9vWq7/9DFwC7zRIJ1DM8DtLdDhw6WFZERI4ghegxKESXuDMM2LkMlj0O3ywEKv8TbNEFBl0PPS8DZ1JctygiIiIiUhvDMCgoDZqheYGPgtLo4NNhM6fNs9OcZKe5cDkUeMrBCVRVwHgD5Fd2q+9fAQSQmmSPHFaa4XGQ5FIFjIiIHD6F6DEoRJcGJX8bfPEUfDkP/CXmWmIG9BtndqentInv/kRERESk2fMHw+wu9JNTOXHuD0a/hExNstMq3QzO090OrJo2lyPAMKpXwATIL/FTVksFTKLTWnlgqTNSAaM/gyIicrAUosegEF0apIpiWPMirHgCCneYa1Y7HH8xDJ4EbXrHd38iIiIi0mxUhZe5lcF5fkkg6n67zULLVGfkUNAEHQYp9aTcH4oE6nklAYpKg+wfaNisZgWMGaw7yHA7cKh/X0REYlCIHoNCdGnQwiGz4mX5HNi5dN/6MYPN3vSu54JVL1JERERE5MgKBMPsLvJHus0rAtETvymJdrLTnLRKd5HhdmC1atJX4i8YMg+zrepWz/cGavTyA6Qk2cmMBOtOklxW9fOLiAjQhEP0GTNmMHPmzKi1Ll268M033xzU8xWiS6Px8xozTP/qPxCu7JpMOwYGXge9R0OC/vyKiIiIyOExDIOS8lCkoiWvJED1V4Y2qzltXtVtrt5paQyq/lznVU6q55cEKPWFajwuwWHdN6nucZKWZNcPhkREmqkmHaK/+uqrfPjhh5E1u91OixYtDur5CtGl0SneBSufhlXPQnmBueb0QO8rYeC1kNEhvvsTERERkUYhGAqzp8hPTqE5cV6+X7+0O8FGdpqLVulOMj1ObAoVpQmo8Icih5XmlQQoLI3+gRGAzQrpyWagbgbrDpyqgBERaRYONiu21+Oejhi73U6rVq3ivQ2R+pHSGs68HYZMhfUvm9PpezfDijlmh3rXc82ql3Yngd6SKCIiIiKVDMPAWxGKVLTsLfYTrhYeWi2QVa3bPDmhUb48FDmgBKeNozJsHJWRAEAoXFUBU9mt7g0QCBrsLQmwt1r/vyfRRqbHGZlYT3bZVAEjItKMNcrvkr799lvatGlDQkICgwcP5p577uGYY46p9bE+nw+fzxf5fXFxcX1tU+TIciZBv3HQdyxsXQTLZpu/fvOWeWt9ohmmH38x2J3x3q2IiIiIxEEobLCnWrf5/lUWSS4brSorWrJSNW0uzY/NaqFFipMWKU4g2ayAqQhFDivNLwngrQhRUh6ipLyc7bvLAXA5rGS4HZFJ9bRkh/77ERFpRhpdncs777yD1+ulS5cu7Nq1i5kzZ/LTTz/x1Vdf4fF4ajy+tg51QHUu0jTs/sacSF+3AIIV5po7G/pPhH7jITkzvvsTERERkTpXWjltnlPoY09RzWnzzBRnJDh3J2iaVuSX+ALhyGGled4Ahd5A1H9XYP63leZ2kOne163ucqgCRkSksWmynej7KywspF27djz44INcffXVNe6vbRK9bdu2CtGlaSnNg9XPwRdPgzfHXLMnQM/LYND10LJbfPcnIiIiIkdMKGyQV+Inp8CcOPdWRE+bJzqtZrd5mpOsVCd2m4I9kV8jFDYoLA1EJtXzSvz4gzWjFHeCLRKoZ3oc+qGViEgj0GxCdID+/fszdOhQ7rnnnl98rA4WlSYt6IeNb8Cyx2HX2n3rx50Bg24wf7XqRZSIiIhIY1Pm29dtvrvIT6jaWKzFApluB9npLlqlufAkKrgTqUuGYVBaESKvclI9v8RPSXmoxuOcdovZqe42u9XT3aqAERFpaJr0waLVeb1etm7dyujRo+O9FZH4szuh56XQ4xLYuRyWPw7fLIStH5m3Fp3NyfSel5sd6yIiIiLSIIXDBvneADmVwXlxWTDqfpfDGqloaZnqxGHXoIRIfbFYLLgT7bgT7bRrmQiAPxiOTKnnlQQo8AbwBw1yCsx3jZjPg7Rks/4l0+Mgw+0gwWmL56ciIiIHqdFNok+dOpXzzz+fdu3a8fPPP3PHHXewdu1aNm7cSFZW1i8+X5Po0uwUbIcVT8GXL4C/xFxLTIe+42DAREhpE9ftiYiIiIipwh8it9BPTqGP3UV+gqHol2oZbget0s3gPDXJrmlzkQYsHDYoLA2S7zVD9bySAL5AuMbjkl02c1rd4yDT49Q7SURE6lmTrXO5/PLL+fTTT8nLyyMrK4vf/OY33H333Rx33HEH9XyF6NJsVRTD2vmwfA4U7jDXrHY4fgQMmgRH9Ynv/kRERESaGcMwp81zC81u88LS6Glzp91S2W3uomWaE6emzUUaLcMwKPOF9vWqewM13mEC4LBZIqF6httJutuB3aZQXUSkrjTZEP3XUoguzV44BJvfgeWzYceSfettB8HgSdDlXLA1+qYnERERkQbJFwhHus1zi3wE9jucMD3ZTnaai+x0F+nJmjYXacr8wTAF3kDlpLqfAm+A0H7D6hYLpCXZI4eVZngcJKoCRkTkiFGIHoNCdJFqfl5rTqZ/9R8IB8y11GNg4LXQZzQkpMZ1eyIiIiKNnWGYlQ65hT5yCs2QrDqHzUJ2tW5z9SOLNF9hw6CoNLivW90boMJfswImyWUl0+Mkw21OrKeo3klE5LApRI9BIbpILYp3wapnYOUzUJ5vrjnd0PtKM1DPODa++xMRERFpRPzBMLurdZvv34OcmmROm7dKN6sarAq/RKQWhmFQ7g+TV+KvDNYDFNVSAWO3WSKBeobHQbrbgcOm+icRkYOhED0GhegiBxAoh/Uvm9Ppe76pXLRAl3PMqpd2J5vvJxQRERGRCMMwKCoLRrrN80qip83tNgstU81p8+w0p6oYROSwBUL7KmDySwLkewM1DiEG84d1VYeVZngcJLn0/x0RkdooRI9BIbrIQTAM2PqRGaZ/98G+9VY9zUNIT7gY7K747U9EREQkzgLBMLuL/eQWmP3mFftNm3sSbbRKc5Gd5iLT48Bq1SCCiBx5VT/Eq5pUzy/xU1ZLBUyi01p5YKkzUgGjd8GIiChEj0khusgh2rPZDNPXLYBgubnmzob+E6DfeEhuEd/9iYiIiNQDwzAoKQ9Vdpub0+bVX0nZrJCV6owE55r6FJF4KfeHIpPqeSV+ikqD7B/82KxmBYwZrDvIcDtw2FUBIyLNj0L0GBSiixymsnxY/Rx88TSU7DLXbC7oeak5nZ7dPb77ExERETnCgiGDPZXT5jmFPsr3m+50J9jMbvM0J5kpTmyaNheRBigYMiorYPyRCphALRUwKUl2MiPBupMkl1UHlopIk6cQPQaF6CK/UigAX78Byx+Hn9fsWz/2dDNM7zgUrJpgEBERkcbJWx4kp7LbfG+xn3C1V0tWC2SlOMlON7vN3Qn2+G1UROQwVb2zJq/EH5lYL/WFajwuwWHdN6nucZKWZFc1lYg0OQrRY1CILnKEGAb8sAKWPQ7fvAVG5WRWZicYdB2cOBKcyfHdo4iIiMgvCIUN9hb7ySn0kVvgrxEkJbmskYqWFilO7DYFSCLS9FT4Q+R5A5Fu9cLS6MoqMGur0pPNQN0M1h04VQEjIo2cQvQYFKKL1IGCHfDFU/DlC+ArNtcS0qDfOOg/EVKPiuv2RERERKorrdjXbb632E+oWkuLxQItPE6y081+c3eCTXUGItLshMJVFTDmYaV53gCBYM34yJNoI9PjjEysJ7v0/0wRaVwUosegEF2kDvlKYM18WDEHCraba1Y7dL/IrHo5um88dyciIiLNVDhssLfET25lTUtJefS0eaLTSnaaWdGSlerEYdNkpYhIdYZhUFIRihxWml8SwFtRswLG5bCS4XZEJtXTkh06L0JEGjSF6DEoRBepB+EQbHkXls2GHZ/vW2870AzTu54HNnWIioiISN0p94XMipZCP3uK/ASrlZtbgAyPw6xpSXeSkmjX5KSIyCHyBcKRQD3PG6DQG4g6RwLMsyTS3A4y3fu61V0O/aBSRBoOhegxKEQXqWe71sHyObDhVQgHzLXUY2DgNdB7NCSmxXV7IiIi0jSEwwb53kAkOC8uC0bd73JYyU4zK1qyUp3q8RUROcJCYYPC0kDksNK8Ej/+Wipg3Am2yvoXs1tdtVkiEk8K0WNQiC4SJyW5sPKfsOoZKMsz15xu6DUKBl4LmcfFd38iIiLS6FT4Q5GKlt1FfgKh6Jc2GW6HGZynu0hN0rS5iEh9MgyD0ooQeZWT6vkl/hp1WgBOu8UM1d1mt3q6WxUwIlJ/FKLHoBBdJM4C5bDhFbPqZc+mykULdDnbrHpp/xvzRC8RERGR/RiGOW1eFZwXlkZPmzvtlki3ectUlyoDREQaGH8wHJlSzysJUFBLBYzFAmnJZv1LpsdBhttBgtMWnw2LSJOnED0GhegiDYRhwPeLYfls+Pb9feutephh+gm/A7srbtsTERGRhsEXCLO7yEdOgZ/dRb4a1QBpyXaz2zzNRbpb0+YiIo1JOGxQWBYkvzJUzysJ4AuEazwu2VVVAWPWwHgSVQEjIkeGQvQYFKKLNEB7tsCKJ2DtSxAsN9eSW0L/CdBvPLiz4rs/ERERqTeGYVBYGiS30EdOoZ8CbyDqfofNQstUJ9npLrJTnZpOFBFpQgzDoMwX2ter7g3UOOMCzL8LMiKT6k7S3Q7sNoXqInLoFKLHoBBdpAEry4cvn4cVT0HJz+aazQU9LzGn07OPj+/+REREpE74g2F2F/nJLfCRW+SvMYWYmmQnO81JdpqLDI8Dq6YPRUSaDX8wTIF334Gl+d4Aof06YCwWSEuyk1F5WGmGx0GifsgqIgdBIXoMCtFFGoFQADa+Ccseh5+/3Lfe4VQYfAN0/C1Y1XEqIiLSWBmGQXFZkJzKbvP8kgDVX5TYbRayUpy0SjeDcwUhIiJSJWwYFJUG93WrewNU+GtWwCS5rGS4nZFu9RQdMC0itVCIHoNCdJFGxDDghy/M3vRN/wWj8hujzI4w8DrodQU4k+O7RxERETkogVCYPUX+SHC+f+DhSbSRneaiVZqLTI8Dq1VBh4iIHByzAsZfGawHKKqlAsZus5DhdkQm1dPdDhw2DWeJNHcK0WNQiC7SSBXuhBVPwpcvgK/YXEtIhb5jYcA1kHp0XLcnIiIi0QzDoKQ8RG6hj9xCP3tL/FR/5WGzQlaKOWmeneYiOUHT5iIicmQEQjUrYIKhmvFXapI9clhphsdBkkt/F4k0NwrRY1CILtLI+UrMA0iXz4GCbeaaxQbdLzSrXo7uF9/9iYiINGPBkMGeYnPSPLfAR9l+0+bJCTZapbnITnPSIsWJTdPmIiJSD6pqxPJKqoJ1f42/owASndbKA0udkQoYncMh0rQpRI9BIbpIExEOwZb3zKqX7Z/tWz96AAy6HrpdADZ7/PYnIiLSTHgrguQW+Mkp9LG32E/1s96sFmiR4owE5+5E/d0sIiINQ7k/FJlUzyvxU1QaZP+AzGY1K2AyKnvVM9wOHHZVwIg0JQrRY1CILtIE7VpvTqZ/9SqE/OZaaluz5qXPGEhMi+v2REREmpJQ2GBv5bR5TqGf0opQ1P1JTivZ6Wa3eYsUJ3abJvhERKThC4aMygoYP/leM1wP1FIBk5JkJzMSrDtJcll1YKlII6YQPQaF6CJNWEkurHoGVj4DZXvNNUcy9B5lHkSaeVx89yciItJIlVZUdZv72FPsJ1TtHfAWC7TwOMlOc5Kd7sKTYFOYICIijV7V2R55Jf7IxHqpL1TjcQkO675JdY+TtCS7DscWaUQUosegEF2kGQhUwIZXzKqX3RsrFy3QeTgMngTth5iv+EVERKRW4bBBXkmAnMrgvKQ8OjRIcFrJTnPRKs1JVopTb20XEZFmocIfIs9bVQEToLA0wP6pms0K6clmoG4G6w6c+ntSpMFSiB6DQnSRZsQw4PvFZtXLt+/tW8/uYfam9/g92F1x256IiEhDUu43p81zCvzsKfITrFZubgEyPI5IcJ6SZNe0uYiINHuhcFUFjHlYaZ43QCBYM2bzJNrI9DjNiXW3g2S9a0ukwVCIHoNCdJFmau+3sOIJWPsSBMrMteQs6D8B+l0N7qz47k9ERKSehQ2D/JJAZU2Ln6KyYNT9LofVrGhJc9Ey1akpOhERkV9gGAYlFaHIYaX5JQG8FTUrYFwOKxluR2RSPS3ZgU0VMCJxoRA9BoXoIs1cWT58+QJ88RQU/2Su2ZzQ41JzOr3VCfHdn4iISB2q8IfILfKTW+Bjd5G/xoFp6W4HrSqD87RkTZuLiIj8Wr5AOBKo53kDFHoDhPdL4qwWSHObU+pV3eouh354LVIfFKLHoBBdRAAIBWDTf2HZbPhp1b71DqfAoBug01lg1TctIiLSuBmG+TbznEI/uYU+Ckujp80ddgvZqS5apTtpmerSC3YREZE6FgobFJYGIoeV5pX48ddSAeNOsFUeWGp2q7tVASNSJxSix6AQXURq+OEL8xDSjf8Fo/KtdhnHmZPpJ44Elzu++xMRETkEvkCY3UVmRUtuoa/GC/O0ZHuk2zzd7dALchERkTgyDIPSihB5lZPq+SX+Ggd6AzjtFjI8DjLcZqie7lYFjMiRoBA9BoXoIhJT4Q9mzcvq58FXZK4lpEKfq2DANZDWNr77ExERqYVhGBSVBckpMIPzfG8g6n6HzULLVLOiJTvNSYLTFqedioiIyMHwB8ORKfW8kgAFtVTAWCyQlryvVz3T7dDf8SKHQSF6DArRReQX+bzmAaQr5kD+9+aaxQbdLzCrXtr2j+/+RESk2fMHw+wp8pNTeSioLxCOuj8lyR7pNs9wO7BqUk1ERKTRCocNCsuC5FeG6nklgRp/9wMku6oqYMwaGE+iKmBEfolC9BgUoovIQQuH4dv3zKqXbZ/uWz+qHwyeBN0uBJs9fvsTEZFmwzAMisuC5BaawXl+SYDq38TbrRayUp1kpzlpleYi0aVJNBERkabKMAzKfNEHlhaXBWs8zmGzREL1DLdZ42a3KVQXqU4hegwK0UXksORsgOVzYMMrEPKbaylHw4CJ0PcqSEyP7/5ERKTJCYTMafOq4LzCHz1x5km0RSpaMj1O9aKKiIg0Y/5gmALvvgNL870BQvt1wFgskJpkjxxWmuFxkKgKGGnmFKLHoBBdRH4V725Y+Qys/CeU7TXXHEnQ6woYeD206Bjf/YmISKNlGAbeipBZ0VLgZ2+Jn+rfqdus0CLFnDTPTnORnKAXvSIiIlK7sGFQVBqsnFQ3J9bL/TUrYJJc1shhpZkeBylJdlXASLOiED0GhegickQEKuCrV2HZbNj99b71zsNh0PXQ4VTzx/wiIiIHEAwZ7C2u6jb3UeaLfnGb7LLRKt3sNm+RomlzEREROXxlvtC+CpiSAEW1VMDYbRYy3I5IDUy624HDZo3DbkXqh0L0GBSii8gRZRhmX/ry2bDl3X3rLY83w/Qel4AjIX77ExGRBsdbYXab5xb42FPsp/o7ra0Wc9o8O81FqzQn7kSdvSEiIiJ1IxCqWQETDNWMCc0KGPOw0gyPgySdvSJNiEL0GBSii0id2fsdrHgC1s6HQJm5lpwF/a6G/leDu2V89yciInERCpvT5lXd5qUVoaj7E51Ws6Il3UVWilMHfomIiEhcVB1knldSFaz7KaulAibRaa2cVHdGKmCseie2NFIK0WNQiC4ida68AL58AVY8BcU/mms2pzmVPuh6aNUjvvsTEZE6V+YLkVvoI6fQx54iP6Fqrz8tFsj0OCLd5p5Em7pHRUREpEEq94ci9S95JX6KSoPsHyTarNEVMBluBw67KmCkcVCIHoNCdBGpN6EAbPqfWfXy48p96+2HwOAboNMwsOobCxGRpiAcNsgrCUS6zUvKo6fNExxWstOcZKe7aJni1AtLERERaZSCIYMCb4B8rz9SAxOopQImJclOZiRYd5LksmpoQBokhegxKEQXkbj4YaUZpm98E4zKYCXjWBh4PfS6Alzu+O5PREQOWbk/ZHabF/rYXeSv0SGa6XFEus1Tkux64SgiIiJNjmEYlJSbB5ZWheqlvlCNxyU4rPsm1T1O0pLsWHVgujQACtFjUIguInFV+AOsfBpWz4WKInPNlQp9x8CAayGtbVy3JyIisYUNg/ySALmFPnIL/RSVBaPudzmstEx10irNRcs0J05Nm4uIiEgzVOEPkecNRGpgCksD7J8+2qyQnrxvUj3D49D3ThIXCtFjUIguIg2Czwvr/gXL50D+VnPNYoNu55tVL20HxHd/IiICgC8QjnSb7y7013i7crrbQas0J9lpLtKSNW0uIiIisr9Q2KyAqTqsNM8bIBCsGUd6Em2RQD3T7SA5QefGSN1TiB6DQnQRaVDCYfj2fbPqZdsn+9aP6mceQtr9QrA54rc/EZFmxjAMCrzBymlzHwWl0dPmDpuF7DSX2W+e5sLl0MSUiIiIyKEwDANvRShyWGl+SQBvRc0KGJfDSoa7qgLGQVqyA5sqYOQIU4geg0J0EWmwcr6CFXNg/SsQ8plrKUfBgInQdywkpsd1eyIiTZUvEGZ3kS/Sb+7fbzIqLdkeCc4z3A5NRImIiIgcYb5A2JxSLwmQ5w1Q6A0Q3i+xtFogzW1OqVd1q2ugQX4thegxKEQXkQbPuwdWPWt2p5fuMdccSXDiSHM6vUWn+O5PRKSRMwyDorIgOQVmcJ7vDUTdb7dZIt3m2WlOEpy2OO1UREREpHkKhQ0KS/f1queV+GsMOgC4E2yRXvVMjwO3KmDkEClEj0Ehuog0GkEfbHjVrHrJ/WrfeqezYNAkOPY00DcHIiIHJRAMs7vIT07loaC+QDjq/pQkO9lpZnCe4XZg1VuFRURERBoMwzAorayAyfeaoXpJec0KGIfdQqbbnFLP9DhId6sCRg5MIXoMCtFFpNExDNj+GSybDVveBSr/t92yuzmZ3uNScCTEdYsiIg2NYRiUlIfM0LzAR543QPXvem1Wc9q8qts8yaVpcxEREZHGxB8MV06qm73qBaUBQtFzElgskJa8r1c90+3QuwwlikL0GBSii0ijlrcVVjwBa+ZDoNRcS2oB/cZD/wngyY7v/kRE4igYMqfNq7rNy/3Rr6LcCTay01y0SneS6XFqKklERESkCQmHDQrLgvu61UsCNd59CJDsqqqAMWtgPImqgGnOFKLHoBBdRJqE8kL48gX44iko+sFcsznhhN+b0+mte8Z1eyIi9cEwDLwVIXILfeQU+skr9kcdQGW1QFa1bvPkBHv8NisiIiIi9cowDMp84cikep43QHFZsMbjHDZLJFTPcDtJdzuw2xSqNxcK0WNQiC4iTUooCN/8z6x6+fGLfevth5hheufhYNVb1USk6QiGDPYW+yPBeZkvugsz2WWLVLRkpWraXERERET28QfDFHjNKfX8yn71UDg6GrVYIDXJHjmsNMPjIFEVME2WQvQYFKKLSJP14yrzENKv3wCjMlRK72CG6b2uAJcnrtsTETlcpRVBciorWvYU1Zw2b5Gyr9vcnaC344qIiIjIwQkbBkWlwchhpfklgRqVgABJTmvksNJMj4OUJLu+52wiFKLHoBBdRJq8oh/hi6dh9VyoKDTXXKnQZzQMuAbS28VzdyIivygUNsgr9keCc29F9LR5otNqdpunOclKdWK3WeO0UxERERFpasp8oX0VMCUBimqpgLHbLGS4HZEamHS3A4e+J22UFKLHoBBdRJoNfyms+xcsnwN535lrFit0Ox8G3QBtB5jvUxMRaQDKfPu6zfcU+aPeVmuxQKbHURmcu3T4k4iIiIjUm0CoZgVMMFQzTjUrYKqCdSdJLlXANAYK0WNQiC4izU44DN99YFa9fL9433qbPjD4Buh+IdgccdueiDRP4bBBXkmgMjj3UVIePW3uclhpVVnR0jLVicOuyR4RERERiT/DMCguC5JXUhWs+ymrpQIm0WmNBOoZbgepyXasGgRpcBSix6AQXUSatdyvzcn09f+GkM9c87SBAROh71hIyojr9kSkaSv3h8itrGjZXeSvMcGT4XHQKs1FdpqTVPVMioiIiEgjUe4PRepf8kr8FJUG2T9wtVmjK2Ay3A4NijQACtFjUIguIgJ498CqZ2HlP6F0t7lmT4ReI2Hg9ZDVOb77E5EmIWwYFHgD5Bb6ySnw1eiTdNotkYqWlmlOnHoRISIiIiJNQDBkfh+c7/VHamACtVTApCTZyXRXr4CxapCknilEj0EhuohINUEffPUfWDYbcjfsW+/4Wxg8CY49Xb3pInJIfIEwuYW+ypu/xouF9GQ72ekustNcpCdr2lxEREREmj7DMCgpNw8srQrVS32hGo9LcFj3Tap7nKQl2bFa9f1yXVKIHoNCdBGRWhgGbP/c7E3f/A5UvfEsqxsMuh56XgqOxLhuUUQaJsMwKCgNkltgBucFpdHT5g6bhezKbvPsNBcuh6bNRUREREQq/CHyvIFIDUxhaYD9U1qbFdKT902qZ3gcevfmEaYQPQaF6CIivyBvK3zxFKx5Efxecy0pE/qNh/4TwNMqvvsTkbjzB8PsLvSTUzlx7g9GfzuZmmQ3a1rSnaS7HTpASURERETkF4TCZgVM1WGled4AgWDN2NaTaIscVprpcZCcYNO7O38FhegxKEQXETlI5YWwZh6seAqKdpprVgf0+L05nd76xLhuT0Tqj2EYFJUFzW7zQh/5JYGo++02Cy1Tq6bNnSQ6bXHaqYiIiIhI02AYBt6KUOSw0vySAN6KmhUwLoc1EqhneBykJTuwqQLmoClEj0EhuojIIQoF4Zu3YPkc+GH5vvV2J8OgSdDlbLAqMBNpagLBMLuL/JFu84pAOOr+lER7pKYl0+NQV6OIiIiISB3zBcLmlHpJgDxvgEJvgPB+ya7VAmluB5nufd3qqlSMTSF6DArRRUR+hR9Xm73pG9+AcGXvcXp7GHgd9L4SXJ547k5EfoWqw46qKlrySqI7GW1WyEp10aoyOE9y6YdnIiIiIiLxFAobFJbu61XPK/HXqFoEcCfYIr3qmR4HblXARChEj0EhuojIEVD0E6x8GlY9BxWF5porBXqPhoHXQnq7uG5PRA5OMBRmT5GfnEJz4rzcHz1t7k6wmd3maU4yU5x6W6iIiIiISANmGAallRUw+V4zVC8pr1kB47BbyHSbU+qZHgfp7uZbAdMsQvS//e1v/OUvf+GPf/wjDz/88EE9RyG6iMgR5C+FdQvMqpe8b801ixW6nmdWvRwzCPTTbZEGo6pXsaqiZW+xP+rtn1YLZFXrNncn2OO3WRERERER+dX8wXDlpLrZq15QGiAUPTvD0Zku+ndKi8v+4u1gs+JG+8po5cqVPPnkk/Ts2TPeWxERab6cydD/aug7DrYugmWPw/cfw6b/mrc2vc0wvftFYHfGe7cizVIobLCnWrd5qS96EiXJZYtUtLRIcWK36QdfIiIiIiJNhdNupVW6i1bpLgDCYYPCsuC+bvWSABkevV7/JY1yEt3r9dKnTx9mz57NXXfdRa9evTSJLiLSUORuhBVzYN3LEPKZa57WMGCiGbYnZcR3fyLNQGlFkNxCPzmFPvYURU+bWyzQIsUZCc7VhygiIiIi0nwZhoEBWJvpa4ImXedy1VVXkZGRwUMPPcRpp512wBDd5/Ph8/kivy8uLqZt27YK0UVE6lrpXrMzfeXT4M011+yJcOLlMOh6yOoS3/2JNCGhsEFeiZ+cAnPi3FsRPW2e6LRGKlqyUp04bNY47VRERERERKThaLJ1LgsWLODLL79k5cqVB/X4e+65h5kzZ9bxrkREpIbkFnDqn+HkG+Hr182ql5z1sPo589ZxqFn1ctwZ6k0XOQxlvn3d5ruL/ISqjZtbgEyPg+x0MzhPSbRr2lxEREREROQwNapJ9B9++IF+/frxwQcfRLrQNYkuItJIGAbsWGIeQvrNQqDyr5+sruZkes/LwJEY1y2KNGThsEG+N0BOZXBeXBaMut/lsJKd5qRVmousVCdOu6bNRUREREREDqRJ1rm88cYbjBgxApvNFlkLhUJYLBasVis+ny/qvtqoE11EpAHI/x5WPAlrXgS/11xLzIB+483udE+r+O5PpIGo8Ici3ea7i/wEQ9HftmW4HWZwnu4iNUnT5iIiIiIiIoeiSYboJSUl7NixI2pt3LhxdO3alWnTpnHCCSf84sdQiC4i0oBUFMGX88xAvWinuWZ1wAkXm1UvbXrFdXsi9c0wzGnz3EKz27ywNHra3Gm3RLrNW6a6cDk0bS4iIiIiInK4mmQnusfjqRGUJycnk5mZeVABuoiINDAJqXDSZBh4HWxeCMtmww/LYf3L5q3dyWbVS5dzwHrgdxqJNFa+QDjSbZ5b5CMQjJ5vSE+2VwbnLtLdmjYXERERERGpb40qRBcRkSbKZofuF5q3n1abvelfv252qO9YAmntzKC995WQoHcRSeNmGAaFpUFyC33kFPop8Aai7nfYLLSs7DZvmeokwakfIImIiIhI3QuHw/j9/nhvQ+SIcjgcv1j/fTDqrc4lGAyyePFitm7dyhVXXIHH4+Hnn38mJSUFt9tdH1sAVOciItJoFP8MXzwNq5+D8gJzzemBPmNg4DWQ3j6u2xM5FP5gmN3Vus19gXDU/alJdrLTnGSnucjwOLBq2lxERERE6pHf72fbtm2Ew+FffrBII5OWlkarVq1qfVdvg+pE37FjB8OHD2fnzp34fD62bNnCscceyx//+Ed8Ph9PPPFEXW8hQiG6iEgj4y+D9QvM6fS9W8w1ixW6nmv2ph8zGBQ4SgNjGAZFZcFIt3leSfS0ud1moWWqM9JvnqhpcxERERGJE8Mw2LlzJ4FAgDZt2mC16twdaRoMw6CsrIzdu3eTlpZG69atazymQXWi//GPf6Rfv36sW7eOzMzMyPqIESOYOHFifWxBREQaK2cS9BsPfcbC1o9g+ePmr5v+Z95a9zLD9ONHgN0Z791KMxYIhtld7Ce3wOw3r9hv2tyTaCM7zUWrNBeZHgdWq374IyIiIiLxFwwGKSsro02bNiQlJcV7OyJHVGJiIgC7d++mZcuWh13tUi8h+meffcbSpUtxOqPDjfbt2/PTTz/VxxZERKSxs1qh01DztnuTOZm+/mXYtRZevwY+uB0GTIC+4yE58xc/nMivZRgGJeWhym5zc9q8+vv7bFbISjW7zbPTXCS5NG0uIiIiIg1PKBQCqJHbiTQVVT8cCgQCDTtED4fDkf8gq/vxxx/xeDz1sQUREWlKWnaDCx6FM++A1c/CF/8Ebw58dBd8ej+ceDkMvB5ado33TqWJCYYM9lROm+cU+ij3R0+bJyfYKkNzJy1SnNg0bS4iIiIijURtfdEiTcGR+LNdLyH6WWedxcMPP8xTTz0FmBv3er3ccccdnHPOOfWxBRERaYqSM+GUP8NJf4SvXzerXnatg9VzzdtxZ5pVLx3PVG+6HDZveZCcym7zvcV+wtWmza0WyErZ123uTqyXb61ERERERESkHtXLwaI//PADw4cPxzAMvv32W/r168e3335LixYt+PTTT2nZsmVdbyFCB4uKiDRhhgE7l8Gyx+GbhUDlX3EtusCg66HnZWbHusgBhMIGe4v95BT6yC3wU+qLfjddkssa6TZvkeLEbtMPaERERESk8aqoqGDbtm106NCBhISEeG9HDsKMGTN44403WLt2bZ18/O3bt9OhQwfWrFlDr1696uQa9elAf8Yb1MGibdu2Zd26dbz88susW7cOr9fL1VdfzahRoyLl7iIiIr+axQLtTjJv+dvgi6fgy3mwdzO8NQUW3Qn9xkH/CZDSJt67lQaktGJft/neYj+hai0tFgu08DjJTjcnzj0JNr3VVURERESkgXj88ce57777yMnJ4cQTT+Sxxx5jwIAB8d5WszN27FgKCwt544036vW67du3Z8qUKUyZMqVOr1PnIXogEKBr16689dZbjBo1ilGjRtX1JUVERCCjAwy/B077C6x5EVY8AYU74LMHYMkjcPzFMHgStOkd751KHITDBntL/OQW+skp8OGtiJ42T3BaI93mWalOHDZrnHYqIiIiIiKxvPzyy9x888088cQTDBw4kIcffphhw4axefPmem2+kKavzl8ROhwOKioq6voyIiIitUtIMcPyG9fApfPgmJMgHIQN/4anToNnh8PG/0K45gHY0rSU+0Jsyy1j+eZCFq7aw5JNhXy3qwxvRQgLkOlx0L2tmzN6ZDC8dwt6H5tCm4wEBegiIiIiIg3Ugw8+yMSJExk3bhzdu3fniSeeICkpiWefffYXn1tYWMiECRPIysoiJSWFM844g3Xr1gGwZ88eWrVqxaxZsyKPX7p0KU6nk0WLFgGwdetWLrzwQrKzs3G73fTv358PP/ww6hrt27fnrrvuYsyYMbjdbtq1a8d///tf9uzZw4UXXojb7aZnz56sWrUq8py5c+eSlpbGG2+8QadOnUhISGDYsGH88MMPB/x8/vnPf9KtWzcSEhLo2rUrs2fPPuiv4xdffEHv3r1JSEigX79+rFmzJur+UCjE1VdfTYcOHUhMTKRLly488sgjkftnzJjB888/z5tvvonFYsFisbB48WIApk2bRufOnUlKSuLYY49l+vTpBAKByHPXrVvH6aefjsfjISUlhb59+0Z9PT7//HOGDBlCYmIibdu25cYbb6S0tBSA0047jR07dnDTTTdFrltX6uVV4Q033MC9995LMBisj8uJiIjUZLVB9wtg/DtwzWKzH91qNzvU/z0aHu1ldqlXFMd7p3KEhCu7zb/aWcKi9Xm8u2Yva7eVsKvARzBs4HJYOSYrgQGdUjmnXxanHJ9Bl6OSSU12qK5FRERERJotwzAo8wfjcjuUoxv9fj+rV69m6NChkTWr1crQoUNZtmzZLz7/kksuYffu3bzzzjusXr2aPn36cOaZZ5Kfn09WVhbPPvssM2bMYNWqVZSUlDB69GgmT57MmWeeCYDX6+Wcc85h0aJFrFmzhuHDh3P++eezc+fOqOs89NBDnHzyyaxZs4Zzzz2X0aNHM2bMGK688kq+/PJLjjvuOMaMGRP1uZeVlXH33XfzwgsvsGTJEgoLC7n88stjfi7z58/n9ttv5+6772bTpk3MmjWL6dOn8/zzz//i18Hr9XLeeefRvXt3Vq9ezYwZM5g6dWrUY8LhMEcffTSvvPIKGzdu5Pbbb+fWW2/l3//+NwBTp07l0ksvZfjw4ezatYtdu3Zx0kknAeDxeJg7dy4bN27kkUce4emnn+ahhx6KfOxRo0Zx9NFHs3LlSlavXs3//d//4XA4APMHFcOHD+d3v/sd69ev5+WXX+bzzz9n8uTJALz22mscffTR3HnnnZHr1pV6OVh0xIgRLFq0CLfbTY8ePUhOTo66/7XXXqvrLUToYFEREYko3gUrn4ZVz0J5gbnm9EDvK2HgtWYljDQqFf4QuYV+cgt97C7yEwhFf5uT7nbQKs3sNk9LtissFxEREZFmb/9DF8v8Qbrf/l5c9rLxzmEkOQ+uffrnn3/mqKOOYunSpQwePDiyfsstt/DJJ5+wYsWKmM/9/PPPOffcc9m9ezculyuy3rFjR2655RauueYawBwM/vDDD+nXrx8bNmxg5cqVUY/f3wknnMB1110XCXnbt2/PkCFDmDdvHgA5OTm0bt2a6dOnc+eddwKwfPlyBg8ezK5du2jVqhVz585l3LhxLF++nIEDBwLwzTff0K1bN1asWMGAAQNqHCzasWNH/t//+3+MHDkyspe77rqLt99+m6VLlx7w6/jUU09x66238uOPP0YO3XziiSe4/vrrD3iw6OTJk8nJyeHVV18FDr4T/f7772fBggWRafOUlBQee+wxrrrqqhqPnTBhAjabjSeffDKy9vnnn3PqqadSWlpKQkLCQXWiN5qDRdPS0vjd735XH5cSERE5eCmt4czbYchUWP8yLJ9jHkK6Yo7Zod71XBg0yTyoVGFrg2QYBvneQCQ4LyyNfteb024hu7LbvGWqC5dD1SwiIiIiIs3dunXr8Hq9ZGZmRq2Xl5ezdevWyO/vv/9+TjjhBF555RVWr14dFaB7vV5mzJjBwoUL2bVrF8FgkPLy8hqT6D179oz8c3Z2NgA9evSosbZ7925atWoFgN1up3///pHHdO3albS0NDZt2lTj0NTS0lK2bt3K1VdfzcSJEyPrwWCQ1NTUX/xabNq0iZ49e0aFy9V/KFHl8ccf59lnn2Xnzp2Ul5fj9/tjBuzVvfzyyzz66KNs3boVr9dLMBiMCqtvvvlmJkyYwLx58xg6dCiXXHIJxx13HGD+e1q/fj3z58+PPN4wDMLhMNu2baNbt26/eP0jpV5C9Oeee64+LiMiInJ4nEnQbxz0HQtbF8Gy2eav37xl3lqfaIbpx18Mdme8d9vs+QJhdhf5yCnws7vIhz8YPW2elmwnO81FqzQn6W5Vs4iIiIiIHIpEh42Ndw6L27UPVosWLbDZbOTm5kat5+bmRsLoWLxeL61bt470dleXlpYW+eetW7fy888/Ew6H2b59e1T4PXXqVD744APuv/9+OnbsSGJiIr///e/x+/1RH6+qmgSIvDapbS0cDh/4Ez7A5wLw9NNPRybXq9hsB//1PJAFCxYwdepUHnjgAQYPHozH4+G+++474LQ/wLJlyxg1ahQzZ85k2LBhpKamsmDBAh544IHIY2bMmMEVV1zBwoULeeedd7jjjjtYsGABI0aMwOv1cu2113LjjTfW+NjHHHPMEfncDla9hOhV9uzZw+bNmwHo0qULWVlZ9Xl5ERGRA7NYoONQ87b7G3Mifd0C2LUOXr8WPrgd+k+EfuMhOfOXP54cEYZhUFgaJLfQR06hnwJvIOp+h81Cy1Qn2ekuslOdJDiPzDeKIiIiIiLNkcViOehKlXhyOp307duXRYsWcdFFFwFmEL1o0aJInUosffr0IScnB7vdTvv27Wt9jN/v58orr+Syyy6jS5cuTJgwgQ0bNtCyZUsAlixZwtixYxkxYgRghtnbt28/Ip9bMBhk1apVkanzzZs3U1hYWOvkdXZ2Nm3atOH7779n1KhRh3ytbt26MW/ePCoqKiLT6MuXL496zJIlSzjppJOYNGlSZK36xD6Y/z5CoVDU2tKlS2nXrh233XZbZG3Hjh019tC5c2c6d+7MTTfdxMiRI3nuuecYMWIEffr0YePGjXTs2DHm/mu7bl2ol/c0l5aWMn78eFq3bs0pp5zCKaecQps2bbj66qspKyurjy2IiIgcmpZd4fxH4KaNcMZ0cLcCby58fBc81B3+eyPs3hTvXTY5obBBYWmAHXvK2bCjhCWbCnh79R4Wf5XPph9LIwF6SpKdzm2SGNI9nXP6ZTGgcxrtshIVoIuIiIiINCM333wzTz/9NM8//zybNm3i+uuvp7S0lHHjxh3weUOHDmXw4MFcdNFFvP/++2zfvp2lS5dy2223Rbq6b7vtNoqKinj00UeZNm0anTt3Zvz48ZGP0alTJ1577TXWrl3LunXruOKKKw57mnx/DoeDP/zhD6xYsYLVq1czduxYBg0aVKPKpcrMmTO55557ePTRR9myZQsbNmzgueee48EHH/zFa11xxRVYLBYmTpzIxo0befvtt7n//vujHtOpUydWrVrFe++9x5YtW5g+fTorV66Mekz79u1Zv349mzdvZu/evQQCATp16sTOnTtZsGABW7du5dFHH+X111+PPKe8vJzJkyezePFiduzYwZIlS1i5cmXkhwXTpk1j6dKlTJ48mbVr1/Ltt9/y5ptvRv2QpH379nz66af89NNP7N2796C/xoeqXkL0m2++mU8++YT//e9/FBYWUlhYyJtvvsknn3zCn/70p/rYgoiIyOFJzoRTpsKUDXDx09C6FwQr4MvnYfYgmDcCvv0AjtA3S82FYRiU+ULsKvCx+adSVn5byIfr9vK/L3bz8YZ8vtxazHe7ythd5McfNLBbLbROd9Grg4fhvVtwZs9Mjj/GQ4sUJ1bVtYiIiIiINEuXXXYZ999/P7fffju9evVi7dq1vPvuu5Ge8VgsFgtvv/02p5xyCuPGjaNz585cfvnl7Nixg+zsbBYvXszDDz/MvHnzSElJwWq1Mm/ePD777DPmzJkDwIMPPkh6ejonnXQS559/PsOGDaNPnz5H5PNKSkpi2rRpXHHFFZx88sm43W5efvnlmI+fMGEC//znP3nuuefo0aMHp556KnPnzqVDhw6/eC23283//vc/NmzYQO/evbntttu49957ox5z7bXXcvHFF3PZZZcxcOBA8vLyoqbSASZOnEiXLl3o168fWVlZLFmyhAsuuICbbrqJyZMn06tXL5YuXcr06dMjz7HZbOTl5TFmzBg6d+7MpZdeytlnn83MmTMBs0/+k08+YcuWLQwZMoTevXtz++2306ZNm8jHuPPOO9m+fTvHHXdcnbaeWAzDMH75Yb9OixYtePXVVznttNOi1j/++GMuvfRS9uzZU9dbiDjYE1dFRERqZRiwcxksnw3fLASjMjxv0RkGXgcnjjQ71iUiEApTXBakuCxIUeWvxWVBAqHavwVx2CykJtlJSbabvyY6SEu2Y7UqLBcREREROdIqKirYtm0bHTp0iDpcUuJj7ty5TJkyhcLCwnhvpck40J/xg82K66XgqKysrNafALVs2VJ1LiIi0rhYLNDuJPNWsB1WPAVfvgB7t8DCm+Gj/2ceUDrgGkhp80sfrUkxDANvRahGWF7qq72fzmIBT6KdlMTKsDzJTmqynQSHVYeBioiIiIiISINRLyH64MGDueOOO3jhhRciaX95eTkzZ85k8ODB9bEFERGRIy+9PQyfBaf9H6ydD8vnQOEO+PwhWPoYHD8CBl0PR/WN906POF8gXC0sD5iBeXmQUIxWmwSH1QzJq8LyJDueRE2Xi4iIiIjIkTd//nyuvfbaWu9r164dX3/9dT3vKH5mzZrFrFmzar1vyJAhvPPOO/W8o8apXupcvvrqK4YNG4bP5+PEE08EYN26dSQkJPDee+9x/PHH1/UWIlTnIiIidSYcgs3vmFUvO5bsW287CAZPgi7ngq3hn3JfXThsUFK+b7K86teKQO1puc0KKYlmUJ6S5IiE5i5HvRzDIiIiIiIih6gp1rmUlJSQm5tb630Oh4N27drV847iJz8/n/z8/FrvS0xM5KijjqrnHdW/I1HnUi8hOpiVLvPnz+ebb74BoFu3bowaNYrExMT6uHyEQnQREakXP681J9O/+g+EA+Za6jEw8FroMxoSUuO6vf0ZhkFFIExRabWwvDxISXmQWN8pJLtslWH5vglzd4JNVSwiIiIiIo1IUwzRRaprVCF6Q6EQXURE6lXxLlj1DKx8Bsorf/rvdEPvK81APePYet9SMGRQXG7WsFSfMA8EYx/0uX9YnpJkx2HTdLmIiIiISGOnEF2aukZzsOg999xDdnY248ePj1p/9tln2bNnD9OmTauPbYiIiNS/lNZwxl9hyJ9g/cvmdPqeb2DFE7DiSehyjln10u5k86TNI8gwDEp90Qd9FpUFKa2IcdAn4E60VYbljkhonujUQZ8iIiIiIiLSfNVLiP7kk0/y0ksv1Vg//vjjufzyyxWii4hI0+dIhL5joc9VsPUjM0z/7gPYvNC8teoJgybBCReD3XXIH94fDNcIy4vLgoTCtU+XuxzWqKnyqoM+bTroU0RERERERCRKvYToOTk5tG7dusZ6VlYWu3btqo8tiIiINAwWC3Q807zt2WyG6esWQM56eOM6+PAO6D8B+o2H5BY1nh4OG3grQhSVBaLC8nJ/7Qd9Wi1EgvJIHUuinQSnra4/UxEREREREZEmoV5C9LZt27JkyRI6dOgQtb5kyRLatGlTH1sQERFpeLK6wPkPw5m3w+rn4IunoWQXfHw3xqf3EzrhEgp7TKQgqVMkLC8pDxJjuJwkp5WUZDspiQ4zLE82D/q0qopFRERERERE5LDVS4g+ceJEpkyZQiAQ4IwzzgBg0aJF3HLLLfzpT3+qjy2IiIg0WKGEdIp730hxx4lYNr1B+oan8RSsx77uRVqse5FQ5snsaX8VRS1OAYsVu9VS45DPlCQ7TrsO+hQREREREWnq5s6dy5QpUygsLKyza1gsFl5//XUuuuiiOrtGY1IvIfqf//xn8vLymDRpEn6/H4CEhASmTZvGX/7yl/rYgoiISNwZhkGZL0xxWSCqu9xb/aDPpGEw4CwyCr+k4/a5tMn9gOy8JWTnLSGQ3pFw/+tw9h6JxeWO3yciIiIiIiLSAHz66afcd999rF69ml27din0jaMZM2bwxhtvsHbt2nq97mmnnUavXr14+OGH6/Q69RKiWywW7r33XqZPn86mTZtITEykU6dOuFyHfnCaiIhIYxAIhikuD1JUWu2gz/IgwVDtXSxOu4XUJEe17vLf4jnrbCzFO+GLp+DLF3AUfAfvT4VP74J+46D/REg9qp4/MxERERERkYahtLSUE088kfHjx3PxxRfHezvShNXr+77dbjf9+/fH4/GwdetWwuHaD0ETERFpLMKGQUl5kB/zKtj4g5dlmwt478s9vLVqD59+XcC67SVs211OvjdAMGRgsUBqkp22LRI44Rg3J3VN4+w+LTinbxa/6Z5Oz/Ye2rdMJN3twG6zQHo7GHY33LwRht8L6e2hohA+fwge6QmvXg0/ro73l0FERERERKTenX322dx1112MGDHikJ/r8/mYOnUqRx11FMnJyQwcOJDFixcDUFFRwfHHH88111wTefzWrVvxeDw8++yzAOTl5TFy5EiOOuookpKS6NGjB//617+irnHaaafxhz/8gSlTppCenk52djZPP/00paWljBs3Do/HQ8eOHXnnnXciz1m8eDEWi4WFCxfSs2dPEhISGDRoEF999dUBP58333yTPn36kJCQwLHHHsvMmTMJBoMH9bX49ttvOeWUU0hISKB79+588MEHNR4zbdo0OnfuTFJSEsceeyzTp08nEAgAZr3MzJkzWbduHRaLBYvFwty5cwF48MEH6dGjB8nJybRt25ZJkybh9XojH3fHjh2cf/75pKenk5yczPHHH8/bb78duf+rr77i7LPPxu12k52dzejRo9m7dy8AY8eO5ZNPPuGRRx6JXHf79u0H9TkfqjqdRH/22WcpLCzk5ptvjqxdc801PPPMMwB06dKF9957j7Zt29blNkRERI4IXyBMUVlg32R55S3WQZ+JTmtUd3lqksM86NN6GAd9ujww6DoYMBG2vAvLZsOOz+GrV81b24EwaBJ0PQ9s9fJGMxERERERaYIMwyAUp7lXm9VstKgPkydPZuPGjSxYsIA2bdrw+uuvM3z4cDZs2ECnTp2YP38+AwcO5Nxzz+W8887jyiuv5Le//S3jx48HzKC9b9++TJs2jZSUFBYuXMjo0aM57rjjGDBgQOQ6zz//PLfccgtffPEFL7/8Mtdffz2vv/46I0aM4NZbb+Whhx5i9OjR7Ny5k6SkpMjz/vznP/PII4/QqlUrbr31Vs4//3y2bNmCw+Go8bl89tlnjBkzhkcffZQhQ4awdevWyA8A7rjjjgN+HcLhMBdffDHZ2dmsWLGCoqIipkyZUuNxHo+HuXPn0qZNGzZs2MDEiRPxeDzccsstXHbZZXz11Ve8++67fPjhhwCkpqYCYLVaefTRR+nQoQPff/89kyZN4pZbbmH27NkA3HDDDfj9fj799FOSk5PZuHEjbrdZX1pYWMgZZ5zBhAkTeOihhygvL2fatGlceumlfPTRRzzyyCNs2bKFE044gTvvvBOArKysg/r3f6gshmHEeOn/6w0aNIhrr72WcePGAfDuu+9y/vnnM3fuXLp168bkyZPp3r07//znP+tqCzUUFxeTmppKUVERKSkp9XZdERFpPEJhc7q8elheVBbEF6j9O0mbFVKSHNXC8no66PPntbDiCdjwKoTNCQBSj4GB10Dv0ZCYVrfXFxERERGRRq+iooJt27bRoUMHEhISCIYM/rdyd1z2cn7/luY7cg/DoRyEuXPnTo499lh27txJmzZtIutDhw5lwIABzJo1C4D77ruPv//971x++eX85z//YcOGDWRmZsb8uOeddx5du3bl/vvvB8xJ9FAoxGeffQZAKBQiNTWViy++mBdeeAGAnJwcWrduzbJlyxg0aBCLFy/m9NNPZ8GCBVx22WUA5Ofnc/TRRzN37lwuvfTSGgeLDh06lDPPPDPq7MkXX3yRW265hZ9//vmAX4v333+fc889lx07dkS+Fu+++y5nn332Ab+e999/PwsWLGDVqlXAwXeiv/rqq1x33XWRafKePXvyu9/9rtaw/6677uKzzz7jvffei6z9+OOPtG3bls2bN9O5c+eD6kTf/894dQebFdfpqNq3335Lv379Ir9/8803ufDCCxk1ahQAs2bNigTsIiIi9c0wDMr94WphuXngp7c8RKyfMCcn2CIheVVgnuyy1du0RJQ2vWDEEzB0Bqx8BlY9A0U74f2/wsf3QO8rYeC1kHlc/e9NRERERESkgdqwYQOhUIjOnTtHrft8vqiQ/E9/+hNvvPEG//jHP3jnnXei7guFQsyaNYt///vf/PTTT/j9fnw+X9Q0OZghcRWbzUZmZiY9evSIrGVnZwOwe3f0Dy4GDx4c+eeMjAy6dOnCpk2bav181q1bx5IlS7j77ruj9ldRUUFZWVmNPVW3adMm2rZtG/XDhOrXrvLyyy/z6KOPsnXrVrxeL8Fg8KAGlD/88EPuuecevvnmG4qLiwkGg1H7uvHGG7n++ut5//33GTp0KL/73e8iX7N169bx8ccfRybTq9u6dWuNf391qU5D9PLy8qgv5tKlS7n66qsjvz/22GPJycmpyy2IiIgAEAyFa0yWF5cFCcQ46NNhs5hheXLlZHmig5QkG3ZbvR4ncnA8reCM22DIzbDhFbPqZc8m+OJJ81DSLmfDoOuh/RCIR9gvIiIiIiKNhs1qToTH69r1wev1YrPZWL16NTabLeq+6oHt7t272bJlCzabjW+//Zbhw4dH7rvvvvt45JFHePjhhyOd31OmTMHv90d9vP3rVywWS9Ra1UDWrzk70uv1MnPmzFoPV91/8vpwLFu2jFGjRjFz5kyGDRtGamoqCxYs4IEHHjjg87Zv3855553H9ddfz913301GRgaff/45V199NX6/n6SkJCZMmMCwYcNYuHAh77//Pvfccw8PPPAAf/jDH/B6vZx//vnce++9NT5269atf/XndSjqNERv164dq1evpl27duzdu5evv/6ak08+OXJ/Tk5OpB9HRETkSDAMg9KKUI2wvNQXqvXxFgt4Euz7dZfbSXBa4zNd/ms4EqHPGLPK5fvFsHw2fPs+bH7bvLXqYfamn/A7sLvivVsREREREWmALBYLdtsvP64x6927N6FQiN27dzNkyJCYjxs/fjw9evTg6quvZuLEiQwdOpRu3boBsGTJEi688EKuvPJKwAzBt2zZQvfu3Y/IHpcvX84xxxwDQEFBAVu2bIlce399+vRh8+bNdOzY8ZCv061bN3744Qd27doVCaaXL18e9ZilS5fSrl07brvttsjajh07oh7jdDoJhaJfd69evZpwOMwDDzyA1Wr+hOTf//53jT20bduW6667juuuu46//OUvPP300/zhD3+gT58+/Oc//6F9+/bY7bXH2LVdty7UaYh+1VVXccMNN/D111/z0Ucf0bVrV/r27Ru5f+nSpZxwwgl1uQUREWnCfIFw5HDPqgM/i8uDMQ/BSXDsf9CnHXeiHdvhHPTZkFkscNzp5m3PFrM3fe1LkLMB3rgePrgD+k+AfuPBXTeHroiIiIiIiNQ1r9fLd999F/n9tm3bWLt2LRkZGZEAujadO3dm1KhRjBkzhgceeIDevXuzZ88eFi1aRM+ePTn33HN5/PHHWbZsGevXr6dt27YsXLiQUaNGsXz5cpxOJ506deLVV19l6dKlpKen8+CDD5Kbm3vEQvQ777yTzMxMsrOzue2222jRokXMfvLbb7+d8847j2OOOYbf//73WK1W1q1bx1dffcVdd911wOsMHTqUzp07c9VVV3HfffdRXFwcFZYDdOrUiZ07d7JgwQL69+/PwoULef3116Me0759+8jX/+ijj8bj8dCxY0cCgQCPPfYY559/PkuWLOGJJ56Iet6UKVM4++yz6dy5MwUFBXz88ceRHxbccMMNPP3004wcOZJbbrmFjIwMvvvuOxYsWMA///lPbDYb7du3Z8WKFWzfvh23201GRkYksD+S6vRNErfccgsTJ07ktddeIyEhgVdeeSXq/iVLljBy5Mi63IKIiDQB4bBBUWmAH/aW89WOEpZuKuCd1Xt4e/UePt9UwPodJezYU0FBqRmgWy2QlmynXVYCPdp5+E23dM7pm8XZfbM4uVs6J7TzcExWIqnJjqYXoO8vqzOc9yDcvNHsTve0gdLdsHgWPHQ8vHkD5H4d712KiIiIiIgcslWrVtG7d2969+4NwM0330zv3r25/fbbf/G5zz33HGPGjOFPf/oTXbp04aKLLmLlypUcc8wxfPPNN/z5z39m9uzZtG3bFoDZs2ezd+9epk+fDsBf//pX+vTpw7BhwzjttNNo1arVQR1qerD+9re/8cc//pG+ffuSk5PD//73P5xOZ62PHTZsGG+99Rbvv/8+/fv3Z9CgQTz00EO0a9fuF69jtVp5/fXXKS8vZ8CAAUyYMCGqWx3gggsu4KabbmLy5Mn06tWLpUuXRr4OVX73u98xfPhwTj/9dLKysvjXv/7FiSeeyIMPPsi9997LCSecwPz587nnnnuinhcKhbjhhhvo1q0bw4cPp3PnzsyePRuANm3asGTJEkKhEGeddRY9evRgypQppKWlRYLyqVOnYrPZ6N69O1lZWezcufOgv8aHwmIYRqyz05qkgz1xVURE6p9hGFQEanaXl5QHifW3VZLLFjVZnpJkx50Qp4M+G4NQADa+Ccseh5+/3Lfe4VQYfAN0/C3UwU/tRURERESkYaqoqGDbtm106NDhiPRny6+zePFiTj/9dAoKCkhLS4v3dpqEA/0ZP9isuE7rXERERGIJhgyKy4OVdSwBiioD80Cw9rTcXnXQZ7WwPCXRjsOuwPeQ2BzQ4/dmL/oPX5i96Zv+C9s+MW+ZHWHgddDrCnAmx3u3IiIiIiIiInGnEF1EROqUYRiU+kLVusvNW2lF7IM/PIm2yrDcEQnNExvjQZ8NmcUCxww0b4U7YcWT8OULkPcdvD0VPvp/0HcsDLgGUo+O925FREREREQO2meffcbZZ58d836v11uPu4mv+fPnc+2119Z6X7t27fj6a9V7HgzVuYiIyBHjD4ZrhOXFZUFC4dr/qnHVctCnpyke9NlY+ErMA0iXz4GCbeaaxQbdLzSrXo7uF9/9iYiIiIjIEdcU61zKy8v56aefYt7fsWPHetxNfJWUlJCbm1vrfQ6H46B60xs71bmIiEhchMMG3opQZVgeiITl5f5wrY+3WsCTWBmWJ1f+mmgnwWmr553LAbk8MPBa6D8BtrxnVr1s/wy+fs28HT0ABl0P3S4Am76FEBERERGRhikxMbFZBeUH4vF48Hg88d5Go6dXwCIiEpNhGPgC4ahDPosrD/qMMVxOotNaOVnuiITm7gQbVlWxNB5WG3Q9x7ztWm9Opn/1Kvz4Bbz6BaS2NWte+oyBxLR471ZERERERESkTtVLiB4KhZg7dy6LFi1i9+7dhMPRk4offfRRfWxDREQOIBQ2oqpYqqbM/bEO+rRazMM9qx/0mWTHqYM+m5bWPWHEHBg6A1Y9AyufgaIf4IPpsPhv0HuUeRBp5nHx3qmIiIiIiIhInaiXEP2Pf/wjc+fO5dxzz+WEE07QwXAiInFkGAZlvjDFZQGKy4MUlZqhufcAB326E2z7dZc7SHLpoM9mxZMNp98Kv7kZNrxiVr3s3ghfPAVfPA2dh8PgSdB+iHloqYiIiIiIiEgTUS8h+oIFC/j3v//NOeecUx+XExGRSoFgmOLymgd9BkO1T5c77ZZISF79oE+7TaGoVHIkQJ/R0PtK+H6xWfXy7Xuw5R3zlt3D7E3v8Xuwu+K9WxEREREREZFfrV5CdKfTqTJ/EZE6FDYMSitC+3WXByjz1X7Qp6X6QZ+VYXlqkh2XQ9PlcpAsFjjudPO291tY8QSsfQlyN8Cbk+DDO8wDSvuNB3fLeO9WRERERERE5LBZDMOIcTTckfPAAw/w/fff849//CPu4UxxcTGpqakUFRWRkpIS172IiBwO86DPQFR3eXFZ7IM+EyIHfVaF5Q7zoE+rwnI5wsry4cvnzXqX4p/MNZsTelxqTqe3OiG++xMRERERkRoqKirYtm0bHTp0ICEhId7bkYMwY8YM3njjDdauXVsnH3/79u106NCBNWvW0KtXrzq5Rn060J/xg82K62US/fPPP+fjjz/mnXfe4fjjj8fhcETd/9prr9XHNkREGpVQ2KCkfP+DPoP4ArVPl9uskJJoJyXZEZks10GfUq+SMuA3N8HgybDxTbM3/afVsPZF89bhFBh0A3Q6C6z6cykiIiIiIofvnnvu4bXXXuObb74hMTGRk046iXvvvZcuXbrEe2vN0tixYyksLOSNN96o1+u2b9+eKVOmMGXKlDq9Tr2E6GlpaYwYMaI+LiUi0ugYhkG5P7xfWB7AWx4i1luFkl3VDvpMNn9Ndtni/m4fEQBsDrMTvcfv4YcvzDB9439h26fmLeM4czL9xJHgcsd7tyIiIiIi0gh98skn3HDDDfTv359gMMitt97KWWedxcaNG0lOTo739qSJqZcxsOeee+6ANxGR5iIYCpNf4mdbbhnrthXz6df5LFy1h/fW7GXZ5kI2/uDlx7wKSioDdIfNQqbHwbHZifTq4OHU4zM4v38WZ/VuwaAuaXRr6+aojATcCXYF6NIwtR0Al8yFP66Dk24EVyrkb4W3p8JD3eH96VD4Q7x3KSIiIiIijcy7777L2LFjOf744znxxBOZO3cuO3fuZPXq1Qf1/MLCQiZMmEBWVhYpKSmcccYZrFu3DoA9e/bQqlUrZs2aFXn80qVLcTqdLFq0CICtW7dy4YUXkp2djdvtpn///nz44YdR12jfvj133XUXY8aMwe12065dO/773/+yZ88eLrzwQtxuNz179mTVqlWR58ydO5e0tDTeeOMNOnXqREJCAsOGDeOHHw78uumf//wn3bp1IyEhga5duzJ79uyD+joAfPHFF/Tu3ZuEhAT69evHmjVrou4PhUJcffXVdOjQgcTERLp06cIjjzwSuX/GjBk8//zzvPnmm1gsFiwWC4sXLwZg2rRpdO7cmaSkJI499limT59OIBCIPHfdunWcfvrpeDweUlJS6Nu3b9TX4/PPP2fIkCEkJibStm1bbrzxRkpLSwE47bTT2LFjBzfddFPkunWlXibRq+zZs4fNmzcD0KVLF7Kysurz8iIi9cao9aDPIKW+UK2Pt1jAnWAjNckRddBnglMHfUoTkdYWzvp/cOo08wDSFXMg/3tY+igsexy6X2BWvbTtH++dioiIiIg0b4YBgbL4XNuRZL5APgxFRUUAZGRkHNTjL7nkEhITE3nnnXdITU3lySef5Mwzz2TLli1kZWXx7LPPctFFF3HWWWfRpUsXRo8ezeTJkznzzDMB8Hq9nHPOOdx99924XC5eeOEFzj//fDZv3swxxxwTuc5DDz3ErFmzmD59Og899BCjR4/mpJNOYvz48dx3331MmzaNMWPG8PXXX0de/5eVlXH33Xfzwgsv4HQ6mTRpEpdffjlLliyp9XOZP38+t99+O//4xz/o3bs3a9asYeLEiSQnJ3PVVVcd8Ovg9Xo577zz+O1vf8uLL77Itm3b+OMf/xj1mHA4zNFHH80rr7xCZmYmS5cu5ZprrqF169ZceumlTJ06lU2bNlFcXBwZmK769+DxeJg7dy5t2rRhw4YNTJw4EY/Hwy233ALAqFGj6N27N3PmzMFms7F27dpIFfjWrVsZPnw4d911F88++yx79uxh8uTJTJ48meeee47XXnuNE088kWuuuYaJEyce1L/3w1UvB4uWlpbyhz/8gRdeeIFw2OzytdlsjBkzhscee4ykpKS63kKEDhYVkSPNFwhHDvcsKg9SXBqguDxIqPbqchIcVlKiDvq04060Y9NBn9KchMPw7Xtm1cu2T/etH9UPBk+CbheCrV5/1i8iIiIi0izVOHTRXwqz2sRnM7f+DM5Dr2IJh8NccMEFFBYW8vnnn//i4z///HPOPfdcdu/ejcvliqx37NiRW265hWuuuQaAG264gQ8//JB+/fqxYcMGVq5cGfX4/Z1wwglcd911TJ48GTAn0YcMGcK8efMAyMnJoXXr1kyfPp0777wTgOXLlzN48GB27dpFq1atmDt3LuPGjWP58uUMHDgQgG+++YZu3bqxYsUKBgwYUONg0Y4dO/L//t//Y+TIkZG93HXXXbz99tssXbr0gF+Lp556iltvvZUff/wxcujmE088wfXXX3/Ag0UnT55MTk4Or776KnDwnej3338/CxYsiEybp6Sk8Nhjj9Ua9k+YMAGbzcaTTz4ZWfv888859dRTKS0tJSEh4aA60RvNwaI333wzn3zyCf/73/84+eSTAfMTvvHGG/nTn/7EnDlz6mMbIiK/SjhsUFJR86DPCn/tabnVwn5huTll7nLoQEURrFbocrZ5y9kAy+fAhlfgp1Xw6nhIORoGTIS+V0Fierx3KyIiIiIiDdgNN9zAV199dVABOpgVIl6vl8zMzKj18vJytm7dGvn9/fffzwknnMArr7zC6tWrowJ0r9fLjBkzWLhwIbt27SIYDFJeXs7OnTujPmbPnj0j/5ydnQ1Ajx49aqzt3r2bVq1aAWC32+nff9+7dLt27UpaWhqbNm1iwIABUR+/tLSUrVu3cvXVV0dNYweDQVJTU3/xa7Fp0yZ69uwZFS4PHjy4xuMef/xxnn32WXbu3El5eTl+vz9mwF7dyy+/zKOPPsrWrVvxer0Eg8GosPrmm29mwoQJzJs3j6FDh3LJJZdw3HHHAea/p/Xr1zN//vzI4w3DIBwOs23bNrp16/aL1z9S6iVE/89//sOrr77KaaedFlk755xzSExM5NJLL1WILiINimEYVAT2P+gzSEl5kFjv3UmqdtBnamVw7k7QQZ8iB6VVD7hoNgydASufgZX/hOIf4cM74JN7odcV0OEU8LQBTyvzZnPEe9ciIiIiIk2TI8mcCI/XtQ/R5MmTeeutt/j00085+uijD+o5Xq+X1q1bR3q7q0tLS4v889atW/n5558Jh8Ns3749KvyeOnUqH3zwAffffz8dO3YkMTGR3//+9/j9/uhPybHvtUtVRlDbWlV7x6Hyer0APP3005HJ9So2m+2wPub+FixYwNSpU3nggQcYPHgwHo+H++67jxUrVhzwecuWLWPUqFHMnDmTYcOGkZqayoIFC3jggQcij5kxYwZXXHEFCxcu5J133uGOO+5gwYIFjBgxAq/Xy7XXXsuNN95Y42NXr8ypD/USopeVlUV+qlJdy5YtKSuLU8eSiAgQDBmUlFeF5YFIaO4P1p6W222WSEhe9WtKoh2HXdPlIr+auyWc/hf4zU3w1auwbDbs/toM1Vf+s9oDLZCcBSmtzWA9pTV4Km/V1xLSDrtPUURERESk2bJYDqtSpb4ZhsEf/vAHXn/9dRYvXkyHDh0O+rl9+vQhJycHu91O+/bta32M3+/nyiuv5LLLLqNLly5MmDCBDRs20LJlSwCWLFnC2LFjGTFiBGCG2du3b/+1nxZgTpGvWrUqMnW+efNmCgsLa528zs7Opk2bNnz//feMGjXqkK/VrVs35s2bR0VFRWQaffny5VGPWbJkCSeddBKTJk2KrFWf2AdwOp2EQtHnwC1dupR27dpx2223RdZ27NhRYw+dO3emc+fO3HTTTYwcOZLnnnuOESNG0KdPHzZu3EjHjh1j7r+269aFegnRBw8ezB133MELL7wQ+ZdRXl7OzJkza317gIjIkWYYBmW+mgd9eiti/4/Wk2gjJdFOarKdlCQHqUl2EnXQp0jdcyRA7yuh1yizL33tfMjfBiW7zFs4CKW7zduudbE/jj0xdsAemWpvDXZn/X1uIiIiIiJyRNxwww289NJLvPnmm3g8HnJycgBITU0lMTHxgM8dOnQogwcP5qKLLuLvf/87nTt35ueff2bhwoWMGDGCfv36cdttt1FUVMSjjz6K2+3m7bffZvz48bz11lsAdOrUiddee43zzz8fi8XC9OnTD3uafH8Oh4M//OEPPProo9jtdiZPnsygQYNqVLlUmTlzJjfeeCOpqakMHz4cn8/HqlWrKCgo4Oabbz7gta644gpuu+02Jk6cyF/+8he2b9/O/fffH/WYTp068cILL/Dee+/RoUMH5s2bx8qVK6N+cNG+fXvee+89Nm/eTGZmJqmpqXTq1ImdO3eyYMEC+vfvz8KFC3n99dcjzykvL+fPf/4zv//97+nQoQM//vgjK1eu5He/+x0A06ZNY9CgQUyePJkJEyaQnJzMxo0b+eCDD/jHP/4Rue6nn37K5ZdfjsvlokWLFof1Nf8l9RKiP/LIIwwbNoyjjz6aE088ETA7bRISEnjvvffqYwsi0oz4g9UO+qz8tbgsSDBc+3S5024hNdlhBuZJZmju0UGfIvFnscCxp5q3KuEwlOVByc9QvCv615Kcff9cXgDBcsj/3rwdSFKLGFPtbfb9mpiuqXYRERERkQakqh66en00wHPPPcfYsWMP+FyLxcLbb7/Nbbfdxrhx49izZw+tWrXilFNOITs7m8WLF/Pwww/z8ccfR/q7582bx4knnsicOXO4/vrrefDBBxk/fjwnnXQSLVq0YNq0aRQXFx+Rzy0pKYlp06ZxxRVX8NNPPzFkyBCeeeaZmI+fMGECSUlJ3Hffffz5z38mOTmZHj16HPCwzSput5v//e9/XHfddfTu3Zvu3btz7733RoJsgGuvvZY1a9Zw2WWXYbFYGDlyJJMmTeKdd96JPGbixIksXryYfv364fV6+fjjj7ngggu46aabmDx5Mj6fj3PPPZfp06czY8YMwKybycvLY8yYMeTm5tKiRQsuvvhiZs6cCZh98p988gm33XYbQ4YMwTAMjjvuOC677LLIde+8806uvfZajjvuOHw+H0asHt5fyWLU1UfeT1lZGfPnz+ebb74BzLcKjBo16hd/MnSkHeyJqyLS8IUNA295qDIsD0RC8/IDHPTpSbTX6C5PcB6ZjjARaUAC5ebUevGufRPsUaF75S3k/+WPBWBzmZPr1YP1yHR7tZsj4Zc/loiIiIhIA1JRUcG2bdvo0KFD1OGSEh9z585lypQpFBYWxnsrTcaB/owfbFZcL5PoYP4EpfoJsSIih6LCH4qE5FXT5SXlQWIMl5PotFbrLndEDvq0arpcpHlwJELGseYtFsOAsvxaptl/jg7dy/Ig5IPCHebtQBIz9gXstYbubczHWHWOgoiIiIiISGNRZyH6f//7X84++2wcDgf//e9/D/jYCy64oK62ISKNTChcedBnaWVYXm4G5r5A7dPldqvFPNyz+kGfSXacOuhTRH6JxQLJmeatVY/Yjwv6KifXqwfsP9ecdA9WQHm+ecv9KvbHsznB3WrfFHskdG8dveao33friYiIiIg0FfPnz+faa6+t9b527drx9ddf1/OO4mfWrFnMmjWr1vuGDBkSVckisdVZnYvVaiUnJ4eWLVtiPcC0lcViqZcTVKuozkWkYTAMg3J/uHKqPEBRqRmYe8tDxPqfkjvBFhWWpybZSXLZdNCniMSfYZg97NUn2GsL3Uv3HPzHTEiLHbBX/ZrUQlPtIiIiIvKrNMU6l5KSEnJzc2u9z+Fw0K5du3reUfzk5+eTn59f632JiYkcddRR9byj+teg61yqn0Z7pE6mFZHGKRAMRybKq9exBEO1x+UOu6VaZ7mD1CTzoE+7TWG5iDRQFgskZZi37ONjPy7oB2/uftPsP9ecdA+UQUWhedu9MfbHs9p/Yaq98rBUZ/KR/oxFRERERBosj8eDx+OJ9zYahIyMDDIyMuK9jUavXjrRX3jhBS677DJcLlfUut/vZ8GCBYwZM6Y+tiEidcwwDLwVNbvLy3y1v9vEUnnQZ+p+dSwJDqumy0WkabI7Ia2teYvFMKCiqFrAnhN9IGpV0O7dDeEgFP9o3g7ElVoZqrfaF6zvH7q7W4JVBy2LiIiIiIjsr87qXKqz2Wzs2rWLli1bRq3n5eXRsmXLQ6pzmTNnDnPmzGH79u0AHH/88dx+++2cffbZB/V81bmIHBm+QLgyLA9EwvListgHfSZEHfRp/upJsOugTxGRwxUKHmCqvVpfu997cB/PYgN39n4Be/XQvfJXlyZ6RERERJqSpljnIlJdg65zqc4wjFqnSn/88UdSU1MP6WMdffTR/O1vf6NTp04YhsHzzz/PhRdeyJo1azj++AO8fVpEDksobOAtj54sLy4LUhHjoE+bFVIS7aQkO0hN3HfQp8uhzl4RkSPKZofUo8zbgVQUH8RUey4Yocou958P/PGcHjNcrx6sV59u97Q2w3hbvXybKSIiIiIiUufq9NVN7969sVgsWCwWzjzzTOz2fZcLhUJs27aN4cOHH9LHPP/886N+f/fddzNnzhyWL1+uEF3kVzAMg4rKgz6rwvKisgDeihCx3q+S7Kp50Gdygg76FBFpUBJSzFtWl9iPCYfMepgaAft+obuvGPwlkFcCed/G/ngWKyS33BewxwrdXSlmt5eIiIiIiEgDVqch+kUXXQTA2rVrGTZsGG63O3Kf0+mkffv2/O53vzvsjx8KhXjllVcoLS1l8ODBtT7G5/Ph8/kivy8uLj7s64k0FcFQmOKyEEVlgUh/eXFZkECsgz5tlhpheUqSHbtN0+UiIk2C1WaG2imt4UCD7T7vfsH6/r/m/P/27jw+qupw//gz2feENYtskR1ZBEEFxaVSFikiWkVEcANcSBGoyFcpClZxww1UXFpAKf5otYhWpBQpSxFBQFZFVAigkgAC2ffM/f1xk2EmmTuZQDLZPu++7ivJnTPnnjs5UHzOuedIWanmWu1ZJd9rp3V9geElAXuC62aozuci4yT/wKq+YwAAAADwWrWG6E888YQkqU2bNho5cmSVrau0d+9e9e3bV3l5eYqIiNBHH32kLl26uC37zDPPaPbs2VVyXaCuMQxD2fnFZkiefTYsz7ba6FNSRKi/osMCXcLy0CA2+gQASAqOkILbSU3bWZex26Xsk64Be2Zq+dA9L10qzJZOHzQPSzYpvJnrcjFlQ/eoeCkkhlntAAAAqPdmzZqlFStWaNeuXdVS/+HDh5WYmKidO3fq4osvrpZr1EU+WazyzjvvrNL6OnbsqF27dik9PV0ffvih7rzzTm3YsMFtkP7oo49q6tSpjp8zMjLUsmXLKm0PUBsUFNkds8rN0LxQGblFKna/dLmCA91s9BkaIH82+gQAnA8/Pyky1jwSelqXK8gpswmqu9A9RbIXStknzCNlt3V9AaFezGqPlwKCqv6eAQAA4HMLFizQggULdPjwYUnSRRddpMcff1xDhgyp2YY1QHfddZfS0tK0YsUKn163TZs2mjx5siZPnlzt1/JJiF5cXKyXX35Z//jHP3T06FEVFBS4vH769OlK1RcUFKR27cwZUJdccom2bdumV199VW+99Va5ssHBwQoODj73xgO1jN1uKCvPXIrl7NrlRcorcJ+W+9nk2NzzbGgeyEafAICaFRQmNWlrHlbsdinnlOu67M4bopaG7rlnpKJc6UyyeXgS1tRiVrtT0B7WmFntAAAAtVyLFi307LPPqn379jIMQ++++66GDx+unTt3sm8iqpxPUrTZs2frpZde0siRI5Wenq6pU6fqpptukp+fn2bNmnXe9dvtdpd1z4H6wNzos1jH0/L1/bFsbf8xXWv3nNIn205o7Z5T2v5jhn44lqPjaQWOAD0s2E9xjYLV8YJwXdo+WgN6NNGwS5vr2m5NdEnbaLWLD1fz6GACdABA3eDnJ0U0k+J7SB0HS73vlq59TBr+mnTHP6UHN0vTD0szjkuTdkl3r5Ju/qs08Cnp8onSRTdJrfpKMa0l/5IZ6Dm/Sql7pR/+I339rrT+Gelfk6Slv5fevEJ64ULpqVjple7SwsHSB3dLq2dIm+dLez+UjmyWTidLhXk1+ckAAAA0eMOGDdP111+v9u3bq0OHDnr66acVERGhLVu2VPjetLQ0jRs3Ts2aNVNUVJR+85vfaPdu86nHkydPKi4uTnPmzHGU37x5s4KCgrR27VpJ0sGDBzV8+HDFxsYqIiJCffr00eeff+5yjTZt2uipp57S2LFjFRERodatW+uTTz7RyZMnNXz4cEVERKh79+7avn274z2LFy9WTEyMVqxYofbt2yskJESDBg3STz/95PF+/vKXv6hz584KCQlRp06d9MYbb3j9OX711Vfq2bOnQkJC1Lt3b+3c6bqnUXFxse69914lJiYqNDRUHTt21Kuvvup4fdasWXr33Xf18ccfy2azyWazaf369ZKk6dOnq0OHDgoLC9OFF16omTNnqrCw0PHe3bt369prr1VkZKSioqJ0ySWXuHwemzZtUv/+/RUaGqqWLVtq0qRJys7OliRdc801OnLkiKZMmeK4bnXyyUz0pUuX6p133tHQoUM1a9YsjRo1Sm3btlX37t21ZcsWTZo0yeu6Hn30UQ0ZMkStWrVSZmam3n//fa1fv16rV6+uxjsAqldRsaHM3CKnmeXmhp8FRe43+gxw2uizdHZ5VGiAAgMIxwEADVBgiNQ40TysGIaUc7riWe05p6TifCntiHl4EtrYaemYsrPa48zlZMKamIMBAAAAdYVhmEvq1QS/wHN6IrC4uFgffPCBsrOz1bdv3wrL33LLLQoNDdWqVasUHR2tt956S9ddd52+//57NWvWTAsXLtSNN96ogQMHqmPHjhozZoySkpJ03XXXSZKysrJ0/fXX6+mnn1ZwcLDee+89DRs2TAcOHFCrVq0c13n55Zc1Z84czZw5Uy+//LLGjBmjfv366Z577tELL7yg6dOna+zYsfrmm28cIXBOTo6efvppvffeewoKCtKDDz6o2267TV988YXbe1m6dKkef/xxvfbaa+rZs6d27typ8ePHKzw8vMIltrOysvS73/1Ov/3tb/W3v/1NycnJeuihh1zK2O12tWjRQh988IGaNGmizZs3a8KECYqPj9ett96qhx9+WPv371dGRoYWLVokSWrcuLEkKTIyUosXL1ZCQoL27t2r8ePHKzIyUo888ogkafTo0erZs6cWLFggf39/7dq1S4GBgZLMgYrBgwfrqaee0sKFC3Xy5EklJSUpKSlJixYt0vLly9WjRw9NmDBB48ePr/B3fr5shmG4T+mqUHh4uPbv369WrVopPj5eK1euVK9evXTo0CH17NlT6enpXtd17733au3atUpJSVF0dLS6d++u6dOn67e//a1X78/IyFB0dLTS09MVFRV1rrcEnBPDMJSTb3eE5KWheVae+40+JSkixN8My8MDFBUWqKjQAIUFs9EnAADVoijfXJPdJWA/Vv5ckZcz0f0C3QTtcWc3RC09FxhavfcFAABgIS8vT8nJyUpMTFRISIhUXCCte6JmGnPt7LNPEHph79696tu3r/Ly8hQREaH3339f119/vcf3bNq0SUOHDtWJEydcloBu166dHnnkEU2YMEGSNHHiRH3++efq3bu39u7dq23btnlcMrpr1666//77lZSUJMmcid6/f38tWbJEkpSamqr4+HjNnDlTTz75pCRpy5Yt6tu3r1JSUhQXF6fFixfr7rvv1pYtW3TZZZdJkr777jt17txZW7du1aWXXlpuY9F27drpz3/+s0aNGuVoy1NPPaXPPvtMmzdv9vhZvP3223rsscf0888/m797SW+++aYeeOABjxuLJiUlKTU1VR9++KEk73M9rNsAAEqSSURBVNdEnzt3rpYtW+aYbR4VFaX58+e7DfvHjRsnf39/l+W7N23apKuvvlrZ2dkKCQnxek30cn3cibdZsU9mordo0UIpKSlq1aqV2rZtq//85z/q1atXhZ3Pnb/+9a/V1EqgahUW2V3WLM8oOYrs7setggJsig4LdFm7PCqMjT4BAPCpgGCpUWvzsGIY5jrsLhujugnds0+as7jSj5qHJyExrhuiugvdw5sxqx0AAMBJx44dtWvXLqWnp+vDDz/UnXfeqQ0bNqhLly6W79m9e7eysrLUpEkTl/O5ubk6ePCg4+e5c+eqa9eu+uCDD7Rjxw6XDDMrK0uzZs3SypUrlZKSoqKiIuXm5uroUdd/83Xv3t3xfWxsrCSpW7du5c6dOHFCcXFxkqSAgAD16dPHUaZTp06KiYnR/v37demll7rUn52drYMHD+ree+91mY1dVFSk6Ohoy8+g1P79+9W9e3eXYNndTP7XX39dCxcu1NGjR5Wbm6uCggLLgN3Z3//+d82bN08HDx5UVlaWioqKXILqqVOnaty4cVqyZIkGDBigW265RW3bmnsm7d69W3v27NHSpUsd5Q3DkN1uV3Jysjp37lzh9auST0L0ESNGaO3atbrsssv0hz/8QXfccYf++te/6ujRo5oyZYovmgBUG7thbvSZkV0alpsbfuZ62OgzMjSg3HIswYHMLgcAoE6w2czNR8MaS7EeNq0qLiw/g91d6F6YI+WlmcfJ/db1+QVIEXFlNkZ1Dt1LvgaFV/UdAwCAhsQv0JwRXlPXroSgoCC1a9dOknTJJZdo27ZtevXVV11mL5eVlZWl+Ph4x7rdzmJiYhzfHzx4UMeOHZPdbtfhw4ddwu+HH35Ya9as0dy5c9WuXTuFhobq97//vQoKClzqK12aRJIj83F3zm53nyFVJCsrS5L0zjvvOGaul/L39z+nOstatmyZHn74Yb344ovq27evIiMj9cILL2jr1q0e3/fll19q9OjRmj17tgYNGqTo6GgtW7ZML774oqPMrFmzdPvtt2vlypVatWqVnnjiCS1btkwjRoxQVlaW7rvvPrfLgDsvmeMrPgnRn332Wcf3I0eOVKtWrfTll1+qffv2GjZsmC+aAFSJ/EJzKZb0nCJHaJ6ZWySLyeUKDfJzCsvNWeYRIf7yY3Y5AAD1n3+gFNPSPKwYhpSXXhK2Hzu7NrvLuu0pUtZxyV4kZfxsHp4ER7mf1e58LqK55Fc1/2EFAADqGZutUkuq1CZ2u135+fkey/Tq1UupqakKCAhQmzZt3JYpKCjQHXfcoZEjR6pjx44aN26c9u7dq+bNm0uSvvjiC911110aMWKEJDPMPnz4cJXcQ1FRkbZv3+6YdX7gwAGlpaW5nXkdGxurhIQEHTp0SKNHj670tTp37qwlS5YoLy/PMRu97MasX3zxhfr166cHH3zQcc55xr5kDmYUF7suVbx582a1bt1aM2bMcJw7cqT8nkMdOnRQhw4dNGXKFI0aNUqLFi3SiBEj1KtXL3377beOQRJ33F23uvgkRC+rb9++Xi3yD9SUYnvZjT7Nr/mF7kcG/f3cbPQZFqAgNvoEAACe2GxSaIx5NO9kXa64yAzSXdZodxO6F2RJ+Rnm8esBD9f1lyJiK57VHhxZ1XcMAABQJR599FENGTJErVq1UmZmpt5//32tX79eq1ev9vi+AQMGqG/fvrrxxhv1/PPPq0OHDjp27JhWrlypESNGqHfv3poxY4bS09M1b948RURE6LPPPtM999yjTz/9VJLUvn17LV++XMOGDZPNZtPMmTPPeTZ5WYGBgfrDH/6gefPmKSAgQElJSbr88svLLeVSavbs2Zo0aZKio6M1ePBg5efna/v27Tpz5oymTp3q8Vq33367ZsyYofHjx+vRRx/V4cOHNXfuXJcy7du313vvvafVq1crMTFRS5Ys0bZt25SYmOgo06ZNG61evVoHDhxQkyZNFB0drfbt2+vo0aNatmyZ+vTpo5UrV+qjjz5yvCc3N1fTpk3T73//eyUmJurnn3/Wtm3bdPPNN0uSpk+frssvv1xJSUkaN26cwsPD9e2332rNmjV67bXXHNfduHGjbrvtNgUHB6tp06bn9Jl7o9pC9E8++cTrsjfccEN1NQPwyDAM5RY4r11ubviZlVssqx13w0s3+gw7uyRLWLA/S7EAAIDq4x8gRV9gHp7kZ5YJ1i1mtRvF5muZxzzXFxThPmCPjCtZrz3eDOP9a2RuDgAAaMBOnDihsWPHKiUlRdHR0erevbtWr16t3/72tx7fZ7PZ9Nlnn2nGjBm6++67dfLkScXFxemqq65SbGys1q9fr1deeUXr1q1zrN+9ZMkS9ejRQwsWLNADDzygl156Sffcc4/69eunpk2bavr06crIyKiS+woLC9P06dN1++2365dfflH//v097hE5btw4hYWF6YUXXtC0adMUHh6ubt26VbjZpiRFREToX//6l+6//3717NlTXbp00XPPPecIsiXpvvvu086dOzVy5EjZbDaNGjVKDz74oFatWuUoM378eK1fv169e/dWVlaW1q1bpxtuuEFTpkxRUlKS8vPzNXToUM2cOVOzZs2SZC43c+rUKY0dO1bHjx9X06ZNddNNN2n2bHMpoe7du2vDhg2aMWOG+vfvL8Mw1LZtW40cOdJx3SeffFL33Xef2rZtq/z8fBmGVZp3/mxGNdXuV2bTI5vNVu5GSkNHX027l7zfcRX1T2Gx3bG5p/NGn4XF7v8IBAbYnMLyQEWHBSgyNEAB/oTlAACgDrMXS1knSkL0VKcNUcuE7vle/oegzU8Kb+55VntknBQSbc68BwAAtUpeXp6Sk5OVmJjossEkfG/x4sWaPHmy0tLSarop9YqnPu5tVlxtU0acH2H4/PPPNX36dM2ZM8exjMuXX36pP/3pT5ozZ051NQENlFG60WeO63IsOfnuB2tsJRt9Os8sjwoLUAgbfQIAgPrIz98Mt6PiPZfLz/JirfZUc632rFTz0E7r+gLDSgL2BKegPd71XGScuZY8AAAAUIv45LnLyZMn680339SVV17pODdo0CCFhYVpwoQJ2r9/vy+agXoov9DuFJYXOjb6LLZYhiok0O9sWB5ufo0MCWCjTwAAgLKCI6TgdlJT682cZLdL2SetZ7WXnstLkwpzpNMHzcOSTQpv5rpcjLvQPbQRs9oBAIBbS5cu1X333ef2tdatW+ubb77xcYtqzpw5cywnMPfv399lSRZ4Vm3LuTgLDQ3Vtm3b1LVrV5fze/bs0WWXXabc3NzqboIDy7nUTcV2Q1luNvrMs9zoU4oKLZ1ZHujY6DM4kI0+AQAAfK4gx3UGu+P7Y67n7IXe1RcQ6hq0uwvdI+OkgODqvS8AAOqB+racS2Zmpo4fP+72tcDAQLVu3drHLao5p0+f1unTp92+FhoaqgsuqGC/nXqiVi/n4qxPnz6aOnWqlixZotjYWEnS8ePHNW3aNMudZdEwGYahPJeNPs2vmXlFshruCQ/2d1mGJTosQOEhbPQJAABQawSFSU3amocVu13KOeUmYC/5WjqrPfe0VJQrnUk2D0/Cmjitze4csDut2x7WmFntAADUI5GRkYqMjKzpZtQKjRs3VuPGjWu6GfWCT0L0hQsXasSIEWrVqpVatmwpSfrpp5/Uvn17rVixwhdNQC1UVGyYm3vmmsuwZGSbobnlRp/+tnJheWRYgAL9mV0OAABQ5/n5SRHNzCO+u3W5wrwyQXuqm9A9VSrON0P5nFPS8b3W9fkHO81kj7MO3QPr/sw8AAAAnBufhOjt2rXTnj17tGbNGn333XeSpM6dO2vAgAHMFm4ADMNQdn6xY1Z56Qzz7DyLjT4lRYT6l4TlgY7QPDSIjT4BAAAavMAQqXGieVgxDCn3TPnZ7I7QvWST1JxfzbA97Yh5eBLayE3A7hy6J5gz3/2Y4AEAAFDf+CRElySbzaaBAwdq4MCBvrokakBBkd1lGZbSr8V297PLg503+iz5GhkaIH82+gQAAMC5stnMZVrCGktxXa3LFeWfncnuvCFqRpklZYryzFA+94x0wsNmZH6BTkvFlAnYnTdGDQqr+nsGAABAtam2EH3evHmaMGGCQkJCNG/ePI9lJ02aVF3NQDWx2w1l5RUrPafQJSzPLXC/0aefTY7NPc+G5oFs9AkAAICaExAsNWptHlYMQ8pLswjYnWa1Z580N0ZNP2oenoREuwnYy4Tu4c2Y1Q4AAFBL2AzDarvG85OYmKjt27erSZMmSky0ftTSZrPp0KFD1dEEt7zdcRUmwzCUX1h+o8+MXOuNPsOC/RwhufNGn34sxQIAAID6qrjQu1nthTne1ecXIEXElazXHm89qz04onrvCwBQ7+Xl5Sk5OVmJiYkKCWEPENQ/nvq4t1lxtc1ET05Odvs9aq9iu+EalOcUKT2nUAVF7tPyAH+bokKdZpaHBygqNECBAcyYAQAAQAPjHyjFtDQPK4Yh5WeUCdhLZrQ7n8s+IdmLpIyfzeMXD9cNjnJaQibBaZNUp3MRzSU//yq/ZQAAUDNmzZqlFStWaNeuXdVS/+HDh5WYmKidO3fq4osvrpZr1DU+WxMdtYdhGMrJtzuWYikNzrMsNvqUpIgQ/zJrlwcqLJiNPgEAAACv2WzmUi4h0VLzTtbliovMIL1s2F42dC/INEP5/Azp1wMerusnRcQ6bYga735WewhP6gIA6q5nn31Wjz76qB566CG98sorNd2cBueuu+5SWlqaVqxY4dPrtmnTRpMnT9bkyZOr9TrVFqJPnTrV67IvvfRSdTUDZRw7nacdBzNUVOx+dnlQgM1lGZbSdczZ6BMAAADwEf8AM+yOSpB0iXW5/Mzyy8Y4Lx2TkSJlHZeM4rOvHfvaur6gCA+z2ku+RsSa7QMAoBbZtm2b3nrrLXXv3r2mm4J6qtrW3di5c6dXR3U9dgD3QgL9VFRsyM8mRYcFqGXTEHVtFaF+nWI0pFdTXX9JM13ZpZG6t4lU6+ahahQRSIAOAAAA1EbBkVKzDtKF10gXj5L6T5Wuf0G6bak0/r/SH/dLM09KfzwgjV8n3fa+dP1cqf8fpR63SxdeKzXrJAVHm/UVZEmnfpCSN0p7lklfvCKtekT6xxjpL9dJL3eR/txUmttBeutq6f+Nkj6dKm18Qdq5VPpxrXRiv5SbJssNlAAAqGJZWVkaPXq03nnnHTVq1Mjr96WlpWncuHFq1qyZoqKi9Jvf/Ea7d++WJJ08eVJxcXGaM2eOo/zmzZsVFBSktWvXSpIOHjyo4cOHKzY2VhEREerTp48+//xzl2u0adNGTz31lMaOHauIiAi1bt1an3zyiU6ePKnhw4crIiJC3bt31/bt2x3vWbx4sWJiYrRixQq1b99eISEhGjRokH766SeP9/OXv/xFnTt3VkhIiDp16qQ33njD68/iq6++Us+ePRUSEqLevXtr586dLq8XFxfr3nvvVWJiokJDQ9WxY0e9+uqrjtdnzZqld999Vx9//LFsNptsNpvWr18vSZo+fbo6dOigsLAwXXjhhZo5c6YKCwsd7929e7euvfZaRUZGKioqSpdcconL57Fp0yb1799foaGhatmypSZNmqTs7GxJ0jXXXKMjR45oypQpjutWl2qbQrBu3brqqhrnITo8UNd1b6KIEH/5EY4DAAAA9ZufvzmjPDLOc7mC7DLLxRxzM6s91VyrPeu4eaTssq4vMMxpBntcmaVknGa1BwRV6e0CAM6fYRjKLcqtkWuHBoRWOgidOHGihg4dqgEDBuipp57y+n233HKLQkNDtWrVKkVHR+utt97Sddddp++//17NmjXTwoULdeONN2rgwIHq2LGjxowZo6SkJF133XWSzPD++uuv19NPP63g4GC99957GjZsmA4cOKBWrVo5rvPyyy9rzpw5mjlzpl5++WWNGTNG/fr10z333KMXXnhB06dP19ixY/XNN9847j0nJ0dPP/203nvvPQUFBenBBx/Ubbfdpi+++MLtvSxdulSPP/64XnvtNfXs2VM7d+7U+PHjFR4erjvvvNPj55CVlaXf/e53+u1vf6u//e1vSk5O1kMPPeRSxm63q0WLFvrggw/UpEkTbd68WRMmTFB8fLxuvfVWPfzww9q/f78yMjK0aNEiSVLjxo0lSZGRkVq8eLESEhK0d+9ejR8/XpGRkXrkkUckSaNHj1bPnj21YMEC+fv7a9euXQoMDJRkDlQMHjxYTz31lBYuXKiTJ08qKSlJSUlJWrRokZYvX64ePXpowoQJGj9+vNe/+3NhM4yGNUXA2x1XAQAAAABO7HYp59fyAXtpyF56Li/NywptUnhTNwF7nOt67aGNzPXkAQDVIi8vT8nJyUpMTFRISIhyCnN02fuX1Uhbtt6+VWGBYV6XX7ZsmZ5++mlt27ZNISEhuuaaa3TxxRdXuCb6pk2bNHToUJ04cULBwcGO8+3atdMjjzyiCRMmSDID+s8//1y9e/fW3r17tW3bNpfyZXXt2lX333+/kpKSJJkz0fv3768lS5ZIklJTUxUfH6+ZM2fqySeflCRt2bJFffv2VUpKiuLi4rR48WLdfffd2rJliy67zPw9fPfdd+rcubO2bt2qSy+9tNzGou3atdOf//xnjRo1ytGWp556Sp999pk2b97s8bN4++239dhjj+nnn39WSEiIJOnNN9/UAw884HFj0aSkJKWmpurDDz+U5P2a6HPnztWyZcscs82joqI0f/58t2H/uHHj5O/vr7feestxbtOmTbr66quVnZ2tkJAQr9ZEL9vHnXmbFftsMbvt27frH//4h44ePaqCggKX15YvX+6rZgAAAAAAzoWfnxTR3Dx0sXW5gpyz4brVrPbMFMleKGWfNI/UPdb1BYSc3fy0NFgvF7rHSwHWoQYAoP756aef9NBDD2nNmjXlgtGK7N69W1lZWWrSpInL+dzcXB08eNDx89y5c9W1a1d98MEH2rFjh0uAnpWVpVmzZmnlypVKSUlRUVGRcnNzdfToUZc6nddpj42NlSR169at3LkTJ04oLs58ciwgIEB9+vRxlOnUqZNiYmK0f/9+XXrppS71Z2dn6+DBg7r33ntdZmMXFRUpOjq6ws9i//796t69u8tn2Ldv33LlXn/9dS1cuFBHjx5Vbm6uCgoKLAN2Z3//+981b948HTx4UFlZWSoqKnIJq6dOnapx48ZpyZIlGjBggG655Ra1bdtWkvl72rNnj5YuXeoobxiG7Ha7kpOT1blz5wqvX1V8EqIvW7ZMY8eO1aBBg/Sf//xHAwcO1Pfff6/jx49rxIgRvmgCAAAAAMAXgsKkJm3Nw4rdLuWernhWe+5pqShPOpNsHp6ENXGdwe68SWrp17DGzGoHgAqEBoRq6+1ba+za3tqxY4dOnDihXr16Oc4VFxdr48aNeu2115Sfny9/f3+3783KylJ8fLxj3W5nMTExju8PHjyoY8eOyW636/Dhwy7h98MPP6w1a9Zo7ty5ateunUJDQ/X73/++3OTh0qVJJDmWa3F3zm63e33vZe9Fkt555x3HzPVSVvdfWcuWLdPDDz+sF198UX379lVkZKReeOEFbd3quZ98+eWXGj16tGbPnq1BgwYpOjpay5Yt04svvugoM2vWLN1+++1auXKlVq1apSeeeELLli3TiBEjlJWVpfvuu0+TJk0qV7fzkjm+4JMQfc6cOXr55Zc1ceJERUZG6tVXX1ViYqLuu+8+xcfH+6IJAAAAAIDaws/PXMolvKkU3926XGHe2RntmcdcA3bn0L04X8o5ZR7H91rX5x9UZrkYi9A9sHIzGgGgPrHZbJVaUqWmXHfdddq71/Xv/LvvvludOnXS9OnTPQbIvXr1UmpqqgICAtSmTRu3ZQoKCnTHHXdo5MiR6tixo8aNG6e9e/eqefPmkqQvvvhCd911l2OCcFZWlg4fPlwl91ZUVKTt27c7Zp0fOHBAaWlpbmdex8bGKiEhQYcOHdLo0aMrfa3OnTtryZIlysvLc8xG37Jli0uZL774Qv369dODDz7oOOc8Y1+SgoKCVFxc7HJu8+bNat26tWbMmOE4d+TIkXJt6NChgzp06KApU6Zo1KhRWrRokUaMGKFevXrp22+/Vbt27Szb7+661cEnIfrBgwc1dOhQSeaNZWdny2azacqUKfrNb36j2bNn+6IZAAAAAIC6JDBEapxoHlYMQ8o9U2ZWu3PoXvI151epuEBKO2oenoQ28mJWexNzMAAAUCMiIyPVtWtXl3Ph4eFq0qRJufNlDRgwQH379tWNN96o559/Xh06dNCxY8e0cuVKjRgxQr1799aMGTOUnp6uefPmKSIiQp999pnuueceffrpp5Kk9u3ba/ny5Ro2bJhsNptmzpx5zrPJywoMDNQf/vAHzZs3TwEBAUpKStLll19ebimXUrNnz9akSZMUHR2twYMHKz8/X9u3b9eZM2c0depUj9e6/fbbNWPGDI0fP16PPvqoDh8+rLlz57qUad++vd577z2tXr1aiYmJWrJkibZt26bExLP//9ymTRutXr1aBw4cUJMmTRQdHa327dvr6NGjWrZsmfr06aOVK1fqo48+crwnNzdX06ZN0+9//3slJibq559/1rZt23TzzTdLkqZPn67LL79cSUlJGjdunMLDw/Xtt99qzZo1eu211xzX3bhxo2677TYFBweradOm5/SZV8QnIXqjRo2UmZkpSbrgggu0b98+devWTWlpacrJyfFFEwAAAAAA9ZHNZi7TEtZYivMQmhTlS1nHXYN1x1en0L0o1wzlc89IJ76xrs8v8Ox67GUD9si4s+u2B9X+2ZwA0NDYbDZ99tlnmjFjhu6++26dPHlScXFxuuqqqxQbG6v169frlVde0bp16xzrdy9ZskQ9evTQggUL9MADD+ill17SPffco379+qlp06aaPn26MjIyqqR9YWFhmj59um6//Xb98ssv6t+/v/76179alh83bpzCwsL0wgsvaNq0aQoPD1e3bt08brZZKiIiQv/61790//33q2fPnurSpYuee+45R5AtSffdd5927typkSNHymazadSoUXrwwQe1atUqR5nx48dr/fr16t27t7KysrRu3TrdcMMNmjJlipKSkpSfn6+hQ4dq5syZmjVrliRzuZlTp05p7NixOn78uJo2baqbbrrJMeG6e/fu2rBhg2bMmKH+/fvLMAy1bdtWI0eOdFz3ySef1H333ae2bdsqPz9fhmFU8tP2js2orpqd3H777erdu7emTp2qP//5z5o/f76GDx+uNWvWqFevXj7dWNTbHVcBAAAAAA2MYUh5aWcD9sxU96F79klJXv6ndEh0+WDdZYZ7ghTeTPKrmnVrAaCy8vLylJycrMTExEpv0Imqt3jxYk2ePFlpaWk13ZR6w1Mf9zYrrtaZ6Pv27VPXrl312muvKS8vT5I0Y8YMBQYGavPmzbr55pv1pz/9qTqbAAAAAACAd2w2cymX0EZSbBfrcsWF7me1Z6Y6LSuTIhVmS3np5nFyv4fr+pfMao87G6y7W0YmOLLq7xkAAFSoWkP07t27q0+fPho3bpxuu+02SZKfn5/+7//+rzovCwAAAABA9fEPlKJbmIcVw5DyM8oE68fObpRa+n3WcckoljJ+MQ9PgiJLZrS72xS1JHiPiJX8fbJyKwDUWkuXLtV9993n9rXWrVvrm288LNdVz8yZM0dz5sxx+1r//v1dlmSBtWpdzuV///ufFi1apA8//FB2u10333yzxo0bp/79+1fXJSvEci4AAAAAgFqjuMhcHsYxo93d5qgpZiDvFZsU0dxiRrvTuZBoc+Y9gAavPi7nkpmZqePHj7t9LTAwUK1bt/Zxi2rO6dOndfr0abevhYaG6oILLvBxi3yvKpZz8cma6NnZ2frHP/6hxYsX63//+5/atWune++9V3feeafi4uKq+/IuCNEBAAAAAHVOflaZYN3N8jFZqZK9yLv6AsNKlo8pO6PdaZZ7RJwUEFS99wWgxtXHEB1wVmdCdGc//vijFi1apCVLlig1NVWDBw/WJ59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" ] @@ -80672,19 +80672,19 @@ { "cell_type": "code", "execution_count": 14, - "id": "eee04c1c", + "id": "5878397a", "metadata": { "execution": { - "iopub.execute_input": "2024-11-07T15:49:37.423076Z", - "iopub.status.busy": "2024-11-07T15:49:37.422632Z", - "iopub.status.idle": "2024-11-07T15:49:38.710105Z", - "shell.execute_reply": "2024-11-07T15:49:38.709442Z" + "iopub.execute_input": "2024-11-07T16:12:41.045700Z", + "iopub.status.busy": "2024-11-07T16:12:41.045277Z", + "iopub.status.idle": "2024-11-07T16:12:42.360858Z", + "shell.execute_reply": "2024-11-07T16:12:42.360124Z" } }, "outputs": [ { "data": { - "image/png": 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", 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t2rXL38fr9VqzZs2yunbtasXExFjnnHOO9c0331iZmZm2U0zW+uyzz6zzzz/fSkhIsOLi4qwBAwZYf/rTn/ztNTU11rRp06zOnTtbDoejTj7TT6aYtCzLWrt2rZWbm2vFx8dbsbGx1rnnnmt98cUXAZ2bhmJsbRyW1crv2gcAhJV58+bptttu044dO+pMXwcACB2KeADAMausrKzzhbeqqioNGjRIXq9X//nPf1owMgBo27gnHgBwzMaPH6/u3bvrlFNOUXFxsf7+979rw4YNDU5JBwAIDYp4AMAxy83N1d/+9jf94x//kNfr1YknnqhFixbpiiuuaOnQAKBN43YaAAAAIMwwTzwAAAAQZijiAQAAgDDT5u+J9/l82rVrlxISEljOG0aWZam0tFTp6emKiOB3XJBDEBxyCI6GPIJgBJxHWmyG+iA8/vjj/oWKsrOzrdWrVwf83Pz8fEsSG1tQW35+fhO+o9ESjjWPkEPYjmUjh7Q91CJszb2Z8kirvxL/0ksvafr06XryySc1ZMgQzZs3T7m5udq4caO6dOlifH5CQoIkKfOeexXxf0uBH02H7yzbcZKu3mE81qb/pBv7nDfoW2Of3ORvbNsf/sNE4xh7hviMfWJ2mn/8Qy/62rb9083HG8fwlUYZ+zxw7kvGPh8UnWTb/sm6/sYx+hy/y7a9pqJaKy5/xv++QdvQmDxS+14YrjGKlPm9jPatRof0md4jh7QxoapFek6dIae74VokosZ+HHeRfa0iSV6X+Up/x7HmmqZH/H7b9jMStxjHOCtmu7HPBUumGvvEbbevV6oHlhnH6PlgtbHP/lM7GPuEQkSN/c/Re6hKX78+x5hHWn0R/8gjj+j666/X5MmTJUlPPvmk3n33XT3zzDO6++67jc+v/bNVRHS0bRHvdNmf0Mg4t/FYETENj1/LFe8y9olNcNq2O13m40TEmIt4p9v84zfFGxFrjkU15sLH9JolyVVjiCWA8x/Iz1ESf+5sYxqTR2rfC5GKUqSDIh4G//dRQg5pW0JVizjd0fZFvOGj0FSrSJICKOID+Sw0ff7HxJtriIRY8y1lgXx2m+qViFjDbz+SIp3m8xJIfRUKEREB/BxlziOt+oa96upqffXVV8rJyfHvi4iIUE5OjlauXNmCkQEIF+QRAI1BDkFr1aqvxO/bt09er1epqal19qempmrDhg1HfY7H45HH4/E/LikpadIYAbRuweYRcgiAI1GLoLVq1Vfij8XcuXOVlJTk3zIyMlo6JABhhBwCoLHII2gOrbqI79Spk5xOpwoLC+vsLywsVFpa2lGfc88996i4uNi/5efnN0eoAFqpYPMIOQTAkahF0Fq16iLe5XJp8ODBWrp0qX+fz+fT0qVLNXTo0KM+x+12KzExsc4GoP0KNo+QQwAciVoErVWrvidekqZPn65JkybptNNOU3Z2tubNm6fy8nL/N8QBwIQ8AqAxyCFojVp9EX/FFVdo7969mjFjhgoKCnTKKadoyZIl9b5gYuJNr5IV23D7by+zn6d85ndjgzpeQy7sYD/vuiTdtvoK23bfMPP0kRGJh4x9XGeYv2jTO3aPbfu29BTjGIUrM419flMyydjHJLnfQWOfX2css22vKPXKvgfCUajyCID2KVQ5JHG7T86ohj/D95xu/3xnlfkGCiuAeyw2bzn6bUBHyovrZNv+TceuxjH++NHlxj7uIaXGPh3fsZ/6sbgi3jjGD+PNfSozzLWTw+21bT8uzVyL7CuNs233VlRJ5uVzWn8RL0lTp07V1KnmxQAAoCHkEQCNQQ5Ba9Oq74kHAAAAUB9FPAAAABBmKOIBAACAMEMRDwAAAIQZingAAAAgzFDEAwAAAGEmLKaYDIWe1/9LkY6oBttv+fMvbJ8fVeI0HqPh0X8UyHzzzh3286H6Eu3nKJWkvr8vM/bZ+N/mOVP/vHykbftj5//dOMa7kwYa+yz75BRjn57Z223bM+MPGMcwzsFfUSXpW+M4AAAEK+KQJaesBttjCu1rDV8AhYbTY+4Tv8k8kNNj32fXQHNd5BxcYezjK7RZxOf/lGbYX3P2mUORe7+5j+k1S5Inxb50LnCZV+dNSqi0bff6zPPVS1yJBwAAAMIORTwAAAAQZijiAQAAgDBDEQ8AAACEGYp4AAAAIMxQxAMAAABhhiIeAAAACDMU8QAAAECYaTeLPTn79pLT6W6wPSLRMLF+AIs9mRYjkqT8jzKNfbr+u8a2/fZHzQssPfye/eJVkuQrMXZRcjf7Ths9XY1j3J76sbHPh4knm4Mx+CHbfvEESdqy61nb9pJSnzo0OhIAAOrzJEaoxtXw9dPyTPvP/8RN5rLN4Wt4MalavkiHsc+hBPM4Jonx5s/lsm0xxj5JW6pt251V5kUw959kPk5sgbGLEs8rtG3PSCgyjrG30n6xzRpvACt2iSvxAAAAQNihiAcAAADCDEU8AAAAEGYo4gEAAIAwQxEPAAAAhJljLuI9Ho88nsC+PQsAAAAgdIIq4j/66CONGTNGHTp0UGxsrGJjY9WhQweNGTNGH39snkYQAAAAQOMFXMQ/99xzGjNmjJKSkvToo4/qnXfe0TvvvKNHH31UycnJGjNmjP7nf/6nKWMFAAAAoCAWe/r973+vefPm6aabbqrXdu2112r48OGaPXu2rr766pAGGCrbL+okpzu6wfY+6dtsn5//rXmRpk27upgD6W9e+KDkDPs+f84/1zjGCTP+bT7ObvNryu5qf17++s4o4xjvfDbS2GdUAPGuMcS7btd64xhZi2+wbfdVVkmaYRwHAIBglZ5brohYm4WJShpelFKSokrNCzBZ5rUpFbMngIWcDJd5e/7PAeMQVVkp5sN0N4dS1THKtr0m2mUcw+EzH2d/bpWxj7Uvybb9kNf8A+idvM9+DJ/94la1Ar4Sv337duXk5DTYPnLkSO3YsSPQ4QAAAAAco4CL+P79++vpp59usP2ZZ57RiSeeGJKgAAAAADQs4NtpHn74YV100UVasmSJcnJylJqaKkkqLCzU0qVLtXXrVr377rtNFigAAACAwwIu4s855xx98803euKJJ7Rq1SoVFBRIktLS0jR69Gj96le/Uo8ePZoqTgAAAAD/J+AiXpJ69OihBx54oKliAQAAABAAVmwFAAAAwgxFPAAAABBmKOIBAACAMBPUPfHN7b777tOsWbPq7Ovbt682bNgQ9Fg1iZZ80Q0vbrDkBPuZdbI22C8SJEkqsV+MQJJGnWZe1Ojj/5xg277u9EXGMc6+wRxv9cnmH/+aEvuFGnpeZr8YlCT9+rJlxj4zvxtr7KMP7WM545VfmccYFsBqD2hTQplHALRPocojnd6MUWRUwwtPlmbYLxRUlmE+RsQhcx+HzXpTtVzF9u27z08zjhFdZP7MreroMI9z0H6c8nTzNelI81qb8hWZF406vp/9mkj/2W4+Lz2T99vHIfM5kY7hSvzs2bNVUVFRb39lZaVmz54d7HBG/fv31+7du/3bZ599FvJjAGjbyCMAGos8gtYm6CJ+1qxZKisrq7e/oqKi3m+poRAZGam0tDT/1qlTp5AfA0DbRh4B0FjkEbQ2QRfxlmXJ4ah/mf/rr79WSor97Q7HYtOmTUpPT1fPnj111VVXafv27bb9PR6PSkpK6mwA2rdg8gg5BMDRkEfQ2gRcxHfo0EEpKSlyOBw6/vjjlZKS4t+SkpJ0/vnn6/LLLw9pcEOGDNHChQu1ZMkSPfHEE8rLy9OIESNUWlra4HPmzp2rpKQk/5aREcANZADarGDzCDkEwE+RR9AaBfzF1nnz5smyLF133XWaNWuWkpKS/G0ul0s9evTQ0KFDQxrc6NGj/f8eMGCAhgwZoszMTL388sv65S9/edTn3HPPPZo+fbr/cUlJCf95gHYs2DxCDgHwU+QRtEYBF/GTJk2SJGVlZWnYsGGKijLPxBJqycnJOv7447V58+YG+7jdbrnd7maMCkA4MeURcggAE/IIWoOg74k/++yzFRERoddee01z5szRnDlz9MYbb8jrDWC+okYqKyvTli1b1LVr1yY/FoC2iTwCoLHII2gNgp4nfvPmzRozZox27typvn37Sjp871dGRobeffdd9erVK2TB/eY3v9HFF1+szMxM7dq1SzNnzpTT6dTEiRODHiu2b5GcsQ3/VnzBhgttn9/9PfMx9gUy7/q3g4x9Ohfbz4faS5ONY/jGGLto1GnrjH2+nj/Qtn3rmu7GMf6sc419srua55v/sH+ibXtyN/MXh7r/1X6MmkOS/QywCDehzCMA2qdQ5ZFDcRHyuRq+fpqw3f6CaMQh+3nkJcnpaXhNnFoHTjbP356w3X6u8uLe5uvAzkMBzAG/3xzv/pPtX3fSZvPrKTy3xhxLvnme+I2R6bbtJ5+Qbxzju72ptu3eCo9xDOkYivibb75ZvXr10qpVq/yz0ezfv1+/+MUvdPPNN+vdd+0XTQrGjh07NHHiRO3fv1+dO3fW8OHDtWrVKnXu3DlkxwDQtpFHADQWeQStUdBF/D//+c86BbwkdezYUX/4wx905plnhjS4RYvMK5MCgB3yCIDGIo+gNQr6nni3233UKZXKysrkcpn/DAEAAACgcYIu4i+66CLdcMMNWr16tSzLkmVZWrVqlX71q19p7NixTREjAAAAgCMEXcTPnz9fvXr10tChQxUdHa3o6GideeaZ6t27tx577LGmiBEAAADAEYK+Jz45OVlvvvmmNm3apA0bNkiS+vXrp969e4c8OAAAAAD1BV3E1+rTp4/69OkTylgAAAAABCDoIt7r9WrhwoVaunSp9uzZI5+v7tycn3zySciCAwAAAFBf0EX8LbfcooULF+rCCy/USSedJIfDPJF/a+B+I0lOV3SD7VtP6mD7/LjrDxqPUbHTfiEhSUr4wvw1hL0X2E/y/6tTlhvH+Os7o4x9ln1yirFPz+u227Z7Xss0jrFV5gWhMs87YOwTVWK/2EORzOc/dfpO2/aaco+0xDgMAABBi6zwKfJQwwsTxe6qsn2+JynWeIzqBHNd1u1TYxftO9m+XoktMC/SFFVm7hN9wLwIky8qyra9sqO5tjr+L+YFlCJ37Tb2KRp6nG37pt09jWNUZdrH4qsM7CurQRfxixYt0ssvv6wxYwJYEhQAAABAyAU9O43L5eJLrAAAAEALCrqIv/322/XYY4/Jssx/IgEAAAAQegHdTjN+/Pg6jz/55BO9//776t+/v6J+cp/S66+/HrroAAAAANQTUBGflJRU5/Gll17aJMEAAAAAMAuoiH/22WebOg4AAAAAAQr6nvjKykpVVFT4H2/btk3z5s3Thx9+GNLAAAAAABxd0EX8JZdcoueff16SVFRUpOzsbD388MO65JJL9MQTT4Q8QAAAAAB1BT1P/Nq1a/Xoo49Kkl599VWlpaVp3bp1eu211zRjxgzdeOONIQ8yFMZOX6bo+IZf7gtbTrd9fklpTEji8CSZf2/q+/sy2/Y/TxtpHCO22LzYQ6d/mxdYMC3CNPLGDcYxAll4as1zg4x94kbZx9LlEnMsmWvsf47VqjaOAQDAsShPd8rpbnjhwoMnxNk+v6qz13iM6D32CyNKUsEZ5lrEVWTsYuQ8ZJ7JcH9/lzmWUvtxIgI4zp7TEox94grNi2kdPMH+/PoizbG44+xrDa8jsFok6CvxFRUVSkg4fCI+/PBDjR8/XhERETrjjDO0bdu2YIcDAAAAEKSgi/jevXtr8eLFys/P1wcffKBRow5fZd2zZ48SE83L3gMAAABonKCL+BkzZug3v/mNevTooezsbA0dOlTS4avygwaZb4kAAAAA0DhB3xP/s5/9TMOHD9fu3bs1cOBA//6RI0cyfzwAAADQDIIu4iUpLS1NaWlpys/PlyRlZGQoOzs7pIEBAAAAOLqgb6epqanRvffeq6SkJPXo0UM9evRQUlKSfve73+nQoUNNESMAAACAIwR9JX7atGl6/fXX9eCDD/rvh1+5cqXuu+8+7d+/n7niAQAAgCYWdBH/wgsvaNGiRRo9erR/34ABA5SRkaGJEydSxAMAAABNLOgi3u12q0ePHvX2Z2VlyeUyT9jfUl55eqScrugG20uGVto+v9cT5sn7995ZYg6km7nLtsTOtu0RifaxSlJFhvk4O/qbb3+KuzDetn3bSynGMbzHVRn7FCVGGfv8utf/2ra/cFOucYzi+T7bdm91laSXjOMAABCszmsrFBnZ8OfQ5kn2n4XxG82fld4zzLVI0gfmhY8k+7qnpJd5BK/Nwla1KtLtP5clKWqT/QKWsXvMY/gCqHiTvjCvd5S02v41VfVNM45xcId9beWtDqw8D/qe+KlTp+r++++Xx+Px7/N4PPr973+vqVOnBjscAAAAgCAFfSV+3bp1Wrp0qY477jj/FJNff/21qqurNXLkSI0fP97f9/XXXw9dpAAAAAAkHUMRn5ycrMsuu6zOvoyMAO7dAAAAABASQRfxzz77bFPEAQAAACBAQd8TDwAAAKBlHdOKra+++qpefvllbd++XdXV1XXa1q5dG5LAAAAAABxd0Ffi58+fr8mTJys1NVXr1q1Tdna2OnbsqK1bt9aZOx4AAABA0wj6Svyf//xnPfXUU5o4caIWLlyoO++8Uz179tSMGTN04MCBpoixWfzqlOW27UvvP8E4Rna8+fV/+OXJxj5RSfZzs5pilaQn159l7BP9bYyxz8ZHj7PvsMs4hBITzPPal+9oeA7/Wi9sOd22vTrRHEtRf/u5ZH2VPqaJBwA0iR0jYxQR3fDnXefP7Z9/oL95zZroL80fhuXpxi5yeuzbO/7LPDe75TDH2+nf1cY+hYPta4SKNPN89MmbzPFW9jcv5hNVYh+ve5d5nv6Dlyfbtvsqa4xjSMdwJX779u0aNmyYJCkmJkalpaWSpKuvvlovvvhiUGMtX75cF198sdLT0+VwOLR48eI67ZZlacaMGeratatiYmKUk5OjTZs2BRsygDaMPAKgMcghCFdBF/FpaWn+K+7du3fXqlWrJEl5eXmyLPNvXEcqLy/XwIEDtWDBgqO2P/jgg5o/f76efPJJrV69WnFxccrNzVVVlXkFUADtA3kEQGOQQxCugr6d5rzzztNbb72lQYMGafLkybrtttv06quv6ssvv6yz0FMgRo8e3eB99JZlad68efrd736nSy65RJL0/PPPKzU1VYsXL9aECROCDR1AG0QeAdAY5BCEq6CL+Keeeko+3+H7im666SZ17NhRX3zxhcaOHaspU6aELLC8vDwVFBQoJyfHvy8pKUlDhgzRypUrG/yP4/F45PH8eCNXSYn53iQAbdOx5BFyCIBa1CJozYK+nSYiIkKRkT/W/hMmTND8+fM1bdo0uVyukAVWUFAgSUpNTa2zPzU11d92NHPnzlVSUpJ/YzVZoP06ljxCDgFQi1oErdkxzRNfVFSkNWvWaM+ePf6r8rWuueaakAR2rO655x5Nnz7d/7ikpIT/PAACRg4B0FjkETSHoIv4t99+W1dddZXKysqUmJgoh8Phb3M4HCEr4tPS0iRJhYWF6tq1q39/YWGhTjnllAaf53a75Xa7QxIDgPB2LHmEHAKgFrUIWrOgb6e5/fbbdd1116msrExFRUU6ePCgfwvlPPFZWVlKS0vT0qVL/ftKSkq0evVqDR06NGTHAdB2kUcANAY5BK1Z0Ffid+7cqZtvvlmxsbGNPnhZWZk2b97sf5yXl6f169crJSVF3bt316233qo5c+aoT58+ysrK0r333qv09HSNGzcu6GOVnl6piNiGp8D88/KRx/IS6sjPzzR3yjBP4B9V7LBt/2jKCOMYiaeYF3JyF5sXPnCdYVio6cMU4xhF/aOMffpmbzf2yf/I/vxW9TcvKqUScywIL82ZRwC0Pc2ZQ6JOLJEztuFVlHqcW2j7/P/q+L3xGN+Umxcs2uNJMPZZvaGnbXtVR/PnqbeTeSGnTlm7jX08u7vYtv/mlI+MY3xW1Nvc5z/mPhF742zbnVXxxjH6/dH+51zj9WiHcZRjKOJzc3P15ZdfqmdP+x9uIL788kude+65/se1949NmjTJvxpseXm5brjhBhUVFWn48OFasmSJom1WOwPQvpBHADQGOQThKugi/sILL9Qdd9yh7777TieffLKiour+JjZ27NiAxzrnnHNsF4hyOByaPXu2Zs+eHWyYANoJ8giAxiCHIFwFXcRff/31knTUN7PD4ZDX6218VAAAAAAaFHQR/9MpJQEAAAA0r6BnpwEAAADQsgIu4leuXKl33nmnzr7nn39eWVlZ6tKli2644YY6SwwDAAAAaBoBF/GzZ8/Wt99+63/873//W7/85S+Vk5Oju+++W2+//bbmzp3bJEECAAAA+FHARfz69es1cuSPc6kvWrRIQ4YM0V//+ldNnz5d8+fP18svv9wkQQIAAAD4UcBfbD148KBSU1P9j//5z39q9OjR/senn3668vPzQxtdCCX8b4ycrobndH3pzodsnz/1ypuMx9h7Z4mxT8XORGMfl2GYnWeZF9o6lNTwdFm1Soaab3/yGeLtvs28eFXqZTuNfYqeyTD2cSXZt1cEsJBTn3/YLzxRU1Md0AILAAAE69A3SfLZzC//v13sFwr60mdejMiKNs8SGJtsXhzxwgH/tm1/vNtq4xgD1kw09vnu2+7GPjKUNPMXjjMOUXmS+TXHbDDP/R9bYB+M5TQOoW0/S7Nt93qqpIfN4wR8JT41NVV5eXmSpOrqaq1du1ZnnHGGv720tLTenPEAAAAAQi/gIn7MmDG6++67tWLFCt1zzz2KjY3ViBEj/O3/+te/1KtXryYJEgAAAMCPAr6d5v7779f48eN19tlnKz4+Xs8995xcLpe//ZlnntGoUaOaJEgAAAAAPwq4iO/UqZOWL1+u4uJixcfHy+mse9PPK6+8ovh4+3u5AAAAADRe0Cu2JiUd/duFKSkpjQ4GAAAAgBkrtgIAAABhhiIeAAAACDMU8QAAAECYCfqe+HBVenqlImIbnqB/9Iqpts+PPivGeIxuD5rjqD7LfMpn3brQtn3mvGuNY7jOOGjsk5pQauyjdPvmjepmHCJiVxdjH98wn7FP8rf2v3OmfmH+ndS0UJbXEyGtMg4DAEDQ4ndYcroarkU6bLD/HPMkO4zHODjAvNijZ6t54cllXw22bT9z0ynGMaws88pHadvMn/8dVu+ybc/7xXHGMbr/PYBVmByHjF2qUuzHcZhfjkp72Lf7nOafocSVeAAAACDsUMQDAAAAYYYiHgAAAAgzFPEAAABAmKGIBwAAAMIMRTwAAAAQZijiAQAAgDDTbuaJd+6KVkR0dIPtPbO32z5/Y4l5PvS9d1YZ+2Qk7DX2Mc0DX55hnj805ZVkY5/8jBRjn6r+lbbtj53/d+MYv3ltkrFPALO3qqh/jW17v5l5xjE8l/exbfdWBxAIAADHIKrSkrOm4c/wg5eW2z7f5zVfe420zHPJu7bbr5kiSfE77WuNsnTzJ7fTXBapspP5NUWdlGbb3uOlAuMY+4emGvv4AihGOv6rxLZ957lJxjGcHkOHavPPUOJKPAAAABB2KOIBAACAMEMRDwAAAIQZingAAAAgzFDEAwAAAGGGIh4AAAAIMxTxAAAAQJihiAcAAADCTIsu9rR8+XI99NBD+uqrr7R792698cYbGjdunL/92muv1XPPPVfnObm5uVqyZEnQx+r5wn5FOt0Ntmecd8D2+ZsSuxiPkZpQauyzcYN50ahRk9bZti/75BTjGCkfbDH2SX7JZexjiveeZ641jnH9VR8Y+/x5+Uhjn+7v2bdvfPQ44xjOHfaLV/iqzAtpoXVpzjwCoO1pzhxS3DNCTnfD1089BxtelFKSUtaay7bOa+wXI5Ika90Xxj6HcgbbtndcedA4RtGp5tqpqI/5evK+JPvX7So2L7DU6cOtxj6efuYarahfgm27FUBlHVlh3+4wLQb1f1r0Snx5ebkGDhyoBQsWNNjnggsu0O7du/3biy++2IwRAmjtyCMAGoMcgnDVolfiR48erdGjR9v2cbvdSkuzX24XQPtFHgHQGOQQhKtWf0/8p59+qi5duqhv37668cYbtX///pYOCUCYIY8AaAxyCFqjFr0Sb3LBBRdo/PjxysrK0pYtW/Tb3/5Wo0eP1sqVK+V0Oo/6HI/HI4/nx5uJSkrM94YBaLuCzSPkEABHohZBa9Wqi/gJEyb4/33yySdrwIAB6tWrlz799FONHHn0L0LOnTtXs2bNaq4QAbRyweYRcgiAI1GLoLVq9bfTHKlnz57q1KmTNm/e3GCfe+65R8XFxf4tPz+/GSME0NqZ8gg5BIAdahG0Fq36SvxP7dixQ/v371fXrl0b7ON2u+V2NzyVJID2zZRHyCEA7FCLoLVo0SK+rKyszm+yeXl5Wr9+vVJSUpSSkqJZs2bpsssuU1pamrZs2aI777xTvXv3Vm5ubsDHsKzD837XeO0n3awuq7Zt91VUGY9VU26e2NNXaR7HGEtVALH47MeQJF+5eU50U7xej/ktVFVW0+jjSFLNIcMYFYYOkhyGw9Se29r3DVq/ps4j/hyiQxJvCxjU6HAeIoeEj+asRXwe+w8hX6X955i32vyZa6p3Dsdj/rysqbGPNZDj1Bwyf7Z7PeabQhw+w3EMsUpSRAB1USDjeKvtgwnk9ZhOv7c6wFrEakHLli2zdPhjsc42adIkq6Kiwho1apTVuXNnKyoqysrMzLSuv/56q6CgIKhj5OfnH/UYbGx2W35+fhO96xFqTZ1HyCFsx7KRQ8IHtQhba91MecRhWW37coHP59OuXbuUkJAgh8OhkpISZWRkKD8/X4mJiS0dXpsT7ufXsiyVlpYqPT1dERFh9ZURNJGf5hAp/N/nrVm4n1tyCI6GWqR5hfv5DTSPhNU98cciIiJCxx13XL39iYmJYfmDDRfhfH6TkpJaOgS0Ig3lECm83+etXTifW3IIfopapGWE8/kNJI9wmQAAAAAIMxTxAAAAQJhpd0W82+3WzJkzmfqpiXB+0R7wPm86nFu0B7zPm1Z7Ob9t/outAAAAQFvT7q7EAwAAAOGOIh4AAAAIMxTxAAAAQJihiAcAAADCTLsr4hcsWKAePXooOjpaQ4YM0Zo1a1o6pLC0fPlyXXzxxUpPT5fD4dDixYvrtFuWpRkzZqhr166KiYlRTk6ONm3a1DLBAiFEDgkNcgjaM/JIaLT3PNKuiviXXnpJ06dP18yZM7V27VoNHDhQubm52rNnT0uHFnbKy8s1cOBALViw4KjtDz74oObPn68nn3xSq1evVlxcnHJzc1VVVdXMkQKhQw4JHXII2ivySOi0+zxitSPZ2dnWTTfd5H/s9Xqt9PR0a+7cuS0YVfiTZL3xxhv+xz6fz0pLS7Meeugh/76ioiLL7XZbL774YgtECIQGOaRpkEPQnpBHmkZ7zCPt5kp8dXW1vvrqK+Xk5Pj3RUREKCcnRytXrmzByNqevLw8FRQU1DnXSUlJGjJkCOcaYYsc0nzIIWiryCPNpz3kkXZTxO/bt09er1epqal19qempqqgoKCFomqbas8n5xptCTmk+ZBD0FaRR5pPe8gj7aaIBwAAANqKdlPEd+rUSU6nU4WFhXX2FxYWKi0trYWiaptqzyfnGm0JOaT5kEPQVpFHmk97yCPtpoh3uVwaPHiwli5d6t/n8/m0dOlSDR06tAUja3uysrKUlpZW51yXlJRo9erVnGuELXJI8yGHoK0ijzSf9pBHIls6gOY0ffp0TZo0Saeddpqys7M1b948lZeXa/LkyS0dWtgpKyvT5s2b/Y/z8vK0fv16paSkqHv37rr11ls1Z84c9enTR1lZWbr33nuVnp6ucePGtVzQQCORQ0KHHIL2ijwSOu0+j7T09DjN7U9/+pPVvXt3y+VyWdnZ2daqVataOqSwtGzZMktSvW3SpEmWZR2e2unee++1UlNTLbfbbY0cOdLauHFjywYNhAA5JDTIIWjPyCOh0d7ziMOyLKsFfncAAAAAcIzazT3xAAAAQFtBEQ8AAACEGYp4AAAAIMxQxAMAAABhhiIeAAAACDNtfp54n8+nXbt2KSEhQQ6Ho6XDQStnWZZKS0uVnp6uiAh+xwU5BMEhh+BoyCMIRsB5pEUnuAzQ448/bmVmZlput9vKzs62Vq9eHfBz8/PzjzqHKBub3Zafn9+E72i0hGPNI+QQtmPZyCFtD7UIW3NvpjzS6q/Ev/TSS5o+fbqefPJJDRkyRPPmzVNubq42btyoLl26GJ+fkJAgSRp00X/LGRV9zHHEv/GlsY8n91Rjn/0nRh1zDLU6fnfI2KeqQ/P8aMu7mq8oJP7gNfapSHUa+7iLLNv2g+dXGsdwfRNr2+71VGnLn2f73zdoGxqTR2rfC8M1RpFq/P9ftG01OqTP9B45pI0JVS0yOPe3irSpRRw19uOUp5s/K32R5s9lV4n956kkRXjt+3gSzcep7Gzu4zCHIp/hZUcYzpsk1cQHcKBu5joiOanCtr3qkLn+Ki+xr0d9lR7tvPUBYx5p9UX8I488ouuvv96/HPGTTz6pd999V88884zuvvtu4/Nr/2zljIq2/Y9jEukwf3h7Axjf6W58ERAZZf5P7HQ1z4/W6Tb/B42MMhfxTncgr8n+P2BErPk/qNMd2HuAP3e2LY3JI7XvhUhFBZQH0M79Xxoih7QtoapFIg21iOlt43SZPysdARTxps9TSYqose/jdAVwnABqhECKeIehpIkwX9uULzqAAwVSR8Ta1zTOAIr4iEOhqUVa9Q171dXV+uqrr5STk+PfFxERoZycHK1cubIFIwMQLsgjABqDHILWqlVfid+3b5+8Xq9SU1Pr7E9NTdWGDRuO+hyPxyOPx+N/XFJS0qQxAmjdgs0j5BAAR6IWQWvVqq/EH4u5c+cqKSnJv2VkZLR0SADCCDkEQGORR9AcWnUR36lTJzmdThUWFtbZX1hYqLS0tKM+55577lFxcbF/y8/Pb45QAbRSweYRcgiAI1GLoLVq1UW8y+XS4MGDtXTpUv8+n8+npUuXaujQoUd9jtvtVmJiYp0NQPsVbB4hhwA4ErUIWqtWfU+8JE2fPl2TJk3SaaedpuzsbM2bN0/l5eX+b4gDgAl5BEBjkEPQGrX6Iv6KK67Q3r17NWPGDBUUFOiUU07RkiVL6n3BxCT6QI0iIxueSDT6h/32A6SZj1eUYj6dSVvN0y3WRNv/gaS0m/k4CTvNk6Y6K33GPruGu23bU75v/OuRpNIe5liK+9n3CeTN7DijyL69wmPbjvAUqjwCoH0KVQ7xRjnkiGp42sCqLvaflxWp5ikbawKYJjFup3kcd7F9uyfFPIY3xhyLL5DpLqvtj+XwBRBLrLnOuOj4b419VhX2sG0/sXOhbbsk/e/W423bHVXmWKUwKOIlaerUqZo6dWpLhwEgjJFHADQGOQStTau+Jx4AAABAfRTxAAAAQJihiAcAAADCDEU8AAAAEGYo4gEAAIAwQxEPAAAAhJmwmGIyFPac6pbT3fCc55Enpds+P263eT70PcPMfU54osTYpzIjwbY9kPndA1HU22XsE1tgP39r9H7zfPSFp9vPNS9JaSvNr2nCfR/Ytj+yPNc4hvV1rH27p8o4BgAAxyLhh3JFOm0+N3vG2z6/Js5pPIbTY54z3WGeml1l3ezHqQlkDvgocx9vyiFjH8de+3qluneleYz95lok3mleK2ZC5le27R8Unmgcw5leYdvuqAisFuFKPAAAABBmKOIBAACAMBP07TQFBQVavXq1CgoKJElpaWkaMmSI0tLSQh4cAAAAgPoCLuLLy8s1ZcoULVq0SA6HQykpKZKkAwcOyLIsTZw4UX/5y18UG2t/zzEAAACAxgn4dppbbrlFa9as0bvvvquqqioVFhaqsLBQVVVVeu+997RmzRrdcsstTRkrAAAAAAVRxL/22mtauHChcnNz5XT++O1op9OpUaNG6ZlnntGrr77aJEECAAAA+FHARbzP55PL1fAUPy6XSz5faKY+BAAAANCwgIv4iy66SDfccIPWrVtXr23dunW68cYbdfHFF4c0OAAAAAD1BfzF1scff1xXXnmlBg8erA4dOqhLly6SpD179qioqEi5ubl6/PHHmyzQxkr8wavIKPNiTA0p7mleYOGEJw4Y+xwY2MHYJ+aA/QJKkRXmhREiKqqNfZKNPcwLNe0abl48wbRglCSVdzWf36f/cqFte3SycQhFGtaDcJjXeQAA4JiUZcYpMiq6wfaqDvbXVn3mj0o5Argpotp+TUlJUqRhvSEr0ryolM9cIsi1w7zwZGSF/bGqjzPXGcm9zTXasoI+xj5Zifbj5O1NMY5h+ex/zqb2WgEX8R06dND777+v77//XqtWraozxeTQoUN1wgknBDoUAAAAgEYIep74fv36qV+/fk0RCwAAAIAABFXEV1dXa/HixVq5cmWdK/HDhg3TJZdcYvvFVwAAAAChEfAXWzdv3qx+/fpp0qRJWrdunXw+n3w+n9atW6drrrlG/fv31+bNm5syVgAAAAAK4kr8jTfeqJNPPlnr1q1TYmJinbaSkhJdc801uummm/TBBx+EPEgAAAAAPwq4iP/888+1Zs2aegW8JCUmJur+++/XkCFDQhocAAAAgPoCvp0mOTlZP/zwQ4PtP/zwg5KTk0MQEgAAAAA7AV+J/6//+i9dc801uvfeezVy5EilpqZKkgoLC7V06VLNmTNH06ZNa7JAAQAAABwWcBE/e/ZsxcXF6aGHHtLtt98uh+PwxPuWZSktLU133XWX7rzzziYLtLEqUp1yuhteJSFut/1CUHG7zAsJ7DnDvJCTabEhSYr7fq9te9HgVOMY8fnm4+w/ybwKQ6dv7BeWOhRr/mPOgX7m1Sm8MebzG7vbfrEHd5FxCAAAWq2ocvvPQiuA+ycq0s19DiWYP3OdVfafuYEsKuUqMi8IFZ9vjqUm1r5P9Y4Y4xilVeY+1d3NC2V6DtmXzknxhlWyJB0sibVtN5+Rw4KaYvKuu+7SXXfdpa1bt6qwsFDS4Skms7KyghkGAAAAQCMEvdiTJPXs2VM9e/YMdSwAAAAAAhDwF1sl6bvvvtOvf/1rDRo0SF27dlXXrl01aNAg/frXv9Z3333XVDECAAAAOELAV+Lff/99jRs3TqeeeqouueSSOl9s/eijj3TqqafqzTffVG5ubpMFCwAAACCIIv7uu+/WXXfdpdmzZ9dru++++3TffffpjjvuoIgHAAAAmljAt9P85z//0VVXXdVg+8SJE7Vp06aQBAUAAACgYQEX8T169NC7777bYPu7776rzMzMkAQFAAAAoGFBzRN/5ZVX6tNPP1VOTk69xZ6WLFmiF154ockCBQAAAHBYwEX8z3/+c3Xr1k3z58/Xww8/rIKCAkmH54kfOnSoPv30Uw0dOjSkwd13332aNWtWnX19+/bVhg0bgh4rttCryKiGF3RK/qrQ9vk1XRKNx9g3IM7YJ36neSGBqh4dbduj99cYx/B0NC/k1PEbj7FPWTeXbXuNee0EpX5lv2CUFJpFo6qTzStPuIrsj+M1nxKEmVDmEQDtU6jySHVChLyuhj+HamLtF0fyJJuPEWH+yFVMoXkRpuj99ksOBXKcmmjzskUx+8w1jTfa/rO7tIe5hgjkNXu6mBenLKuwr6+8NeYxYuPsiw2vFcDJVZDzxA8bNkzDhg0L5imN1r9/f3388cf+x5GRxzS1PYB2jDwCoLHII2htWv07MDIyUmlpaS0dBoAwRh4B0FjkEbQ2QS32ZOf7779vklVcN23apPT0dPXs2VNXXXWVtm/fbtvf4/GopKSkzgagfQsmj5BDABwNeQStTciK+Orqam3bti1Uw0mShgwZooULF2rJkiV64oknlJeXpxEjRqi0tLTB58ydO1dJSUn+LSMjI6QxAQgvweYRcgiAnyKPoDVyWJZl/taBpOnTp9u27927Vy+88IK83oa/PNpYRUVFyszM1COPPKJf/vKXR+3j8Xjk8fz4hYGSkhJlZGTotEvvV2RUdINjN9cXW5M3m7/YGgreGPPvZ85K8xdBQ/HF1oSd5i+ttJ4vtlZpyx9+q+LiYiUmmn/mCD+mPNJQDjlHlyjSEdWcoSIM1ViH9KneJIe0cceaRwZe/Xs5XQ3XIqH4YqvM398M6Eupofliq7lP4rbGf7G1MNtcQ8TtMJ+YopPNsUQlV9m2h+SLrRUebbzyAWMeCfie+Mcee0ynnHJKg4OVlZUFOtQxS05O1vHHH6/Nmzc32MftdsvtNs/MAqB9MuURcggAE/IIWoOAi/jevXvrtttu0y9+8Yujtq9fv16DBw8OWWBHU1ZWpi1btujqq69u0uMAaLvIIwAaizyC1iDgIv60007TV1991WAR73A4FOCdOQH7zW9+o4svvliZmZnatWuXZs6cKafTqYkTJwY9VvSBGkVGNvxnkvJ+nW2fX9rNfKq80QH8qaa3/e0pklSRZj9Oz6e2GMfYeXkvY59A5onfN8j+Z9ppnfk17zvJfAtCZKWxi2ILTO8v85/Tks7YY9vuLWei+LYmlHkEQPsUqjwSu6dGkVEN1yLV8fa3YsTsMx8jqsx8a2lUWQC3ucbb1z2B1Dyuhr/C+GMspeb7cpzVhvNSYP6rR1yh+XZv02uWpPKe9sdyxJiP4/Uabu31BnBPlIIo4h9++OE693f91MCBA+Xzmd84wdixY4cmTpyo/fv3q3Pnzho+fLhWrVqlzp3tC24AqEUeAdBY5BG0RgEX8S0xN+qiRYua/ZgA2hbyCIDGIo+gNQrZFJMAAAAAmgdFPAAAABBmKOIBAACAMEMRDwAAAISZoIv42bNnq6Kiot7+yspKzZ49OyRBAQAAAGhY0EX8rFmzjro6a0VFhWbNmhWSoAAAAAA0LOApJmtZliWHo/4k9F9//bVSUlJCElRLcFbaz3EfyGJEknmxq4Sd5gUWTD+WbZPMCzm5D5pj+eEi8yJMCT/Y/55XkmUcQlVdA3nNZqNP+5dt+/vf9DeOUVMUb9vuqwj6vwQAAAFxeC05HA1/Pntd9ov8WAFcevVG2S+MJEkVXcx9agyLOTmrzXWGu8Tcx+Z0+B0yLIJVE2ceoyQzgPPSzbxQU3RKlW17cnz9u1V+aliXPNt2T9khbTaOEkQR36FDBzkcDjkcDh1//PF1Cnmv16uysjL96le/CnQ4AAAAAMco4CJ+3rx5sixL1113nWbNmqWkpCR/m8vlUo8ePTR06NAmCRIAAADAjwIu4idNmiRJysrK0rBhwxQVZb4VAwAAAEDoBX0D8Nlnny2v16vXXntN33//vSSpf//+Gjt2rJxO8/1GAAAAABon6CJ+8+bNGjNmjHbu3Km+fftKkubOnauMjAy9++676tXL/KVLAAAAAMcu6Ckmb775ZvXq1Uv5+flau3at1q5dq+3btysrK0s333xzU8QIAAAA4AhBX4n/5z//qVWrVtWZTrJjx476wx/+oDPPPDOkwQEAAACoL+gr8W63W6WlpfX2l5WVyeVyhSQoAAAAAA0L+kr8RRddpBtuuEFPP/20srOzJUmrV6/Wr371K40dOzbkAYbKgX5uOd3uBttrYuyfn/nKLuMx9p3Z1dintJv5lB8YWm3bnrLS/MtSp3+Vm2PJMq+O4Em2b0/MM6/SUJ1s/l0xIs1+8QRJWrv3ONv2qwatMY5x4JD9a64uq9azxlEAAAheZJVXkZENLyjkrLavEWpi7BdgOjyG+XM5Zn8AixrtNi9aZLJvcKKxT9wOc7yxu+xX3CzOSjCOkfSDeeHJqFLzBC0Hk+xnZywoMi986k7bZN8hIrBFMoO+Ej9//nz16tVLQ4cOVXR0tKKjo3XmmWeqd+/eeuyxx4IdDgAAAECQgr4Sn5ycrDfffFObNm3Shg0bJEn9+vVT7969Qx4cAAAAgPqCLuJr9enTR3369AllLAAAAAACEHQR7/V6tXDhQi1dulR79uyRz+er0/7JJ5+ELDgAAAAA9QVdxN9yyy1auHChLrzwQp100klyOMxfsgAAAAAQOkEX8YsWLdLLL7+sMWPGNEU8AAAAAAyCnp3G5XLxJVYAAACgBQVdxN9+++167LHHZFnmeT0BAAAAhF5At9OMHz++zuNPPvlE77//vvr376+oqLqT3r/++uuhiy6E3EWWnK6Gf/FwHzQMUGG/0IAklaebvx+QtNW8wELmq/a/Wx2K9dm2S1JRH/NCTs5Kc7zeGPtf1jwdzGO4igI4jsew2pak/Yb2fxRmG8d459zHbdvLon0s9gQAaBIRHq8ivA3XAXG7PLbPr062X2hIktwH7BeMlKSoHaZPVMmqso/FSutoHCN+VwALLG0tMPapKSi0bU+vPME4xqFOscY+1XHma9uO/fYLbnY/yfx6vtjb07a9ptz+3NcKqIhPSkqq8/jSSy8NaHAAAAAAoRdQEf/ss1ybBAAAAFqLoO+Jr6ysVEVFhf/xtm3bNG/ePH344YchDQwAAADA0QVdxF9yySV6/vnnJUlFRUXKzs7Www8/rEsuuURPPPFEyAMEAAAAUFfQRfzatWs1YsQISdKrr76qtLQ0bdu2Tc8//7zmz58f8gABAAAA1BV0EV9RUaGEhARJ0ocffqjx48crIiJCZ5xxhrZt2xbyAAEAAADUFXQR37t3by1evFj5+fn64IMPNGrUKEnSnj17lJiYGPIAAQAAANQV0Ow0R5oxY4auvPJK3XbbbTrvvPM0dOhQSYevyg8aNCjkAYZKeVeHnO6G5ys3zd++9YZexmO4i8xxRFWY53ivTAn6x1JPIPO315xQYezT5Q23bXtxT2fAMTVW5Ab7OV5rsszzqvZ32c9HX+Iy/3wAADgWzgPlcjobnjvdkWT/OedNtZ+jXJI8KeY+knmOd/ns14lxlpo/c6tSzDVC7KFDxj6Raam27ZVp5rVxLKe5LnKaQ5GrxLCWj898ffzC9G9t26vKDmmlOZTgr8T/7Gc/0/bt2/Xll1/qgw8+8O8fOXKkHn300aDGWr58uS6++GKlp6fL4XBo8eLFddoty9KMGTPUtWtXxcTEKCcnR5s2bQo2ZABtGHkEQGOQQxCugi7iJSktLU2DBg3Szp07lZ+fL0nKzs7WCSeYV8w6Unl5uQYOHKgFCxYctf3BBx/U/Pnz9eSTT2r16tWKi4tTbm6uqqqqjiVsAG0QeQRAY5BDEK6Cvm+jpqZGs2bN0vz581VWViZJio+P17Rp0zRz5kxFRZmXBK41evRojR49+qhtlmVp3rx5+t3vfqdLLrlEkvT8888rNTVVixcv1oQJE4INHUAbRB4B0BjkEISroK/ET5s2TU899ZQefPBBrVu3TuvWrdODDz6op59+WjfffHPIAsvLy1NBQYFycnL8+5KSkjRkyBCtXNnwnUIej0clJSV1NgDt07HkEXIIgFrUImjNgi7iX3jhBS1cuFBTpkzRgAEDNGDAAE2ZMkVPP/20XnjhhZAFVlBQIElKTa37ZYbU1FR/29HMnTtXSUlJ/i0jIyNkMQEIL8eSR8ghAGpRi6A1C7qId7vd6tGjR739WVlZcrkC+UZ007rnnntUXFzs32rv2QeAQJBDADQWeQTNIegifurUqbr//vvl8fw4tZDH49Hvf/97TZ06NWSBpaWlSZIKCwvr7C8sLPS3HY3b7VZiYmKdDUD7dCx5hBwCoBa1CFqzoIv4devW6Z133tFxxx2nnJwc5eTk6LjjjtPbb7+tr7/+WuPHj/dvjZGVlaW0tDQtXbrUv6+kpESrV6/2z00PAHbIIwAagxyC1izo2WmSk5N12WWX1dl3rPd6lZWVafPmzf7HeXl5Wr9+vVJSUtS9e3fdeuutmjNnjvr06aOsrCzde++9Sk9P17hx44I+VtxuS05XwwsXJH9V2GCbJMXnm3+Ljtxj/uJKVQ/zAgvl6fYLEpgWppKkkmTzAgvur+0XlTjM/lidvjGvjOCsNC+gtGdapbFPRZ79z+CqQWuMY3xbbX+csmoWewo3zZlHALQ9zZlDrBiXLGfDtx5Xptl/Ljs99gswSZIVyOVZw0JOkmRF2g9U3cW8wFJ5mjmYjknm+soqLbdtdx4yf3Z7I8x1UfQ+c01jOewXwfRZ5kWlEpz205NG2SwIdqSgi/hnn3022Kc06Msvv9S5557rfzx9+nRJ0qRJk7Rw4ULdeeedKi8v1w033KCioiINHz5cS5YsUXR0dMhiABDeyCMAGoMcgnAVdBEfSuecc44sq+HfBh0Oh2bPnq3Zs2c3Y1QAwgl5BEBjkEMQro6piH/11Vf18ssva/v27aqurq7Ttnbt2pAEBgAAAODogv5i6/z58zV58mSlpqZq3bp1ys7OVseOHbV169YGVzwDAAAAEDpBF/F//vOf9dRTT+lPf/qTXC6X7rzzTn300Ue6+eabVVxc3BQxAgAAADhC0EX89u3bNWzYMElSTEyMSktLJUlXX321XnzxxdBGBwAAAKCeoIv4tLQ0HThwQJLUvXt3rVq1StLhKZnsvhgCAAAAIDSCLuLPO+88vfXWW5KkyZMn67bbbtP555+vK664QpdeemnIAwQAAABQV9Cz0zz11FPy+Q5Pqn/TTTepY8eO+uKLLzR27FhNmTIl5AGGSk2MZDW8voJxEaatV5uP0eWTrsY+kVXmBQnKe9hP8h9Zaf6xxRaY/ypSmmXsogP97BdHqE4OYFGDH6KMfcoKzIswxGfZL6b1n7IuxjGUbO4CAEBT8Ma75YhseH75mhj7z9T4bRXGY/hizDWCL9r8mVuRalM0SYoqN9czsYWhuUPDEWkfb00Arye6wH7BqEC5SuwXexrSZZtxjCrL/mdUFeBpC7qIj4iIUETEjxfwJ0yYoAkTJgQ7DAAAAIBjdEzzxBcVFWnNmjXas2eP/6p8rWuuuSYkgQEAAAA4uqCL+LfffltXXXWVysrKlJiYKIfjxz/9OBwOingAAACgiQX9xdbbb79d1113ncrKylRUVKSDBw/6t9pZawAAAAA0naCL+J07d+rmm29WbGxsU8QDAAAAwCDoIj43N1dffvllU8QCAAAAIABB3xN/4YUX6o477tB3332nk08+WVFRdacPHDt2bMiCAwAAAFBf0EX89ddfL0maPXt2vTaHwyGv19v4qAAAAAA0KOgi/qdTSoYLd5FPkVENx+6Nsb+zKGmdecGifYPM5+aExwuNfWqi7ReN8nQwDiFvtHkRps5rzb9wFfe0X0Ah4QfzHVk1McYuivvB/FasqEy0bS9OtF8MSpL6u+yDKXGF5/sbABAGfNbhrQHOavtVfmoS7BdgkiRPsvnzNLLS/FkXUWMfi9dlrjO85nBVnWEuaqK+3W7bbkWaY5FlXkGpJrHhhbhqHYo3H8pk36EE23bPoUMBjRP0PfEAAAAAWlbARfzKlSv1zjvv1Nn3/PPPKysrS126dNENN9wgj8cT8gABAAAA1BVwET979mx9++23/sf//ve/9ctf/lI5OTm6++679fbbb2vu3LlNEiQAAACAHwVcxK9fv14jR470P160aJGGDBmiv/71r5o+fbrmz5+vl19+uUmCBAAAAPCjgIv4gwcPKjU11f/4n//8p0aPHu1/fPrppys/Pz+00QEAAACoJ+AiPjU1VXl5eZKk6upqrV27VmeccYa/vbS0tN6c8QAAAABCL+AifsyYMbr77ru1YsUK3XPPPYqNjdWIESP87f/617/Uq1evJgkSAAAAwI8Cnif+/vvv1/jx43X22WcrPj5ezz33nFyuHycAfeaZZzRq1KgmCTIUDvZ1yulueM7z7h9W2j6/21f2c5RK0r7zexr7bPt5urFP0lb7+dvjv6kxjlHWzTw5q2kOeEkq72E+lkl8WpmxT1WlOd7YmGrb9l0l9vPIS9KiUvv5aCvKWKwMANA0Irw+RajhOdodho+gUM0BL/OU6Yo4ZN/pUJz5OrA3xjx/e2SxeWZDh+FOD1+U+ThVXc0TvEeVmWsen8v+vBRUmWuRER022bZXHgqs9gq4iO/UqZOWL1+u4uJixcfHy+msWwC+8sorio8PwQz4AAAAAGwFvWJrUlLSUfenpKQ0OhgAAAAAZqzYCgAAAIQZingAAAAgzFDEAwAAAGGGIh4AAAAIMxTxAAAAQJihiAcAAADCTNBTTIbS8uXL9dBDD+mrr77S7t279cYbb2jcuHH+9muvvVbPPfdcnefk5uZqyZIlQR+r60qPIiMbXgwgosJ+IaGaHqnGY8QcME/OH7/TvAiDaaGm/Se5jWMkbzlk7FMTY/7xd/rGflGDol72CzBIUnmSeSGnGo954akbBn5u277iYG/jGGmRxbbt5U4Wewo3zZlHALQ9zZlDfFFO+SIb/rzzuewXLQpkIaeo8gAWLIo0X8N12pdFspzmBZbidps/U60AYqnJ6GzbXtnBXEOkFFQZ+zg85ngdNbG27d2ii4xj3JScb9te4vTpLuMoLXwlvry8XAMHDtSCBQsa7HPBBRdo9+7d/u3FF19sxggBtHbkEQCNQQ5BuGrRK/GjR4/W6NGjbfu43W6lpaU1U0QAwg15BEBjkEMQrlr9PfGffvqpunTpor59++rGG2/U/v37WzokAGGGPAKgMcghaI1a9Eq8yQUXXKDx48crKytLW7Zs0W9/+1uNHj1aK1eulNN59PufPB6PPB6P/3FJSUlzhQugFQo2j5BDAByJWgStVasu4idMmOD/98knn6wBAwaoV69e+vTTTzVy5MijPmfu3LmaNWtWc4UIoJULNo+QQwAciVoErVWrv53mSD179lSnTp20efPmBvvcc889Ki4u9m/5+fbfAAbQvpjyCDkEgB1qEbQWrfpK/E/t2LFD+/fvV9euXRvs43a75Xabp2AE0D6Z8gg5BIAdahG0Fi1axJeVldX5TTYvL0/r169XSkqKUlJSNGvWLF122WVKS0vTli1bdOedd6p3797Kzc0N+BiWdXie85oaj22/CK99u6/GPDdrzSH7OdUlyQpgHG+1fR+vxzw3a80h8zzx3mrzj7/mkP18s94A5lT1VZjnZvVVm+d4rSqzj+VQuWFSW0nlpfbxVpQdPve17xu0fk2dR/w5RIck3hYwqNHh3EsOCR+tqRYx1RHWIXMN4agJYJ74ENyIUXPIXENY5nJFNV5zjWD57AcKqJ6pCWCeeG8ANVqV/TieMnP9VVJqf5ySQGsRqwUtW7bM0uGPxTrbpEmTrIqKCmvUqFFW586draioKCszM9O6/vrrrYKCgqCOkZ+ff9RjsLHZbfn5+U30rkeoNXUeIYewHctGDgkf1CJsrXUz5RGHZbXtywU+n0+7du1SQkKCHA6HSkpKlJGRofz8fCUmJrZ0eG1OuJ9fy7JUWlqq9PR0RUSE1VdG0ER+mkOk8H+ft2bhfm7JITgaapHmFe7nN9A8Elb3xB+LiIgIHXfccfX2JyYmhuUPNlyE8/lNSkpq6RDQijSUQ6Twfp+3duF8bskh+ClqkZYRzuc3kDzCZQIAAAAgzFDEAwAAAGGm3RXxbrdbM2fOZOqnJsL5RXvA+7zpcG7RHvA+b1rt5fy2+S+2AgAAAG1Nu7sSDwAAAIQ7ingAAAAgzFDEAwAAAGGGIh4AAAAIM+2uiF+wYIF69Oih6OhoDRkyRGvWrGnpkMLS8uXLdfHFFys9PV0Oh0OLFy+u025ZlmbMmKGuXbsqJiZGOTk52rRpU8sEC4QQOSQ0yCFoz8gjodHe80i7KuJfeuklTZ8+XTNnztTatWs1cOBA5ebmas+ePS0dWtgpLy/XwIEDtWDBgqO2P/jgg5o/f76efPJJrV69WnFxccrNzVVVVVUzRwqEDjkkdMghaK/II6HT7vOI1Y5kZ2dbN910k/+x1+u10tPTrblz57ZgVOFPkvXGG2/4H/t8PistLc166KGH/PuKioost9ttvfjiiy0QIRAa5JCmQQ5Be0IeaRrtMY+0myvx1dXV+uqrr5STk+PfFxERoZycHK1cubIFI2t78vLyVFBQUOdcJyUlaciQIZxrhC1ySPMhh6CtIo80n/aQR9pNEb9v3z55vV6lpqbW2Z+amqqCgoIWiqptqj2fnGu0JeSQ5kMOQVtFHmk+7SGPtJsiHgAAAGgr2k0R36lTJzmdThUWFtbZX1hYqLS0tBaKqm2qPZ+ca7Ql5JDmQw5BW0UeaT7tIY+0myLe5XJp8ODBWrp0qX+fz+fT0qVLNXTo0BaMrO3JyspSWlpanXNdUlKi1atXc64RtsghzYccgraKPNJ82kMeiWzpAJrT9OnTNWnSJJ122mnKzs7WvHnzVF5ersmTJ7d0aGGnrKxMmzdv9j/Oy8vT+vXrlZKSou7du+vWW2/VnDlz1KdPH2VlZenee+9Venq6xo0b13JBA41EDgkdcgjaK/JI6LT7PNLS0+M0tz/96U9W9+7dLZfLZWVnZ1urVq1q6ZDC0rJlyyxJ9bZJkyZZlnV4aqd7773XSk1NtdxutzVy5Ehr48aNLRs0EALkkNAgh6A9I4+ERnvPIw7LsqwW+N0BAAAAwDFqN/fEAwAAAG0FRTwAAAAQZijiAQAAgDBDEQ8AAACEGYp4AAAAIMxQxAMAAABhhiIeAAAACDMU8QAAAECYoYgHAAAAwgxFPAAAABBmKOIBAACAMEMRDwAAAISZ/w+Ef3UO6g1GeAAAAABJRU5ErkJggg==", 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", 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"text/plain": [ "
" ] @@ -80779,7 +80779,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4345f4f5", + "id": "b99a6f07", "metadata": {}, "outputs": [], "source": [] diff --git a/cosem_starter.html b/cosem_starter.html index fd21cafe4..fe88716c0 100644 --- a/cosem_starter.html +++ b/cosem_starter.html @@ -158,6 +158,61 @@

Full Example

Available COSEM Pretrained Models

Below is a table of the COSEM pretrained models available, along with their details:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Available COSEM Pretrained Models

Model

Checkpoints

Best Checkpoint

Classes

Input Res

Output Res

Model

setup04

975000, 625000, 1820500

1820500

ecs, pm, mito, mito_mem, ves, ves_mem, endo, endo_mem, er, er_mem, eres, nuc, mt, mt_out

8 nm

4 nm

Upsample U-Net

setup26.1

650000, 2580000

2580000

mito, mito_mem, mito_ribo

8 nm

4 nm

Upsample U-Net

setup28

775000

775000

er, er_mem

8 nm

4 nm

Upsample U-Net

setup36

500000, 1100000

1100000

nuc, nucleo

8 nm

4 nm

Upsample U-Net

setup45

625000, 1634500

1634500

ecs, pm

4 nm

4 nm

U-Net

Notes

diff --git a/notebooks/minimal_tutorial.html b/notebooks/minimal_tutorial.html index c631e1b40..4dbf5ba4f 100644 --- a/notebooks/minimal_tutorial.html +++ b/notebooks/minimal_tutorial.html @@ -605,13 +605,13 @@

Visualize
Creating FileStatsStore:
 	path    : /home/runner/dacapo/stats
 <xarray.DataArray (iterations: 2000)>
-array([0.65979844, 0.61128896, 0.58934087, ..., 0.35486192, 0.37893647,
-       0.34515283])
+array([0.78323734, 0.73412889, 0.75879186, ..., 0.59049326, 0.35759771,
+       0.4338592 ])
 Coordinates:
   * iterations  (iterations) int64 0 1 2 3 4 5 ... 1994 1995 1996 1997 1998 1999
-../_images/861cf92042db801d4447df36db45bb7b2c06c0dc7e6752090fa59dd6078fccd0.png +../_images/f0b777515a067bf92625a6b5c7fc1d6f53803b2d1d4a45eb577f801db4d909da.png
@@ -643,7 +643,7 @@

Visualize +../_images/51cdf388f441d27cb394fc34355bc1b5620cd50c7403731d3668a9bbc58b6489.png

@@ -722,8 +722,8 @@

Visualize -../_images/a2617a0b50e674893d8d0dc94875b031801dc021d21bbeb63a687c2ae205b309.png -../_images/543df62e8ee0c046852195b03c5991c63d742fc7c4022c3069509bb1a20dd599.png +../_images/fa87b4210262a0cea0da17811d74cf359d26fb6dcff2709fc01c3b5f8de02768.png +../_images/99d5fdd1d05e74662f078ac6762d4f77eace3ab471f33e8133780fc9a2e6e1bd.png

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