Detectron__2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zakeZlxAHRo",
        "outputId": "939ae122-48ab-4a31-c234-96ef65cf2e00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIeWKZXBIbu-"
      },
      "source": [
        "#redimensionnement d'image pour le détecteur Detectron"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YgZDAuxVIT6l"
      },
      "outputs": [],
      "source": [
        "def resize(scale_percent, image_path):\n",
        "    # Charger l'image\n",
        "    image = cv2.imread(image_path)\n",
        "\n",
        "    # Calculer les nouvelles dimensions en fonction du pourcentage de redimensionnement\n",
        "    width = int(image.shape[1] * scale_percent / 100)\n",
        "    height = int(image.shape[0] * scale_percent / 100)\n",
        "    dim = (width, height)\n",
        "\n",
        "    # Redimensionner l'image en utilisant l'interpolation de l'aire\n",
        "    resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)\n",
        "\n",
        "    return resized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODaxaykfdG6h"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXyaa-tO-8bi"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#detectron Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUH8PWuo-7PD",
        "outputId": "276dbe1b-0b6a-45f6-bde7-443518054b4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyyaml==5.1\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pyyaml\n",
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            "  Created wheel for pyyaml: filename=PyYAML-5.1-cp310-cp310-linux_x86_64.whl size=44090 sha256=f7a15f2c76f16e4d31a391e127d337600d8a8989d3dc3887111c45b0cf482a01\n",
            "  Stored in directory: /root/.cache/pip/wheels/70/83/31/975b737609aba39a4099d471d5684141c1fdc3404f97e7f68a\n",
            "Successfully built pyyaml\n",
            "Installing collected packages: pyyaml\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "dask 2022.12.1 requires pyyaml>=5.3.1, but you have pyyaml 5.1 which is incompatible.\n",
            "flax 0.6.9 requires PyYAML>=5.4.1, but you have pyyaml 5.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed pyyaml-5.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/facebookresearch/detectron2.git\n",
            "  Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-req-build-glr7emxd\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/detectron2.git /tmp/pip-req-build-glr7emxd\n",
            "  Resolved https://github.com/facebookresearch/detectron2.git to commit 94113be6e12db36b8c7601e13747587f19ec92fe\n",
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            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->detectron2==0.6) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard->detectron2==0.6) (3.2.2)\n",
            "Building wheels for collected packages: detectron2, fvcore, antlr4-python3-runtime\n",
            "  Building wheel for detectron2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for detectron2: filename=detectron2-0.6-cp310-cp310-linux_x86_64.whl size=7802931 sha256=f756083f89e84642aa23fbf5bb729df5b74133040995e42262e2220c194b29ed\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-j2u89g6g/wheels/47/e5/15/94c80df2ba85500c5d76599cc307c0a7079d0e221bb6fc4375\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fvcore: filename=fvcore-0.1.5.post20221221-py3-none-any.whl size=61405 sha256=1213dd9893199b34ff23b29dc30830306ee4b1e1b00a1e5a9d86586b49cc706b\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/c0/af/77c1cf53a1be9e42a52b48e5af2169d40ec2e89f7362489dd0\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=06ca1c6043a4e3ff19b6cab83d3eb3df59c5c037ce05153ec7fdb4057292abef\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "Successfully built detectron2 fvcore antlr4-python3-runtime\n",
            "Installing collected packages: antlr4-python3-runtime, yacs, portalocker, pathspec, omegaconf, mypy-extensions, iopath, hydra-core, black, fvcore, detectron2\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 black-23.3.0 detectron2-0.6 fvcore-0.1.5.post20221221 hydra-core-1.3.2 iopath-0.1.9 mypy-extensions-1.0.0 omegaconf-2.3.0 pathspec-0.11.1 portalocker-2.7.0 yacs-0.1.8\n"
          ]
        }
      ],
      "source": [
        "!python -m pip install pyyaml==5.1\n",
        "!python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Vvq0-Ol_C1k",
        "outputId": "d470e54d-e7c6-452c-c77a-f1c331fbad37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2022 NVIDIA Corporation\n",
            "Built on Wed_Sep_21_10:33:58_PDT_2022\n",
            "Cuda compilation tools, release 11.8, V11.8.89\n",
            "Build cuda_11.8.r11.8/compiler.31833905_0\n",
            "torch:  2.0 ; cuda:  cu118\n",
            "detectron2: 0.6\n"
          ]
        }
      ],
      "source": [
        "import torch, detectron2\n",
        "!nvcc --version\n",
        "TORCH_VERSION = \".\".join(torch.__version__.split(\".\")[:2])\n",
        "CUDA_VERSION = torch.__version__.split(\"+\")[-1]\n",
        "print(\"torch: \", TORCH_VERSION, \"; cuda: \", CUDA_VERSION)\n",
        "print(\"detectron2:\", detectron2.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UQzOx3Iv_50b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "387d8c35-306c-49e9-fdf5-55532c6c0ce2"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-6a81eb09ac8b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdetectron2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdetectron2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msetup_logger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msetup_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# import some common libraries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'detectron2'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "import detectron2\n",
        "from detectron2.utils.logger import setup_logger\n",
        "setup_logger()\n",
        "\n",
        "# import some common libraries\n",
        "import numpy as np\n",
        "import os, json, cv2, random\n",
        "import matplotlib.pyplot as plt\n",
        "# import some common detectron2 utilities\n",
        "from detectron2 import model_zoo\n",
        "from detectron2.engine import DefaultPredictor\n",
        "from detectron2.config import get_cfg\n",
        "from detectron2.utils.visualizer import Visualizer\n",
        "from detectron2.data import MetadataCatalog, DatasetCatalog\n",
        "from detectron2.structures import BoxMode\n",
        "from detectron2.utils.visualizer import ColorMode\n",
        "from detectron2.engine import DefaultTrainer\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-f7IpmQA_Jz"
      },
      "source": [
        "#Enregistrement de jeu de données et configuration de métadonnées pour l'entraînement et la validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGKfbRLFBw-N"
      },
      "source": [
        "la structure du fichier json resultat de l'annotation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWJY_GgiBgMQ"
      },
      "source": [
        "![Capture d’écran du 2023-06-06 10-24-44.png](data:image/png;base64,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)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vlZlgB5QANUY"
      },
      "outputs": [],
      "source": [
        "def get_tree_dicts(img_dir):\n",
        "    # Charger le fichier JSON contenant les annotations des images\n",
        "    json_file = os.path.join(img_dir, \"annotations_json.json\")\n",
        "    with open(json_file) as f:\n",
        "        imgs_anns = json.load(f)\n",
        "\n",
        "    classes = ['Tree', 'Person']\n",
        "    dataset_dicts = []\n",
        "\n",
        "    # Parcourir chaque image annotée\n",
        "    for idx, v in enumerate(imgs_anns.values()):\n",
        "        record = {}\n",
        "\n",
        "        # Chemin d'accès complet à l'image\n",
        "        filename = os.path.join(img_dir, v[\"filename\"])\n",
        "        height, width = cv2.imread(filename).shape[:2]\n",
        "\n",
        "        record[\"file_name\"] = filename\n",
        "        record[\"image_id\"] = idx\n",
        "        record[\"height\"] = height\n",
        "        record[\"width\"] = width\n",
        "\n",
        "        annos = v[\"regions\"]\n",
        "        print(len(annos))\n",
        "        objs = []\n",
        "\n",
        "        # Parcourir chaque annotation dans l'image\n",
        "        for x in range(len(annos)):\n",
        "            anno = annos[x][\"shape_attributes\"]\n",
        "            px = anno[\"all_points_x\"]\n",
        "            py = anno[\"all_points_y\"]\n",
        "            poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]\n",
        "            poly = [p for x in poly for p in x]\n",
        "\n",
        "            # Créer l'objet d'annotation avec les informations nécessaires\n",
        "            obj = {\n",
        "                \"bbox\": [np.min(px), np.min(py), np.max(px), np.max(py)],\n",
        "                \"bbox_mode\": BoxMode.XYXY_ABS,\n",
        "                \"segmentation\": [poly],\n",
        "                \"category_id\": classes.index(annos[x][\"region_attributes\"]['Object']),\n",
        "                \"iscrowd\": 0\n",
        "            }\n",
        "            objs.append(obj)\n",
        "\n",
        "        record[\"annotations\"] = objs\n",
        "        dataset_dicts.append(record)\n",
        "\n",
        "    return dataset_dicts\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rumPFcLrCRKp"
      },
      "source": [
        " l'ajout de 0.5 aux coordonnées x et y des points dans poly est une pratique courante pour améliorer la précision et l'alignement des annotations de régions dans certains cas, notamment lors de la détection d'objets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPV_CD5lCXjM"
      },
      "source": [
        "get_tree_dicts ouput :\n",
        "\n",
        "dataset_dicts[\n",
        "  record {\n",
        "    filename : ,image_id , heigth: , width: ,\n",
        "annotations : {bbox: [min(px), min(py),max(px),max(py)] ,bbox_mode:  the bounding box coordinates the top-left and bottom-right corners of the box within the image , segmentation : [(x+0.5,y+0.5),.....],category_id: Person , iscrowd:0}\n",
        "      }\n",
        "      ]\n",
        "\n",
        "\n",
        "\n",
        "  dataset of detectron:\n",
        "  1. datax_train :\n",
        "\n",
        "*   dataset catalog : dataset_dicts(Data_Resized/train)\n",
        "*   metadataCatalogue : thing_classes=[\"Tree\",\"Person\"]\n",
        "2. datax_test :\n",
        "\n",
        "*   dataset catalog : dataset_dicts(Data_Resized/test)\n",
        "*   metadataCatalogue : thing_classes=[\"Tree\",\"Person\"]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Enregistrer les datasets d'entraînement et de validation avec leurs annotations\n",
        "for d in [\"train\", \"val\"]:\n",
        "    DatasetCatalog.register(\"datax_\" + d, lambda d=d: get_tree_dicts(\"/content/drive/MyDrive/olive/data_object_detection_olivier/\" + d))\n",
        "    MetadataCatalog.get(\"datax_\" + d).set(thing_classes=[\"Tree\",\"Person\"])\n",
        "\n",
        "# Obtenir les métadonnées du dataset d'entraînement\n",
        "tree_metadata = MetadataCatalog.get(\"datax_train\")"
      ],
      "metadata": {
        "id": "TIeKgMzL4M5Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1H0NwRNrEvHJ"
      },
      "source": [
        "#Entraînement d'un modèle de détection d'instances avec Detectron2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from detectron2.engine import DefaultTrainer\n",
        "from detectron2.evaluation import COCOEvaluator\n",
        "import os\n",
        "from detectron2 import model_zoo\n",
        "from detectron2.config import get_cfg\n",
        "from detectron2.engine import DefaultTrainer"
      ],
      "metadata": {
        "id": "-qAtr0LnyKWI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0AxA9OPFqgOV"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "class CocoTrainer(DefaultTrainer):\n",
        "\n",
        "  @classmethod\n",
        "  def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n",
        "\n",
        "    if output_folder is None:\n",
        "        os.makedirs(\"coco_eval\", exist_ok=True)\n",
        "        output_folder = \"coco_eval\"\n",
        "\n",
        "    return COCOEvaluator(dataset_name, cfg, False, output_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvGhNH_lDltH",
        "outputId": "bab8fb92-3b7d-4049-de1e-0e4e9a08a2cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[06/19 11:07:04 d2.engine.defaults]: Model:\n",
            "GeneralizedRCNN(\n",
            "  (backbone): FPN(\n",
            "    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (top_block): LastLevelMaxPool()\n",
            "    (bottom_up): ResNet(\n",
            "      (stem): BasicStem(\n",
            "        (conv1): Conv2d(\n",
            "          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
            "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "        )\n",
            "      )\n",
            "      (res2): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res3): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res4): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (4): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (5): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (6): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (7): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (8): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (9): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (10): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (11): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (12): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (13): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (14): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (15): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (16): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (17): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (18): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (19): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (20): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (21): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (22): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res5): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            2048, 2048, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (proposal_generator): RPN(\n",
            "    (rpn_head): StandardRPNHead(\n",
            "      (conv): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (anchor_generator): DefaultAnchorGenerator(\n",
            "      (cell_anchors): BufferList()\n",
            "    )\n",
            "  )\n",
            "  (roi_heads): StandardROIHeads(\n",
            "    (box_pooler): ROIPooler(\n",
            "      (level_poolers): ModuleList(\n",
            "        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
            "        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
            "        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
            "        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
            "      )\n",
            "    )\n",
            "    (box_head): FastRCNNConvFCHead(\n",
            "      (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "      (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n",
            "      (fc_relu1): ReLU()\n",
            "      (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "      (fc_relu2): ReLU()\n",
            "    )\n",
            "    (box_predictor): FastRCNNOutputLayers(\n",
            "      (cls_score): Linear(in_features=1024, out_features=3, bias=True)\n",
            "      (bbox_pred): Linear(in_features=1024, out_features=8, bias=True)\n",
            "    )\n",
            "    (mask_pooler): ROIPooler(\n",
            "      (level_poolers): ModuleList(\n",
            "        (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
            "        (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
            "        (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
            "        (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
            "      )\n",
            "    )\n",
            "    (mask_head): MaskRCNNConvUpsampleHead(\n",
            "      (mask_fcn1): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn2): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn3): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (mask_fcn4): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n",
            "      (deconv_relu): ReLU()\n",
            "      (predictor): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "  )\n",
            ")\n",
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            "[06/19 11:07:55 d2.data.build]: Removed 0 images with no usable annotations. 66 images left.\n",
            "[06/19 11:07:55 d2.data.build]: Distribution of instances among all 2 categories:\n",
            "|  category  | #instances   |  category  | #instances   |\n",
            "|:----------:|:-------------|:----------:|:-------------|\n",
            "|    Tree    | 66           |   Person   | 73           |\n",
            "|            |              |            |              |\n",
            "|   total    | 139          |            |              |\n",
            "[06/19 11:07:55 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]\n",
            "[06/19 11:07:55 d2.data.build]: Using training sampler TrainingSampler\n",
            "[06/19 11:07:55 d2.data.common]: Serializing the dataset using: <class 'detectron2.data.common._TorchSerializedList'>\n",
            "[06/19 11:07:55 d2.data.common]: Serializing 66 elements to byte tensors and concatenating them all ...\n",
            "[06/19 11:07:55 d2.data.common]: Serialized dataset takes 0.18 MiB\n",
            "[06/19 11:07:55 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/model_final_2d9806.pkl ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "model_final_2d9806.pkl: 431MB [00:01, 256MB/s]                           \n",
            "WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.cls_score.weight' to the model due to incompatible shapes: (81, 1024) in the checkpoint but (3, 1024) in the model! You might want to double check if this is expected.\n",
            "WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.cls_score.bias' to the model due to incompatible shapes: (81,) in the checkpoint but (3,) in the model! You might want to double check if this is expected.\n",
            "WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (320, 1024) in the checkpoint but (8, 1024) in the model! You might want to double check if this is expected.\n",
            "WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (320,) in the checkpoint but (8,) in the model! You might want to double check if this is expected.\n",
            "WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.mask_head.predictor.weight' to the model due to incompatible shapes: (80, 256, 1, 1) in the checkpoint but (2, 256, 1, 1) in the model! You might want to double check if this is expected.\n",
            "WARNING:fvcore.common.checkpoint:Skip loading parameter 'roi_heads.mask_head.predictor.bias' to the model due to incompatible shapes: (80,) in the checkpoint but (2,) in the model! You might want to double check if this is expected.\n",
            "WARNING:fvcore.common.checkpoint:Some model parameters or buffers are not found in the checkpoint:\n",
            "roi_heads.box_predictor.bbox_pred.{bias, weight}\n",
            "roi_heads.box_predictor.cls_score.{bias, weight}\n",
            "roi_heads.mask_head.predictor.{bias, weight}\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "# Obtenir la configuration par défaut\n",
        "cfg = get_cfg()\n",
        "cfg.OUTPUT_DIR = \"/content/drive/MyDrive/olive/data_object_detection_olivier/\"\n",
        "# Fusionner la configuration avec un fichier de configuration pré-entraîné spécifique (mask_rcnn_X_101_32x8d_FPN_3x.yaml).\n",
        "# Cela permet de charger les paramètres et les hyperparamètres prédéfinis pour ce modèle.\n",
        "cfg.merge_from_file(model_zoo.get_config_file(\"COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml\"))\n",
        "\n",
        "# Définir le dataset d'entraînement\n",
        "cfg.DATASETS.TRAIN = (\"datax_train\",)\n",
        "\n",
        "# Définir le dataset de test (aucun dans ce cas)\n",
        "cfg.DATASETS.TEST = ()\n",
        "\n",
        "# Configurer le nombre de travailleurs de chargement des données (NUM_WORKERS),\n",
        "# qui détermine le nombre de processus utilisés pour charger les données en parallèle.\n",
        "cfg.DATALOADER.NUM_WORKERS = 2\n",
        "\n",
        "# Poids initiaux du modèle\n",
        "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml\")  # Let training initialize from model zoo\n",
        "\n",
        "# Set the number of images per batch\n",
        "cfg.SOLVER.IMS_PER_BATCH = 2\n",
        "\n",
        "# Set the base learning rate\n",
        "cfg.SOLVER.BASE_LR = 0.00025\n",
        "\n",
        "# Set the number of warm-up iterations\n",
        "cfg.SOLVER.WARMUP_ITERS = 200\n",
        "\n",
        "# Set the total number of training iterations\n",
        "cfg.SOLVER.MAX_ITER = 500\n",
        "\n",
        "# Set the learning rate schedule steps\n",
        "cfg.SOLVER.STEPS = (400,)\n",
        "\n",
        "# Set the learning rate decay\n",
        "cfg.SOLVER.GAMMA = 0.1\n",
        "\n",
        "# Set the batch size per image\n",
        "cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128\n",
        "\n",
        "# Set the number of classes (e.g., 2: Tree and Person)\n",
        "cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2\n",
        "\n",
        "# Use the CPU for training\n",
        "cfg.MODEL.DEVICE = 'cuda'\n",
        "\n",
        "# Create the output directory if it doesn't exist\n",
        "os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\n",
        "\n",
        "# Create the DefaultTrainer object with the configuration\n",
        "trainer = DefaultTrainer(cfg)\n",
        "\n",
        "# Load pre-trained weights or resume training from a checkpoint\n",
        "trainer.resume_or_load(resume=False)\n",
        "\n",
        "# Start training the model\n",
        "#trainer.train()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyISCDDnEqVd",
        "outputId": "7ceab25f-bedd-46b5-940a-4155917f5fdb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[06/19 11:07:59 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from /content/drive/MyDrive/olive/data_object_detection_olivier/model_final.pth ...\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Chemin d'accès au fichier de poids du modèle pré-entrainé\n",
        "cfg.MODEL.WEIGHTS = \"/content/drive/MyDrive/olive/data_object_detection_olivier/model_final.pth\"\n",
        "\n",
        "# Seuil de score pour les prédictions lors du test\n",
        "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.95\n",
        "\n",
        "# Définir le dataset de test (dans ce cas, le dataset de validation)\n",
        "cfg.DATASETS.TEST = (\"datax_val\", )\n",
        "\n",
        "# Créer un prédicteur par défaut en utilisant la configuration\n",
        "predictor = DefaultPredictor(cfg)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qvCZCJzHJ9Z"
      },
      "source": [
        "#Visualisation des prédictions d'instances sur le dataset de validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "YzdCmzv4HK_r",
        "outputId": "d6d0efe5-3cb9-4c77-f4e6-b33c8a82d333"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/ImageFile.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[1;32m    503\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m         \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileno\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    505\u001b[0m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: '_idat' object has no attribute 'fileno'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-b92466452bb5>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2RGB\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    444\u001b[0m     \"\"\"\n\u001b[1;32m    445\u001b[0m     \u001b[0m_warn_if_gui_out_of_main_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_get_backend_mod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m             display(\n\u001b[0m\u001b[1;32m     91\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-2>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2364\u001b[0m                 \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2366\u001b[0;31m                     result = print_method(\n\u001b[0m\u001b[1;32m   2367\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2230\u001b[0m                 \"bbox_inches_restore\"}\n\u001b[1;32m   2231\u001b[0m             \u001b[0mskip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptional_kws\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m             print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(\n\u001b[0m\u001b[1;32m   2233\u001b[0m                 *args, **{k: v for k, v in kwargs.items() if k not in skip}))\n\u001b[1;32m   2234\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Let third-parties do as they see fit.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_to_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m_print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    456\u001b[0m         \"\"\"\n\u001b[1;32m    457\u001b[0m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 458\u001b[0;31m         mpl.image.imsave(\n\u001b[0m\u001b[1;32m    459\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m             dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m   1687\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"format\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1688\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dpi\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1689\u001b[0;31m         \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpil_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2240\u001b[0;31m             \u001b[0msave_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2241\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2242\u001b[0m             \u001b[0;31m# do what we can to clean up\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/PngImagePlugin.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(im, fp, filename, chunk, save_all)\u001b[0m\n\u001b[1;32m   1346\u001b[0m         \u001b[0m_write_multiple_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m         \u001b[0mImageFile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_idat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zip\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/ImageFile.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[1;32m    516\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    517\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 518\u001b[0;31m                     \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbufsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    519\u001b[0m                     \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "\n",
        "# Obtenir les dictionnaires de données du dataset de validation\n",
        "dataset_dicts = get_tree_dicts(\"/content/drive/MyDrive/olive/data_object_detection_olivier/val\")\n",
        "\n",
        "# Sélectionner aléatoirement 2 exemples d'images du dataset de validation\n",
        "for d in random.sample(dataset_dicts, 2):\n",
        "    # Charger l'image\n",
        "    im = cv2.imread(d[\"file_name\"])\n",
        "\n",
        "    # Effectuer des prédictions sur l'image en utilisant le prédicteur\n",
        "    outputs = predictor(im)\n",
        "\n",
        "    # Créer un visualiseur avec l'image et les métadonnées\n",
        "    v = Visualizer(im[:, :, ::-1], metadata=tree_metadata, scale=1, instance_mode=ColorMode.IMAGE_BW)\n",
        "\n",
        "    # Dessiner les prédictions d'instances sur l'image\n",
        "    v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
        "\n",
        "    # Afficher l'image avec les prédictions\n",
        "    plt.figure(figsize=(15, 18))\n",
        "    plt.imshow(cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "    # Get the segmented masks\n",
        "    masks = outputs[\"instances\"].pred_masks\n",
        "\n",
        "    # Convert masks to numpy array\n",
        "    masks_np = masks.cpu().numpy()\n",
        "\n",
        "    # Create a blank image to store the segmented part\n",
        "    segmented_image = np.zeros_like(im)\n",
        "\n",
        "    # Apply the masks to the segmented image\n",
        "    for mask in masks_np:\n",
        "      segmented_image[mask] = im[mask]\n",
        "\n",
        "    # Display the segmented part\n",
        "    plt.imshow(segmented_image)\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "import os\n",
        "import cv2\n",
        "# Obtenir les dictionnaires de données du dataset de validation\n",
        "directory='/content/drive/MyDrive/olive/data/'\n",
        "save_dir='/content/drive/MyDrive/olive/boxe/'\n",
        "# Sélectionner aléatoirement 2 exemples d'images du dataset de validation\n",
        "for filename in os.listdir(directory):\n",
        "    # Full path of the image\n",
        "    filepath = os.path.join(directory, filename)\n",
        "    # Load the image\n",
        "    im = cv2.imread(filepath)\n",
        "    # Effectuer des prédictions sur l'image en utilisant le prédicteur\n",
        "    outputs = predictor(im)\n",
        "\n",
        "    # Créer un visualiseur avec l'image et les métadonnées\n",
        "    v = Visualizer(im[:, :, ::-1], metadata=tree_metadata, scale=1, instance_mode=ColorMode.IMAGE_BW)\n",
        "\n",
        "    # Dessiner les prédictions d'instances sur l'image\n",
        "    v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
        "\n",
        "    save_path = os.path.join(save_dir, filename)\n",
        "    cv2.imwrite(save_path, cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n",
        "\n",
        "\n",
        "    # Afficher l'image avec les prédictions\n",
        "\n",
        "    plt.figure(figsize=(15, 18))\n",
        "    plt.imshow(cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "oY7Y0zrSoX1p",
        "outputId": "f68b4852-2fac-49d0-cd49-03548a2e1da1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nimport os\\nimport cv2\\n# Obtenir les dictionnaires de données du dataset de validation\\ndirectory=\\'/content/drive/MyDrive/olive/data/\\'\\nsave_dir=\\'/content/drive/MyDrive/olive/boxe/\\'\\n# Sélectionner aléatoirement 2 exemples d\\'images du dataset de validation\\nfor filename in os.listdir(directory):\\n    # Full path of the image\\n    filepath = os.path.join(directory, filename)\\n    # Load the image\\n    im = cv2.imread(filepath)\\n    # Effectuer des prédictions sur l\\'image en utilisant le prédicteur\\n    outputs = predictor(im)\\n\\n    # Créer un visualiseur avec l\\'image et les métadonnées\\n    v = Visualizer(im[:, :, ::-1], metadata=tree_metadata, scale=1, instance_mode=ColorMode.IMAGE_BW)\\n\\n    # Dessiner les prédictions d\\'instances sur l\\'image\\n    v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\\n\\n    save_path = os.path.join(save_dir, filename)\\n    cv2.imwrite(save_path, cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\\n\\n\\n    # Afficher l\\'image avec les prédictions\\n\\n    plt.figure(figsize=(15, 18))\\n    plt.imshow(cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\\n    plt.show()\\n\\n\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2PECGrsDTm4e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eadc005f-9af9-40a0-c1c7-38bc5ceb3e20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "import os\n",
        "import cv2\n",
        "# Obtenir les dictionnaires de données du dataset de validation\n",
        "directory='/content/drive/MyDrive/olive/augmented_data/'\n",
        "save_dir='/content/drive/MyDrive/olive/bounding_box/'\n",
        "# Sélectionner aléatoirement 2 exemples d'images du dataset de validation\n",
        "for filename in os.listdir(directory):\n",
        "    # Full path of the image\n",
        "    filepath = os.path.join(directory, filename)\n",
        "    # Load the image\n",
        "    im = cv2.imread(filepath)\n",
        "    # Effectuer des prédictions sur l'image en utilisant le prédicteur\n",
        "    outputs = predictor(im)\n",
        "\n",
        "    # Créer un visualiseur avec l'image et les métadonnées\n",
        "    v = Visualizer(im[:, :, ::-1], metadata=tree_metadata, scale=1, instance_mode=ColorMode.IMAGE_BW)\n",
        "\n",
        "    # Dessiner les prédictions d'instances sur l'image\n",
        "    v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
        "\n",
        "    save_path = os.path.join(save_dir, filename)\n",
        "    cv2.imwrite(save_path, cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n",
        "\n",
        "\n",
        "    # Afficher l'image avec les prédictions\n",
        "    plt.figure(figsize=(15, 18))\n",
        "    plt.imshow(cv2.cvtColor(v.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLpqzXSzON0z"
      },
      "source": [
        "#Dataset for resnet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sgrgv6JBOUGT"
      },
      "source": [
        "Tree(h1,w1)\n",
        "Person(h2,w2)\n",
        "\n",
        "Tree_height = (h1/h2)*1.7\n",
        "Tree_width= (Tree_height/h1)*w1\n",
        "\n",
        "\n",
        "\n",
        "[\n",
        "   {\n",
        "      \"filename\": \"/content/output/bounding_box/bbox_0.jpg\",\n",
        "      \"Tree_height\": 3.8214771364184092,\n",
        "      \"Tree_Width\": 4.996759928962184\n",
        "   },\n",
        "   {\n",
        "      \"filename\": \"/content/output/bounding_box/bbox_1.jpg\",\n",
        "      \"Tree_height\": 4.289722472181375,\n",
        "      \"Tree_Width\": 5.056172223075741\n",
        "   }\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekUduBefE2hL"
      },
      "source": [
        "#Annotation pour ResNet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eu90IKT57ubj"
      },
      "outputs": [],
      "source": [
        "\n",
        "from PIL import Image\n",
        "import glob\n",
        "cuda_device = torch.device(\"cuda:0\" if torch.cuda.is_available else \"cpu\")\n",
        "\n",
        "def bound_box(data_path):\n",
        "    j = 0\n",
        "\n",
        "\n",
        "    # Parcourir toutes les images dans le répertoire spécifié\n",
        "    liste=[]\n",
        "    for x in glob.glob(data_path + \"/*.jpg\"):\n",
        "        print(x)\n",
        "        im = resize(60, x)\n",
        "        # Effectuer des prédictions sur l'image en utilisant le prédicteur\n",
        "        outputs = predictor(im)\n",
        "        print('outputs=',outputs)\n",
        "        y1=0\n",
        "        y2=0\n",
        "        x1=0\n",
        "        x2=0\n",
        "        y21=0\n",
        "        y22=0\n",
        "        print(\"******************************\",len(outputs[\"instances\"].pred_classes))\n",
        "        # Parcourir toutes les instances prédites\n",
        "        for i in range(len(outputs[\"instances\"].pred_classes)):\n",
        "\n",
        "            if outputs[\"instances\"].pred_classes[i] == 0:\n",
        "                y1 = int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][1])\n",
        "                y2 = int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][3])\n",
        "                x1 = int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][0])\n",
        "                x2 = int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][2])\n",
        "                print(\"y1\",y1)\n",
        "                print(\"y2\",y2)\n",
        "                h1=y2-y1\n",
        "                w1=x2-x1\n",
        "            if outputs[\"instances\"].pred_classes[i] == 1:\n",
        "                if ((int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][3])-int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][1]))>y22-y21):\n",
        "                  y22=int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][3])\n",
        "                  y21=int(outputs[\"instances\"].pred_boxes[i].tensor.cpu().numpy()[0][1])\n",
        "                  h2=y22-y21\n",
        "                  print(\"y21\",y21)\n",
        "                  print(\"y22\",y22)\n",
        "        print(\"h1\", h1 )\n",
        "        print(\"w1\", w1 )\n",
        "        print(\"h2\", h2 )\n",
        "        if(h1!=0):\n",
        "          Tree_height=(h1/h2)*1.7\n",
        "          Tree_Width=Tree_height/h1*w1\n",
        "        else:\n",
        "          Tree_height=0\n",
        "          Tree_Width=0\n",
        "        liste.append({\"filename\":data_path + x,\"Tree_height\":Tree_height ,\"Tree_Width\":Tree_Width})\n",
        "\n",
        "    with open('/content/drive/MyDrive/olive/Tree_height.txt', 'w') as f:\n",
        "                        f.write(json.dumps(liste))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yd7XPKa35gF1",
        "outputId": "4db18bef-8e95-442e-a7e1-3233c4c253d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mLe flux de sortie a été tronqué et ne contient que les 5000 dernières lignes.\u001b[0m\n",
            "        [ 268.1675,   35.7920, 1701.7531, 1261.6241]], device='cuda:0')), scores: tensor([0.9908, 0.9842], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 896\n",
            "y22 1379\n",
            "y1 35\n",
            "y2 1261\n",
            "h1 1226\n",
            "w1 1433\n",
            "h2 483\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im4_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[488.7245, 587.1692, 612.3060, 948.5039],\n",
            "        [144.6703, 160.4066, 917.5867, 855.8655]], device='cuda:0')), scores: tensor([0.9951, 0.9873], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 587\n",
            "y22 948\n",
            "y1 160\n",
            "y2 855\n",
            "h1 695\n",
            "w1 773\n",
            "h2 361\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_092104_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[1898.3138,  979.0450, 2102.0005, 1566.7983],\n",
            "        [ 124.3209, 1015.6772,  498.8844, 1848.7075],\n",
            "        [  41.6287,   31.7820, 2070.9177, 1420.3416]], device='cuda:0')), scores: tensor([0.9941, 0.9887, 0.9836], device='cuda:0'), pred_classes: tensor([1, 1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y21 979\n",
            "y22 1566\n",
            "y21 1015\n",
            "y22 1848\n",
            "y1 31\n",
            "y2 1420\n",
            "h1 1389\n",
            "w1 2029\n",
            "h2 833\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_083350_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[1505.7433,  896.5679, 1722.8878, 1580.1847],\n",
            "        [ 483.4688,   40.9445, 1980.5479, 1307.3733]], device='cuda:0')), scores: tensor([0.9927, 0.9863], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 896\n",
            "y22 1580\n",
            "y1 40\n",
            "y2 1307\n",
            "h1 1267\n",
            "w1 1497\n",
            "h2 684\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_142748_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 442.8009,  160.1831, 1500.8369, 1100.2274],\n",
            "        [1368.6526,  753.8657, 1560.1217, 1320.6428]], device='cuda:0')), scores: tensor([0.9906, 0.9895], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 160\n",
            "y2 1100\n",
            "y21 753\n",
            "y22 1320\n",
            "h1 940\n",
            "w1 1058\n",
            "h2 567\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_100624_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[1077.3749,  897.9883, 1252.7037, 1360.8320],\n",
            "        [ 317.5559,  128.4828, 1737.0781, 1206.0653]], device='cuda:0')), scores: tensor([0.9931, 0.9871], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 897\n",
            "y22 1360\n",
            "y1 128\n",
            "y2 1206\n",
            "h1 1078\n",
            "w1 1420\n",
            "h2 463\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im14_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[2165.5710, 1222.5498, 3686.0000, 3262.0522],\n",
            "        [ 443.3138,  515.7393, 1670.9277, 4874.0547]], device='cuda:0')), scores: tensor([0.9898, 0.9821], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 1222\n",
            "y2 3262\n",
            "y21 515\n",
            "y22 4874\n",
            "h1 2040\n",
            "w1 1521\n",
            "h2 4359\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im13_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[2419.9224,  439.8888, 3686.0000, 4655.9087],\n",
            "        [ 194.3884,  491.2608, 2486.5042, 3270.9978]], device='cuda:0')), scores: tensor([0.9845, 0.9820], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 439\n",
            "y22 4655\n",
            "y1 491\n",
            "y2 3270\n",
            "h1 2779\n",
            "w1 2292\n",
            "h2 4216\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_133501_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 856.8768,  222.6442, 1945.9230, 1073.2784],\n",
            "        [ 857.0478,  764.9920, 1109.1520, 1439.7762],\n",
            "        [   0.0000,  827.3683,  109.5655, 1302.4901]], device='cuda:0')), scores: tensor([0.9936, 0.9929, 0.9761], device='cuda:0'), pred_classes: tensor([0, 1, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y1 222\n",
            "y2 1073\n",
            "y21 764\n",
            "y22 1439\n",
            "h1 851\n",
            "w1 1089\n",
            "h2 675\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/O_124141_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 254.3797, 2013.3588,  651.0151, 3608.3870],\n",
            "        [ 375.7320,   45.0041, 3686.0000, 3325.5344]], device='cuda:0')), scores: tensor([0.9924, 0.9814], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 2013\n",
            "y22 3608\n",
            "y1 45\n",
            "y2 3325\n",
            "h1 3280\n",
            "w1 3311\n",
            "h2 1595\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_100624_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[1057.8058,  920.4710, 1217.0471, 1344.4121],\n",
            "        [ 393.2291,  197.7948, 1670.0529, 1211.3589]], device='cuda:0')), scores: tensor([0.9931, 0.9911], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 920\n",
            "y22 1344\n",
            "y1 197\n",
            "y2 1211\n",
            "h1 1014\n",
            "w1 1277\n",
            "h2 424\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_135633.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 877.3344,  247.9878, 1777.6213, 1083.0851],\n",
            "        [ 908.9628,  734.3819, 1100.2899, 1241.0885]], device='cuda:0')), scores: tensor([0.9905, 0.9896], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 247\n",
            "y2 1083\n",
            "y21 734\n",
            "y22 1241\n",
            "h1 836\n",
            "w1 900\n",
            "h2 507\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/O_125721_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 689.8218, 2401.7759, 1328.6555, 4309.3672],\n",
            "        [ 314.5266,   62.6673, 3686.0000, 3852.3657]], device='cuda:0')), scores: tensor([0.9923, 0.9837], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 2401\n",
            "y22 4309\n",
            "y1 62\n",
            "y2 3852\n",
            "h1 3790\n",
            "w1 3372\n",
            "h2 1908\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/O_131625_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 860.3862, 2401.5728, 1366.8424, 3988.8213],\n",
            "        [ 384.1928,  591.9849, 3583.7871, 3466.7773]], device='cuda:0')), scores: tensor([0.9952, 0.9842], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 2401\n",
            "y22 3988\n",
            "y1 591\n",
            "y2 3466\n",
            "h1 2875\n",
            "w1 3199\n",
            "h2 1587\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im13_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[2405.0503,  596.7304, 3686.0000, 4538.8208],\n",
            "        [ 272.5215,  578.0313, 2489.4519, 3239.5808]], device='cuda:0')), scores: tensor([0.9825, 0.9653], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 596\n",
            "y22 4538\n",
            "y1 578\n",
            "y2 3239\n",
            "h1 2661\n",
            "w1 2217\n",
            "h2 3942\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_111447_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 716.6600,  469.9269, 1596.0527, 1166.5615],\n",
            "        [1405.1637,  861.6280, 1580.2061, 1319.7501]], device='cuda:0')), scores: tensor([0.9937, 0.9913], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 469\n",
            "y2 1166\n",
            "y21 861\n",
            "y22 1319\n",
            "h1 697\n",
            "w1 880\n",
            "h2 458\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md8_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[ 93.5673, 304.1403, 546.8141, 785.0797],\n",
            "        [539.1691, 411.9001, 708.0623, 878.5975]], device='cuda:0')), scores: tensor([0.9944, 0.9900], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 304\n",
            "y2 785\n",
            "y21 411\n",
            "y22 878\n",
            "h1 481\n",
            "w1 453\n",
            "h2 467\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im9.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 285.9876,  638.4982,  498.4313, 1124.3606],\n",
            "        [ 191.2420,   29.2940, 1074.1963,  976.5984],\n",
            "        [ 466.9893,  604.8282,  631.0120, 1105.1665]], device='cuda:0')), scores: tensor([0.9901, 0.9842, 0.9834], device='cuda:0'), pred_classes: tensor([1, 0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y21 638\n",
            "y22 1124\n",
            "y1 29\n",
            "y2 976\n",
            "y21 604\n",
            "y22 1105\n",
            "h1 947\n",
            "w1 883\n",
            "h2 501\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md7_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[217.8370, 446.0037, 371.5373, 884.6890],\n",
            "        [345.6151, 344.0966, 826.6234, 742.4906]], device='cuda:0')), scores: tensor([0.9927, 0.9859], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 446\n",
            "y22 884\n",
            "y1 344\n",
            "y2 742\n",
            "h1 398\n",
            "w1 481\n",
            "h2 438\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 496.1497,  621.2103,  663.9854, 1177.8615],\n",
            "        [  77.8363,   63.5909, 1068.4249,  907.0414]], device='cuda:0')), scores: tensor([0.9911, 0.9882], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 621\n",
            "y22 1177\n",
            "y1 63\n",
            "y2 907\n",
            "h1 844\n",
            "w1 991\n",
            "h2 556\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im5_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 605.0305,  737.0167,  715.1744, 1129.0452],\n",
            "        [  48.0036,  133.9996,  989.2184,  966.8534]], device='cuda:0')), scores: tensor([0.9938, 0.9811], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 737\n",
            "y22 1129\n",
            "y1 133\n",
            "y2 966\n",
            "h1 833\n",
            "w1 941\n",
            "h2 392\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[652.3733, 262.6552, 839.1571, 804.3970],\n",
            "        [ 29.3701,   0.0000, 711.4174, 642.3460]], device='cuda:0')), scores: tensor([0.9842, 0.9783], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 262\n",
            "y22 804\n",
            "y1 0\n",
            "y2 642\n",
            "h1 642\n",
            "w1 682\n",
            "h2 542\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_083105_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[1575.0815,  935.8484, 1765.2155, 1549.5219],\n",
            "        [  32.8340,    8.7769, 1711.3029, 1375.6901]], device='cuda:0')), scores: tensor([0.9951, 0.9868], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 935\n",
            "y22 1549\n",
            "y1 8\n",
            "y2 1375\n",
            "h1 1367\n",
            "w1 1679\n",
            "h2 614\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md3_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[380.7311, 220.7319, 914.9271, 662.7858],\n",
            "        [269.8059, 428.5133, 402.3663, 774.2136]], device='cuda:0')), scores: tensor([0.9940, 0.9935], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 220\n",
            "y2 662\n",
            "y21 428\n",
            "y22 774\n",
            "h1 442\n",
            "w1 534\n",
            "h2 346\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_080515_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2496, image_width=1872, fields=[pred_boxes: Boxes(tensor([[ 720.2070, 1153.6857,  947.2125, 1946.0773],\n",
            "        [  42.2642,  238.8628, 1715.1713, 1586.0848]], device='cuda:0')), scores: tensor([0.9960, 0.9902], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1153\n",
            "y22 1946\n",
            "y1 238\n",
            "y2 1586\n",
            "h1 1348\n",
            "w1 1673\n",
            "h2 793\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im8_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 414.0426,  622.8549,  584.3376, 1173.7587],\n",
            "        [  12.1936,   63.2995, 1000.5837,  910.4240]], device='cuda:0')), scores: tensor([0.9928, 0.9884], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 622\n",
            "y22 1173\n",
            "y1 63\n",
            "y2 910\n",
            "h1 847\n",
            "w1 988\n",
            "h2 551\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im9_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 284.1922,  641.9954,  501.2093, 1126.6704],\n",
            "        [ 465.1386,  609.2897,  631.8846, 1110.0306],\n",
            "        [ 176.5682,   31.7536, 1080.0000,  966.2188]], device='cuda:0')), scores: tensor([0.9909, 0.9845, 0.9831], device='cuda:0'), pred_classes: tensor([1, 1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y21 641\n",
            "y22 1126\n",
            "y21 609\n",
            "y22 1110\n",
            "y1 31\n",
            "y2 966\n",
            "h1 935\n",
            "w1 904\n",
            "h2 501\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im10_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 791.1833,  732.6761,  994.0001, 1201.1187],\n",
            "        [  52.1530,  128.5678,  956.2326, 1052.9064]], device='cuda:0')), scores: tensor([0.9921, 0.9837], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 732\n",
            "y22 1201\n",
            "y1 128\n",
            "y2 1052\n",
            "h1 924\n",
            "w1 904\n",
            "h2 469\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_101502_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 326.0527,  186.8018, 1578.9614, 1136.1049],\n",
            "        [ 822.1412,  888.0750,  983.6669, 1302.3755]], device='cuda:0')), scores: tensor([0.9940, 0.9909], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 186\n",
            "y2 1136\n",
            "y21 888\n",
            "y22 1302\n",
            "h1 950\n",
            "w1 1252\n",
            "h2 414\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_093428_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 898.2822,  895.1616, 1055.8016, 1355.0409],\n",
            "        [ 335.1704,  142.3112, 1665.8691, 1246.7787]], device='cuda:0')), scores: tensor([0.9926, 0.9886], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 895\n",
            "y22 1355\n",
            "y1 142\n",
            "y2 1246\n",
            "h1 1104\n",
            "w1 1330\n",
            "h2 460\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 950.7385,  762.9495, 1144.8762, 1204.9186],\n",
            "        [1032.4177,  412.7401, 1688.0709, 1019.6142]], device='cuda:0')), scores: tensor([0.9895, 0.9831], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 762\n",
            "y22 1204\n",
            "y1 412\n",
            "y2 1019\n",
            "h1 607\n",
            "w1 656\n",
            "h2 442\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_084643_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 729.2939, 1062.7618,  882.5038, 1565.0118],\n",
            "        [ 356.1361,  277.2646, 1794.1559, 1418.1248]], device='cuda:0')), scores: tensor([0.9879, 0.9874], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1062\n",
            "y22 1565\n",
            "y1 277\n",
            "y2 1418\n",
            "h1 1141\n",
            "w1 1438\n",
            "h2 503\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_091003_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 369.4426,  926.0035,  676.2288, 1734.9122],\n",
            "        [ 381.4742,    7.7230, 1988.6375, 1424.5338]], device='cuda:0')), scores: tensor([0.9935, 0.9833], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 926\n",
            "y22 1734\n",
            "y1 7\n",
            "y2 1424\n",
            "h1 1417\n",
            "w1 1607\n",
            "h2 808\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_080515.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2496, image_width=1872, fields=[pred_boxes: Boxes(tensor([[ 930.8724, 1152.9337, 1159.1177, 1956.8290],\n",
            "        [ 138.9688,  243.5829, 1829.3866, 1568.7057]], device='cuda:0')), scores: tensor([0.9947, 0.9877], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1152\n",
            "y22 1956\n",
            "y1 243\n",
            "y2 1568\n",
            "h1 1325\n",
            "w1 1691\n",
            "h2 804\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im1_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[185.6329, 231.3945, 883.6418, 745.7301],\n",
            "        [438.5017, 585.5431, 504.5309, 793.9841],\n",
            "        [513.1782, 608.7247, 604.0271, 798.2696]], device='cuda:0')), scores: tensor([0.9950, 0.9854, 0.9757], device='cuda:0'), pred_classes: tensor([0, 1, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y1 231\n",
            "y2 745\n",
            "y21 585\n",
            "y22 793\n",
            "h1 514\n",
            "w1 698\n",
            "h2 208\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im14_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 655.6483,  789.4463, 1686.5936, 4506.4502],\n",
            "        [2122.4155, 1354.9845, 3518.6289, 3141.9573]], device='cuda:0')), scores: tensor([0.9879, 0.9869], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 789\n",
            "y22 4506\n",
            "y1 1354\n",
            "y2 3141\n",
            "h1 1787\n",
            "w1 1396\n",
            "h2 3717\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_080733_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2496, image_width=1872, fields=[pred_boxes: Boxes(tensor([[1125.3625, 1119.6011, 1373.8187, 1932.5905],\n",
            "        [  13.6420,  176.9988, 1610.6517, 1562.6051]], device='cuda:0')), scores: tensor([0.9945, 0.9873], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1119\n",
            "y22 1932\n",
            "y1 176\n",
            "y2 1562\n",
            "h1 1386\n",
            "w1 1597\n",
            "h2 813\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_084643_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 753.0352, 1086.2498,  888.7351, 1506.7153],\n",
            "        [ 429.1214,  406.6246, 1685.6141, 1366.7360]], device='cuda:0')), scores: tensor([0.9919, 0.9914], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1086\n",
            "y22 1506\n",
            "y1 406\n",
            "y2 1366\n",
            "h1 960\n",
            "w1 1256\n",
            "h2 420\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md3_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[396.2391, 222.4697, 911.1082, 660.3240],\n",
            "        [271.2764, 421.4633, 402.1826, 775.9927]], device='cuda:0')), scores: tensor([0.9945, 0.9926], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 222\n",
            "y2 660\n",
            "y21 421\n",
            "y22 775\n",
            "h1 438\n",
            "w1 515\n",
            "h2 354\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_101502_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 811.9526,  851.4496,  979.2946, 1309.4921],\n",
            "        [ 266.2398,   68.2883, 1654.8792, 1168.7476]], device='cuda:0')), scores: tensor([0.9938, 0.9895], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 851\n",
            "y22 1309\n",
            "y1 68\n",
            "y2 1168\n",
            "h1 1100\n",
            "w1 1388\n",
            "h2 458\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im5_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 602.8937,  735.0357,  710.4555, 1111.9052],\n",
            "        [  71.5997,  171.3516,  980.2236,  960.8571]], device='cuda:0')), scores: tensor([0.9943, 0.9883], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 735\n",
            "y22 1111\n",
            "y1 171\n",
            "y2 960\n",
            "h1 789\n",
            "w1 909\n",
            "h2 376\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/O_130123_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[2778.3154, 1904.2931, 3210.5100, 3251.6797],\n",
            "        [ 702.1515,  164.1733, 3233.1343, 2958.0627]], device='cuda:0')), scores: tensor([0.9956, 0.9798], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1904\n",
            "y22 3251\n",
            "y1 164\n",
            "y2 2958\n",
            "h1 2794\n",
            "w1 2531\n",
            "h2 1347\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_102305_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 403.6449,  312.0746, 1668.8640, 1171.0687],\n",
            "        [ 798.6675,  916.1041,  939.6219, 1295.5813]], device='cuda:0')), scores: tensor([0.9946, 0.9913], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 312\n",
            "y2 1171\n",
            "y21 916\n",
            "y22 1295\n",
            "h1 859\n",
            "w1 1265\n",
            "h2 379\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_100624.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[1075.2578,  890.4287, 1247.2223, 1356.5614],\n",
            "        [ 335.3561,  121.5573, 1744.3467, 1207.1787]], device='cuda:0')), scores: tensor([0.9929, 0.9850], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 890\n",
            "y22 1356\n",
            "y1 121\n",
            "y2 1207\n",
            "h1 1086\n",
            "w1 1409\n",
            "h2 466\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_102305_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 780.8012,  851.8394,  939.8633, 1309.2886],\n",
            "        [ 333.7930,  176.4653, 1802.3225, 1180.6625]], device='cuda:0')), scores: tensor([0.9915, 0.9880], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 851\n",
            "y22 1309\n",
            "y1 176\n",
            "y2 1180\n",
            "h1 1004\n",
            "w1 1469\n",
            "h2 458\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_132145.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 981.0193,  848.3577, 1168.8160, 1429.6442],\n",
            "        [ 993.3391,  364.5842, 1916.5461, 1198.8173]], device='cuda:0')), scores: tensor([0.9942, 0.9921], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 848\n",
            "y22 1429\n",
            "y1 364\n",
            "y2 1198\n",
            "h1 834\n",
            "w1 923\n",
            "h2 581\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_133501_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 569.2334,  257.4385, 1594.6304, 1094.6559],\n",
            "        [1381.3522,  776.7784, 1620.7303, 1410.7355],\n",
            "        [2331.3853,  836.3910, 2496.0000, 1393.7666]], device='cuda:0')), scores: tensor([0.9922, 0.9905, 0.9749], device='cuda:0'), pred_classes: tensor([0, 1, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y1 257\n",
            "y2 1094\n",
            "y21 776\n",
            "y22 1410\n",
            "h1 837\n",
            "w1 1025\n",
            "h2 634\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[392.6462, 221.4241, 910.1149, 662.0352],\n",
            "        [272.2239, 421.1204, 401.8688, 775.7083]], device='cuda:0')), scores: tensor([0.9944, 0.9927], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 221\n",
            "y2 662\n",
            "y21 421\n",
            "y22 775\n",
            "h1 441\n",
            "w1 518\n",
            "h2 354\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/O_124141_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[2982.2312, 2074.0269, 3363.4136, 3533.6047],\n",
            "        [   0.0000,  155.1652, 3220.4614, 3279.7957]], device='cuda:0')), scores: tensor([0.9916, 0.9740], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 2074\n",
            "y22 3533\n",
            "y1 155\n",
            "y2 3279\n",
            "h1 3124\n",
            "w1 3220\n",
            "h2 1459\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 934.4641,  751.5609, 1130.7823, 1216.4866],\n",
            "        [ 997.0060,  382.9662, 1693.7449, 1021.4957]], device='cuda:0')), scores: tensor([0.9919, 0.9821], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 751\n",
            "y22 1216\n",
            "y1 382\n",
            "y2 1021\n",
            "h1 639\n",
            "w1 696\n",
            "h2 465\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_132145_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[1323.5443,  847.9750, 1514.0906, 1427.5802],\n",
            "        [ 575.3630,  360.7151, 1490.7185, 1205.9189]], device='cuda:0')), scores: tensor([0.9923, 0.9914], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 847\n",
            "y22 1427\n",
            "y1 360\n",
            "y2 1205\n",
            "h1 845\n",
            "w1 915\n",
            "h2 580\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[375.0787, 293.6967, 785.7928, 592.2413],\n",
            "        [279.4391, 474.0028, 371.3327, 734.7869]], device='cuda:0')), scores: tensor([0.9922, 0.9908], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 293\n",
            "y2 592\n",
            "y21 474\n",
            "y22 734\n",
            "h1 299\n",
            "w1 410\n",
            "h2 260\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_094943.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 855.3542,  938.9186, 1025.0067, 1382.8384],\n",
            "        [ 432.1576,   59.9751, 1761.0385, 1302.8043]], device='cuda:0')), scores: tensor([0.9905, 0.9821], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 938\n",
            "y22 1382\n",
            "y1 59\n",
            "y2 1302\n",
            "h1 1243\n",
            "w1 1329\n",
            "h2 444\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.9511, 435.1250, 413.4091, 805.9351],\n",
            "        [366.1094, 334.6053, 889.1938, 656.7972]], device='cuda:0')), scores: tensor([0.9930, 0.9913], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 435\n",
            "y22 805\n",
            "y1 334\n",
            "y2 656\n",
            "h1 322\n",
            "w1 523\n",
            "h2 370\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_075921_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2496, image_width=1872, fields=[pred_boxes: Boxes(tensor([[ 410.3793, 1388.1516,  638.9875, 2224.7852],\n",
            "        [   0.0000,  377.5515, 1479.9497, 1784.3877]], device='cuda:0')), scores: tensor([0.9951, 0.9761], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1388\n",
            "y22 2224\n",
            "y1 377\n",
            "y2 1784\n",
            "h1 1407\n",
            "w1 1479\n",
            "h2 836\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im7_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 265.2950,  656.7894,  529.6939, 1273.7502],\n",
            "        [ 157.9761,   82.8926,  954.8969,  974.9551]], device='cuda:0')), scores: tensor([0.9892, 0.9799], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 656\n",
            "y22 1273\n",
            "y1 82\n",
            "y2 974\n",
            "h1 892\n",
            "w1 797\n",
            "h2 617\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im11.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1497, image_width=1123, fields=[pred_boxes: Boxes(tensor([[ 559.2068,  692.0281,  694.9965, 1173.7524],\n",
            "        [  79.2320,  148.1243, 1092.8865,  952.5930]], device='cuda:0')), scores: tensor([0.9943, 0.9887], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 692\n",
            "y22 1173\n",
            "y1 148\n",
            "y2 952\n",
            "h1 804\n",
            "w1 1013\n",
            "h2 481\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md8_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[367.4521, 311.8210, 815.8372, 784.4816],\n",
            "        [216.9359, 411.0939, 382.9245, 879.2346]], device='cuda:0')), scores: tensor([0.9945, 0.9896], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 311\n",
            "y2 784\n",
            "y21 411\n",
            "y22 879\n",
            "h1 473\n",
            "w1 448\n",
            "h2 468\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_121059_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[1470.2152,  802.6939, 1644.4791, 1326.4796],\n",
            "        [ 797.6509,  526.5993, 1474.6069, 1187.3773]], device='cuda:0')), scores: tensor([0.9876, 0.9876], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 802\n",
            "y22 1326\n",
            "y1 526\n",
            "y2 1187\n",
            "h1 661\n",
            "w1 677\n",
            "h2 524\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_101502_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 806.2997,  830.9780,  979.4879, 1317.8411],\n",
            "        [ 231.2495,   17.5701, 1692.3527, 1146.5641]], device='cuda:0')), scores: tensor([0.9938, 0.9841], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 830\n",
            "y22 1317\n",
            "y1 17\n",
            "y2 1146\n",
            "h1 1129\n",
            "w1 1461\n",
            "h2 487\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_123400_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[1373.9852,  810.7361, 1562.5157, 1317.6431],\n",
            "        [ 756.7172,  511.6787, 1430.9630, 1163.6738]], device='cuda:0')), scores: tensor([0.9922, 0.9793], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 810\n",
            "y22 1317\n",
            "y1 511\n",
            "y2 1163\n",
            "h1 652\n",
            "w1 674\n",
            "h2 507\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im5_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 369.7768,  735.4078,  477.8889, 1112.1105],\n",
            "        [ 107.7840,  170.3487, 1011.5115,  940.5807]], device='cuda:0')), scores: tensor([0.9937, 0.9843], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 735\n",
            "y22 1112\n",
            "y1 170\n",
            "y2 940\n",
            "h1 770\n",
            "w1 904\n",
            "h2 377\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_084643_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 734.2968, 1067.4607,  882.0559, 1549.3047],\n",
            "        [ 382.6170,  311.4924, 1762.5107, 1412.5988]], device='cuda:0')), scores: tensor([0.9898, 0.9845], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1067\n",
            "y22 1549\n",
            "y1 311\n",
            "y2 1412\n",
            "h1 1101\n",
            "w1 1380\n",
            "h2 482\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[505.0075, 437.2061, 641.2404, 805.7397],\n",
            "        [ 10.5181, 339.0529, 554.5554, 658.8535]], device='cuda:0')), scores: tensor([0.9941, 0.9915], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 437\n",
            "y22 805\n",
            "y1 339\n",
            "y2 658\n",
            "h1 319\n",
            "w1 544\n",
            "h2 368\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_100624_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[1082.6631,  877.3909, 1265.6636, 1368.7908],\n",
            "        [ 272.9035,   54.4567, 1783.6017, 1217.7565]], device='cuda:0')), scores: tensor([0.9929, 0.9862], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 877\n",
            "y22 1368\n",
            "y1 54\n",
            "y2 1217\n",
            "h1 1163\n",
            "w1 1511\n",
            "h2 491\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_074427_3.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=2496, image_width=1872, fields=[pred_boxes: Boxes(tensor([[1056.5861, 1332.0449, 1282.6191, 2019.7277],\n",
            "        [ 508.6067, 1291.8997,  703.6422, 2013.9167],\n",
            "        [ 286.8466,  541.7184, 1619.9974, 1658.9615]], device='cuda:0')), scores: tensor([0.9944, 0.9943, 0.9890], device='cuda:0'), pred_classes: tensor([1, 1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y21 1332\n",
            "y22 2019\n",
            "y21 1291\n",
            "y22 2013\n",
            "y1 541\n",
            "y2 1658\n",
            "h1 1117\n",
            "w1 1333\n",
            "h2 722\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im6_1.jpg\n",
            "outputs= {'instances': Instances(num_instances=4, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 320.9618,  685.9221,  434.1749,  997.6137],\n",
            "        [ 223.0520,  720.1926,  297.2755,  893.9720],\n",
            "        [ 264.1515,  202.4467, 1028.8088,  928.4631],\n",
            "        [  62.1060,  702.7452,  128.4462,  941.9432]], device='cuda:0')), scores: tensor([0.9898, 0.9769, 0.9751, 0.9710], device='cuda:0'), pred_classes: tensor([1, 1, 0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 4\n",
            "y21 685\n",
            "y22 997\n",
            "y1 202\n",
            "y2 928\n",
            "h1 726\n",
            "w1 764\n",
            "h2 312\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_130903_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 713.0127,  830.8031,  919.1492, 1375.9476],\n",
            "        [ 878.3622,  324.9316, 1897.6310, 1275.2125]], device='cuda:0')), scores: tensor([0.9900, 0.9899], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 830\n",
            "y22 1375\n",
            "y1 324\n",
            "y2 1275\n",
            "h1 951\n",
            "w1 1019\n",
            "h2 545\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_094149.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 836.7048,  920.4293,  988.2784, 1367.3656],\n",
            "        [ 266.9044,   93.5872, 1802.6925, 1279.2605]], device='cuda:0')), scores: tensor([0.9922, 0.9850], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 920\n",
            "y22 1367\n",
            "y1 93\n",
            "y2 1279\n",
            "h1 1186\n",
            "w1 1536\n",
            "h2 447\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_084643.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 733.2903, 1071.5601,  888.9821, 1542.1027],\n",
            "        [ 377.8069,  302.0822, 1777.3597, 1423.4301]], device='cuda:0')), scores: tensor([0.9926, 0.9822], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1071\n",
            "y22 1542\n",
            "y1 302\n",
            "y2 1423\n",
            "h1 1121\n",
            "w1 1400\n",
            "h2 471\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_075921.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2496, image_width=1872, fields=[pred_boxes: Boxes(tensor([[ 434.5223, 1385.0260,  651.4006, 2169.5908],\n",
            "        [   5.6088,  438.8596, 1492.7402, 1731.9948]], device='cuda:0')), scores: tensor([0.9957, 0.9819], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1385\n",
            "y22 2169\n",
            "y1 438\n",
            "y2 1731\n",
            "h1 1293\n",
            "w1 1487\n",
            "h2 784\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/O_124141.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 327.9589, 2032.0880,  698.1251, 3546.1133],\n",
            "        [ 420.6246,  137.6179, 3686.0000, 3265.4336]], device='cuda:0')), scores: tensor([0.9853, 0.9753], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 2032\n",
            "y22 3546\n",
            "y1 137\n",
            "y2 3265\n",
            "h1 3128\n",
            "w1 3266\n",
            "h2 1514\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im3_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[195.9497, 626.1398, 324.5587, 969.8033],\n",
            "        [137.7918,  88.2267, 959.1431, 827.3333]], device='cuda:0')), scores: tensor([0.9918, 0.9901], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 626\n",
            "y22 969\n",
            "y1 88\n",
            "y2 827\n",
            "h1 739\n",
            "w1 822\n",
            "h2 343\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_093428_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 898.5649,  924.4740, 1042.6825, 1351.8873],\n",
            "        [ 377.7618,  246.6927, 1632.4979, 1239.6389]], device='cuda:0')), scores: tensor([0.9915, 0.9907], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 924\n",
            "y22 1351\n",
            "y1 246\n",
            "y2 1239\n",
            "h1 993\n",
            "w1 1255\n",
            "h2 427\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20210603_075921_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2496, image_width=1872, fields=[pred_boxes: Boxes(tensor([[ 486.0192, 1371.0762,  682.5170, 2076.0312],\n",
            "        [  81.5287,  500.3603, 1412.1453, 1718.8401]], device='cuda:0')), scores: tensor([0.9938, 0.9821], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 1371\n",
            "y22 2076\n",
            "y1 500\n",
            "y2 1718\n",
            "h1 1218\n",
            "w1 1331\n",
            "h2 705\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 981.0026,  779.5250, 1156.7532, 1177.3977],\n",
            "        [1035.4447,  463.2020, 1640.7180, 1018.4774]], device='cuda:0')), scores: tensor([0.9917, 0.9836], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 779\n",
            "y22 1177\n",
            "y1 463\n",
            "y2 1018\n",
            "h1 555\n",
            "w1 605\n",
            "h2 398\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_132145_5.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 981.5313,  847.5317, 1168.1256, 1428.5883],\n",
            "        [1009.3575,  363.3940, 1914.1510, 1204.3345]], device='cuda:0')), scores: tensor([0.9939, 0.9912], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 847\n",
            "y22 1428\n",
            "y1 363\n",
            "y2 1204\n",
            "h1 841\n",
            "w1 905\n",
            "h2 581\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220116_094943_2.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=2448, image_width=1843, fields=[pred_boxes: Boxes(tensor([[ 860.3931,  969.1471, 1013.5634, 1365.3588],\n",
            "        [ 468.6265,  185.4081, 1680.6295, 1252.7180]], device='cuda:0')), scores: tensor([0.9931, 0.9899], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 969\n",
            "y22 1365\n",
            "y1 185\n",
            "y2 1252\n",
            "h1 1067\n",
            "w1 1212\n",
            "h2 396\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im1_4.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[187.3317, 241.2225, 880.1285, 756.3972],\n",
            "        [478.2430, 607.3756, 570.5998, 799.1610],\n",
            "        [573.2290, 585.7651, 642.7662, 792.7962]], device='cuda:0')), scores: tensor([0.9950, 0.9870, 0.9852], device='cuda:0'), pred_classes: tensor([0, 1, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y1 241\n",
            "y2 756\n",
            "y21 607\n",
            "y22 799\n",
            "y21 585\n",
            "y22 792\n",
            "h1 515\n",
            "w1 693\n",
            "h2 207\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_2_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[633.5736, 296.0245, 798.1711, 785.5975],\n",
            "        [ 68.4840,  56.5885, 674.4785, 626.6067]], device='cuda:0')), scores: tensor([0.9878, 0.9839], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 296\n",
            "y22 785\n",
            "y1 56\n",
            "y2 626\n",
            "h1 570\n",
            "w1 606\n",
            "h2 489\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_4_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[373.3199, 289.5238, 812.1836, 591.1799],\n",
            "        [278.7916, 474.2764, 370.6788, 732.4686]], device='cuda:0')), scores: tensor([0.9913, 0.9884], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 289\n",
            "y2 591\n",
            "y21 474\n",
            "y22 732\n",
            "h1 302\n",
            "w1 439\n",
            "h2 258\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_2_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[633.9881, 294.9685, 796.7482, 786.2297],\n",
            "        [ 66.8218,  54.7296, 685.1250, 627.8477]], device='cuda:0')), scores: tensor([0.9892, 0.9869], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 294\n",
            "y22 786\n",
            "y1 54\n",
            "y2 627\n",
            "h1 573\n",
            "w1 619\n",
            "h2 492\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_4_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[375.9173, 292.9910, 808.1532, 592.4368],\n",
            "        [278.9602, 474.9203, 370.7003, 733.4543]], device='cuda:0')), scores: tensor([0.9891, 0.9882], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 292\n",
            "y2 592\n",
            "y21 474\n",
            "y22 733\n",
            "h1 300\n",
            "w1 433\n",
            "h2 259\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_4_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[375.9173, 292.9910, 808.1532, 592.4368],\n",
            "        [278.9602, 474.9203, 370.7003, 733.4543]], device='cuda:0')), scores: tensor([0.9891, 0.9882], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 292\n",
            "y2 592\n",
            "y21 474\n",
            "y22 733\n",
            "h1 300\n",
            "w1 433\n",
            "h2 259\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_4_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[373.3199, 289.5238, 812.1836, 591.1799],\n",
            "        [278.7916, 474.2764, 370.6788, 732.4686]], device='cuda:0')), scores: tensor([0.9913, 0.9884], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 289\n",
            "y2 591\n",
            "y21 474\n",
            "y22 732\n",
            "h1 302\n",
            "w1 439\n",
            "h2 258\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_4_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[280.1333, 473.9238, 371.1750, 730.8777],\n",
            "        [371.7415, 291.1566, 808.1941, 589.9659]], device='cuda:0')), scores: tensor([0.9907, 0.9904], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 473\n",
            "y22 730\n",
            "y1 291\n",
            "y2 589\n",
            "h1 298\n",
            "w1 437\n",
            "h2 257\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_133501_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 863.5809,  266.4970, 1911.9474, 1052.8555],\n",
            "        [ 871.9449,  770.6617, 1115.2184, 1413.9415],\n",
            "        [   0.0000,  836.1909,  165.7392, 1387.4785]], device='cuda:0')), scores: tensor([0.9926, 0.9924, 0.9827], device='cuda:0'), pred_classes: tensor([0, 1, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y1 266\n",
            "y2 1052\n",
            "y21 770\n",
            "y22 1413\n",
            "h1 786\n",
            "w1 1048\n",
            "h2 643\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_133501_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[8.6745e+02, 7.8234e+02, 1.1163e+03, 1.4035e+03],\n",
            "        [8.7936e+02, 2.5395e+02, 1.9150e+03, 1.0750e+03],\n",
            "        [2.9264e-01, 8.3685e+02, 1.6562e+02, 1.3884e+03]], device='cuda:0')), scores: tensor([0.9929, 0.9924, 0.9824], device='cuda:0'), pred_classes: tensor([1, 0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y21 782\n",
            "y22 1403\n",
            "y1 253\n",
            "y2 1075\n",
            "h1 822\n",
            "w1 1035\n",
            "h2 621\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_133501_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=3, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[8.6745e+02, 7.8234e+02, 1.1163e+03, 1.4035e+03],\n",
            "        [8.7936e+02, 2.5395e+02, 1.9150e+03, 1.0750e+03],\n",
            "        [2.9264e-01, 8.3685e+02, 1.6562e+02, 1.3884e+03]], device='cuda:0')), scores: tensor([0.9929, 0.9924, 0.9824], device='cuda:0'), pred_classes: tensor([1, 0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 3\n",
            "y21 782\n",
            "y22 1403\n",
            "y1 253\n",
            "y2 1075\n",
            "h1 822\n",
            "w1 1035\n",
            "h2 621\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_123400_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 933.9112,  802.0026, 1128.2047, 1316.1023],\n",
            "        [1043.5392,  516.5278, 1716.4597, 1163.8744]], device='cuda:0')), scores: tensor([0.9868, 0.9772], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 802\n",
            "y22 1316\n",
            "y1 516\n",
            "y2 1163\n",
            "h1 647\n",
            "w1 673\n",
            "h2 514\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_123400_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 935.4634,  813.1045, 1127.5948, 1311.3944],\n",
            "        [1041.6599,  517.5988, 1716.9965, 1162.3557]], device='cuda:0')), scores: tensor([0.9874, 0.9779], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 813\n",
            "y22 1311\n",
            "y1 517\n",
            "y2 1162\n",
            "h1 645\n",
            "w1 675\n",
            "h2 498\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_123400_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 935.4634,  813.1045, 1127.5948, 1311.3944],\n",
            "        [1041.6599,  517.5988, 1716.9965, 1162.3557]], device='cuda:0')), scores: tensor([0.9874, 0.9779], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 813\n",
            "y22 1311\n",
            "y1 517\n",
            "y2 1162\n",
            "h1 645\n",
            "w1 675\n",
            "h2 498\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_123400_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 935.4634,  813.1045, 1127.5948, 1311.3944],\n",
            "        [1041.6599,  517.5988, 1716.9965, 1162.3557]], device='cuda:0')), scores: tensor([0.9874, 0.9779], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 813\n",
            "y22 1311\n",
            "y1 517\n",
            "y2 1162\n",
            "h1 645\n",
            "w1 675\n",
            "h2 498\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_123400_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 935.5579,  813.4290, 1128.1354, 1310.5742],\n",
            "        [1046.6334,  518.5649, 1715.8783, 1159.6943]], device='cuda:0')), scores: tensor([0.9870, 0.9765], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 813\n",
            "y22 1310\n",
            "y1 518\n",
            "y2 1159\n",
            "h1 641\n",
            "w1 669\n",
            "h2 497\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_1_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[1358.7214,  769.7847, 1547.5563, 1196.1863],\n",
            "        [ 813.5737,  416.7288, 1480.4675, 1022.9759]], device='cuda:0')), scores: tensor([0.9889, 0.9827], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 769\n",
            "y22 1196\n",
            "y1 416\n",
            "y2 1022\n",
            "h1 606\n",
            "w1 667\n",
            "h2 427\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_132145_4_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 979.2052,  846.1716, 1170.0450, 1429.2733],\n",
            "        [ 997.0654,  367.4188, 1930.4723, 1206.9062]], device='cuda:0')), scores: tensor([0.9941, 0.9934], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 846\n",
            "y22 1429\n",
            "y1 367\n",
            "y2 1206\n",
            "h1 839\n",
            "w1 933\n",
            "h2 583\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_132145_4_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 979.0817,  847.0966, 1169.6520, 1430.0471],\n",
            "        [1000.6338,  367.2797, 1930.7073, 1207.2577]], device='cuda:0')), scores: tensor([0.9941, 0.9933], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 847\n",
            "y22 1430\n",
            "y1 367\n",
            "y2 1207\n",
            "h1 840\n",
            "w1 930\n",
            "h2 583\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_132145_4_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 978.8880,  846.3493, 1169.7612, 1427.2725],\n",
            "        [ 999.6881,  366.7665, 1928.5223, 1208.4595]], device='cuda:0')), scores: tensor([0.9941, 0.9936], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 846\n",
            "y22 1427\n",
            "y1 366\n",
            "y2 1208\n",
            "h1 842\n",
            "w1 929\n",
            "h2 581\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_132145_4_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 979.2052,  846.1716, 1170.0450, 1429.2733],\n",
            "        [ 997.0654,  367.4188, 1930.4723, 1206.9062]], device='cuda:0')), scores: tensor([0.9941, 0.9934], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 846\n",
            "y22 1429\n",
            "y1 367\n",
            "y2 1206\n",
            "h1 839\n",
            "w1 933\n",
            "h2 583\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md3_1_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[518.1336, 424.6888, 647.4210, 777.7181],\n",
            "        [  5.3334, 227.2048, 509.0094, 644.4756]], device='cuda:0')), scores: tensor([0.9934, 0.9925], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 424\n",
            "y22 777\n",
            "y1 227\n",
            "y2 644\n",
            "h1 417\n",
            "w1 504\n",
            "h2 353\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md7_2_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[539.8111, 464.6581, 682.1271, 852.3721],\n",
            "        [133.4614, 368.1908, 570.6941, 724.3773]], device='cuda:0')), scores: tensor([0.9942, 0.9816], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 464\n",
            "y22 852\n",
            "y1 368\n",
            "y2 724\n",
            "h1 356\n",
            "w1 437\n",
            "h2 388\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[652.5657, 262.1470, 838.8769, 804.3726],\n",
            "        [ 26.2512,   0.0000, 698.1640, 647.7796]], device='cuda:0')), scores: tensor([0.9845, 0.9796], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 262\n",
            "y22 804\n",
            "y1 0\n",
            "y2 647\n",
            "h1 647\n",
            "w1 672\n",
            "h2 542\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[653.8852, 257.5103, 830.3214, 805.8591],\n",
            "        [ 25.9934,   0.0000, 697.4093, 644.6833]], device='cuda:0')), scores: tensor([0.9813, 0.9795], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 257\n",
            "y22 805\n",
            "y1 0\n",
            "y2 644\n",
            "h1 644\n",
            "w1 672\n",
            "h2 548\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[652.5679, 262.2673, 838.9945, 802.9718],\n",
            "        [ 28.4865,   0.0000, 695.9285, 645.3374]], device='cuda:0')), scores: tensor([0.9846, 0.9808], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 262\n",
            "y22 802\n",
            "y1 0\n",
            "y2 645\n",
            "h1 645\n",
            "w1 667\n",
            "h2 540\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[652.5679, 262.2673, 838.9945, 802.9718],\n",
            "        [ 28.4865,   0.0000, 695.9285, 645.3374]], device='cuda:0')), scores: tensor([0.9846, 0.9808], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 262\n",
            "y22 802\n",
            "y1 0\n",
            "y2 645\n",
            "h1 645\n",
            "w1 667\n",
            "h2 540\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[653.8852, 257.5103, 830.3214, 805.8591],\n",
            "        [ 25.9934,   0.0000, 697.4093, 644.6833]], device='cuda:0')), scores: tensor([0.9813, 0.9795], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 257\n",
            "y22 805\n",
            "y1 0\n",
            "y2 644\n",
            "h1 644\n",
            "w1 672\n",
            "h2 548\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_1_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[ 83.6871, 254.7545, 268.6301, 805.8513],\n",
            "        [214.0807,   0.0000, 872.8921, 645.2373]], device='cuda:0')), scores: tensor([0.9835, 0.9799], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 254\n",
            "y22 805\n",
            "y1 0\n",
            "y2 645\n",
            "h1 645\n",
            "w1 658\n",
            "h2 551\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md2_1_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[ 83.6871, 254.7545, 268.6301, 805.8513],\n",
            "        [214.0807,   0.0000, 872.8921, 645.2373]], device='cuda:0')), scores: tensor([0.9835, 0.9799], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 254\n",
            "y22 805\n",
            "y1 0\n",
            "y2 645\n",
            "h1 645\n",
            "w1 658\n",
            "h2 551\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 950.1831,  762.9537, 1143.9198, 1203.7325],\n",
            "        [1020.7324,  409.9641, 1689.7150, 1017.3516]], device='cuda:0')), scores: tensor([0.9897, 0.9838], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 762\n",
            "y22 1203\n",
            "y1 409\n",
            "y2 1017\n",
            "h1 608\n",
            "w1 669\n",
            "h2 441\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 949.6605,  762.2250, 1144.3878, 1203.1855],\n",
            "        [1021.7931,  409.3515, 1687.9650, 1013.4990]], device='cuda:0')), scores: tensor([0.9899, 0.9831], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 762\n",
            "y22 1203\n",
            "y1 409\n",
            "y2 1013\n",
            "h1 604\n",
            "w1 666\n",
            "h2 441\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 949.6365,  763.2512, 1144.9288, 1203.4037],\n",
            "        [1022.4509,  409.6978, 1689.6642, 1014.5323]], device='cuda:0')), scores: tensor([0.9900, 0.9834], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 763\n",
            "y22 1203\n",
            "y1 409\n",
            "y2 1014\n",
            "h1 605\n",
            "w1 667\n",
            "h2 440\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 949.6365,  763.2512, 1144.9288, 1203.4037],\n",
            "        [1022.4509,  409.6978, 1689.6642, 1014.5323]], device='cuda:0')), scores: tensor([0.9900, 0.9834], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 763\n",
            "y22 1203\n",
            "y1 409\n",
            "y2 1014\n",
            "h1 605\n",
            "w1 667\n",
            "h2 440\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/IMG_20220223_113845_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1872, image_width=2496, fields=[pred_boxes: Boxes(tensor([[ 949.6365,  763.2512, 1144.9288, 1203.4037],\n",
            "        [1022.4509,  409.6978, 1689.6642, 1014.5323]], device='cuda:0')), scores: tensor([0.9900, 0.9834], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 763\n",
            "y22 1203\n",
            "y1 409\n",
            "y2 1014\n",
            "h1 605\n",
            "w1 667\n",
            "h2 440\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[369.2375, 291.6165, 785.1886, 597.9508],\n",
            "        [278.9684, 473.2285, 370.6142, 732.8293]], device='cuda:0')), scores: tensor([0.9925, 0.9905], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 291\n",
            "y2 597\n",
            "y21 473\n",
            "y22 732\n",
            "h1 306\n",
            "w1 416\n",
            "h2 259\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[369.2375, 291.6165, 785.1886, 597.9508],\n",
            "        [278.9684, 473.2285, 370.6142, 732.8293]], device='cuda:0')), scores: tensor([0.9925, 0.9905], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 291\n",
            "y2 597\n",
            "y21 473\n",
            "y22 732\n",
            "h1 306\n",
            "w1 416\n",
            "h2 259\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[369.2375, 291.6165, 785.1886, 597.9508],\n",
            "        [278.9684, 473.2285, 370.6142, 732.8293]], device='cuda:0')), scores: tensor([0.9925, 0.9905], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 291\n",
            "y2 597\n",
            "y21 473\n",
            "y22 732\n",
            "h1 306\n",
            "w1 416\n",
            "h2 259\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[373.8006, 291.7531, 789.8489, 598.5721],\n",
            "        [279.2100, 473.9020, 371.1127, 734.5598]], device='cuda:0')), scores: tensor([0.9922, 0.9910], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 291\n",
            "y2 598\n",
            "y21 473\n",
            "y22 734\n",
            "h1 307\n",
            "w1 416\n",
            "h2 261\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md1_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[369.2375, 291.6165, 785.1886, 597.9508],\n",
            "        [278.9684, 473.2285, 370.6142, 732.8293]], device='cuda:0')), scores: tensor([0.9925, 0.9905], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 291\n",
            "y2 597\n",
            "y21 473\n",
            "y22 732\n",
            "h1 306\n",
            "w1 416\n",
            "h2 259\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im7_2_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1440, image_width=1080, fields=[pred_boxes: Boxes(tensor([[ 555.1513,  660.3218,  782.8046, 1223.7725],\n",
            "        [ 156.4174,  161.9001,  884.9042,  930.2080]], device='cuda:0')), scores: tensor([0.9885, 0.9835], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 660\n",
            "y22 1223\n",
            "y1 161\n",
            "y2 930\n",
            "h1 769\n",
            "w1 728\n",
            "h2 563\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md3_3_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[258.4015, 409.3447, 396.6871, 783.7357],\n",
            "        [385.9395, 200.5149, 908.3130, 664.3575]], device='cuda:0')), scores: tensor([0.9947, 0.9941], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 409\n",
            "y22 783\n",
            "y1 200\n",
            "y2 664\n",
            "h1 464\n",
            "w1 523\n",
            "h2 374\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1238, image_width=928, fields=[pred_boxes: Boxes(tensor([[279.6877, 442.9734, 419.2323, 815.7332],\n",
            "        [365.7302, 330.0577, 885.8919, 658.6910]], device='cuda:0')), scores: tensor([0.9923, 0.9897], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 442\n",
            "y22 815\n",
            "y1 330\n",
            "y2 658\n",
            "h1 328\n",
            "w1 520\n",
            "h2 373\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1150, image_width=862, fields=[pred_boxes: Boxes(tensor([[259.5844, 409.9745, 390.5816, 758.3190],\n",
            "        [340.8114, 306.6630, 822.7632, 609.5815]], device='cuda:0')), scores: tensor([0.9930, 0.9899], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 409\n",
            "y22 758\n",
            "y1 306\n",
            "y2 609\n",
            "h1 303\n",
            "w1 482\n",
            "h2 349\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1101, image_width=826, fields=[pred_boxes: Boxes(tensor([[250.2352, 392.1395, 372.8512, 723.5215],\n",
            "        [322.8443, 294.5160, 787.4038, 585.7637]], device='cuda:0')), scores: tensor([0.9922, 0.9897], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 392\n",
            "y22 723\n",
            "y1 294\n",
            "y2 585\n",
            "h1 291\n",
            "w1 465\n",
            "h2 331\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[286.2920, 450.1020, 430.0147, 834.4781],\n",
            "        [373.6154, 346.9807, 901.6864, 670.6747]], device='cuda:0')), scores: tensor([0.9925, 0.9905], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 450\n",
            "y22 834\n",
            "y1 346\n",
            "y2 670\n",
            "h1 324\n",
            "w1 528\n",
            "h2 384\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[266.6323, 425.3439, 402.6072, 782.0798],\n",
            "        [350.3734, 315.7301, 850.0073, 630.7656]], device='cuda:0')), scores: tensor([0.9932, 0.9896], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 425\n",
            "y22 782\n",
            "y1 315\n",
            "y2 630\n",
            "h1 315\n",
            "w1 500\n",
            "h2 357\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=5057, image_width=3808, fields=[pred_boxes: Boxes(tensor([[1877.1870,  839.5411, 3345.9875, 4363.3730],\n",
            "        [ 475.0674, 2065.7974, 1872.6556, 3344.3804]], device='cuda:0')), scores: tensor([0.9859, 0.9772], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 839\n",
            "y22 4363\n",
            "y1 2065\n",
            "y2 3344\n",
            "h1 1279\n",
            "w1 1397\n",
            "h2 3524\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4476, image_width=3370, fields=[pred_boxes: Boxes(tensor([[1681.4117,  730.7678, 2948.0681, 3903.8457],\n",
            "        [ 430.3452, 1825.3812, 1662.9718, 2968.7920]], device='cuda:0')), scores: tensor([0.9798, 0.9739], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 730\n",
            "y22 3903\n",
            "y1 1825\n",
            "y2 2968\n",
            "h1 1143\n",
            "w1 1232\n",
            "h2 3173\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=5574, image_width=4197, fields=[pred_boxes: Boxes(tensor([[2097.6599,  900.5968, 3672.4980, 4867.1719],\n",
            "        [ 506.9279, 2270.5969, 2086.2483, 3700.0540]], device='cuda:0')), scores: tensor([0.9798, 0.9703], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 900\n",
            "y22 4867\n",
            "y1 2270\n",
            "y2 3700\n",
            "h1 1430\n",
            "w1 1580\n",
            "h2 3967\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_zoom_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1469, image_width=1102, fields=[pred_boxes: Boxes(tensor([[603.7460, 523.7047, 767.9292, 964.0665],\n",
            "        [ 21.0592, 406.5207, 663.3127, 783.2300]], device='cuda:0')), scores: tensor([0.9940, 0.9905], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 523\n",
            "y22 964\n",
            "y1 406\n",
            "y2 783\n",
            "h1 377\n",
            "w1 642\n",
            "h2 441\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_rotation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[504.5986, 437.5037, 641.5853, 813.1424],\n",
            "        [ 22.6712, 334.5232, 557.5070, 655.5940]], device='cuda:0')), scores: tensor([0.9931, 0.9899], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 437\n",
            "y22 813\n",
            "y1 334\n",
            "y2 655\n",
            "h1 321\n",
            "w1 535\n",
            "h2 376\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_translation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[504.2270, 429.0320, 643.2200, 808.4016],\n",
            "        [ 13.7680, 338.7907, 552.7903, 652.4944]], device='cuda:0')), scores: tensor([0.9927, 0.9896], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 429\n",
            "y22 808\n",
            "y1 338\n",
            "y2 652\n",
            "h1 314\n",
            "w1 539\n",
            "h2 379\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_translation_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[500.6954, 434.6010, 641.2689, 802.7081],\n",
            "        [ 13.4669, 337.6487, 548.6646, 655.5705]], device='cuda:0')), scores: tensor([0.9940, 0.9885], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 434\n",
            "y22 802\n",
            "y1 337\n",
            "y2 655\n",
            "h1 318\n",
            "w1 535\n",
            "h2 368\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_flip_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.8297, 435.4107, 415.0397, 807.2753],\n",
            "        [362.4429, 335.5306, 880.6805, 646.7349]], device='cuda:0')), scores: tensor([0.9925, 0.9919], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 435\n",
            "y22 807\n",
            "y1 335\n",
            "y2 646\n",
            "h1 311\n",
            "w1 518\n",
            "h2 372\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_rotation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[504.0781, 438.0576, 641.6523, 805.9393],\n",
            "        [ 14.8990, 339.0852, 554.7289, 655.0079]], device='cuda:0')), scores: tensor([0.9939, 0.9914], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 438\n",
            "y22 805\n",
            "y1 339\n",
            "y2 655\n",
            "h1 316\n",
            "w1 540\n",
            "h2 367\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_zoom_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1382, image_width=1036, fields=[pred_boxes: Boxes(tensor([[569.2411, 490.8935, 721.2778, 910.4869],\n",
            "        [ 20.8818, 382.6394, 622.4504, 735.5164]], device='cuda:0')), scores: tensor([0.9931, 0.9910], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 490\n",
            "y22 910\n",
            "y1 382\n",
            "y2 735\n",
            "h1 353\n",
            "w1 602\n",
            "h2 420\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_flip_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.8297, 435.4107, 415.0397, 807.2753],\n",
            "        [362.4429, 335.5306, 880.6805, 646.7349]], device='cuda:0')), scores: tensor([0.9925, 0.9919], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 435\n",
            "y22 807\n",
            "y1 335\n",
            "y2 646\n",
            "h1 311\n",
            "w1 518\n",
            "h2 372\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_flip_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.8297, 435.4107, 415.0397, 807.2753],\n",
            "        [362.4429, 335.5306, 880.6805, 646.7349]], device='cuda:0')), scores: tensor([0.9925, 0.9919], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 435\n",
            "y22 807\n",
            "y1 335\n",
            "y2 646\n",
            "h1 311\n",
            "w1 518\n",
            "h2 372\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_zoom_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1414, image_width=1061, fields=[pred_boxes: Boxes(tensor([[581.6736, 501.0251, 741.9059, 932.9572],\n",
            "        [ 17.8268, 391.7941, 638.4231, 752.4020]], device='cuda:0')), scores: tensor([0.9921, 0.9909], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 501\n",
            "y22 932\n",
            "y1 391\n",
            "y2 752\n",
            "h1 361\n",
            "w1 621\n",
            "h2 431\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_flip_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.8297, 435.4107, 415.0397, 807.2753],\n",
            "        [362.4429, 335.5306, 880.6805, 646.7349]], device='cuda:0')), scores: tensor([0.9925, 0.9919], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 435\n",
            "y22 807\n",
            "y1 335\n",
            "y2 646\n",
            "h1 311\n",
            "w1 518\n",
            "h2 372\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_rotation_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[506.3605, 436.0091, 640.4090, 807.7970],\n",
            "        [ 16.6310, 345.1515, 551.4344, 659.0206]], device='cuda:0')), scores: tensor([0.9925, 0.9896], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 436\n",
            "y22 807\n",
            "y1 345\n",
            "y2 659\n",
            "h1 314\n",
            "w1 535\n",
            "h2 371\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_flip_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[1812.8347,  825.8362, 3232.0493, 4232.7266],\n",
            "        [ 462.3325, 1995.1714, 1820.2814, 3233.1116]], device='cuda:0')), scores: tensor([0.9838, 0.9773], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 825\n",
            "y22 4232\n",
            "y1 1995\n",
            "y2 3233\n",
            "h1 1238\n",
            "w1 1358\n",
            "h2 3407\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_rotation_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 477.2041,  822.9895, 1861.4370, 4308.4941],\n",
            "        [1797.2085, 2012.8302, 3230.2007, 3231.9507]], device='cuda:0')), scores: tensor([0.9859, 0.9766], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 822\n",
            "y22 4308\n",
            "y1 2012\n",
            "y2 3231\n",
            "h1 1219\n",
            "w1 1433\n",
            "h2 3486\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_rotation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 477.2041,  822.9895, 1861.4370, 4308.4941],\n",
            "        [1797.2085, 2012.8302, 3230.2007, 3231.9507]], device='cuda:0')), scores: tensor([0.9859, 0.9766], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 822\n",
            "y22 4308\n",
            "y1 2012\n",
            "y2 3231\n",
            "h1 1219\n",
            "w1 1433\n",
            "h2 3486\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_zoom_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4854, image_width=3655, fields=[pred_boxes: Boxes(tensor([[ 457.5890,  830.9124, 1864.3752, 4273.8110],\n",
            "        [1824.9403, 2009.1355, 3187.3660, 3206.1948]], device='cuda:0')), scores: tensor([0.9848, 0.9780], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 830\n",
            "y22 4273\n",
            "y1 2009\n",
            "y2 3206\n",
            "h1 1197\n",
            "w1 1363\n",
            "h2 3443\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_translation_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 436.2516,  838.7563, 1869.2288, 4301.4497],\n",
            "        [1838.8571, 2036.0326, 3206.9253, 3226.0720]], device='cuda:0')), scores: tensor([0.9819, 0.9782], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 838\n",
            "y22 4301\n",
            "y1 2036\n",
            "y2 3226\n",
            "h1 1190\n",
            "w1 1368\n",
            "h2 3463\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_flip_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[1812.8347,  825.8362, 3232.0493, 4232.7266],\n",
            "        [ 462.3325, 1995.1714, 1820.2814, 3233.1116]], device='cuda:0')), scores: tensor([0.9838, 0.9773], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 825\n",
            "y22 4232\n",
            "y1 1995\n",
            "y2 3233\n",
            "h1 1238\n",
            "w1 1358\n",
            "h2 3407\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_rotation_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 477.2041,  822.9895, 1861.4370, 4308.4941],\n",
            "        [1797.2085, 2012.8302, 3230.2007, 3231.9507]], device='cuda:0')), scores: tensor([0.9859, 0.9766], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 822\n",
            "y22 4308\n",
            "y1 2012\n",
            "y2 3231\n",
            "h1 1219\n",
            "w1 1433\n",
            "h2 3486\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_translation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[506.6237, 437.2414, 644.6696, 810.9196],\n",
            "        [ 19.1684, 342.9877, 560.6041, 660.6973]], device='cuda:0')), scores: tensor([0.9916, 0.9903], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 437\n",
            "y22 810\n",
            "y1 342\n",
            "y2 660\n",
            "h1 318\n",
            "w1 541\n",
            "h2 373\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_flip_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.2615, 438.3568, 415.1627, 806.5392],\n",
            "        [364.8940, 342.2986, 898.2224, 656.6654]], device='cuda:0')), scores: tensor([0.9918, 0.9896], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 438\n",
            "y22 806\n",
            "y1 342\n",
            "y2 656\n",
            "h1 314\n",
            "w1 534\n",
            "h2 368\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_rotation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[505.8224, 440.9384, 640.5356, 808.2393],\n",
            "        [ 14.8157, 339.8023, 557.9235, 660.1325]], device='cuda:0')), scores: tensor([0.9916, 0.9897], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 440\n",
            "y22 808\n",
            "y1 339\n",
            "y2 660\n",
            "h1 321\n",
            "w1 543\n",
            "h2 368\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_zoom_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1230, image_width=922, fields=[pred_boxes: Boxes(tensor([[506.9851, 441.8983, 642.0595, 809.7983],\n",
            "        [ 14.0531, 340.8366, 559.2429, 659.4525]], device='cuda:0')), scores: tensor([0.9917, 0.9897], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 441\n",
            "y22 809\n",
            "y1 340\n",
            "y2 659\n",
            "h1 319\n",
            "w1 545\n",
            "h2 368\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_translation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[510.0182, 432.4608, 642.9136, 806.8945],\n",
            "        [ 22.5083, 336.8394, 559.8069, 659.7401]], device='cuda:0')), scores: tensor([0.9926, 0.9903], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 432\n",
            "y22 806\n",
            "y1 336\n",
            "y2 659\n",
            "h1 323\n",
            "w1 537\n",
            "h2 374\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_flip_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.2615, 438.3568, 415.1627, 806.5392],\n",
            "        [364.8940, 342.2986, 898.2224, 656.6654]], device='cuda:0')), scores: tensor([0.9918, 0.9896], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 438\n",
            "y22 806\n",
            "y1 342\n",
            "y2 656\n",
            "h1 314\n",
            "w1 534\n",
            "h2 368\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_zoom_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1387, image_width=1040, fields=[pred_boxes: Boxes(tensor([[570.9352, 497.0469, 723.8126, 912.9339],\n",
            "        [ 17.3947, 384.6474, 630.2227, 745.6311]], device='cuda:0')), scores: tensor([0.9915, 0.9893], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 497\n",
            "y22 912\n",
            "y1 384\n",
            "y2 745\n",
            "h1 361\n",
            "w1 613\n",
            "h2 415\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_rotation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[507.4807, 433.6865, 644.0446, 805.7451],\n",
            "        [ 14.7186, 344.1811, 552.9706, 663.9002]], device='cuda:0')), scores: tensor([0.9918, 0.9898], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 433\n",
            "y22 805\n",
            "y1 344\n",
            "y2 663\n",
            "h1 319\n",
            "w1 538\n",
            "h2 372\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_translation_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[509.3205, 442.2703, 644.1019, 807.3620],\n",
            "        [ 20.2052, 347.2760, 557.1950, 661.3708]], device='cuda:0')), scores: tensor([0.9933, 0.9906], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 442\n",
            "y22 807\n",
            "y1 347\n",
            "y2 661\n",
            "h1 314\n",
            "w1 537\n",
            "h2 365\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_zoom_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1282, image_width=961, fields=[pred_boxes: Boxes(tensor([[527.4878, 459.0048, 668.4833, 843.8311],\n",
            "        [ 14.6506, 355.7521, 582.6217, 687.2985]], device='cuda:0')), scores: tensor([0.9918, 0.9900], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 459\n",
            "y22 843\n",
            "y1 355\n",
            "y2 687\n",
            "h1 332\n",
            "w1 568\n",
            "h2 384\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_translation_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[504.5104, 441.6013, 642.9917, 806.0662],\n",
            "        [ 13.9195, 342.9504, 554.9725, 664.1880]], device='cuda:0')), scores: tensor([0.9916, 0.9877], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 441\n",
            "y22 806\n",
            "y1 342\n",
            "y2 664\n",
            "h1 322\n",
            "w1 541\n",
            "h2 365\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_zoom_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1180, image_width=885, fields=[pred_boxes: Boxes(tensor([[485.9633, 423.4140, 616.1819, 776.8361],\n",
            "        [ 13.9095, 326.9714, 535.4615, 632.9454]], device='cuda:0')), scores: tensor([0.9914, 0.9898], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 423\n",
            "y22 776\n",
            "y1 326\n",
            "y2 632\n",
            "h1 306\n",
            "w1 522\n",
            "h2 353\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_4_flip_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1228, image_width=921, fields=[pred_boxes: Boxes(tensor([[277.2615, 438.3568, 415.1627, 806.5392],\n",
            "        [364.8940, 342.2986, 898.2224, 656.6654]], device='cuda:0')), scores: tensor([0.9918, 0.9896], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 438\n",
            "y22 806\n",
            "y1 342\n",
            "y2 656\n",
            "h1 314\n",
            "w1 534\n",
            "h2 368\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_translation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[378.6422, 356.0057, 924.3537, 675.0958],\n",
            "        [287.9890, 448.6000, 431.7500, 839.5196]], device='cuda:0')), scores: tensor([0.9932, 0.9925], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 356\n",
            "y2 675\n",
            "y21 448\n",
            "y22 839\n",
            "h1 319\n",
            "w1 546\n",
            "h2 391\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_flip_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[519.7037, 452.8591, 663.1801, 832.9662],\n",
            "        [ 16.6052, 353.9673, 570.3977, 676.0763]], device='cuda:0')), scores: tensor([0.9939, 0.9909], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 452\n",
            "y22 832\n",
            "y1 353\n",
            "y2 676\n",
            "h1 323\n",
            "w1 554\n",
            "h2 380\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_rotation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[285.0677, 448.6441, 431.2798, 828.6410],\n",
            "        [378.9405, 356.4755, 929.6980, 681.3932]], device='cuda:0')), scores: tensor([0.9935, 0.9881], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 448\n",
            "y22 828\n",
            "y1 356\n",
            "y2 681\n",
            "h1 325\n",
            "w1 551\n",
            "h2 380\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_translation_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[284.7741, 446.9786, 429.9284, 833.1510],\n",
            "        [372.1031, 340.5483, 897.0103, 669.7092]], device='cuda:0')), scores: tensor([0.9924, 0.9887], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 446\n",
            "y22 833\n",
            "y1 340\n",
            "y2 669\n",
            "h1 329\n",
            "w1 525\n",
            "h2 387\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_rotation_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[286.4849, 450.0813, 429.9903, 834.4409],\n",
            "        [373.6292, 346.8749, 901.8666, 670.6100]], device='cuda:0')), scores: tensor([0.9925, 0.9905], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 450\n",
            "y22 834\n",
            "y1 346\n",
            "y2 670\n",
            "h1 324\n",
            "w1 528\n",
            "h2 384\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_zoom_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1500, image_width=1125, fields=[pred_boxes: Boxes(tensor([[ 339.0894,  531.7430,  508.2162,  986.4597],\n",
            "        [ 445.4409,  411.4069, 1075.8118,  791.2991]], device='cuda:0')), scores: tensor([0.9923, 0.9915], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 531\n",
            "y22 986\n",
            "y1 411\n",
            "y2 791\n",
            "h1 380\n",
            "w1 630\n",
            "h2 455\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_rotation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[285.3276, 446.7761, 427.7357, 831.0688],\n",
            "        [377.3805, 354.9628, 922.9739, 676.9276]], device='cuda:0')), scores: tensor([0.9952, 0.9876], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 446\n",
            "y22 831\n",
            "y1 354\n",
            "y2 676\n",
            "h1 322\n",
            "w1 545\n",
            "h2 385\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_translation_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[293.4944, 447.9196, 432.3870, 831.7573],\n",
            "        [376.5300, 348.9081, 919.0171, 668.9621]], device='cuda:0')), scores: tensor([0.9943, 0.9897], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 447\n",
            "y22 831\n",
            "y1 348\n",
            "y2 668\n",
            "h1 320\n",
            "w1 543\n",
            "h2 384\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_rotation_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[285.3276, 446.7761, 427.7357, 831.0688],\n",
            "        [377.3805, 354.9628, 922.9739, 676.9276]], device='cuda:0')), scores: tensor([0.9952, 0.9876], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 446\n",
            "y22 831\n",
            "y1 354\n",
            "y2 676\n",
            "h1 322\n",
            "w1 545\n",
            "h2 385\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_translation_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[286.7761, 447.1027, 428.9638, 829.8571],\n",
            "        [375.0448, 342.7384, 907.0195, 669.2432]], device='cuda:0')), scores: tensor([0.9926, 0.9914], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 447\n",
            "y22 829\n",
            "y1 342\n",
            "y2 669\n",
            "h1 327\n",
            "w1 532\n",
            "h2 382\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_9_rotation_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1269, image_width=951, fields=[pred_boxes: Boxes(tensor([[286.4849, 450.0813, 429.9903, 834.4409],\n",
            "        [373.6292, 346.8749, 901.8666, 670.6100]], device='cuda:0')), scores: tensor([0.9925, 0.9905], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 450\n",
            "y22 834\n",
            "y1 346\n",
            "y2 670\n",
            "h1 324\n",
            "w1 528\n",
            "h2 384\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_zoom_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1192, image_width=894, fields=[pred_boxes: Boxes(tensor([[267.0257, 427.1421, 405.0445, 786.3649],\n",
            "        [355.2041, 319.6870, 861.7340, 627.1115]], device='cuda:0')), scores: tensor([0.9930, 0.9920], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 427\n",
            "y22 786\n",
            "y1 319\n",
            "y2 627\n",
            "h1 308\n",
            "w1 506\n",
            "h2 359\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_rotation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[266.4696, 423.9460, 402.7101, 781.8156],\n",
            "        [350.3244, 315.8973, 850.3486, 630.5452]], device='cuda:0')), scores: tensor([0.9922, 0.9895], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 423\n",
            "y22 781\n",
            "y1 315\n",
            "y2 630\n",
            "h1 315\n",
            "w1 500\n",
            "h2 358\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_flip_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[488.0139, 422.5739, 618.5710, 784.4860],\n",
            "        [ 18.0881, 322.3793, 538.0818, 632.5258]], device='cuda:0')), scores: tensor([0.9933, 0.9890], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 422\n",
            "y22 784\n",
            "y1 322\n",
            "y2 632\n",
            "h1 310\n",
            "w1 520\n",
            "h2 362\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_rotation_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[266.4696, 423.9460, 402.7101, 781.8156],\n",
            "        [350.3244, 315.8973, 850.3486, 630.5452]], device='cuda:0')), scores: tensor([0.9922, 0.9895], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 423\n",
            "y22 781\n",
            "y1 315\n",
            "y2 630\n",
            "h1 315\n",
            "w1 500\n",
            "h2 358\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_translation_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[269.4162, 420.8841, 405.2055, 785.4245],\n",
            "        [358.2304, 322.1521, 862.2291, 626.6516]], device='cuda:0')), scores: tensor([0.9910, 0.9857], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 420\n",
            "y22 785\n",
            "y1 322\n",
            "y2 626\n",
            "h1 304\n",
            "w1 504\n",
            "h2 365\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_flip_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[488.0139, 422.5739, 618.5710, 784.4860],\n",
            "        [ 18.0881, 322.3793, 538.0818, 632.5258]], device='cuda:0')), scores: tensor([0.9933, 0.9890], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 422\n",
            "y22 784\n",
            "y1 322\n",
            "y2 632\n",
            "h1 310\n",
            "w1 520\n",
            "h2 362\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_zoom_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=997, image_width=748, fields=[pred_boxes: Boxes(tensor([[223.8116, 356.9039, 339.0403, 658.3993],\n",
            "        [292.3669, 267.5821, 716.3073, 529.6801]], device='cuda:0')), scores: tensor([0.9930, 0.9888], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 356\n",
            "y22 658\n",
            "y1 267\n",
            "y2 529\n",
            "h1 262\n",
            "w1 424\n",
            "h2 302\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_rotation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[352.3008, 329.9925, 861.0095, 627.8333],\n",
            "        [266.3041, 419.5752, 400.4565, 776.2865]], device='cuda:0')), scores: tensor([0.9921, 0.9921], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 329\n",
            "y2 627\n",
            "y21 419\n",
            "y22 776\n",
            "h1 298\n",
            "w1 509\n",
            "h2 357\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_translation_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[266.4696, 423.9460, 402.7101, 781.8156],\n",
            "        [350.3244, 315.8973, 850.3486, 630.5452]], device='cuda:0')), scores: tensor([0.9922, 0.9895], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 423\n",
            "y22 781\n",
            "y1 315\n",
            "y2 630\n",
            "h1 315\n",
            "w1 500\n",
            "h2 358\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_zoom_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1129, image_width=847, fields=[pred_boxes: Boxes(tensor([[255.4701, 404.1180, 382.2871, 744.1264],\n",
            "        [335.3630, 305.7219, 810.3184, 592.6510]], device='cuda:0')), scores: tensor([0.9922, 0.9879], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 404\n",
            "y22 744\n",
            "y1 305\n",
            "y2 592\n",
            "h1 287\n",
            "w1 475\n",
            "h2 340\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_flip_8.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[488.0139, 422.5739, 618.5710, 784.4860],\n",
            "        [ 18.0881, 322.3793, 538.0818, 632.5258]], device='cuda:0')), scores: tensor([0.9933, 0.9890], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 422\n",
            "y22 784\n",
            "y1 322\n",
            "y2 632\n",
            "h1 310\n",
            "w1 520\n",
            "h2 362\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_zoom_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1369, image_width=1027, fields=[pred_boxes: Boxes(tensor([[309.8479, 489.9984, 463.4422, 902.6029],\n",
            "        [403.6294, 363.9662, 979.8432, 727.5946]], device='cuda:0')), scores: tensor([0.9923, 0.9895], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 489\n",
            "y22 902\n",
            "y1 363\n",
            "y2 727\n",
            "h1 364\n",
            "w1 576\n",
            "h2 413\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_flip_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[488.0139, 422.5739, 618.5710, 784.4860],\n",
            "        [ 18.0881, 322.3793, 538.0818, 632.5258]], device='cuda:0')), scores: tensor([0.9933, 0.9890], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 422\n",
            "y22 784\n",
            "y1 322\n",
            "y2 632\n",
            "h1 310\n",
            "w1 520\n",
            "h2 362\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_translation_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[266.5231, 423.8796, 403.6710, 781.3022],\n",
            "        [350.1648, 313.6362, 845.8658, 628.8586]], device='cuda:0')), scores: tensor([0.9924, 0.9895], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 423\n",
            "y22 781\n",
            "y1 313\n",
            "y2 628\n",
            "h1 315\n",
            "w1 495\n",
            "h2 358\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_zoom_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1082, image_width=811, fields=[pred_boxes: Boxes(tensor([[244.8984, 387.2228, 366.3990, 712.6988],\n",
            "        [321.4617, 299.5242, 793.9256, 566.7103]], device='cuda:0')), scores: tensor([0.9921, 0.9886], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 387\n",
            "y22 712\n",
            "y1 299\n",
            "y2 566\n",
            "h1 267\n",
            "w1 472\n",
            "h2 325\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/md4_5_10_flip_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=1186, image_width=889, fields=[pred_boxes: Boxes(tensor([[488.0139, 422.5739, 618.5710, 784.4860],\n",
            "        [ 18.0881, 322.3793, 538.0818, 632.5258]], device='cuda:0')), scores: tensor([0.9933, 0.9890], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 422\n",
            "y22 784\n",
            "y1 322\n",
            "y2 632\n",
            "h1 310\n",
            "w1 520\n",
            "h2 362\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im14_5_translation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 524.2247,  583.5621, 1649.0009, 4704.9292],\n",
            "        [2157.2515, 1238.8844, 3679.7417, 3200.5254]], device='cuda:0')), scores: tensor([0.9876, 0.9852], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 583\n",
            "y22 4704\n",
            "y1 1238\n",
            "y2 3200\n",
            "h1 1962\n",
            "w1 1522\n",
            "h2 4121\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im14_5_rotation_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[2154.6575, 1301.4679, 3673.6960, 3222.3528],\n",
            "        [ 527.1169,  560.1462, 1621.4680, 4688.3545]], device='cuda:0')), scores: tensor([0.9878, 0.9878], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 1301\n",
            "y2 3222\n",
            "y21 560\n",
            "y22 4688\n",
            "h1 1921\n",
            "w1 1519\n",
            "h2 4128\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im14_5_flip_9.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[2039.0157,  614.3430, 3158.9236, 4668.5972],\n",
            "        [   0.0000, 1259.7535, 1517.7360, 3195.9250]], device='cuda:0')), scores: tensor([0.9869, 0.9846], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 614\n",
            "y22 4668\n",
            "y1 1259\n",
            "y2 3195\n",
            "h1 1936\n",
            "w1 1517\n",
            "h2 4054\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im14_5_translation_10.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4896, image_width=3686, fields=[pred_boxes: Boxes(tensor([[ 515.1179,  594.8150, 1660.4713, 4667.4072],\n",
            "        [2143.9377, 1252.4901, 3686.0000, 3200.4280]], device='cuda:0')), scores: tensor([0.9881, 0.9856], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 594\n",
            "y22 4667\n",
            "y1 1252\n",
            "y2 3200\n",
            "h1 1948\n",
            "w1 1543\n",
            "h2 4073\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_8_translation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=5574, image_width=4197, fields=[pred_boxes: Boxes(tensor([[2095.4158,  902.1958, 3672.4712, 4869.6782],\n",
            "        [ 529.0126, 2287.5017, 2077.1699, 3702.3696]], device='cuda:0')), scores: tensor([0.9797, 0.9753], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 902\n",
            "y22 4869\n",
            "y1 2287\n",
            "y2 3702\n",
            "h1 1415\n",
            "w1 1548\n",
            "h2 3967\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_8_rotation_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=5574, image_width=4197, fields=[pred_boxes: Boxes(tensor([[2068.5703,  926.1398, 3688.4756, 4809.4854],\n",
            "        [ 520.4863, 2283.3467, 2066.0815, 3683.5110]], device='cuda:0')), scores: tensor([0.9859, 0.9773], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 926\n",
            "y22 4809\n",
            "y1 2283\n",
            "y2 3683\n",
            "h1 1400\n",
            "w1 1546\n",
            "h2 3883\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_8_zoom_6.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=4668, image_width=3514, fields=[pred_boxes: Boxes(tensor([[1752.4847,  756.5754, 3074.8110, 4075.0566],\n",
            "        [ 444.7499, 1920.1759, 1736.1383, 3098.7642]], device='cuda:0')), scores: tensor([0.9802, 0.9751], device='cuda:0'), pred_classes: tensor([1, 0], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y21 756\n",
            "y22 4075\n",
            "y1 1920\n",
            "y2 3098\n",
            "h1 1178\n",
            "w1 1292\n",
            "h2 3319\n",
            "/content/drive/MyDrive/olive/eq_augmented_data/im12_2_8_rotation_7.jpg\n",
            "outputs= {'instances': Instances(num_instances=2, image_height=5574, image_width=4197, fields=[pred_boxes: Boxes(tensor([[ 505.9104, 2209.6433, 2072.9534, 3700.8213],\n",
            "        [2041.0441,  968.7586, 3695.2954, 4817.9648]], device='cuda:0')), scores: tensor([0.9828, 0.9819], device='cuda:0'), pred_classes: tensor([0, 1], device='cuda:0'), pred_masks: tensor([[[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]],\n",
            "\n",
            "        [[False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         ...,\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False],\n",
            "         [False, False, False,  ..., False, False, False]]], device='cuda:0')])}\n",
            "****************************** 2\n",
            "y1 2209\n",
            "y2 3700\n",
            "y21 968\n",
            "y22 4817\n",
            "h1 1491\n",
            "w1 1567\n",
            "h2 3849\n"
          ]
        }
      ],
      "source": [
        "bound_box(\"/content/drive/MyDrive/olive/eq_augmented_data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQBQRdiF6jbj"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "fin = open(\"/content/drive/MyDrive/olive/Tree_height.txt\", \"r\")  # Ouvrir le fichier source en lecture\n",
        "fout = open(\"/content/drive/MyDrive/olive/Tree_height1.txt\", \"wt\")  # Créer un nouveau fichier texte pour écrire les modifications\n",
        "\n",
        "# Parcourir les lignes du fichier source\n",
        "for line in fin:\n",
        "    fout.write(line.replace('/content/drive/MyDrive/olive/eq_augmented_data/content/drive/MyDrive/olive/eq_augmented_data/', ''))  # Remplacer la chaîne '/content/output' par une chaîne vide et écrire la ligne modifiée dans le fichier de sortie\n",
        "\n",
        "fin.close()  # Fermer le fichier source\n",
        "fout.close()  # Fermer le fichier de sortie\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5GkIAgJHj5F",
        "outputId": "94f56102-9442-4dc2-e961-31943786a65f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  \"filename\": \"IMG_20210603_074354.jpg\", \"Tree_height\": 2.657163323782235, \"Tree_Width\": 3.2977077363896847\n"
          ]
        }
      ],
      "source": [
        "\n",
        "my_file = open(\"/content/drive/MyDrive/olive/Tree_height1.txt\", \"r\")  # Ouvrir le fichier en lecture\n",
        "data = my_file.read()  # Lire le contenu du fichier\n",
        "\n",
        "# Remplacer les fins de ligne ('\\n') par un espace et les caractères '[' et ']' par un espace\n",
        "# puis diviser le texte en une liste en utilisant '},' comme délimiteur\n",
        "data_into_list = data.replace('\\n', ' ').replace('[', ' ').replace(']', ' ').split(\"},\")\n",
        "# Parcourir les éléments de la liste et remplacer les caractères '{' et '}' par un espace\n",
        "for i in range(len(data_into_list)):\n",
        "    data_into_list[i] = data_into_list[i].replace('{', ' ').replace('}', ' ')\n",
        "\n",
        "# Imprimer le deuxième élément de la liste\n",
        "print(data_into_list[1])\n",
        "\n",
        "my_file.close()  # Fermer le fichier\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aQWKr2vHpbU",
        "outputId": "0886432e-08ba-44fa-acb6-1643b9f575d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n",
            "<ipython-input-18-7aad1387a4ea>:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Création d'un DataFrame vide avec les colonnes 'filename', 'height' et 'width'\n",
        "df = pd.DataFrame(columns=['filename', 'height', 'width'])\n",
        "\n",
        "# Parcourir les éléments de la liste data_into_list (jusqu'à l'indice 13 inclus)\n",
        "for num in range(len(data_into_list)):\n",
        "    # Nettoyage de l'élément en supprimant les espaces en double, remplaçant '//' par '/' et en divisant par ','\n",
        "    x = data_into_list[num].replace('      ', '').replace('//', '/').split(',')\n",
        "    # Extraction du nom de fichier en supprimant les guillemets et en remplaçant 'jpg ' par 'jpg'\n",
        "    filename = x[0].split(': ')[1].replace('\"', '').replace('jpg ', 'jpg')\n",
        "    # Extraction de la hauteur en convertissant en float et arrondissant à 2 décimales\n",
        "    height = round(float(x[1].split(': ')[1]), 2)\n",
        "    # Extraction de la largeur en convertissant en float et arrondissant à 2 décimales\n",
        "    width = round(float(x[2].split(': ')[1]), 2)\n",
        "    # Ajout des valeurs dans le DataFrame\n",
        "    df = df.append({'filename': filename, 'height': height, 'width': width}, ignore_index=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "UDpA2mlnIoXa",
        "outputId": "163452a5-af2e-4cbe-fa3d-e1cfafff092a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       filename  height  width\n",
              "0     IMG_20210603_083350_5.jpg    2.96   3.90\n",
              "1       IMG_20210603_074354.jpg    2.66   3.30\n",
              "2                     im8_4.jpg    2.68   2.94\n",
              "3     IMG_20220223_140547_3.jpg    2.63   3.31\n",
              "4     IMG_20220223_122127_5.jpg    3.13   3.47\n",
              "..                          ...     ...    ...\n",
              "395   im14_5_translation_10.jpg    0.81   0.64\n",
              "396  im12_2_8_translation_6.jpg    0.61   0.66\n",
              "397     im12_2_8_rotation_6.jpg    0.61   0.68\n",
              "398         im12_2_8_zoom_6.jpg    0.60   0.66\n",
              "399     im12_2_8_rotation_7.jpg    0.66   0.69\n",
              "\n",
              "[400 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5055c90c-c9d3-40d4-9258-10f4c822ba1c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>height</th>\n",
              "      <th>width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IMG_20210603_083350_5.jpg</td>\n",
              "      <td>2.96</td>\n",
              "      <td>3.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IMG_20210603_074354.jpg</td>\n",
              "      <td>2.66</td>\n",
              "      <td>3.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>im8_4.jpg</td>\n",
              "      <td>2.68</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IMG_20220223_140547_3.jpg</td>\n",
              "      <td>2.63</td>\n",
              "      <td>3.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IMG_20220223_122127_5.jpg</td>\n",
              "      <td>3.13</td>\n",
              "      <td>3.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>im14_5_translation_10.jpg</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>im12_2_8_translation_6.jpg</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>im12_2_8_rotation_6.jpg</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>im12_2_8_zoom_6.jpg</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>im12_2_8_rotation_7.jpg</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5055c90c-c9d3-40d4-9258-10f4c822ba1c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5055c90c-c9d3-40d4-9258-10f4c822ba1c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5055c90c-c9d3-40d4-9258-10f4c822ba1c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2nGu_XcvJGI5"
      },
      "outputs": [],
      "source": [
        "# Exporter le DataFrame vers un fichier CSV\n",
        "df.to_csv('/content/drive/MyDrive/olive/Tree_height.csv', index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file\n",
        "data = pd.read_csv('/content/drive/MyDrive/olive/Tree_height.csv')\n",
        "\n",
        "# Initialize counters\n",
        "small_count = 0\n",
        "small_name=[]\n",
        "med_count = 0\n",
        "med_name=[]\n",
        "big_count = 0\n",
        "vbig_count = 0\n",
        "vbig_name=[]\n",
        "print(\"min=\",min(data['height']))\n",
        "print(\"max=\",max(data['height']))\n",
        "# Iterate over the 'height' column\n",
        "i=0\n",
        "for height in data['height']:\n",
        "\n",
        "    if (height <= 1.5) and (height > 0):\n",
        "        small_count += 1\n",
        "        small_name.append(data[\"filename\"][i])\n",
        "    elif (1.5 < height)and (height<=2.5):\n",
        "        med_count += 1\n",
        "        med_name.append(data[\"filename\"][i])\n",
        "    elif (2.5 < height )and (height<= 3.5):\n",
        "        big_count += 1\n",
        "    elif height > 3.5:\n",
        "        vbig_count += 1\n",
        "        vbig_name.append(data[\"filename\"][i])\n",
        "    i=i+1\n",
        "\n",
        "# Print the counts\n",
        "print(\"small++ count:\", small_count)\n",
        "print(\"med+++ count:\", med_count)\n",
        "print(\"big++ count:\", big_count)\n",
        "print(\"vbig++ count:\", vbig_count)\n",
        "print(\"list of small\",small_name)\n",
        "print(\"list of med\",med_name)\n",
        "print(\"list of vbig\",vbig_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEG9wiJBWDyx",
        "outputId": "d4e38b05-bef7-49b3-c3bf-963289954461"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "min= 0.58\n",
            "max= 4.78\n",
            "small++ count: 99\n",
            "med+++ count: 100\n",
            "big++ count: 100\n",
            "vbig++ count: 101\n",
            "list of small ['md4_5.jpg', 'md4_4.jpg', 'im12_2.jpg', 'im14_5.jpg', 'im12.jpg', 'md4_3.jpg', 'im13.jpg', 'im13_5.jpg', 'md4_2.jpg', 'im12_1.jpg', 'im12_5.jpg', 'md7.jpg', 'im14_4.jpg', 'im12_3.jpg', 'im13_2.jpg', 'im13_1.jpg', 'im14_1.jpg', 'im14.jpg', 'im14_3.jpg', 'im13_3.jpg', 'im13_4.jpg', 'im14_2.jpg', 'md4_1.jpg', 'md4.jpg', 'md4_5_6.jpg', 'md4_5_8.jpg', 'md4_5_7.jpg', 'md4_5_9.jpg', 'md4_5_10.jpg', 'im12_2_6.jpg', 'im12_2_7.jpg', 'im12_2_8.jpg', 'md4_5_zoom_6.jpg', 'md4_5_rotation_6.jpg', 'md4_5_translation_8.jpg', 'md4_5_translation_7.jpg', 'md4_5_flip_7.jpg', 'md4_5_rotation_8.jpg', 'md4_5_zoom_8.jpg', 'md4_5_flip_8.jpg', 'md4_5_flip_9.jpg', 'md4_5_zoom_10.jpg', 'md4_5_flip_10.jpg', 'md4_5_rotation_10.jpg', 'im12_2_flip_6.jpg', 'im12_2_rotation_7.jpg', 'im12_2_rotation_8.jpg', 'im12_2_zoom_8.jpg', 'im12_2_translation_9.jpg', 'im12_2_flip_9.jpg', 'im12_2_rotation_10.jpg', 'md4_4_translation_6.jpg', 'md4_4_flip_6.jpg', 'md4_4_rotation_6.jpg', 'md4_4_zoom_6.jpg', 'md4_4_translation_8.jpg', 'md4_4_flip_7.jpg', 'md4_4_zoom_8.jpg', 'md4_4_rotation_8.jpg', 'md4_4_translation_9.jpg', 'md4_4_zoom_9.jpg', 'md4_4_translation_10.jpg', 'md4_4_zoom_10.jpg', 'md4_4_flip_10.jpg', 'md4_5_9_translation_6.jpg', 'md4_5_9_flip_6.jpg', 'md4_5_9_rotation_6.jpg', 'md4_5_9_translation_7.jpg', 'md4_5_9_rotation_7.jpg', 'md4_5_9_zoom_7.jpg', 'md4_5_9_rotation_8.jpg', 'md4_5_9_translation_9.jpg', 'md4_5_9_rotation_9.jpg', 'md4_5_9_translation_10.jpg', 'md4_5_9_rotation_10.jpg', 'md4_5_10_zoom_6.jpg', 'md4_5_10_rotation_6.jpg', 'md4_5_10_flip_6.jpg', 'md4_5_10_rotation_7.jpg', 'md4_5_10_translation_7.jpg', 'md4_5_10_flip_7.jpg', 'md4_5_10_zoom_7.jpg', 'md4_5_10_rotation_8.jpg', 'md4_5_10_translation_8.jpg', 'md4_5_10_zoom_8.jpg', 'md4_5_10_flip_8.jpg', 'md4_5_10_zoom_9.jpg', 'md4_5_10_flip_9.jpg', 'md4_5_10_translation_10.jpg', 'md4_5_10_zoom_10.jpg', 'md4_5_10_flip_10.jpg', 'im14_5_translation_6.jpg', 'im14_5_rotation_9.jpg', 'im14_5_flip_9.jpg', 'im14_5_translation_10.jpg', 'im12_2_8_translation_6.jpg', 'im12_2_8_rotation_6.jpg', 'im12_2_8_zoom_6.jpg', 'im12_2_8_rotation_7.jpg']\n",
            "list of med ['md2_2.jpg', 'md1_4.jpg', 'IMG_20220223_133501.jpg', 'IMG_20220223_123400.jpg', 'IMG_20220223_132145_4.jpg', 'md3_1.jpg', 'md2_1.jpg', 'md7_2.jpg', 'md2.jpg', 'IMG_20220223_121059_1.jpg', 'IMG_20220223_113845.jpg', 'im7_2.jpg', 'md1.jpg', 'md3_3.jpg', 'IMG_20220223_121059.jpg', 'im7_5.jpg', 'md8.jpg', 'im7.jpg', 'IMG_20220223_132145_2.jpg', 'IMG_20210603_074427_2.jpg', 'md8_4.jpg', 'IMG_20220223_113845_4.jpg', 'im7_4.jpg', 'md2_4.jpg', 'im7_3.jpg', 'IMG_20220223_133501_2.jpg', 'md3_2.jpg', 'IMG_20220223_133501_4.jpg', 'md1_3.jpg', 'IMG_20220223_133501_5.jpg', 'md2_3.jpg', 'md8_2.jpg', 'md7_5.jpg', 'IMG_20220223_132145_3.jpg', 'md1_2.jpg', 'md7_4.jpg', 'IMG_20220223_123400_2.jpg', 'md1_1.jpg', 'md8_3.jpg', 'md7_3.jpg', 'IMG_20220223_133501_3.jpg', 'md8_1.jpg', 'md7_1.jpg', 'md2_5.jpg', 'md3_4.jpg', 'IMG_20220223_113845_5.jpg', 'md3_5.jpg', 'IMG_20220223_132145.jpg', 'IMG_20220223_133501_1.jpg', 'md3.jpg', 'IMG_20220223_113845_3.jpg', 'IMG_20220223_132145_1.jpg', 'md1_5.jpg', 'im7_1.jpg', 'md8_5.jpg', 'IMG_20220223_121059_5.jpg', 'IMG_20220223_123400_1.jpg', 'IMG_20220223_113845_2.jpg', 'IMG_20220223_132145_5.jpg', 'md2_2_8.jpg', 'md1_4_7.jpg', 'md2_2_10.jpg', 'md1_4_6.jpg', 'md1_4_8.jpg', 'md1_4_9.jpg', 'md1_4_10.jpg', 'IMG_20220223_133501_7.jpg', 'IMG_20220223_133501_8.jpg', 'IMG_20220223_133501_10.jpg', 'IMG_20220223_123400_6.jpg', 'IMG_20220223_123400_7.jpg', 'IMG_20220223_123400_8.jpg', 'IMG_20220223_123400_9.jpg', 'IMG_20220223_123400_10.jpg', 'IMG_20220223_113845_1_7.jpg', 'IMG_20220223_132145_4_6.jpg', 'IMG_20220223_132145_4_7.jpg', 'IMG_20220223_132145_4_8.jpg', 'IMG_20220223_132145_4_10.jpg', 'md3_1_7.jpg', 'md7_2_6.jpg', 'md2_6.jpg', 'md2_9.jpg', 'md2_8.jpg', 'md2_7.jpg', 'md2_10.jpg', 'md2_1_6.jpg', 'md2_1_9.jpg', 'IMG_20220223_113845_6.jpg', 'IMG_20220223_113845_7.jpg', 'IMG_20220223_113845_8.jpg', 'IMG_20220223_113845_9.jpg', 'IMG_20220223_113845_10.jpg', 'md1_6.jpg', 'md1_8.jpg', 'md1_7.jpg', 'md1_9.jpg', 'md1_10.jpg', 'im7_2_9.jpg', 'md3_3_6.jpg']\n",
            "list of vbig ['IMG_20220223_115152_1.jpg', 'IMG_20220223_115152_3.jpg', 'im5.jpg', 'im3_1.jpg', 'im1_2.jpg', 'im2_4.jpg', 'IMG_20220116_103146_3.jpg', 'IMG_20220116_092845_1.jpg', 'im6_4.jpg', 'IMG_20220116_091216_5.jpg', 'im6_2.jpg', 'IMG_20220116_103146.jpg', 'im3_3.jpg', 'IMG_20210603_082659_5.jpg', 'im1_5.jpg', 'IMG_20220116_093428_3.jpg', 'im3_4.jpg', 'O_130123_3.jpg', 'IMG_20220116_091216_2.jpg', 'IMG_20220116_103146_5.jpg', 'IMG_20220116_093428.jpg', 'IMG_20220116_103146_1.jpg', 'IMG_20220116_094149_2.jpg', 'im2.jpg', 'IMG_20210603_082659_2.jpg', 'IMG_20220116_095920_4.jpg', 'IMG_20220223_115152_4.jpg', 'IMG_20210603_082659_1.jpg', 'IMG_20220116_095920.jpg', 'IMG_20220116_102305_5.jpg', 'IMG_20220116_100624_1.jpg', 'IMG_20220116_095920_5.jpg', 'IMG_20210603_083056_4.jpg', 'IMG_20220116_094943_1.jpg', 'im5_4.jpg', 'IMG_20220116_095920_3.jpg', 'IMG_20220116_103146_2.jpg', 'im1.jpg', 'IMG_20210603_083105_4.jpg', 'O_124141_4.jpg', 'IMG_20220116_090204_5.jpg', 'IMG_20220116_094149_1.jpg', 'im1_3.jpg', 'im2_5.jpg', 'im3.jpg', 'IMG_20220116_094943_5.jpg', 'IMG_20220116_094943_3.jpg', 'IMG_20210603_083105_1.jpg', 'im12_4.jpg', 'IMG_20220116_102305.jpg', 'IMG_20210603_083056_3.jpg', 'IMG_20220116_092845_4.jpg', 'O_125721_4.jpg', 'IMG_20220116_090204_1.jpg', 'IMG_20220116_093428_4.jpg', 'IMG_20220116_091216_3.jpg', 'IMG_20220116_092845_3.jpg', 'IMG_20210603_082659_3.jpg', 'IMG_20220116_094149_5.jpg', 'IMG_20220223_115152.jpg', 'IMG_20220116_092845_2.jpg', 'IMG_20220116_085405_2.jpg', 'IMG_20210603_082659.jpg', 'IMG_20220116_090204_4.jpg', 'IMG_20220116_094943_4.jpg', 'IMG_20220116_101502_5.jpg', 'IMG_20220116_090204_2.jpg', 'IMG_20220116_084643_1.jpg', 'im2_1.jpg', 'IMG_20220116_101502_1.jpg', 'im6_5.jpg', 'im6.jpg', 'IMG_20220116_090204_3.jpg', 'IMG_20220116_100624_5.jpg', 'IMG_20220116_100624_2.jpg', 'im5_3.jpg', 'IMG_20210603_083105_3.jpg', 'IMG_20220116_101502_2.jpg', 'IMG_20220116_093428_5.jpg', 'IMG_20220116_084643_3.jpg', 'im1_1.jpg', 'IMG_20220116_084643_2.jpg', 'IMG_20220116_101502_4.jpg', 'im5_5.jpg', 'O_130123_4.jpg', 'IMG_20220116_102305_2.jpg', 'IMG_20220116_100624.jpg', 'IMG_20220116_102305_3.jpg', 'O_124141_1.jpg', 'IMG_20220116_094943.jpg', 'IMG_20220116_101502_3.jpg', 'IMG_20220116_084643_5.jpg', 'IMG_20220116_100624_3.jpg', 'im6_1.jpg', 'IMG_20220116_094149.jpg', 'IMG_20220116_084643.jpg', 'O_124141.jpg', 'im3_2.jpg', 'IMG_20220116_093428_2.jpg', 'IMG_20220116_094943_2.jpg', 'im1_4.jpg']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZcVl53WiXod_"
      },
      "source": [
        "#segmentation des image pour ResNet50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R8BkgRNLNf7S"
      },
      "outputs": [],
      "source": [
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as datasets\n",
        "import torchvision.models as models\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms, utils\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import glob\n",
        "import torch\n",
        "import pandas as pd\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "zHPJWryLNs_a",
        "outputId": "bd100bdd-0b7b-43b6-e7c0-f381df747109"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nimport random\\nimport cv2\\n\\n# Path to the directory containing the images\\ndirectory = \\'/content/drive/MyDrive/olive/eq_augmented_data\\'\\n\\ncon=0\\n# Traverse all images in the directory\\nfor filename in os.listdir(directory):\\n  filepath = os.path.join(directory, filename)\\n  # Load the image\\n  im= cv2.imread(filepath)\\n  # Effectuer des prédictions sur l\\'image en utilisant le prédicteur\\n  outputs = predictor(im)\\n  # Get the predicted classes and segmented masks\\n  pred_classes = outputs[\"instances\"].pred_classes\\n  masks = outputs[\"instances\"].pred_masks\\n  # Convert masks to numpy array\\n  masks_np = masks.cpu().numpy()\\n  # Create a blank image to store the segmented part\\n  segmented_image = np.zeros_like(im)\\n  # Apply the masks to the segmented image\\n  for i, mask in enumerate(masks_np):\\n    if pred_classes[i] == 0:\\n      segmented_image[mask] = im[mask]\\n\\n  #plt.imshow(segmented_image)\\n  #plt.show()\\n  # Create the output directory if it doesn\\'t exist\\n  output_dir = \"/content/drive/MyDrive/olive/segmeted/\"\\n  os.makedirs(output_dir, exist_ok=True)\\n\\n  # Save the segmented image\\n  output_path = os.path.join(output_dir, filename)\\n  cv2.imwrite(output_path, segmented_image)\\n  con=con+1\\n  print(\"*****************************************con\",con)\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "\"\"\"\n",
        "import random\n",
        "import cv2\n",
        "\n",
        "# Path to the directory containing the images\n",
        "directory = '/content/drive/MyDrive/olive/eq_augmented_data'\n",
        "\n",
        "con=0\n",
        "# Traverse all images in the directory\n",
        "for filename in os.listdir(directory):\n",
        "  filepath = os.path.join(directory, filename)\n",
        "  # Load the image\n",
        "  im= cv2.imread(filepath)\n",
        "  # Effectuer des prédictions sur l'image en utilisant le prédicteur\n",
        "  outputs = predictor(im)\n",
        "  # Get the predicted classes and segmented masks\n",
        "  pred_classes = outputs[\"instances\"].pred_classes\n",
        "  masks = outputs[\"instances\"].pred_masks\n",
        "  # Convert masks to numpy array\n",
        "  masks_np = masks.cpu().numpy()\n",
        "  # Create a blank image to store the segmented part\n",
        "  segmented_image = np.zeros_like(im)\n",
        "  # Apply the masks to the segmented image\n",
        "  for i, mask in enumerate(masks_np):\n",
        "    if pred_classes[i] == 0:\n",
        "      segmented_image[mask] = im[mask]\n",
        "\n",
        "  #plt.imshow(segmented_image)\n",
        "  #plt.show()\n",
        "  # Create the output directory if it doesn't exist\n",
        "  output_dir = \"/content/drive/MyDrive/olive/segmeted/\"\n",
        "  os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "  # Save the segmented image\n",
        "  output_path = os.path.join(output_dir, filename)\n",
        "  cv2.imwrite(output_path, segmented_image)\n",
        "  con=con+1\n",
        "  print(\"*****************************************con\",con)\n",
        "\"\"\""
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}