diff --git a/README.md b/README.md index 9f16a19..3e06cc5 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ This repository contains the accompanying code for [4D-SpatioTemporal ConvNets: 2. Next, preprocess all scannet raw point cloud with the following command after you set the path correctly. ``` -python -m lib.datasets.prepreocessing.scannet +python -m lib.datasets.preprocessing.scannet ``` 3. Train the network with @@ -65,6 +65,7 @@ The above script trains a network. You have to change the arguments accordingly. ## Model Zoo -| Model | Dataset | Voxel Size | Performance | Link | -|:-------------:|:-------:|:----------:|:------------------------:|:------:| -| Mink16UNet34C | ScanNet | 2cm | Test set 73.6% mIoU | [download](https://node1.chrischoy.org/data/publications/minknet/Mink16UNet34C_ScanNet.pth) | +| Model | Dataset | Voxel Size | Conv1 Kernel Size | Performance | Link | +|:-------------:|:-------------------:|:----------:|:-----------------:|:------------------------:|:------:| +| Mink16UNet34C | ScanNet train + val | 2cm | 3 | Test set 73.6% mIoU | [download](https://node1.chrischoy.org/data/publications/minknet/Mink16UNet34C_ScanNet.pth) | +| Mink16UNet34C | ScanNet train | 2cm | 5 | Val 72.219% mIoU without rotation average [per class performance](https://github.com/chrischoy/SpatioTemporalSegmentation/issues/13) | [download](https://node1.chrischoy.org/data/publications/minknet/MinkUNet34C-train-conv1-5.pth) | diff --git a/lib/dataset.py b/lib/dataset.py index 8cb7984..2331da1 100644 --- a/lib/dataset.py +++ b/lib/dataset.py @@ -434,7 +434,7 @@ def initialize_data_loader(DatasetClass, t.ChromaticAutoContrast(), t.ChromaticTranslation(config.data_aug_color_trans_ratio), t.ChromaticJitter(config.data_aug_color_jitter_std), - t.HueSaturationTranslation(config.data_aug_hue_max, config.data_aug_saturation_max), + # t.HueSaturationTranslation(config.data_aug_hue_max, config.data_aug_saturation_max), ] if len(input_transforms) > 0: