Our Method

Task Description

Model

My Improvement

Reproduction

Getting Started

Dataset

Pretrained Model

Final Trained Model

File Organization

# store the whole dataset and pretrained backbonesmkdir -p data/dataset ; mkdir -p data/pretrained_models ;# store the trained modelsmkdir -p outdir/models ; # store predictions of validation set and testing setmkdir -p outdir/masks/val/im1 ; mkdir -p outdir/masks/val/im2 ;mkdir -p outdir/masks/test/im1 ; mkdir -p outdir/masks/test/im2 ;├── data    ├── dataset                    # download from the link above    │   ├── train                  # training set    |   |   ├── im1    |   |   └── ...    │   └── val                    # the final testing set (without labels)    |    └── pretrained_models        ├── resnet18.pth        ├── resnet34.pth        └── ...

Training

# Please refer to utils/options.py for more arguments# If hardware supports, more backbones can be trained, such as resnet50, resnet101CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone "resnet18" --pretrained --model "fcn"

Testing

# Modify the backbones, models and checkpoint paths in L39-44 in test.py manually according to your saved models# Or simply use our final trained modelsCUDA_VISIBLE_DEVICES=0,1,2,3 python test.py```