# store the whole dataset and pretrained backbones
mkdir -p data/dataset ; mkdir -p data/pretrained_models ;
# store the trained models
mkdir -p outdir/models ; 
# store predictions of validation set and testing set
mkdir -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
        └── ...

# Please refer to utils/options.py for more arguments
# If hardware supports, more backbones can be trained, such as resnet50, resnet101
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone "resnet18" --pretrained --model "fcn"

# 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 models
CUDA_VISIBLE_DEVICES=0,1,2,3 python test.py```