# 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 the pseudo masks
mkdir -p outdir/masks/train/im1 ; mkdir -p outdir/masks/train/im2 ;

# 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
        ├── hrnet_w18.pth
        ├── hrnet_w40.pth
        └── ...

# Please refer to utils/options.py for more arguments
# If hardware supports, more backbones can be trained, such as hrnet_w44, hrnet_w48
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone hrnet_w18 --pretrained --model pspnet --lightweight

# This step is optional but important in performance improvement
# Modify the backbones, models and checkpoint paths in L20-40 in label.py manually according to your saved models
# It is better to ensemble multiple trained models for pseudo labeling

# Pseudo labeling
CUDA_VISIBLE_DEVICES=0,1,2,3 python label.py

# Re-training
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone hrnet_w18 --pretrained --model pspnet --lightweight --use-pseudo-label

# 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