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S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal (NTIRE @ CVPR 2024) - Kubiak et al.

image info

The basics

This is the official project repo for the paper S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal, published at the NTIRE workshop at CVPR 2024.

The model was developed in an env based on Pytorch 1.8.1 with CUDA 11.1 (docker image: nvidia/cuda:11.1.1-cudnn8-runtime-ubuntu18.04). Key project dependancies: kornia, scikit-image, wandb.

Testing

  • Download the checkpoints (ISTD | AISTD) and put them in the relevant folder, e.g. checkpoints/best_istd (you need to create the ./checkpoints folder). Rename each checkpoint file to latest_net_G.pth.

  • Update the dataset path - self.image_folder - in tools/datasets/istd_paired_dataset.py to point towards your ISTD/AISTD dataset folder.

  • To test the ISTD model, run:

    python test.py --name best_istd --dataset istd

    For AISTD, replace istd with aistd in the checkpoint & dataset names.

Training

  • Update the dataset path - self.image_folder - in tools/datasets/istd_paired_dataset.py to point towards your ISTD/AISTD dataset folder. Alternatively, create your own dataset in tools/datasets/<YOUR DATASET>, add it to tools/dataloader.py and use the flag --dataset <YOUR DATASET>.

  • To train the model, use the below command:

    python train.py --name <YOUR EXP NAME> --dataset <YOUR DATASET>/istd/aistd

  • To enable wandb logging, use the --tb_log 1 flag; you'll also need to create a ./logs folder. For other training parameters, please refer to tools/args.py.

  • To evaluate, follow the instruction from Testing. To test a model version other than the default latest, use the --which <EPOCH NUMBER> flag.

Citation

If you use or write about S3R-Net, please use the below citation:

@inproceedings{kubiak_2024_s3rnet,
  title={S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal},
  author={Nikolina Kubiak and Armin Mustafa and Graeme Phillipson and Stephen Jolly and Simon Hadfield},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2024}
}

Acknowledgements

This project builds upon pix2pixHD and our previous project - SILT.