RCCR

(ICME 2022) Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization

[Arxiv] [Paper]

Citing RCCR

If you find RCCR useful in your research, please consider citing:

@inproceedings{zhou2022domain,
  title={Domain adaptive semantic segmentation with regional contrastive consistency regularization},
  author={Zhou, Qianyu and Zhuang, Chuyun and Lu, Xuequan and Ma, Lizhuang},
  booktitle={2022 IEEE International Conference on Multimedia and Expo (ICME)},
  year={2022},
  organization={IEEE}
}

Requirements

• CUDA/CUDNN
• Python3
• Pytorch
• Scipy==1.2.0
• Other requirements

  pip install -r requirements.txt

Run training and testing

Example of training a model with unsupervised domain adaptation on GTA5->CityScapes on a single gpu

python3 train_spasr_gtav.py --config ./configs/configUDA_spasr_gtav.json --name UDA

Example of testing a model with domain adaptation with CityScapes as target domain

python3 evaluateUDA.py --model-path checkpoint.pth

Acknowledgements

This project is based on the following open-source projects. We thank their authors for making the source code publically available.

• DACS

License

This project is released under the Apache License 2.0, while some specific features in this repository are with other licenses. Please refer to LICENSES.md for the careful check, if you are using our code for commercial matters.