by Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee, Xianzheng Ma, Xiangwei Zhu, Zhenming Ji
Here is the official project of 🎻CoDA. We are releasing the training code and dataset generated by ourselves in our paper.
CoDA is a UDA methodology that boosts models to understand all adverse scenes (☁️,☔,❄️,🌙) by highlighting the discrepancies between and within these scenes. CoDA achieves state-of-the-art performances on widely used benchmarks.
[2024-7-10]We have released our generated data samples. You can download from here.
[Baidu Netdisk] [Google Drive]
[2024-7-2] We are delighted to inform that CoDA has been accepted by ECCV 2024 main conference 🎉🎉🎉!!!
[2024-3-8] We create the official project of CoDA and release the inference code.
Experiments | mIoU | Checkpoint |
---|---|---|
Cityscapes |
72.6 | - |
Cityscapes |
60.9 | - |
Cityscapes |
61.0 | - |
Cityscapes |
61.2 | - |
Cityscapes |
59.2 | - |
Cityscapes |
41.6 | - |
If you find this project useful in your research, please consider citing:
@article{gong2024coda,
title={CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning},
author={Gong, Ziyang and Li, Fuhao and Deng, Yupeng and Bhattacharjee, Deblina and Zhu, Xiangwei and Ji, Zhenming},
journal={arXiv preprint arXiv:2403.17369},
year={2024}
}
cd CoDA
python ./tools/download_ck.py
or you can manually download checkpoints from Google Drive.
conda create -n coda python=3.8.5 pip=22.3.1
conda activate coda
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.3.7 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7/index.html
Before run demo, first configure the PYTHONPATH, or you may encounter error like 'can not found tools...'.
cd CoDA
export PYTHONPATH=.:$PYTHONPATH
or directly modify the .bashrc file
vi ~/.bashrc
export PYTHONPATH=your path/CoDA:$PYTHONPATH
source ~/.bashrc
python ./tools/image_demo.py --img ./images/night_demo.png --config ./configs/coda/csHR2acdcHR_coda.py --checkpoint ./pretrained/CoDA_cs2acdc.pth
python ./tools/image_demo.py --img_dir ./acdc_dir --config ./configs/coda/csHR2acdcHR_coda.py --checkpoint ./pretrained/CoDA_cs2acdc.pth --out_dir ./workdir/cs2acdc
python ./tools/train.py --config ./configs/coda/csHR2acdcHR_coda.py --work-dir ./workdir/cs2acdc