by Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee, Xianzheng Ma, Xiangwei Zhu, Zhenming Ji
Here is the official project of ๐ปCoDA. 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 (โ๏ธ,โ,โ๏ธ,๐) 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]ย ย ย ย [Google Drive]
[2024-7-2] We are delighted to inform that CoDA has been accepted by ECCV 2024 main conference ๐๐๐!!!
[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