Code for our CVPR 2022 paper Continual Test-Time Domain Adaptation
Please create and activate the following conda envrionment. To reproduce our results, please kindly create and use this environment.
# It may take several minutes for conda to solve the environment
conda update conda
conda env create -f environment.yml
conda activate cotta
# Tested on RTX2080TI
cd cifar
bash run_cifar10.sh
# Tested on RTX2080TI
bash run_cifar10_gradual.sh
# Tested on RTX3090
bash run_cifar100.sh
# Tested on RTX3090
cd imagenet
bash run.sh
Please cite our work if you find it useful.
@inproceedings{wang2022continual,
title={Continual Test-Time Domain Adaptation},
author={Wang, Qin and Fink, Olga and Van Gool, Luc and Dai, Dengxin},
booktitle={Proceedings of Conference on Computer Vision and Pattern Recognition},
year={2022}
}
- TENT code is heavily used. official
- KATANA code is used for augmentation. official
- Robustbench official
- ImageNet-C Download
For questions regarding the code, please contact wang@qin.ee .