CoTTA

Code for our CVPR 2022 paper Continual Test-Time Domain Adaptation

Prerequisite

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

Experiment

CIFAR10-to-CIFAR10C-standard task

# Tested on RTX2080TI
cd cifar
bash run_cifar10.sh

CIFAR10-to-CIFAR10C-gradual task

# Tested on RTX2080TI
bash run_cifar10_gradual.sh

CIFAR100-to-CIFAR100C task

# Tested on RTX3090
bash run_cifar100.sh

ImageNet-to-ImageNetC task

# Tested on RTX3090
cd imagenet
bash run.sh

Citation

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}
}

Acknowledgement

TENT code is heavily used. official
KATANA code is used for augmentation. official
Robustbench official

Data links

ImageNet-C Download
Supplementary

For questions regarding the code, please contact wang@qin.ee .

zongzi13545329/cotta