This is a PyTorch implementation of our CVPR 2024 paper Unified Entropy Optimization for Open-Set Test-Time Adaptation.
This repository also contains the implementation of Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization.
conda create -n unient -y python=3.9
conda activate unient
pip install -r requirements.txtDownload and extract CIFAR-10-C manually or use the commands below:
wget --content-disposition https://zenodo.org/records/2535967/files/CIFAR-10-C.tar?download=1
tar -xvf CIFAR-10-C.tarDownload and extract SVHN-C manually or use the commands below:
gdown 1IxX_2rtw0_RB4ewADEOB4icW7Idkje8P
tar -zxvf SVHN-C.tar.gzAlternatively, following https://github.com/hendrycks/robustness, apply common corruptions and perturbations to SVHN to produce SVHN-C.
Create symbolic links to the datasets:
mkdir cifar/data
ln -s /path/to/CIFAR-10-C cifar/data
ln -s /path/to/SVHN-C cifar/dataMoreover, for EATA, download CIFAR-10 and create a symbolic link:
ln -s /path/to/CIFAR-10 cifar/dataDownload and extract CIFAR-100-C manually or use the commands below:
wget --content-disposition https://zenodo.org/records/3555552/files/CIFAR-100-C.tar?download=1
tar -xvf CIFAR-100-C.tarCreate a symbolic link to the dataset:
ln -s /path/to/CIFAR-100-C cifar/dataMoreover, for EATA, download CIFAR-100 and create a symbolic link:
ln -s /path/to/CIFAR-100 cifar/dataDownload and extract Tiny-ImageNet-C manually or use the commands below:
wget --content-disposition https://zenodo.org/records/2536630/files/Tiny-ImageNet-C.tar?download=1
tar -xvf Tiny-ImageNet-C.tarDownload and extract ImageNet-O-64x64-C manually or use the commands below:
gdown 1MdtUCbC5VknTgYne17YxC0jlSrfSdBkI
tar -zxvf ImageNet-O-64x64-C.tar.gzAlternatively, following https://github.com/hendrycks/robustness, apply common corruptions and perturbations to ImageNet-O to produce ImageNet-O-64x64-C.
Create symbolic links to the datasets:
mkdir tiny_imagenet/data
ln -s /path/to/Tiny-ImageNet-C tiny_imagenet/data
ln -s /path/to/ImageNet-O-64x64-C tiny_imagenet/dataMoreover, for EATA, download Tiny-ImageNet and create a symbolic link:
ln -s /path/to/Tiny-ImageNet tiny_imagenet/dataDownload and extract ImageNet-C manually or use the commands below:
bash imagenet/download.sh -p /path/to/ImageNet-CDownload and extract ImageNet-O-C manually or use the commands below:
gdown 1In4tZQes8YkFN_NEyQmjxFh-YP3iwpE0
tar -zxvf ImageNet-O-C.tar.gzAlternatively, following https://github.com/hendrycks/robustness, apply common corruptions and perturbations to ImageNet-O to produce ImageNet-O-C.
Create symbolic links to the datasets:
mkdir imagenet/data
ln -s /path/to/ImageNet-C imagenet/data
ln -s /path/to/ImageNet-O-C imagenet/dataMoreover, for EATA, download ImageNet and create a symbolic link:
ln -s /path/to/ImageNet imagenet/dataFor more details on the datasets, please refer to https://github.com/hendrycks/robustness.
cd cifar
# perform a hyperparameter grid search
bash run.sh -g 0 -d cifar10cd cifar
# perform a hyperparameter grid search
bash run.sh -g 0 -d cifar100Train the model on the source dataset or use our pre-trained checkpoint from here.
cd tiny_imagenet
# optional
python train.py ./data/tiny-imagenet-200 -a resnet50 --epochs 50 --lr 0.01 --pretrained --gpu 0
# perform a hyperparameter grid search
bash run.sh -g 0cd imagenet
# perform a hyperparameter grid search
bash run.sh -g 0@article{gao2024unified,
title={Unified Entropy Optimization for Open-Set Test-Time Adaptation},
author={Zhengqing Gao and Xu-Yao Zhang and Cheng-Lin Liu},
journal={arXiv preprint arXiv:2404.06065},
year={2024}
}
Our implementation references the codes in the following repositories: