This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset.

FACT

This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset.

To cite, please use:

@InProceedings{Xu_2021_CVPR,
    author    = {Xu, Qinwei and Zhang, Ruipeng and Zhang, Ya and Wang, Yanfeng and Tian, Qi},
    title     = {A Fourier-Based Framework for Domain Generalization},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {14383-14392}
}

Requirements

• Python 3.6
• Pytorch 1.1.0

Evaluation

Firstly create directory ckpt/ and drag your model under it. For running the evaluation code, please download the PACS dataset from http://www.eecs.qmul.ac.uk/~dl307/project_iccv2017. Then update the files with suffix _test.txt in data/datalists for each domain, following styles below:

/home/user/data/images/PACS/kfold/art_painting/dog/pic_001.jpg 0
/home/user/data/images/PACS/kfold/art_painting/dog/pic_002.jpg 0
/home/user/data/images/PACS/kfold/art_painting/dog/pic_003.jpg 0
...

Once the data is prepared, remember to update the path of test files and output logs in shell_test.py:

input_dir = 'path/to/test/files'
output_dir = 'path/to/output/logs'

then simply run:

 python shell_test.py -d=art_painting

You can use the argument -d to specify the held-out target domain.

Training from scratch

After downloading the dataset, update the files with suffix _train.txt and _val.txt in data/datalists for each domain, following the same styles as the test files above. Please make sure you are using the official train-val-split. Then update the the path of train&val files and output logs in shell_train.py:

input_dir = 'path/to/train/files'
output_dir = 'path/to/output/logs'

Then running the code:

python shell_train.py -d=art_painting

Use the argument -d to specify the held-out target domain.

Acknowledgement

Part of our code is borrowed from the following repositories.

• JigenDG: "Domain Generalization by Solving Jigsaw Puzzles", CVPR 2019
• DDAIG: "Deep Domain-Adversarial Image Generation for Domain Generalisation", AAAI 2020

We thank to the authors for releasing their codes. Please also consider citing their works.