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PyTorch implementation for self-supervised co-part segmentation.

Copyright (C) 2019 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

paper

supplementary

The code is developed based on Pytorch v0.4 with TensorboardX as visualization tools. We recommend to use virtualenv to run our code:

$ virtualenv -p python3 scops_env
$ source scops_env/bin/activate
(scops_env)$ pip install -r requirements.txt

To deactivate the virtual environment, run $ deactivate. To activate the environment again, run $ source scops_env/bin/activate.

Download data (Saliency, labels, pretrained model)

$ ./download_CelebA.sh

Download CelebA unaligned from here.

$ ./evaluate_celebAWild.sh and accept all default options. The results are stored in a single webpage at results_CelebA/SCOPS_K8/ITER_100000/web_html/index.html.

$ CUDA_VISIBLE_DEVICES={GPU} python train.py -f exps/SCOPS_K8_retrain.json where {GPU} is the GPU device number.

Please consider citing our paper if you find this code useful for your research.

@inproceedings{hung:CVPR:2019,
	title = {SCOPS: Self-Supervised Co-Part Segmentation},
	author = {Hung, Wei-Chih and Jampani, Varun and Liu, Sifei and Molchanov, Pavlo and Yang, Ming-Hsuan and Kautz, Jan},
	booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
	month = june,
	year = {2019}
}