Progressive Learning for Person Re-Identification with One Example

Pytorch implementation for our paper [Link]. This code is based on the Open-ReID library and Exploit-Unknown-Gradually.

Preparation

Dependencies

• Python 3.6
• PyTorch (version >= 0.4.1)
• scikit-learn, metric-learn, tqdm

Download datasets

• DukeMTMC-VideoReID: [Direct Link]  [Google Drive]  [BaiduYun]. This page contains more details and baseline code.
• MARS: [Google Drive]   [BaiduYun].
• Market-1501: [Direct Link]
• DukeMTMC-reID: [Direct Link]
• Move the downloaded zip files to ./data/ and unzip here.

Train

sh run.sh

Please set the max_frames smaller if your GPU memory is less than 11G.

Performances

The performances varies according to random splits for initial labeled data. To reproduce the performances in our paper, please use the one-shot splits at ./examples/

Citation

Please cite the following paper in your publications if it helps your research:

@article{wu2019progressive,
  title  = {Progressive Learning for Person Re-Identification with One Example},
  author = {Wu, Yu and Lin, Yutian and Dong, Xuanyi and Yan, Yan and Bian, Wei and Yang, Yi},
  journal= {IEEE Transactions on Image Processing (TIP)},
  year   = {2019}, 
  doi    = {10.1109/TIP.2019.2891895}
}

Contact

To report issues for this code, please open an issue on the issues tracker.

If you have further questions about this paper, please do not hesitate to contact me.

Yu Wu's Homepage