feesics / bottom-up-clustering-person-re-identification

A Bottom-Up Clustering Approach to Unsupervised Person Re-identification, AAAI 2019 (Oral)

A Bottom-Up Clustering Approach to Unsupervised Person Re-identification

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

Preparation

Dependencies

• Python 3.6
• PyTorch (version >= 0.4.1)
• h5py, 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.

Usage

sh ./run.sh

--size_penalty parameter lambda to balance the diversity regularization term.

--merge_percent percent of data to merge at each iteration.

Citation

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

@inproceedings{lin2019aBottom,
    title     = {A Bottom-Up Clustering Approach to Unsupervised Person Re-identification},
    author    = {Lin, Yutian and Dong, Xuanyi and Zheng, Liang and Yan, Yan and Yang, Yi},
    booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
    year      = {2019}
}