windroser / luperson

Unsupervised Pre-training for Person Re-identification (LUPerson)

LUPerson

Unsupervised Pre-training for Person Re-identification (LUPerson).

The repository is for our CVPR2021 paper Unsupervised Pre-training for Person Re-identification.

LUPerson Dataset

LUPerson is currently the largest unlabeled dataset for Person Re-identification, which is used for Unsupervised Pre-training. LUPerson consists of 4M images of over 200K identities and covers a much diverse range of capturing environments.

LUPerson can only be used for research, commercial usage is forbidden.

Details can be found at ./LUP.

Pre-trained Models

Model	path
ResNet50	R50
ResNet101	R101
ResNet152	R152

Finetuned Results

For MGN with ResNet50:

Dataset	mAP	cmc1	path
MSMT17	66.06/79.93	85.08/87.63	MSMT
DukeMTMC	82.27/91.70	90.35/92.82	Duke
Market1501	91.12/96.16	96.26/97.12	Market
CUHK03-L	74.54/85.84	74.64/82.86	CUHK03

These numbers are a little different from those reported in our paper, and most are slightly better.

For MGN with ResNet101:

Dataset	mAP	cmc1	path
MSMT17	68.41/81.12	86.28/88.27	-
DukeMTMC	84.15/92.77	91.88/93.99	-
Market1501	91.86/96.21	96.56/97.03	-
CUHK03-L	75.98/86.73	75.86/84.07	-

The numbers are in the format of without RR/with RR.

Citation

If you find this code useful for your research, please cite our paper.

@article{fu2020unsupervised,
  title={Unsupervised Pre-training for Person Re-identification},
  author={Fu, Dengpan and Chen, Dongdong and Bao, Jianmin and Yang, Hao and Yuan, Lu and Zhang, Lei and Li, Houqiang and Chen, Dong},
  journal={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2021}
}

News

We extend our LUPerson to LUPerson-NL with Noisy Labels which are generated from tracking algorithm, Please check for our CVPR22 paper Large-Scale Pre-training for Person Re-identification with Noisy Labels. And LUPerson-NL dataset is available at https://github.com/DengpanFu/LUPerson-NL

Third-party Usage

LUPerson and LUPerson-NL are used by some work and have obtained very good performance.

• [2021-03] TransReID: https://github.com/damo-cv/TransReID
• [2021-12] TransReID-SSL: https://github.com/damo-cv/TransReID-SSL
• [2023-03] SOLIDER: https://github.com/tinyvision/SOLIDER