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.

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.


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

  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},