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Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

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LUPerson-NL

Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

The repository is for our CVPR2022 paper Large-Scale Pre-training for Person Re-identification with Noisy Labels.

LUPerson-NL Dataset

LUPerson-NL is currently the largest noisy annotated Person Re-identification dataset without humuan labelling efforts, which is used for Pre-training. LUPerson-NL consists of 10M images of over 430K identities extracted from 21K street-view videos and covers a much diverse range of capturing environments.

Details can be found at ./LUP-NL.

Pre-trained Models

Model link
ResNet50 R50 code:pr50
ResNet101 R101 code:r101
ResNet152 R152 code:r152

Finetuned Results

For MGN with ResNet50:

Dataset mAP cmc1 link
MSMT17 68.0 86.0 -
DukeMTMC 84.3 92.0 -
Market1501 91.9 96.6 -
CUHK03-L 80.4 80.9 -

For MGN with ResNet101:

Dataset mAP cmc1 path
MSMT17 70.8 87.1 -
DukeMTMC 85.5 92.8 -
Market1501 92.5 96.9 -
CUHK03-L 80.5 81.2 -

For MGN with ResNet152:

Dataset mAP cmc1 path
MSMT17 71.6 87.5 -
DukeMTMC 85.6 92.4 -
Market1501 92.7 96.8 -
CUHK03-L 80.6 81.2 -

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}
}
@article{fu2022large,
  title={Large-Scale Pre-training for Person Re-identification with Noisy Labels},
  author={Fu, Dengpan and Chen, Dongdong and Yang, Hao and Bao, Jianmin and Yuan, Lu and Zhang, Lei and Li, Houqiang and Wen, Fang and Chen, Dong},
  journal={arXiv preprint arXiv:2203.16533},
  year={2022}
}

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