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DSR in FastReID

Deep Spatial Feature Reconstruction for Partial Person Re-identification

Lingxiao He, Xingyu Liao


] [

Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification

Lingxiao He, Xingyu Liao


] [


First install FastReID, and then put Partial Datasets in directory datasets. The whole framework of FastReID-DSR is

and the detail you can refer to


The datasets can find in Google Drive

PartialREID---gallery: 300 images of 60 ids, query: 300 images of 60 ids

PartialiLIDS---gallery: 119 images of 119 ids, query: 119 images of 119 ids

OccludedREID---gallery: 1,000 images of 200 ids, query: 1,000 images of 200 ids

Training and Evaluation

To train a model, run:

python3 projects/PartialReID/ --config-file 

For example, to train the re-id network with IBN-ResNet-50 Backbone one should execute:

CUDA_VISIBLE_DEVICES='0,1,2,3' python3 projects/PartialReID/ --config-file 'projects/PartialReID/configs/partial_market.yml'


| Method | PartialREID | OccludedREID | PartialiLIDS | |:--:|:--:|:--:|:--:| | | [email protected] (mAP)| [email protected] (mAP)| [email protected] (mAP)| | DSR (CVPR’18) |73.7(68.1) |72.8(62.8)|64.3(58.1)| | FPR (ICCV'19) | 81.0(76.6)|78.3(68.0)|68.1(61.8)| | FastReID-DSR | 82.7(76.8)|81.6(70.9)|73.1(79.8) |

Citing DSR and Citing FPR

If you use DSR or FPR, please use the following BibTeX entry.

  title={Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach},
  author={He, Lingxiao and Liang, Jian and Li, Haiqing and Sun, Zhenan},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  title={Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification},
  author={He, Lingxiao and Wang, Yinggang and Liu, Wu and Zhao, He and Sun, Zhenan and Feng, Jiashi},
  booktitle={IEEE International Conference on Computer Vision (ICCV)},

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