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A Strong Baseline for RGB-Infrared Cross-Modality Person Re-Identification

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A Strong Baseline for RGD-Infrared Cross-Modality Person Re-Identification

Dependency

  • Python 3.7
  • PyTorch 1.10
  • Ignite 0.4.7
  • Yacs

Utilization

Download SYSU-MM01 dataset and uncompress it. Change the entry data_root in configs/default.py to the path of the dataset. Put the rand_perm_cam.mat in exp directory in dataset root. This file is used to assign gallery items for each trial while testing. Run

CUDA_VISIBLE_DEVICES=0 python3 train.py configs/baseline.yml --work-dir work_dirs/baseline 

Performance

We evaluate the performance on SYSU-MM01 under the setting of one-shot & all-search.

model mAP rank-1 rank-5 rank-10 rank-20
baseline 54.60 57.51 82.77 90.05 95.28

Reference

L1aoXingyu/reid_baseline

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A Strong Baseline for RGB-Infrared Cross-Modality Person Re-Identification

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