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A simple baseline for Person ReID, it achieves 3rd place in VisDA2020 challenge.

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The repo contains our code for VisDA 2020 challenge

What's new

  • practical post-process: remove camera bias
  • circle loss (class-level and pair-wise)
  • memory bank
  • Mix precision (FP16)
  • Advanced Data augmentation: augmix, auto-augmentation
  • pseudo label based method for unsupervised learning
  • efficient search and re-rank by faiss
  • Multi-GPU (single node DDP)
  • SOTA benchmark
  • Distillation

Requirement

  1. pytorch>=1.2.0
  2. yacs
  3. sklearn
  4. apex
  5. faiss (pip install faiss-gpu)

Reproduce results on VisDA 2020 Challenge

Refer to VISDA20.md and tech_report, trained models can be download from here

  • leaderboard (ranged by rank1)
team mAP rank1
vimar 76.56% 84.25%
xiangyu(ours) 72.39% 83.85%
yxge 74.78% 82.86%
  • Ablation on validation set
method mAP rank1
personx-spgan 37.7% 63.7%
+pseudo label 51.8% 77.7%
+BN finetune 55.5% 81.4%
+re-rank 73.4% 80.9%
+remove camera bias 79.5% 89.1%
ensemble 82.7% 90.7%

Benchmark

Setting: ResNet50-ibn-a, single RTX 2080 Ti, FP16

  • market1501
method mAP rank1
bag-of-tricks 88.2% 95.0%
fast reid 89.3% 95.3%
ours 88.4% 95.1%
  • dukemtmc-reid
method mAP rank1
bag-of-tricks 79.1% 90.1%
fast-reid 81.2% 90.8%
ours 80.1% 90.3%
  • msmt17(v2)
method mAP rank1
Bag of Tricks 54.4% 77.0%
fast reid 60.6% 83.9%
ours 60.6% 83.1%

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A simple baseline for Person ReID, it achieves 3rd place in VisDA2020 challenge.

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