Pytorch-Person-ReID-PCB-Beyond-Part-Models

• A Strong Implementation of PCB (Beyond Part Models, ECCV2018) on Market-1501 and DukeMTMC-reID datasets.

• We support:

  • A strong PCB implementation which outperforms most existing implementations.
  • A simple and clear implementation, and end-to-end training and evaluation.

News

• We re-write a strong Re-ID baseline Bag of Tricks (BoT) with a more simple and clear implementation, which is more friendly with researchers and newers. Our code can be found here. BoT outperforms PCB by using only the global feature. Our implementation of BoT achieves the same performance with the offical one.

Dependencies

• Anaconda (Python 3.7)
• PyTorch 0.4.0
• GPU Memory >= 20G (we use 2 GTX1080ti)
• Memory >= 20G

Dataset Preparation

• Market-1501 Dataset and DukeMTMC-reID Dataset
• Download and extract both anywhere

Train and Test

python main.py --market_path market_path --duke_path duke_path

Experiments

1. Settings

• We conduct our experiments on 2 GTX1080ti GPUs

2. Results

Implementations	market2market	duke2duke	market2duke	duke2market
PCB w/ REA (Ours)	0.939 (0.832) <model.pth>	0.856 (0.753) <model.pth>	0.384 (0.237)	0.555 (0.285)
PCB (Ours)	0.934 (0.809)	0.867 (0.746)	0.440(0.265)	0.592 (0.308)
PCB (layumi)	0.926 (0.774)	0.642 (0.439)	-	-
PCB (huanghoujing)	0.928 (0.785)	0.845 (0.700)	-	-
PCB (Xiaoccer)	0.927 (0.796)	-	-	-
PCB (Paper)	0.924 (0.773)	0.819 (0.653)	-	-

Contacts

If you have any question about the project, please feel free to contact with me.

E-mail: guan.wang0706@gmail.com