deep-person-reid/README.md

deep-person-reid

This repo contains pytorch implementations of deep person re-identification approaches.

We will actively maintain this repo.

Pretrained models

Prepare data

Create a data directory to store reid datasets under this repo via mkdir data/

Market1501 [7]:

1. download dataset to data/ from http://www.liangzheng.org/Project/project_reid.html.
2. extract dataset and rename to market1501.

MARS [8]:

1. create a directory named mars/ under data/
2. download dataset to data/mars/ from http://www.liangzheng.com.cn/Project/project_mars.html.
3. extract bbox_train.zip and bbox_test.zip.
4. download split information from https://github.com/liangzheng06/MARS-evaluation/tree/master/info and put info/ in data/mars.

Train

Test

Results

Setup

• Image size: 256-by-128
  
• Optimizer: Adam [6]
  
• Loss functions:
  xent: cross entropy + label smoothing regularizer [5]
  htri: triplet loss with hard positive/negative mining [4]
  

Market1501

Model	Size (M)	Loss	Rank-1 / -5 / -10 (%)	mAP (%)	Reported Rank	Reported mAP
ResNet50 [1]	25.05	xent	85.8 / 94.4 / 96.3	70.1
ResNet50M [2]	30.01	xent	88.8 / 95.3 / 97.0	74.4	89.9 / - / -	75.6
DenseNet121 [3]	7.72	xent

References

[1] He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
[2] Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching. arXiv:1711.08106.
[3] Huang et al. Densely Connected Convolutional Networks. CVPR 2017.
[4] Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
[5] Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
[6] Kingma and Ba. Adam: A Method for Stochastic Optimization. ICLR 2015.
[7] Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
[8] Zheng et al. MARS: A Video Benchmark for Large-Scale Person Re-identification. ECCV 2016.