• Tiny Person ReID Baseline
• • Updates (Difference from Official Code)
  • Directory layout
  • Pipeline
  • Pretrained Model
  • Get Started
  • Train
  • Test
  • Results

Tiny Person ReID Baseline

Paper: "Bag of Tricks and A Strong Baseline for Deep Person Re-identification"[pdf]

This project refers the official code link and can reproduce the results as good as it on Market1501 when the input size is set to 256x128. If you find this project useful, please cite the offical paper.

@inproceedings{luo2019bag,
  title={Bag of Tricks and A Strong Baseline for Deep Person Re-identification},
  author={Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2019}
}

Updates (Difference from Official Code)

• v0.1.2 (Feb. 2020)
  • Support Harder Example Mining, which achieve better performance. See docs for details.
  • Support visualizing augmented data
  • Support flipped features
  • Support search best parameters for reranking
• v0.1.1 (Sep. 2019)
  • Support ArcFace loss
  • Support visualizing reID results
  • Add comments in config.py
• v0.1.0 (Jun. 2019)
  • Develop based on the pytorch template
  • No need to install ignite and yacs
  • Support computing distance using cosine similarity
  • Set hyperparameters using a configuration class
  • Only support ResNet50 as the backbone

Directory layout

.
├── config                  # hyperparameters settings
│   └── ...                 
├── datasets                # data loader
│   └── ...           
├── log                     # log and model weights             
├── loss                    # loss function code
│   └── ...   
├── model                   # model
│   └── ...  
├── processor               # training and testing procedures
│   └── ...    
├── solver                  # optimization code
│   └── ...   
├── tools                   # tools
│   └── ...
├── utils                   # metrics code
│   └── ...
├── train.py                # train code 
├── test.py                 # test code 
├── get_vis_result.py       # get visualized results 
├── docs                    # docs for readme              
└── README.md

Pipeline

Pretrained Model

The pretrained (128x64) model can be downloaded now.
Extraction code is u3q5.

Get Started

1. cd to folder where you want to download this repo

2. Run git clone https://github.com/lulujianjie/person-reid-tiny-baseline.git

3. Install dependencies:

   • pytorch>=0.4
   • torchvision
   • cv2 (optional)

Train

python train.py

Test

python test.py

To get visualized reID results, first create results folder in log dir, then:

python ./tools/get_vis_result.py

You will get the ranked results (query|rank1|rank2|...), like:

Results

model	method	mAP	Rank1
resnet50	triplet loss + softmax + center loss (B1)	85.8	94.1
resnet50	B1 + flipped feature	86.3	93.9
resnet50	B1 + Harder Example Mining	86.2	94.4
resnet50	B1 + flipped feature + Harder Example Mining	86.6	94.6
resnet50	B1 + Harder Example Mining + reranking	94.1	95.6
resnet50	B1 + Harder Example Mining + searched reranking	94.2	95.8