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}
}

• v0.1.1 (Sep. 2019)
  • Support ArcFace loss, which can converge faster (around 50 epochs) and achieve slightly better performance than softmax+triplet loss+center loss
  • Support visualizing reID results
  • Add comments in config.py
• v0.1.0 (Jun. 2019)
  • Develop based on the pytorch template link
  • No need to install ignite and yacs
  • Support computing DistMap using cosine similarity
  • Set hyperparameters using a configuration class
  • Only support ResNet50 as the backbone

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

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

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)

python train.py

python test.py

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

python get_vis_result.py

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