• Introduction: This project re-implements the strong person re-identification baseline: Bag of Tricks (paper and official code).
• Advantage: This project provides a more simple and clear implementation by only using the best parameters and removing lots of unnecessary modules.

• 2020-03-27: [CVPR'20] Our new work about Occluded ReID has been accepted by CVPR'20. (Paper)
• 2020-01-01: [AAAI'20] Our new work about RGB-Infrared(IR) ReID for dark situation has been accepted by AAAI'20. (Paper, Code).
• 2019-10-25: [ICCV'19] Our new work about RGB-Infrared(IR) ReID for dark situation has been accepted by ICCV'19. (Paper, Code).
• 2019-05-01: We implement PCB and achieve better performance than the offical one. (Code)

• 2020-03-27: this branch is will not be updated, which based on python2.7 and torch-0.4.0. Please find new updating on the master branch, which is based on python3.7 and torch-1.1.0.
• 2019-06-18: we add visualization code to show ranked images

• Anaconda (Python 2.7)
• PyTorch 0.4.0
• GPU Memory >= 10G
• Memory >= 20G

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

# train
python main.py --market_path market_path --duke_path duke_path --output_path output_path/ --mode train 

# test, the output_path should be same with that in training process
python main.py --market_path market_path --duke_path duke_path --output_path output_path/ --mode test --resume_test_epoch resume_test_epoch

# visualize the ranked images, the output_path should be same with that in training process
python main.py --market_path market_path --duke_path duke_path --output_path output_path/ --mode visualize --resume_visualize_epoch resume_visualize_epoch

• Warm up learning rate
• Random erasing augmentation (REA)
• Label smoothing
• Last stride
• BNNeck
• Note that our implementation uses no the center loss and re-ranking.

• We conduct our experiments on 1 GTX1080ti GPU

Query	Top1	Top2	Top3	Top4	Top5	Top6	Top7	Top8	Top9	Top10

• More results can be seen in folder ranked_images/market

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

E-mail: guan.wang0706@gmail.com