Batch DropBlock Network for Person Re-identification and Beyond

Official source code of paper https://arxiv.org/abs/1811.07130

Update on 2019.3.15

Update CUHK03 results.

Update on 2019.1.29

Traning scripts are released. The best Markt1501 result is 95.3%! Please look at the training section of README.md.

Update on 2019.1.23

In-Shop Clothes Retrieval dataset and pretrained model are released!. The rank-1 result is 89.5 which is a litter bit higher than paper reported.

This paper is accepted by ICCV 2019. Please cite if you use this code in your research.

@article{dai2018batch,
  title={Batch DropBlock Network for Person Re-identification and Beyond},
  author={Dai, Zuozhuo and Chen, Mingqiang and Gu, Xiaodong and Zhu, Siyu and Tan, Ping},
  journal={arXiv preprint arXiv:1811.07130},
  year={2018}
}

Setup running environment

This project requires python3, cython, torch, torchvision, scikit-learn, tensorboardX, fire. The baseline source code is borrowed from https://github.com/L1aoXingyu/reid_baseline.

Prepare dataset

Create a directory to store reid datasets under this repo via
```bash
cd reid
mkdir data
```

For market1501 dataset, 
1. Download Market1501 dataset to `data/` from http://www.liangzheng.org/Project/project_reid.html
2. Extract dataset and rename to `market1501`. The data structure would like:
```
market1501/
    bounding_box_test/
    bounding_box_train/
    query/
```

For CUHK03 dataset,
1. Download CUHK03-NP dataset from https://github.com/zhunzhong07/person-re-ranking/tree/master/CUHK03-NP 
2. Extract dataset and rename folers inside it to cuhk-detect and cuhk-label.
For DukeMTMC-reID dataset,
Dowload from https://github.com/layumi/DukeMTMC-reID_evaluation

For In-Shop Clothes dataset,
1. Downlaod clothes dataset from http://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/bfe_models/clothes.tar
2. Extract dataset and put it to `data/` folder.

Results

You can download the pre-trained models from the above table and evaluate on person re-ID datasets. For example, to evaluate CUHK03-Label dataset, you can download the model to './pytorch-ckpt/cuhk_label_bfe' directory and run the following commands.

Evaluate Market1501

python3 main_reid.py train --save_dir='./pytorch-ckpt/market_bfe' --model_name=bfe --train_batch=32 --test_batch=32 --dataset=market1501 --pretrained_model='./pytorch-ckpt/market_bfe/944.pth.tar' --evaluate

Evaluate CUHK03-Label

python3 main_reid.py train --save_dir='./pytorch-ckpt/cuhk_label_bfe' --model_name=bfe --train_batch=32 --test_batch=32 --dataset=cuhk-label  --pretrained_model='./pytorch-ckpt/cuhk_label_bfe/750.pth.tar' --evaluate

Evaluate In-Shop clothes

python main_reid.py train --save_dir='./pytorch-ckpt/clothes_bfe' --model_name=bfe --pretrained_model='./pytorch-ckpt/clothes_bfe/clothes_895.pth.tar' --test_batch=32 --dataset=clothes --evaluate

Training

Traning Market1501

python main_reid.py train --save_dir='./pytorch-ckpt/market-bfe' --max_epoch=400 --eval_step=30 --dataset=market1501 --test_batch=128 --train_batch=128 --optim=adam --adjust_lr

This traning command is tested on 4 GTX1080 gpus. Here is training log. You shoud get a result around 95%.