batch-feature-erasing-network's Introduction

Batch Feature Erasing for Person Re-identification and Beyond

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

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

Results

Dataset	CUHK03-Label	CUHK03-Detect	DukeMTMC re-ID	Market1501
Rank-1	75.0	72.1	88.7	94.4
mAP	70.9	67.9	75.8	85.0
model	aliyun	aliyun	aliyun	aliyun

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 command:

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

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