# MEB-Net

**Repository Path**: lamoxue/MEB-Net

## Basic Information

- **Project Name**: MEB-Net
- **Description**: "Multiple Expert Brainstorming for Domain Adaptive Person Re-identification", ECCV 2020
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-31
- **Last Updated**: 2021-03-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

Yunpeng Zhai, Qixiang Ye, Shijian Lu, Mengxi Jia, Rongrong Ji, Yonghong Tian. "Multiple Expert Brainstorming for Domain Adaptive Person Re-identification", ECCV 2020

Paper [PDF](https://arxiv.org/abs/2007.01546)

<!-- ![avatar](figs/fig1.png) -->

![avatar](figs/fig2.png)

<!-- ![avatar](figs/fig3.png) -->



<!-- ![avatar](figs/tab2.png) -->

## Setup

Datasets (Market-1501 and DukeMTMC-reID).

## Requirements

- PyTorch 1.3.1

## Running the experiments

### Step:1 Supervised learning in the source domain

```
bash pretrain.sh <source dataset> <target dataset> <architecture>
```
For example, (duke->market):
```
bash pretrain.sh dukemtmc market1501 densenet
bash pretrain.sh dukemtmc market1501 resnet50
bash pretrain.sh dukemtmc market1501 inceptionv3
```

### Step:2 Unsupervised adaptation in the target domain

```
bash train.sh <source dataset> <target dataset> <architecture-1> <architecture-2> <architecture-3>
```
For example, (duke->market)
```
bash train.sh dukemtmc market1501 densenet resnet50 inceptionv3
```

### Step:3 Evaluate in the target domain

```
bash test.sh <target dataset> <architecture> <checkpoint_path>
```
For example, (market1501, densenet)
```
bash test.sh market1501 densenet logs/xxxx/xxxx-MEB-Net/checkpoint.pt.pth
```

## Experiment results

![avatar](figs/tab1.png)

<!-- | Src -> Tgt Dataset     | mAP | Rank-1 | Rank-5 | Rank-10 | 
| :-------------------:  | :-------: | :-------------: |  :--------------:| :-------------: |
| DukeMTMC -> Market1501 | 76.0    | 89.9              | 96.0              | 97.5            | 
| Market1501 -> DukeMTMC | 66.1    | 79.6              | 88.3              | 92.2            |  -->

## Acknowledgement

Our code is based on [open-reid](https://github.com/Cysu/open-reid) and [MMT](https://github.com/yxgeee/MMT).

## Citation

If you use this method or this code in your research, please cite as:
```
@article{zhai2020multiple,
  title={Multiple Expert Brainstorming for Domain Adaptive Person Re-identification},
  author={Zhai, Yunpeng and Ye, Qixiang and Lu, Shijian and Jia, Mengxi and Ji, Rongrong and Tian, Yonghong},
  journal={arXiv preprint arXiv:2007.01546},
  year={2020}
}
```