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https://github.com/layumi/2016_person_re-ID

TOMM2017 A Discriminatively Learned CNN Embedding for Person Re-identification
https://github.com/layumi/2016_person_re-ID

matconvnet matlab person-reidentification re-id reidentification tomm2017

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TOMM2017 A Discriminatively Learned CNN Embedding for Person Re-identification

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README 

        
## A Discriminatively Learned CNN Embedding for Person Re-identification



![](https://github.com/layumi/2016_person_re-ID/blob/master/paper.jpg)



In this package, we provide our training and testing code written in [Matconvnet](http://www.vlfeat.org/matconvnet/) for the paper [A Discriminatively Learned CNN Embedding for Person Re-identification](https://arxiv.org/abs/1611.05666).

 

We also include matconvnet-beta23 which has been modified for our paper. All codes have been test on Ubuntu14.04 and Ubuntu16.04 with Matlab R2015b.



**This code is ONLY released for academic use.**



## Resources



* [Weihang Chen](https://github.com/ahangchen) also realizes our paper in [Keras](https://github.com/ahangchen/rank-reid/tree/release). ([![GitHub stars](https://img.shields.io/github/stars/ahangchen/rank-reid.svg?style=flat&label=Star)](https://github.com/ahangchen/rank-reid/tree/release))

* [Xuanyi Dong](https://github.com/D-X-Y) also realizes our paper in [Caffe](https://github.com/D-X-Y/caffe-reid). ([![GitHub stars](https://img.shields.io/github/stars/D-X-Y/caffe-reid.svg?style=flat&label=Star)](https://github.com/D-X-Y/caffe-reid))

* [Zhun Zhong](https://github.com/zhunzhong07/IDE-baseline-Market-1501) provides a extensive [Caffe baseline code](https://github.com/zhunzhong07/IDE-baseline-Market-1501). You may check it. ([![GitHub stars](https://img.shields.io/github/stars/zhunzhong07/IDE-baseline-Market-1501.svg?style=flat&label=Star)](https://github.com/zhunzhong07/IDE-baseline-Market-1501))

* [Zhedong Zheng](https://github.com/layumi) provides a [strong Pytorch baseline](https://github.com/layumi/Person_reID_baseline_pytorch) ([![GitHub stars](https://img.shields.io/github/stars/layumi/Person_reID_baseline_pytorch.svg?style=flat&label=Star)](https://github.com/layumi/Person_reID_baseline_pytorch)) and realizes our paper in [Pytorch](https://github.com/layumi/Person-reID-verification). ([![GitHub stars](https://img.shields.io/github/stars/layumi/Person-reID-verification.svg?style=flat&label=Star)](https://github.com/layumi/Person-reID-verification))



~What's new: We add the data preparation and evaluation codes for CUHK03.



~What's new: We make the code of model structure more easy to follow.



~What's new: We provide a better code for extract feature.



~What's new: We provide a faster evaluation code for Market-1501.



## Installation

1. Clone this repo

 

	```Shell

	git clone https://github.com/layumi/2016_person_re-ID.git

	cd 2016_person_re-ID

	mkdir data

	```



2. Download the pretrained model. 

	

	**This model is ONLY released for academic use.**

	You can find the pretrained model in [GoogleDriver](https://drive.google.com/open?id=0B0VOCNYh8HeRWks0V24xTlpKWkU) or [BaiduYun] (https://pan.baidu.com/s/1miG2OpM). Download and put them into the `./data`.



	BaiduYun sometime changes the link. If you find the url fail, you can contact me to update it.

	

3. Compile matconvnet 

**(Note that I have included my Matconvnet in this repo, so you do not need to download it again. I has changed some codes comparing with the original version. For example, one of the difference is in `/matlab/+dagnn/@DagNN/initParams.m`. If one layer has params, I will not initialize it again, especially for pretrained model.)**



	You just need to uncomment and modify some lines in `gpu_compile.m` and run it in Matlab. Try it~

	(The code does not support cudnn 6.0. You may just turn off the Enablecudnn or try cudnn5.1)



	If you fail in compilation, you may refer to http://www.vlfeat.org/matconvnet/install/



## Dataset

* Download [Market1501 Dataset](http://www.liangzheng.org/Project/project_reid.html). [[Google]](https://drive.google.com/file/d/0B8-rUzbwVRk0c054eEozWG9COHM/view) [[Baidu]](https://pan.baidu.com/s/1ntIi2Op) The photos are taken in Tsinghua University.



* DukeMTMC-reID is a larger dataset in the same format of Market1501. The photos are taken in Duke University.

You can download it from [DukeMTMC-reID Dataset](https://github.com/layumi/DukeMTMC-reID_evaluation). We also upload the result to DukeMTMC-reID leaderboard.



* If you want to rehearsal our result on [CUHK03 Dataset](http://www.ee.cuhk.edu.hk/~rzhao/), you can simply change the number of kernel from 751 to 1367 in `resnet52_market.m` and recreate `net.mat`. Because there are 751 IDs in Market-1501 while 1367 training identities are in CUHK03. More information can be found in `cuhk03-prepare-eval` dir. We add the data preparation and evaluation codes for CUHK03.



* Training dataset for Oxford5k (http://cmp.felk.cvut.cz/cnnimageretrieval/)



## Test 

1. Run `test/test_gallery_query_crazy.m` to extract the features of images in the gallery and query set. They will store in a .mat file. Then you can use it to do evaluation.

2. Evaluate feature on the Market-1501. Run `evaluation/zzd_evaluation_res_faster.m`. You can get the following Single-query Result.



| Methods               | Rank@1 | mAP    | 

| --------              | -----  | ----   | 

| Ours* (SQ)            | 80.82% | 62.30% | 

| Ours* (MQ-avg)        | 86.67% | 70.16% | 

| Ours* (MQ-max)        | 86.76% | 70.68% | 

| Ours* (MQ-max+rerank) | 86.67% | 72.55% | 



*Note that the result is slightly higher than the result reported in our paper.



*For multi-query result, you can use `evaluation/zzd_evaluation_res_fast.m` . It is slower than `evaluation/zzd_evaluation_res_faster.m`  since it need to extract extra features. (The evaluation code is modified from the [Market-1501 Baseline Code](http://www.liangzheng.org/Project/project_reid.html))



## FQA

1. What is multi-query setting?



Actually, we can get a sequence of the query under one camera instead of one image. Then we can use every image in this sequence to extract a query mean feature (mean of feature extracted from several images).

We call it multi-query. If we use this feature to do person retrieval, we usually get a better result.

But it use additional images (in 'Market-1501/gt_bboxes'). You can find more detail in the [original paper](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf). 



## Train

1. Add your dataset path into `prepare_data.m` and run it. Make sure the code outputs the right image path.



2. Run `train_id_net_res_2stream.m` to have fun.



## Citation

Please cite this paper in your publications if it helps your research:

```bibtex

@article{zheng2016discriminatively,

  title={A Discriminatively Learned CNN Embedding for Person Re-identification},

  author={Zheng, Zhedong and Zheng, Liang and Yang, Yi},

  doi={10.1145/3159171},

  note={\mbox{doi}:\url{10.1145/3159171}},

  journal={ACM Transactions on Multimedia Computing Communications and Applications},

  year={2017}

}

```

## Acknowledge

Thanks for Xuanyi Dong to realize our paper in Caffe.



Thanks for Weihang Chen to realize our paper in Keras.



Thanks for Weihang Chen to report the bug in `prepare_data.m`.



## Related Repos

1. [Person re-ID with GAN](https://github.com/layumi/Person-reID_GAN)

2. [Pedestrian Alignment Network](https://github.com/layumi/Pedestrian_Alignment)