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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
Last synced: 6 days ago
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TOMM2017 A Discriminatively Learned CNN Embedding for Person Re-identification
- Host: GitHub
- URL: https://github.com/layumi/2016_person_re-id
- Owner: layumi
- License: mit
- Created: 2017-01-08T08:59:26.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-12-03T06:14:17.000Z (about 1 year ago)
- Last Synced: 2024-11-24T14:04:48.779Z (28 days ago)
- Topics: matconvnet, matlab, person-reidentification, re-id, reidentification, tomm2017
- Language: C
- Homepage: https://dl.acm.org/citation.cfm?id=3159171
- Size: 13.4 MB
- Stars: 264
- Watchers: 27
- Forks: 116
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## A Discriminatively Learned CNN Embedding for Person Re-identification
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). ([](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). ([](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. ([](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) ([](https://github.com/layumi/Person_reID_baseline_pytorch)) and realizes our paper in [Pytorch](https://github.com/layumi/Person-reID-verification). ([](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)