In this package, we provide our training and testing code written in Matconvnet for the paper A Discriminatively Learned CNN Embedding for Person Re-identification.
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.
- Weihang Chen also realizes our paper in Keras. ()
- Xuanyi Dong also realizes our paper in Caffe. ()
- Zhun Zhong provides a extensive Caffe baseline code. You may check it. ()
- Zhedong Zheng provides a strong Pytorch baseline () and realizes our paper in Pytorch. ()
~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.
-
Clone this repo
git clone https://github.com/layumi/2016_person_re-ID.git cd 2016_person_re-ID mkdir data
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Download the pretrained model.
This model is ONLY released for academic use. You can find the pretrained model in GoogleDriver 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.
-
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/
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Download Market1501 Dataset. [Google] [Baidu] The photos are taken in Tsinghua University.
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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. We also upload the result to DukeMTMC-reID leaderboard.
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If you want to rehearsal our result on CUHK03 Dataset, you can simply change the number of kernel from 751 to 1367 in
resnet52_market.m
and recreatenet.mat
. Because there are 751 IDs in Market-1501 while 1367 training identities are in CUHK03. More information can be found incuhk03-prepare-eval
dir. We add the data preparation and evaluation codes for CUHK03. -
Training dataset for Oxford5k (http://cmp.felk.cvut.cz/cnnimageretrieval/)
- 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. - 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)
- 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.
-
Add your dataset path into
prepare_data.m
and run it. Make sure the code outputs the right image path. -
Run
train_id_net_res_2stream.m
to have fun.
Please cite this paper in your publications if it helps your research:
@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}
}
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
.