# DukeMTMC-reID_baseline
Here we provide ResNet-50 baseline training code on DukeMTMC-reID Dataset.
You can download DukeMTMC-reID in https://github.com/layumi/DukeMTMC-reID_baseline.
## Installation
1. Clone this repo
```Shell
git clone https://github.com/layumi/DukeMTMC-reID_baseline.git
cd DukeMTMC-reID_baseline
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=0B0VOCNYh8HeRUHVRaThuRFhMNkU) or [BaiduYun] (http://pan.baidu.com/s/1jIHqSQy). Download and put the files into the `./data`.
BaiduYun sometime changes the link. If you find the url fail, you can contact me to update it.
3. Compile matconvnet
You just need to uncomment and modify some lines in `gpu_compile.m` and run it in Matlab. Try it~
If you fail in compilation, you may refer to http://www.vlfeat.org/matconvnet/install/
## Test
1. After installation, you can run `test/test_duke.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 DukeMTMC-reID. You can directly use the code in https://github.com/layumi/DukeMTMC-reID_evaluation.
## 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_duke.m` to have fun.
## Citation
Please cite this paper in your publications if it helps your research:
```
@inproceedings{zheng2017unlabeled,
title={Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro},
author={Zheng, Zhedong and Zheng, Liang and Yang, Yi},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
year={2017}
}
```
DukeMTMC-reID_baseline (Matlab).zip
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DukeMTMC-reID_baseline (Matlab).zip （240个子文件） 
normalize_cpu.cpp 12KB roipooling_cpu.cpp 12KB pooling_cpu.cpp 10KB bnorm_cpu.cpp 10KB tinythread.cpp 8KB im2row_cpu.cpp 8KB imread_libjpeg.cpp 6KB bilinearsampler_cpu.cpp 6KB imread_gdiplus.cpp 6KB imread_quartz.cpp 5KB subsample_cpu.cpp 3KB imread.cpp 1KB nnbias.cpp 1KB copy_cpu.cpp 1KB vl_nnbilinearsampler.cpp 122B nnfullyconnected.cpp 121B vl_nnnormalize.cpp 116B vl_imreadjpeg_old.cpp 115B vl_imreadjpeg.cpp 115B vl_taccummex.cpp 114B vl_nnroipool.cpp 114B vl_cudatool.cpp 113B nnbilinearsampler.cpp 113B nnroipooling.cpp 113B vl_nnbnorm.cpp 112B vl_nnconvt.cpp 112B nnsubsample.cpp 112B vl_nnconv.cpp 111B nnnormalize.cpp 111B vl_nnpool.cpp 111B vl_tmove.cpp 110B datamex.cpp 109B nnpooling.cpp 107B data.cpp 106B nnbnorm.cpp 103B nnconv.cpp 101B vl_tmove.cu 76KB bnorm_gpu.cu 45KB vl_imreadjpeg.cu 43KB nnconv_cudnn.cu 23KB im2row_gpu.cu 20KB vl_nnconv.cu 17KB nnbnorm_cudnn.cu 17KB data.cu 14KB pooling_gpu.cu 13KB vl_imreadjpeg_old.cu 13KB vl_nnconvt.cu 13KB datamex.cu 13KB roipooling_gpu.cu 13KB nnbilinearsampler_cudnn.cu 12KB bilinearsampler_gpu.cu 12KB datacu.cu 10KB vl_nnbnorm.cu 10KB vl_nnpool.cu 9KB nnpooling_cudnn.cu 9KB nnconv.cu 9KB nnbias_cudnn.cu 8KB vl_nnroipool.cu 8KB nnbnorm.cu 8KB nnfullyconnected.cu 8KB nnsubsample.cu 7KB vl_nnbilinearsampler.cu 7KB nnpooling.cu 6KB normalize_gpu.cu 5KB vl_taccummex.cu 5KB vl_nnnormalize.cu 5KB subsample_gpu.cu 5KB nnroipooling.cu 5KB nnbilinearsampler.cu 5KB nnnormalize.cu 4KB vl_cudatool.cu 4KB nnbias.cu 4KB copy_gpu.cu 1KB sharedmem.cuh 4KB tinythread.h 21KB mexutils.h 19KB fast_mutex.h 7KB imread_helpers.hpp 21KB nnconv_blas.hpp 13KB blashelper.hpp 11KB data.hpp 7KB nnbias_blas.hpp 5KB datacu.hpp 4KB cudnnhelper.hpp 3KB bnorm.hpp 2KB nnbnorm.hpp 2KB nnconv.hpp 2KB roipooling.hpp 2KB pooling.hpp 2KB nnbnorm_cudnn.hpp 2KB datamex.hpp 2KB imread.hpp 1KB nnpooling_cudnn.hpp 1KB bilinearsampler.hpp 1KB im2row.hpp 1KB nnroipooling.hpp 1KB nnconv_cudnn.hpp 1KB nnpooling.hpp 1KB subsample.hpp 1KB nnsubsample.hpp 1KB共 240 条
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