2017-03-22 18:48:01 +08:00
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<<<<<<< HEAD
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# Re-ranking Person Re-identification with k-reciprocal Encoding
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================================================================
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### This code has the IDE baseline for the Market-1501 and CUHK03 new training/testing protocol.
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### The re-ranking code is available upon request.
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If you find this code useful in your research, please consider citing:
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@article{zhong2017re,
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title={Re-ranking Person Re-identification with k-reciprocal Encoding},
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author={Zhong, Zhun and Zheng, Liang and Cao, Donglin and Li, Shaozi},
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booktitle={CVPR},
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year={2017}
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}
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### Requirements: Caffe
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Requirements for `Caffe` and `matcaffe` (see: [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html))
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### Installation
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1. Build Caffe and matcaffe
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```Shell
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cd $Re-ranking_ROOT/caffe
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# Now follow the Caffe installation instructions here:
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# http://caffe.berkeleyvision.org/installation.html
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make -j8 && make matcaffe
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```
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2. Download pre-computed imagenet models, Market-1501 dataset and CUHK03 dataset
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```Shell
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Please download the pre-trained imagenet models and put it in the "data/imagenet_models" folder.
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Please download Market-1501 dataset and unzip it in the "evaluation/data/Market-1501" folder.
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Please download CUHK03 dataset and unzip it in the "evaluation/data/CUHK03" folder.
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```
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- [Pre-trained imagenet models](https://pan.baidu.com/s/1o7YZT8Y)
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- [Market-1501](https://pan.baidu.com/s/1ntIi2Op)
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- CUHK03 [[Baiduyun]](https://pan.baidu.com/s/1o8txURK) [[Google drive]](https://drive.google.com/open?id=0B7TOZKXmIjU3OUhfd3BPaVRHZVE)
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### The new training/testing protocol for CUHK03
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The new training/testing protocol split for CUHK03 in our paper is in the "evaluation/data/CUHK03/" folder.
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- cuhk03_new_protocol_config_detected.mat
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- cuhk03_new_protocol_config_labeled.mat
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### Training and testing IDE model
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1. Training
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```Shell
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cd $Re-ranking_ROOT
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# train IDE ResNet_50 for Market-1501
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./experiments/Market-1501/train_IDE_ResNet_50.sh
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# train IDE ResNet_50 for CUHK03
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./experiments/CUHK03/train_IDE_ResNet_50_labeled.sh
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./experiments/CUHK03/train_IDE_ResNet_50_detected.sh
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```
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2. Feature Extraction
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```Shell
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cd $Re-ranking_ROOT/evaluation
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# extract feature for Market-1501
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matlab Market_1501_extract_feature.m
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# extract feature for CUHK03
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matlab CUHK03_extract_feature.m
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```
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3. Evaluation with our re-ranking method
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```Shell
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# evaluation for Market-1501
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matlab Market_1501_evaluation.m
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# evaluation for CUHK03
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matlab CUHK03_evaluation.m
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```
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### Results
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You can download our pre-trained IDE models and IDE features, and put them in the "out_put" and "evaluation/feat" folder, respectively.
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- IDE models [[Baiduyun]](https://pan.baidu.com/s/1jHVj2C2) [[Google drive]](https://drive.google.com/open?id=0B7TOZKXmIjU3ZTNsWGt3azcxUUU)
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- IDE features [[Baiduyun]](https://pan.baidu.com/s/1c1TtKcw) [[Google drive]](https://drive.google.com/open?id=0B7TOZKXmIjU3ODhaRm8yN2QzRHc)
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Using the above IDE models and IDE features, you can reproduce the results with our re-ranking method as follows:
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- Market-1501
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|Methods | Rank@1 | mAP|
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| -------- | ----- | ---- |
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|IDE_ResNet_50 + Euclidean | 78.92% | 55.03%|
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|IDE_ResNet_50 + XQDA | 77.58% | 56.06%|
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For Market-1501, these results are better than those reported in our paper, since we add a dropout = 0.5 layer after pool5.
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- CUHK03 under the new training/testing protocol
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| | Labeled | Labeled| detected | detected|
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| -------| ----- | ---- |---- |---- |
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|Methods | Rank@1 | mAP| Rank@1 | mAP|
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|IDE_CaffeNet + Euclidean | 15.6% | 14.9%| 15.1% | 14.2%|
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|IDE_CaffeNet + XQDA | 21.9% | 20.0%|21.1% | 19.0%|
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|IDE_ResNet_50 + Euclidean | 22.2% | 21.0%|21.3% | 19.7%|
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|IDE_ResNet_50 + XQDA | 32.0% | 29.6%|31.1% | 28.2%|
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### Contact us
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If you have any questions about this code, please do not hesitate to contact us.
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[Zhun Zhong](http://zhunzhong.site)
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[Liang Zheng](http://liangzheng.com.cn)
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=======
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# person-re-ranking
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>>>>>>> 8479ff10372e05534e4294c41347581dd73ec201
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