mirror of https://github.com/JDAI-CV/fast-reid.git
83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
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# Bag of Tricks and A Strong ReID Baseline in FastReID
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Bag of Tricks and A Strong Baseline for Deep Person Re-identification. CVPRW2019, Oral.
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[Hao Luo\*](https://github.com/michuanhaohao) [Youzhi Gu\*](https://github.com/shaoniangu) [Xingyu Liao\*](https://github.com/L1aoXingyu) [Shenqi Lai](https://github.com/xiaolai-sqlai)
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A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification. IEEE Transactions on Multimedia (Accepted).
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[[Journal Version(TMM)]](https://ieeexplore.ieee.org/document/8930088)
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[[PDF]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Luo_Bag_of_Tricks_and_a_Strong_Baseline_for_Deep_Person_CVPRW_2019_paper.pdf)
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[[Slides]](https://drive.google.com/open?id=1h9SgdJenvfoNp9PTUxPiz5_K5HFCho-V)
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[[Poster]](https://drive.google.com/open?id=1izZYAwylBsrldxSMqHCH432P6hnyh1vR)
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## Training
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To train a model, run
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```bash
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CUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <config.yml>
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```
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For example, to launch a end-to-end baseline training on market1501 dataset on GPU#1,
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one should excute:
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```bash
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CUDA_VISIBLE_DEVICES=1 python train_net.py --config-file='configs/bagtricks_market1501.yml'
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```
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## Evaluation
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To evaluate the model in test set, run similarly:
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```bash
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CUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <configs.yaml> --eval-only MODEL.WEIGHTS model.pth
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```
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## Experimental Results
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You can reproduce the results by simply excute
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```bash
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sh scripts/train_market.sh
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sh scripts/train_duke.sh
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sh scripts/train_msmt.sh
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```
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### Market1501 dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BagTricks | ImageNet | 93.9% | 84.9% | 57.1% |
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### DukeMTMC dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BagTricks | ImageNet | 87.1% | 76.4% | 39.2% |
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### MSMT17 dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BagTricks | ImageNet | 72.2% | 48.4% | 9.6% |
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```
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@InProceedings{Luo_2019_CVPR_Workshops,
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author = {Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
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title = {Bag of Tricks and a Strong Baseline for Deep Person Re-Identification},
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booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
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month = {June},
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year = {2019}
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}
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@ARTICLE{Luo_2019_Strong_TMM,
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author={H. {Luo} and W. {Jiang} and Y. {Gu} and F. {Liu} and X. {Liao} and S. {Lai} and J. {Gu}},
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journal={IEEE Transactions on Multimedia},
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title={A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification},
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year={2019},
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pages={1-1},
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doi={10.1109/TMM.2019.2958756},
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ISSN={1941-0077},
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}
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```
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