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Bag of Tricks and A Strong Baseline for Deep Person Re-identification. CVPRW2019, Oral.
[[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)
[[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)
[[Slides]](https://drive.google.com/open?id=1h9SgdJenvfoNp9PTUxPiz5_K5HFCho-V)
[[Poster]](https://drive.google.com/open?id=1izZYAwylBsrldxSMqHCH432P6hnyh1vR)
[[Journal Version]](https://arxiv.org/pdf/1906.08332)
### We are very grateful for your contribution to our project and hope that this project can help your research or work.
The codes are expanded on a [ReID-baseline](https://github.com/L1aoXingyu/reid_baseline) , which is open sourced by our co-first author [Xingyu Liao](https://github.com/L1aoXingyu).
@ -14,6 +17,9 @@ A tiny repo with simple re-implement. [[link]](https://github.com/lulujianjie/pe
Our baseline also achieves great performance on __Vehicle ReID__ task! [[link]](https://github.com/DTennant/reid_baseline_with_syncbn)
With Ranked List loss(CVPR2019)[[link]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Ranked_List_Loss_for_Deep_Metric_Learning_CVPR_2019_paper.pdf), our baseline can achieve better performance. [[link]](https://github.com/Qidian213/Ranked_Person_ReID)
```
@InProceedings{Luo_2019_CVPR_Workshops,
author = {Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},