This paper explores a simple and efficient baseline for person re-identification (ReID). Person re-identification (ReID) with deep neural networks has made progress and achieved high performance in recent years. However, many state-of-the-arts methods design complex network structure and concatenate multi-branch features. In the literature, some effective training tricks are briefly appeared in several papers or source codes. This paper will collect and evaluate these effective training tricks in person ReID. By combining these tricks together, the model achieves 94.5% rank-1 and 85.9% mAP on Market1501 with only using global features. Our codes and models are available at https://github.com/michuanhaohao/reid-strong-baseline.
Bag of Tricks and a Strong Baseline for Deep Person Re-Identification
Hao Luo,Youzhi Gu,Xingyu Liao,Shenqi Lai,Wei Jiang
Published 2019 in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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- Publication year
2019
- Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publication date
2019-03-17
- Fields of study
Computer Science
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