Bag of Tricks and a Strong Baseline for Deep Person Re-Identification
Open accessAbstract
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.
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The tested retrieval model BoT did not perform well on anonymized images, and for both datasets, the retrieval score is random.
“The tested retrieval model BoT [64] did not perform well on our anonymized images, and for both datasets, the retrieval score is random.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [64]
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“For this experiment, we employ the state-of-the-art person ReId model BOT [64] to evaluate image retrieval and use the test sets of real and simulated datasets.”
MethodEdge-Constrained Video Analytics: A Comprehensive Survey on Detection, Tracking, and Recognition
Toshpulatov, M. A., Lee, W., & Cho, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6614394
- Authors
- Luo, H., Gu, Y., Liao, X., Lai, S., & Jiang, W.
- Affiliations
- Zhejiang University (CN)
- Chinese Academy of Sciences (CN)
- Xi'an Jiaotong University (CN)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1487-1495
- OpenAlex
- W2967515867
- Cited by
- 1,584
- References
- 29
- Citations by year
- 2026: 140 · 2025: 239 · 2024: 252 · 2023: 252 · 2022: 254 · 2021: 267 · 2020: 146 · 2019: 33 · 2016: 1
- FWCI
- 57.09
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61633019)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- person re-identification, deep neural network, global features, baseline model, Market-1501
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