Bag of Tricks and A Strong Baseline for Deep Person Re-identification
arXiv:1903.07071
Abstract
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.
arXiv commentCVPR2019 Workshop. Code is available at https://github.com/michuanhaohao/reid-strong-baseline
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Cited by in corpus (5)
- Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification
- Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
- Collaborative Attention Network for Person Re-identification
- High-Performance Long-Term Tracking with Meta-Updater
- Triplet Online Instance Matching Loss for Person Re-identification