Bag of Tricks and a Strong Baseline for Deep Person Re-Identification

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.

1. Introduction

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 or refinements are briefly appeared in several papers or source codes. This paper will collect and evaluate such effective training tricks in person ReID. With involved in all training tricks, ResNet50 reaches 94.5% rank-1 accuracy and 85.9% mAP on Market1501 [24]. It is worth mentioning that it achieves such surprising performance with global features of the model.