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.