# A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification

## TL;DR

This paper presents a simple yet effective baseline for person re-identification using deep neural networks, introducing a novel batch normalization neck (BNNeck) that improves performance by separating metric and classification features.

## Contribution

It introduces BNNeck, a new structure that enhances person ReID performance, and evaluates effective training tricks to establish a strong baseline.

## Key findings

- Achieves 94.5% rank-1 accuracy on Market1501 with ResNet50.
- BNNeck significantly boosts baseline performance.
- Baseline surpasses existing global- and part-based methods.

## Abstract

This study explores a simple but strong baseline for person re-identification (ReID). Person ReID with deep neural networks has progressed and achieved high performance in recent years. However, many state-of-the-art methods design complex network structures and concatenate multi-branch features. In the literature, some effective training tricks briefly appear in several papers or source codes. The present study collects and evaluates these effective training tricks in person ReID. By combining these tricks, the model achieves 94.5% rank-1 and 85.9% mean average precision on Market1501 with only using the global features of ResNet50. The performance surpasses all existing global- and part-based baselines in person ReID. We propose a novel neck structure named as batch normalization neck (BNNeck). BNNeck adds a batch normalization layer after global pooling layer to separate metric and classification losses into two different feature spaces because we observe they are inconsistent in one embedding space. Extended experiments show that BNNeck can boost the baseline, and our baseline can improve the performance of existing state-of-the-art methods. Our codes and models are available at: https://github.com/michuanhaohao/reid-strong-baseline.