michuanhaohao/reid-strong-baseline

Health v2 · maintenance only

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Adoption not part of the score

2355 stars · 580 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of the 'Bag of Tricks and A Strong Baseline for Deep Person Re-identification' paper (CVPRW 2019), providing end-to-end training and evaluation for person re-identification models. It includes dataset preparation, multi-GPU support, and modular code management for ReID research.

Use cases

• train a person re-identification model
• reproduce CVPR 2019 ReID baseline results
• evaluate person re-identification accuracy on standard datasets
• apply bag of tricks to improve ReID model performance
• use batch normalization neck for person re-identification
• adapt ReID baseline for vehicle re-identification

When to choose

• you need a strong, well-cited baseline for person re-identification research
• you want a PyTorch codebase with end-to-end training and evaluation for ReID
• you want to reproduce or extend the Bag of Tricks paper results
• you need multi-GPU training support for ReID models

When to avoid

• you need a production-ready ReID deployment system rather than research code
• you need a framework for tasks other than person/vehicle re-identification
• you need actively maintained code with recent updates
• you need a no-code or GUI tool for ReID

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision deep-learning machine-learning python person-reidentification pytorch computer-vision deep-learning metric-learning research-code cvpr2019 baseline research linux gpu

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