# michuanhaohao/reid-strong-baseline

Bag of Tricks and A Strong Baseline for Deep Person Re-identification

Repository: https://github.com/michuanhaohao/reid-strong-baseline
Canonical: https://ross.abutalabs.com/products/reid-strong-baseline
Homepage: http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Luo_Bag_of_Tricks_and_a_Strong_Baseline_for_Deep_Person_CVPRW_2019_paper.pdf
Language: Python
License: MIT
License Family: permissive
Last push: 2020-04-23T06:04:46+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2727, "days_push": 2323, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2355, forks 580 (observed 2026-08-28T04:06:40.586181+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: computer-vision, deep-learning, machine-learning
- platform: python
- tags: person-reidentification, pytorch, computer-vision, deep-learning, metric-learning, research-code, cvpr2019, baseline, research, linux, gpu

## Member repositories
- michuanhaohao/reid-strong-baseline (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:40.586181+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:36:27.019763+00:00, confidence not recorded.
  - readme: https://github.com/michuanhaohao/reid-strong-baseline (fetched 2026-08-28T04:06:40.586181+00:00, sha 12df01ac2449)
- Data as of 2026-08-30T08:39:29.467469+00:00.