# layumi/Person_reID_baseline_pytorch

:bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial

Repository: https://github.com/layumi/Person_reID_baseline_pytorch
Canonical: https://ross.abutalabs.com/products/person_reid_baseline_pytorch
Homepage: https://www.zdzheng.xyz
Language: Python
License: MIT
License Family: permissive
Topics: open-reid, pytorch, person-reidentification, image-retrieval, person-reid, re-ranking, random-erasing, image-search, market-1501, tutorial, baseline, msmt17, cuhk-np, vehicle-reid, circle-loss, metric-learning, object-reid, awesome-reid
Last push: 2026-07-18T07:34:37+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 93, release rhythm 8, longevity 100
- inputs: {"age_days": 3169, "days_push": 46, "days_rel": 483, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4446, forks 1028 (observed 2026-08-28T04:08:50.413268+00:00)

## What it is
A small, friendly PyTorch baseline implementation for person and vehicle re-identification (ReID). It reproduces strong top-conference results with simple training code, supports memory-efficient bf16/fp16 training, and includes a beginner tutorial.

## Use cases
- train a person re-identification model in pytorch
- re-identify people across camera views
- build a vehicle re-id baseline
- run image retrieval with re-ranking
- learn person reid with a tutorial
- benchmark on Market-1501 and MSMT17
- apply circle loss and random erasing tricks

## When to choose
- you need a well-cited, easy-to-modify ReID baseline in PyTorch
- you have limited GPU memory (2GB with fp16)
- you want a tutorial-friendly entry into object re-identification
- you need standard benchmarks like Market-1501, MSMT17, CUHK-NP

## When to avoid
- you need production-ready person tracking or detection pipelines
- you want a turnkey commercial surveillance product
- you work outside PyTorch or need non-vision retrieval tasks

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, image-processing, computer-vision, search-engine
- domain: computer-vision, machine-learning, deep-learning, image-processing
- platform: python, cross-platform
- tags: person-reidentification, vehicle-reid, metric-learning, re-ranking, pytorch, market-1501, circle-loss, image-retrieval, baseline, tutorial, gpu

## Member repositories
- layumi/Person_reID_baseline_pytorch (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:50.413268+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 2026-08-29T18:20:47.996303+00:00, confidence not recorded.
  - readme: https://github.com/layumi/Person_reID_baseline_pytorch (fetched 2026-08-28T04:08:50.413268+00:00, sha 3e7793147e0b)
  - homepage: https://www.zdzheng.xyz (fetched 2026-08-29T09:07:53.760792+00:00, sha d611149e1578)
- Data as of 2026-08-30T08:39:29.467469+00:00.