# JDAI-CV/fast-reid

SOTA Re-identification Methods and Toolbox

Repository: https://github.com/JDAI-CV/fast-reid
Canonical: https://ross.abutalabs.com/products/fast-reid
Language: Python
License: Apache-2.0
License Family: permissive
Topics: person-reid, open-reid, re-identification, person-reidentification, image-retrieval, re-ranking, random-erasing, image-search, apex, toolbox, sota, computer-vision, pytorch, baseline, reids
Last push: 2024-07-30T14:37:38+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3010, "days_push": 764, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3981, forks 879 (observed 2026-08-28T04:08:31.666330+00:00)

## What it is
FastReID is a PyTorch-based research platform implementing state-of-the-art re-identification algorithms for persons, vehicles, and faces. It serves as both a training toolbox with SoTA baselines and a library supporting projects like image retrieval, attribute recognition, and TensorRT deployment.

## Use cases
- train a person re-identification model on Market-1501
- build an image retrieval system with re-ranking
- benchmark SoTA reid baselines in PyTorch
- convert reid models to ONNX or TensorRT for deployment
- recognize person attributes from images
- train face recognition models with partial FC
- run distributed multi-GPU training for reid

## When to choose
- you need state-of-the-art re-identification baselines with reproducible configs
- you want a research platform extensible to retrieval, face recognition, and attribute tasks
- you need fast training with mixed precision, distributed GPUs, and TensorRT export

## When to avoid
- you need a plug-and-play production reid API without model training
- you work outside PyTorch (e.g., TensorFlow or JAX)
- you need actively developed features - the project is in maintenance mode

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision, search-engine, benchmarking
- domain: computer-vision, deep-learning, machine-learning, image-processing
- platform: python
- tags: re-identification, person-reid, pytorch, image-retrieval, face-recognition, tensorrt, onnx, vehicle-reid, toolbox, gpu, linux, docker

## Member repositories
- JDAI-CV/fast-reid (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:31.666330+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 2026-08-29T18:24:16.390889+00:00, confidence not recorded.
  - readme: https://github.com/JDAI-CV/fast-reid (fetched 2026-08-28T04:08:31.666330+00:00, sha 56f975526482)
- Data as of 2026-08-30T08:39:29.467469+00:00.