# meituan/YOLOv6

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

Repository: https://github.com/meituan/YOLOv6
Canonical: https://ross.abutalabs.com/products/yolov6
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Topics: object-detection, pytorch, yolo
Last push: 2024-08-07T18:20:33+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1547, "days_push": 756, "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 5895, forks 1062 (observed 2026-08-28T04:09:32.024132+00:00)

## What it is
YOLOv6 is a single-stage object detection framework implemented in PyTorch, designed for industrial applications with a family of pretrained models (N/T/S/M/L) balancing accuracy and speed. It includes training, evaluation, quantization, and deployment tooling, plus variants for segmentation, face detection, and mobile/CPU (YOLOv6Lite).

## Use cases
- train a custom YOLO object detection model on my own dataset
- run fast real-time object detection on GPU with pretrained weights
- deploy an object detection model with TensorRT for production
- detect faces in images with a YOLO model
- run object detection on mobile devices or CPU
- quantize a detection model for faster inference

## When to choose
- you need a well-benchmarked YOLO detector with pretrained models and deployment support
- you want industrial-grade speed/accuracy tradeoffs including TensorRT and quantization
- you need variants like segmentation, face detection, or lightweight mobile models

## When to avoid
- you need the latest actively developed detector (development has slowed since 2023)
- your project requires a permissive license (GPL-3.0)
- you need tasks beyond detection/segmentation such as pose estimation or tracking

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, deep-learning, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: object-detection, yolo, pytorch, model-training, inference, industrial-applications, pretrained-models, gpu

## Member repositories
- meituan/YOLOv6 (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:32.024132+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 2026-08-29T17:51:32.259146+00:00, confidence not recorded.
  - readme: https://github.com/meituan/YOLOv6 (fetched 2026-08-28T04:09:32.024132+00:00, sha 08bc34184c08)
- Data as of 2026-08-30T08:39:29.467469+00:00.