# open-mmlab/mmpose

OpenMMLab Pose Estimation Toolbox and Benchmark.

Repository: https://github.com/open-mmlab/mmpose
Canonical: https://ross.abutalabs.com/products/mmpose
Homepage: https://mmpose.readthedocs.io/en/latest/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pose-estimation, human-pose, pytorch, hrnet, mpii, benchmark, cpm, hourglass, higher-hrnet, crowdpose, ochuman, freihand, mspn, rsn, udp, animal-pose-estimation, mmpose, hand-pose-estimation, face-keypoint, rtmpose
Last push: 2025-08-04T07:30:50+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 35, release rhythm 8, longevity 100
- inputs: {"age_days": 2247, "days_push": 394, "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 7855, forks 1528 (observed 2026-08-28T04:10:07.460491+00:00)

## What it is
MMPose is an open-source pose estimation toolbox and benchmark built on PyTorch as part of the OpenMMLab ecosystem. It provides implementations of state-of-the-art keypoint detection models for humans, animals, hands, and faces, along with training and evaluation pipelines.

## Use cases
- estimate human pose keypoints from images
- train a pose estimation model on COCO or MPII
- benchmark keypoint detection models like HRNet and RTMPose
- detect hand keypoints for gesture recognition
- run face landmark detection
- estimate animal poses in videos
- fine-tune a pretrained pose model on a custom dataset

## When to choose
- you need production-quality pose estimation with a large model zoo
- you want to train or benchmark keypoint detection models in PyTorch
- you need pose estimation for humans, animals, hands, or faces in one toolkit

## When to avoid
- you need a lightweight inference-only solution without the OpenMMLab dependency stack
- your task is object detection or segmentation rather than keypoint estimation
- you work outside Python/PyTorch ecosystems

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, image-processing, benchmarking
- domain: computer-vision, deep-learning, machine-learning, image-processing
- platform: python, cross-platform
- tags: pose-estimation, keypoint-detection, human-pose, pytorch, model-zoo, rtmpose, hand-pose-estimation, face-keypoints, animal-pose-estimation, openmmlab, gpu, linux

## Member repositories
- open-mmlab/mmpose (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:07.460491+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 2026-08-29T17:33:48.114475+00:00, confidence not recorded.
  - readme: https://github.com/open-mmlab/mmpose (fetched 2026-08-28T04:10:07.460491+00:00, sha 75f398f7724a)
  - registry_pypi: https://pypi.org/pypi/mmpose/json (fetched 2026-08-29T08:30:48.170920+00:00, sha 4cb3bf05c675)
- Data as of 2026-08-30T08:39:29.467469+00:00.