# edvardHua/PoseEstimationForMobile

:dancer: Real-time single person pose estimation for Android and iOS.

Repository: https://github.com/edvardHua/PoseEstimationForMobile
Canonical: https://ross.abutalabs.com/products/poseestimationformobile
Language: C++
License: Apache-2.0
License Family: permissive
Topics: pose-estimation, tensorflow, deep-neural-networks, cpm, android, ios, convolutional-neural-networks, human-pose-estimation
Last push: 2023-03-24T22:29:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3065, "days_push": 1258, "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 1024, forks 266 (observed 2026-08-28T04:03:16.443533+00:00)

## What it is
A TensorFlow-based library implementing CPM and Hourglass models with MobileNetV2 inverted residual modules for real-time single-person human pose estimation on mobile devices. It includes training code, pretrained models, and demo apps for Android and iOS achieving roughly 60 FPS on modern hardware.

## Use cases
- detect human body keypoints in real time on a phone camera feed
- build a fitness app that tracks user poses on Android or iOS
- run pose estimation models offline on mobile devices
- train a custom CPM or Hourglass pose model with TensorFlow
- benchmark lightweight pose estimation models on mobile GPUs

## When to choose
- you need real-time single-person pose estimation on Android or iOS
- you want a lightweight MobileNet-based baseline for mobile inference
- you need both training code and mobile demo apps in one project

## When to avoid
- you need multi-person pose estimation
- you need state-of-the-art accuracy rather than a baseline
- you target platforms other than Android or iOS
- you need actively maintained code with recent TensorFlow versions

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: machine-learning, computer-vision, mobile-development, deep-learning
- platform: python, cpp
- tags: pose-estimation, human-pose, tensorflow, cpm, hourglass, mobilenet, real-time-inference, tflite, mace, keypoint-detection, android, ios

## Member repositories
- edvardHua/PoseEstimationForMobile (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:16.443533+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 2026-08-30T07:08:13.715271+00:00, confidence not recorded.
  - readme: https://github.com/edvardHua/PoseEstimationForMobile (fetched 2026-08-28T04:03:16.443533+00:00, sha 9ad9ca4b2c5c)
- Data as of 2026-08-30T08:39:29.467469+00:00.