RRepoGEO

REPOGEO REPORT · LITE

edvardHua/PoseEstimationForMobile

Default branch master · commit e31fb850 · scanned 5/9/2026, 3:17:47 AM

GitHub: 1,020 stars · 269 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface edvardHua/PoseEstimationForMobile, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to clearly state its purpose as a mobile library/solution

    Why:

    CURRENT
    This repository currently implemented the CPM and Hourglass model using TensorFlow. Instead of normal convolution, inverted residuals (also known as Mobilenet V2) module has been used inside the model for **real-time** inference.
    COPY-PASTE FIX
    This repository provides a **real-time single-person human pose estimation library and demo applications for Android and iOS**, leveraging optimized CPM and Hourglass models with inverted residuals (MobileNet V2) for efficient mobile inference. It offers a robust baseline for integrating advanced pose estimation into your mobile projects.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://edvardhua.github.io/PoseEstimationForMobile/
  • lowtopics#3
    Add more specific topics related to mobile application development

    Why:

    CURRENT
    android, convolutional-neural-networks, cpm, deep-neural-networks, human-pose-estimation, ios, pose-estimation, tensorflow
    COPY-PASTE FIX
    android, convolutional-neural-networks, cpm, deep-neural-networks, human-pose-estimation, ios, mobile-development, mobile-apps, pose-estimation, tensorflow

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface edvardHua/PoseEstimationForMobile
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MoveNet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MoveNet · recommended 2×
  2. PoseNet · recommended 2×
  3. MediaPipe Pose · recommended 1×
  4. TensorFlow Lite · recommended 1×
  5. ML Kit Pose Detection · recommended 1×
  • CATEGORY QUERY
    What are the best real-time human pose estimation libraries for mobile applications?
    you: not recommended
    AI recommended (in order):
    1. MediaPipe Pose
    2. TensorFlow Lite
    3. MoveNet
    4. PoseNet
    5. ML Kit Pose Detection
    6. OpenCV
    7. Apple Vision Framework

    AI recommended 7 alternatives but never named edvardHua/PoseEstimationForMobile. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement real-time pose estimation on Android/iOS using TensorFlow models?
    you: not recommended
    AI recommended (in order):
    1. MediaPipe
    2. MoveNet
    3. PoseNet
    4. ML Kit
    5. Core ML

    AI recommended 5 alternatives but never named edvardHua/PoseEstimationForMobile. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of edvardHua/PoseEstimationForMobile?
    pass
    AI named edvardHua/PoseEstimationForMobile explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts edvardHua/PoseEstimationForMobile in production, what risks or prerequisites should they evaluate first?
    pass
    AI named edvardHua/PoseEstimationForMobile explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo edvardHua/PoseEstimationForMobile solve, and who is the primary audience?
    pass
    AI did not name edvardHua/PoseEstimationForMobile — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of edvardHua/PoseEstimationForMobile. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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edvardHua/PoseEstimationForMobile — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite