REPOGEO REPORT · LITE

edvardHua/PoseEstimationForMobile

Default branch master · commit e31fb850 · scanned 6/18/2026, 10:13:16 PM

GitHub: 1,023 stars · 268 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 statement to clearly state purpose and audience

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 **real-time single-person pose estimation for Android and iOS**, implementing CPM and Hourglass models using TensorFlow. It leverages inverted residuals (Mobilenet V2) for efficient, **real-time** inference directly on mobile devices.

mediumhomepage#2
Add homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://github.com/edvardHua/PoseEstimationForMobile
```

lowreadme#3

Explicitly state target audience and use cases in the README

Why:

COPY-PASTE FIX

Add a sentence near the beginning of the README, such as: 'It serves as a practical baseline for mobile application developers and researchers aiming to integrate efficient, on-device human pose estimation into their Android and iOS applications.'

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

Of all named tools, what % are you?

Top rival

OpenPose

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenPose · recommended 2×
MediaPipe Pose · recommended 1×
MoveNet · recommended 1×
TensorFlow Lite · recommended 1×
Vision framework · recommended 1×

CATEGORY QUERY
Looking for a mobile-optimized solution for real-time single-person pose estimation.
you: not recommended
AI recommended (in order):
1. MediaPipe Pose
2. MoveNet
3. TensorFlow Lite
4. OpenPose
5. Vision framework
AI recommended 5 alternatives but never named edvardHua/PoseEstimationForMobile. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are efficient deep learning models for on-device pose estimation on smartphones?
you: not recommended
AI recommended (in order):
1. MediaPipe Pose (BlazePose GHUM)
2. MoveNet (Thunder/Lightning)
3. LiteHRNet
4. YOLO-Pose
5. EfficientPose
6. OpenPose
AI recommended 6 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)

[![RepoGEO](https://repogeo.com/badge/edvardHua/PoseEstimationForMobile.svg)](https://repogeo.com/en/r/edvardHua/PoseEstimationForMobile)

HTML

<a href="https://repogeo.com/en/r/edvardHua/PoseEstimationForMobile"><img src="https://repogeo.com/badge/edvardHua/PoseEstimationForMobile.svg" alt="RepoGEO" /></a>

Pro

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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