REPOGEO REPORT · LITE

CVHub520/X-AnyLabeling

Default branch main · commit 85d250a8 · scanned 5/10/2026, 4:27:26 AM

GitHub: 9,029 stars · 981 forks

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 CVHub520/X-AnyLabeling, 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

Add a clear, descriptive opening paragraph to the README

Why:

CURRENT

The README excerpt shows <div align="center">... followed by links and videos, lacking an immediate textual description.

COPY-PASTE FIX

X-AnyLabeling is an open-source, AI-powered desktop application designed for effortless image and video data labeling. It integrates state-of-the-art models like Segment Anything (SAM), YOLO, and Grounding DINO to provide advanced auto-labeling, auto-training, and promptable concept grounding capabilities for machine learning engineers and researchers.

mediumreadme#2

Clarify the role of the X-AnyLabeling-Server in the README

Why:

COPY-PASTE FIX

Add a sentence to the README, perhaps near the installation or features section, clarifying: 'The X-AnyLabeling-Server, linked as the project homepage, provides the backend infrastructure for advanced AI model inference and auto-training features, complementing the desktop application.'

lowtopics#3

Add 'desktop-application' and 'gui-tool' topics

Why:

CURRENT

artificial-intelligence, clip, computer-vision, deep-learning, groundingdino, image-annotation-tool, image-classification, image-labeling-tool, image-matting, instance-segmentation, machine-learning, object-detection, ocr, onnxruntime, paddlepaddle, pose-estimation, rotated-object-detection, sam, vision-language-model, yolo

COPY-PASTE FIX

artificial-intelligence, clip, computer-vision, deep-learning, desktop-application, groundingdino, gui-tool, image-annotation-tool, image-classification, image-labeling-tool, image-matting, instance-segmentation, machine-learning, object-detection, ocr, onnxruntime, paddlepaddle, pose-estimation, rotated-object-detection, sam, vision-language-model, yolo

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 CVHub520/X-AnyLabeling

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Labelbox

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Labelbox · recommended 2×
SuperAnnotate · recommended 2×
V7 · recommended 2×
opencv/cvat · recommended 2×
Scale AI · recommended 2×

CATEGORY QUERY
What AI-powered tools simplify image and video data annotation for machine learning projects?
you: not recommended
AI recommended (in order):
1. Labelbox
2. SuperAnnotate
3. V7
4. CVAT (opencv/cvat)
5. Scale AI
6. Dataloop
AI recommended 6 alternatives but never named CVHub520/X-AnyLabeling. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Which annotation platforms offer advanced AI models for precise image segmentation and object detection?
you: not recommended
AI recommended (in order):
1. Labelbox
2. SuperAnnotate
3. V7
4. CVAT (opencv/cvat)
5. Scale AI
6. DataLoop
7. Roboflow
AI recommended 7 alternatives but never named CVHub520/X-AnyLabeling. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 CVHub520/X-AnyLabeling?
pass
AI named CVHub520/X-AnyLabeling explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts CVHub520/X-AnyLabeling in production, what risks or prerequisites should they evaluate first?
pass
AI named CVHub520/X-AnyLabeling 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 CVHub520/X-AnyLabeling solve, and who is the primary audience?
pass
AI did not name CVHub520/X-AnyLabeling — 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 CVHub520/X-AnyLabeling. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/CVHub520/X-AnyLabeling.svg)](https://repogeo.com/en/r/CVHub520/X-AnyLabeling)

HTML

<a href="https://repogeo.com/en/r/CVHub520/X-AnyLabeling"><img src="https://repogeo.com/badge/CVHub520/X-AnyLabeling.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

CVHub520/X-AnyLabeling — 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