RRepoGEO

REPOGEO REPORT · LITE

CVHub520/X-AnyLabeling

Default branch main · commit 71f8ad21 · scanned 6/20/2026, 2:46:14 AM

GitHub: 9,479 stars · 1,025 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
40 /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
3 / 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 direct, keyword-rich introductory sentence to the README

    Why:

    CURRENT
    The README starts with visual elements and language links, pushing the core textual description down.
    COPY-PASTE FIX
    X-AnyLabeling is an AI-powered, multi-functional image annotation and data labeling platform for computer vision, integrating models like Segment Anything (SAM) and YOLO to accelerate tasks such as instance segmentation and object detection.
  • mediumreadme#2
    Emphasize core differentiators in the README's introduction

    Why:

    COPY-PASTE FIX
    It stands out by deeply integrating advanced AI models to provide highly efficient, semi-automatic, and intelligent data annotation, significantly accelerating the labeling process for researchers and data annotators.
  • lowreadme#3
    Add context to the homepage link in the README

    Why:

    COPY-PASTE FIX
    For the backend server component, visit: https://github.com/CVHub520/X-AnyLabeling-Server

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
0%
Of all named tools, what % are you?
Top rival
SuperAnnotate
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SuperAnnotate · recommended 2×
  2. opencv/cvat · recommended 2×
  3. Roboflow · recommended 2×
  4. Labelbox · recommended 1×
  5. V7 · recommended 1×
  • CATEGORY QUERY
    Looking for an image annotation tool with AI assistance for deep learning datasets.
    you: not recommended
    AI recommended (in order):
    1. Labelbox
    2. SuperAnnotate
    3. V7
    4. CVAT (opencv/cvat)
    5. Roboflow
    6. Annotate.io

    AI recommended 6 alternatives but never named CVHub520/X-AnyLabeling. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for automated instance segmentation and object detection labeling?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
    2. Label Studio (heartexlabs/label-studio)
    3. CVAT (Computer Vision Annotation Tool) (opencv/cvat)
    4. SuperAnnotate
    5. V7 (V7 Labs)
    6. Roboflow
    7. Scale AI

    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 named CVHub520/X-AnyLabeling explicitly

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

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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