RRepoGEO

REPOGEO REPORT · LITE

liliu-avril/Awesome-Segment-Anything

Default branch main · commit 1b279214 · scanned 5/28/2026, 2:58:12 AM

GitHub: 1,206 stars · 76 forks

AI VISIBILITY SCORE
22 /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
1 / 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 liliu-avril/Awesome-Segment-Anything, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    awesome-list, segment-anything-model, sam, computer-vision, foundation-models, survey, research, ai, machine-learning, deep-learning
  • highreadme#2
    Add an explicit 'Awesome List' statement to the README's opening

    Why:

    CURRENT
    # A Comprehensive Survey on Segment Anything Model for Vision and Beyond
    COPY-PASTE FIX
    # A Comprehensive Survey on Segment Anything Model for Vision and Beyond

This repository serves as the first comprehensive awesome list and curated collection of resources related to Meta AI's Segment Anything Model (SAM) for vision and beyond.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/liliu-avril/Awesome-Segment-Anything

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 liliu-avril/Awesome-Segment-Anything
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Models
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Models · recommended 1×
  2. Papers With Code · recommended 1×
  3. Awesome-Vision-Foundation-Models · recommended 1×
  4. Google AI Blog · recommended 1×
  5. Meta AI Blog · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of generalizable vision foundation models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Models
    2. Papers With Code
    3. Awesome-Vision-Foundation-Models
    4. Google AI Blog
    5. Meta AI Blog
    6. arXiv
    7. Towards Data Science (Medium)

    AI recommended 7 alternatives but never named liliu-avril/Awesome-Segment-Anything. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements in universal object segmentation for various computer vision applications?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM)
    2. DINOv2
    3. DINO
    4. Mask2Former
    5. MaskFormer
    6. OneFormer
    7. Grounding DINO
    8. OWL-ViT
    9. OWL-ViT v2

    AI recommended 9 alternatives but never named liliu-avril/Awesome-Segment-Anything. 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 liliu-avril/Awesome-Segment-Anything?
    pass
    AI did not name liliu-avril/Awesome-Segment-Anything — 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?

  • If a team adopts liliu-avril/Awesome-Segment-Anything in production, what risks or prerequisites should they evaluate first?
    pass
    AI named liliu-avril/Awesome-Segment-Anything 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 liliu-avril/Awesome-Segment-Anything solve, and who is the primary audience?
    pass
    AI did not name liliu-avril/Awesome-Segment-Anything — 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

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liliu-avril/Awesome-Segment-Anything — 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
liliu-avril/Awesome-Segment-Anything — RepoGEO report