RRepoGEO

REPOGEO REPORT · LITE

timojl/clipseg

Default branch master · commit 77c4e2dc · scanned 5/26/2026, 4:42:52 AM

GitHub: 1,333 stars · 123 forks

AI VISIBILITY SCORE
60 /100
Needs work
Category recall
1 / 2
Avg rank #4.0 when recommended
Rule findings
1 pass · 1 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 timojl/clipseg, 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

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

OVERALL DIRECTION
  • highreadme#1
    Refine README's opening sentence to highlight core functionality

    Why:

    CURRENT
    This repository contains the code used in the paper "Image Segmentation Using Text and Image Prompts".
    COPY-PASTE FIX
    CLIPSeg enables image segmentation without training, using arbitrary text queries or image prompts with masks. This repository contains the code from our CVPR 2022 paper "Image Segmentation Using Text and Image Prompts".
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://huggingface.co/docs/transformers/model_doc/clipseg

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
1 / 2
50% of queries surface timojl/clipseg
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 2×
  2. Grounding DINO · recommended 2×
  3. OWL-ViT · recommended 2×
  4. SEEM · recommended 2×
  5. Mask2Former · recommended 2×
  • CATEGORY QUERY
    How to perform image segmentation using natural language text prompts?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model
    2. CLIP
    3. Grounding DINO
    4. OWL-ViT
    5. SEEM
    6. Mask2Former
    7. GLIP
    8. Llama-Adapter V2

    AI recommended 8 alternatives but never named timojl/clipseg. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good libraries for zero-shot image segmentation with text or visual cues?
    you: #4
    AI recommended (in order):
    1. Segment Anything Model (SAM)
    2. Grounding DINO
    3. OWL-ViT
    4. CLIPSeg ← you
    5. CLIP
    6. SEEM
    7. Mask2Former
    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 timojl/clipseg?
    pass
    AI named timojl/clipseg explicitly

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

  • If a team adopts timojl/clipseg in production, what risks or prerequisites should they evaluate first?
    pass
    AI named timojl/clipseg 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 timojl/clipseg solve, and who is the primary audience?
    pass
    AI named timojl/clipseg explicitly

    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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timojl/clipseg — 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