RRepoGEO

REPOGEO REPORT · LITE

yzhuoning/Awesome-CLIP

Default branch main · commit 654df44d · scanned 6/26/2026, 1:53:21 PM

GitHub: 1,230 stars · 59 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)

3 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 yzhuoning/Awesome-CLIP, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#2
    Reposition the README's opening sentence to clarify repo type

    Why:

    CURRENT
    This repo collects the research resources based on CLIP (Contrastive Language-Image Pre-Training) proposed by OpenAI.
    COPY-PASTE FIX
    This is an awesome list collecting comprehensive research resources, papers, and code implementations based on CLIP (Contrastive Language-Image Pre-Training) proposed by OpenAI, designed for AI researchers and developers.
  • mediumhomepage#3
    Add a relevant homepage URL to the 'About' section

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL in the 'About' section to a relevant resource, such as the original CLIP paper's project page or the OpenAI blog post about CLIP (e.g., https://openai.com/blog/clip/).

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 yzhuoning/Awesome-CLIP
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OWL-ViT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OWL-ViT · recommended 1×
  2. Grounding DINO · recommended 1×
  3. CLIP · recommended 1×
  4. Faster R-CNN · recommended 1×
  5. YOLO · recommended 1×
  • CATEGORY QUERY
    How can I achieve zero-shot object detection using current vision-language models?
    you: not recommended
    AI recommended (in order):
    1. OWL-ViT
    2. Grounding DINO
    3. CLIP
    4. Faster R-CNN
    5. YOLO
    6. DETR
    7. ViLD
    8. RegionCLIP
    9. GLIP
    10. Llama-2
    11. GPT-4
    12. GPT-4V
    13. LLaVA
    14. Florence-2

    AI recommended 14 alternatives but never named yzhuoning/Awesome-CLIP. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find open-source implementations for contrastive image-text model training?
    you: not recommended
    AI recommended (in order):
    1. OpenCLIP
    2. Hugging Face Transformers Library
    3. OpenAI's CLIP repository
    4. LAION-5B
    5. PyTorch Lightning

    AI recommended 5 alternatives but never named yzhuoning/Awesome-CLIP. 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 yzhuoning/Awesome-CLIP?
    pass
    AI did not name yzhuoning/Awesome-CLIP — 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 yzhuoning/Awesome-CLIP in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yzhuoning/Awesome-CLIP 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 yzhuoning/Awesome-CLIP solve, and who is the primary audience?
    pass
    AI named yzhuoning/Awesome-CLIP explicitly

    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 yzhuoning/Awesome-CLIP. 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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yzhuoning/Awesome-CLIP — 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