RRepoGEO

REPOGEO REPORT · LITE

rmokady/CLIP_prefix_caption

Default branch main · commit 1ad805a8 · scanned 6/24/2026, 4:02:26 PM

GitHub: 1,420 stars · 224 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 rmokady/CLIP_prefix_caption, 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
    Reposition the README H1 to be more specific

    Why:

    CURRENT
    # CLIP prefix captioning.
    COPY-PASTE FIX
    # ClipCap: CLIP Prefix for Image Captioning
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    image-captioning, clip, vision-language-model, nlp, deep-learning, pytorch, generative-ai, multimodal
  • mediumabout#3
    Expand the repository's 'About' description

    Why:

    CURRENT
    Simple image captioning model
    COPY-PASTE FIX
    Official implementation of ClipCap: CLIP Prefix for Image Captioning. Generates image captions by mapping CLIP embeddings to a language model prefix, achieving fast training and SOTA results without object annotations.

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 rmokady/CLIP_prefix_caption
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. OpenCV · recommended 1×
  3. Keras · recommended 1×
  4. TensorFlow · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How to automatically generate descriptions for images using a simple model?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. OpenCV
    3. Keras
    4. TensorFlow
    5. PyTorch
    6. Azure AI Vision
    7. Google Cloud Vision API

    AI recommended 7 alternatives but never named rmokady/CLIP_prefix_caption. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an efficient image captioning solution that leverages pre-trained vision-language models.
    you: not recommended
    AI recommended (in order):
    1. BLIP-2
    2. InstructBLIP
    3. GIT
    4. ViT-GPT2
    5. OFA
    6. CoCa

    AI recommended 6 alternatives but never named rmokady/CLIP_prefix_caption. 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 rmokady/CLIP_prefix_caption?
    pass
    AI named rmokady/CLIP_prefix_caption explicitly

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

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