RRepoGEO

REPOGEO REPORT · LITE

mlfoundations/open_flamingo

Default branch main · commit 655f693f · scanned 6/23/2026, 12:47:08 PM

GitHub: 4,107 stars · 320 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
35 /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
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 mlfoundations/open_flamingo, 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
  • mediumabout#1
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the project's main website, documentation, or paper link (e.g., the paper link from the README) to the 'Homepage' field in the repository settings.
  • mediumtopics#2
    Add more specific topics related to few-shot vision-language models

    Why:

    CURRENT
    computer-vision, deep-learning, flamingo, in-context-learning, language-model, multimodal-learning, pytorch
    COPY-PASTE FIX
    Add `few-shot-learning`, `vision-language-model`, `vlm` to the existing topics.

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 mlfoundations/open_flamingo
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Hugging Face Accelerate · recommended 1×
  3. Hugging Face Diffusers · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How to train large models that can process both visual and textual information?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. Hugging Face Diffusers
    4. PyTorch Lightning
    5. DeepSpeed
    6. TensorFlow
    7. Keras
    8. TensorFlow Vision
    9. TensorFlow Text
    10. JAX
    11. Flax

    AI recommended 11 alternatives but never named mlfoundations/open_flamingo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch-based solution for developing vision-language models with few-shot capabilities.
    you: not recommended
    AI recommended (in order):
    1. OpenCLIP
    2. Hugging Face Transformers
    3. timm (PyTorch Image Models)
    4. MMDetection
    5. MMYOLO
    6. PyTorch-Lightning

    AI recommended 6 alternatives but never named mlfoundations/open_flamingo. 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 mlfoundations/open_flamingo?
    pass
    AI named mlfoundations/open_flamingo explicitly

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

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

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

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mlfoundations/open_flamingo — 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