RRepoGEO

REPOGEO REPORT · LITE

kakaobrain/coyo-dataset

Default branch main · commit 3dc6afb6 · scanned 5/28/2026, 2:02:57 AM

GitHub: 1,254 stars · 38 forks

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 kakaobrain/coyo-dataset, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the appropriate license text (e.g., Apache-2.0, MIT, or a custom data license if applicable). If a custom license, also clarify its terms directly in the README.
  • mediumreadme#2
    Strengthen the unique value proposition in the README introduction

    Why:

    CURRENT
    COYO-700M is a large-scale dataset that contains 747M image-text pairs as well as many other meta-attributes to increase the usability to train various models. Our dataset follows a similar strategy to previous vision-and-language datasets, collecting many informative pairs of alt-text and its associated image in HTML documents. We expect COYO to be used to train popular large-scale foundation models complementary to other similar datasets.
    COPY-PASTE FIX
    COYO-700M is a large-scale, high-quality dataset containing 747M image-text pairs, designed to train various multimodal foundation models. Unlike other datasets that prioritize raw scale, COYO-700M emphasizes stringent image and text-level filtering, including meta-attributes, to deliver a cleaner, more usable dataset complementary to existing resources. This focus on quality makes it ideal for researchers seeking robust data for advanced vision-language model pre-training.

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 kakaobrain/coyo-dataset
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LAION-5B
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LAION-5B · recommended 2×
  2. WIT (WebImageText) · recommended 2×
  3. YFCC100M (Yahoo Flickr Creative Commons 100M) · recommended 2×
  4. Stable Diffusion · recommended 1×
  5. LAION-400M · recommended 1×
  • CATEGORY QUERY
    What are good large-scale image-text datasets for training multimodal foundation models?
    you: not recommended
    AI recommended (in order):
    1. LAION-5B
    2. Stable Diffusion
    3. LAION-400M
    4. LAION-2B-en
    5. ALIGN
    6. JFT-300M
    7. JFT-3B
    8. Conceptual Captions (CC3M)
    9. Conceptual Captions (CC12M)
    10. WIT (WebImageText)
    11. COYO-700M
    12. YFCC100M (Yahoo Flickr Creative Commons 100M)

    AI recommended 12 alternatives but never named kakaobrain/coyo-dataset. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find extensive image-text data for vision-language model pre-training?
    you: not recommended
    AI recommended (in order):
    1. LAION-5B
    2. Conceptual Captions (CC3M/CC12M)
    3. WIT (WebImageText)
    4. COCO (Common Objects in Context)
    5. Visual Genome
    6. SBU Captions
    7. YFCC100M (Yahoo Flickr Creative Commons 100M)

    AI recommended 7 alternatives but never named kakaobrain/coyo-dataset. 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 kakaobrain/coyo-dataset?
    pass
    AI did not name kakaobrain/coyo-dataset — 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 kakaobrain/coyo-dataset in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kakaobrain/coyo-dataset 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 kakaobrain/coyo-dataset solve, and who is the primary audience?
    pass
    AI named kakaobrain/coyo-dataset explicitly

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

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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