RRepoGEO

REPOGEO REPORT · LITE

apple/pico-banana-400k

Default branch main · commit 27384f40 · scanned 5/20/2026, 2:57:51 AM

GitHub: 1,822 stars · 81 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 apple/pico-banana-400k, 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
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    A large-scale dataset of ~400K text–image–edit triplets designed to advance research in text-guided image editing.
  • mediumreadme#2
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## ⚖️ License

This dataset is released under [insert specific license name(s) here, e.g., a custom research license or specific open-source licenses if applicable]. Please refer to the LICENSE file for full details.

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 apple/pico-banana-400k
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. COCO (Common Objects in Context) · recommended 2×
  3. Visual Genome · recommended 1×
  4. Flickr30k · recommended 1×
  5. Conceptual Captions (CC3M/CC12M) · recommended 1×
  • CATEGORY QUERY
    What are large-scale datasets for training models on text-guided image manipulation tasks?
    you: not recommended
    AI recommended (in order):
    1. LAION-5B
    2. COCO (Common Objects in Context)
    3. Visual Genome
    4. Flickr30k
    5. Conceptual Captions (CC3M/CC12M)
    6. GLIDE (Guided Language to Image Diffusion for Editing)

    AI recommended 6 alternatives but never named apple/pico-banana-400k. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need an image dataset with original, text instruction, and edited versions for AI training.
    you: not recommended
    AI recommended (in order):
    1. DiffusionDB
    2. LAION-5B
    3. COCO (Common Objects in Context)
    4. Flickr30k Entities
    5. ImageNet-R (ImageNet-Rendition)
    6. PASCAL VOC
    7. Adobe DeepFill v2 Dataset

    AI recommended 7 alternatives but never named apple/pico-banana-400k. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 apple/pico-banana-400k?
    pass
    AI named apple/pico-banana-400k explicitly

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

  • If a team adopts apple/pico-banana-400k in production, what risks or prerequisites should they evaluate first?
    pass
    AI named apple/pico-banana-400k 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 apple/pico-banana-400k solve, and who is the primary audience?
    pass
    AI named apple/pico-banana-400k explicitly

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

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apple/pico-banana-400k — 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