RRepoGEO

REPOGEO REPORT · LITE

apple/ml-ferret

Default branch main · commit 3c9e5c93 · scanned 5/25/2026, 10:48:04 AM

GitHub: 8,683 stars · 519 forks

AI VISIBILITY SCORE
23 /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
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 apple/ml-ferret, 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
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    Ferret is an end-to-end Multimodal Large Language Model (MLLM) designed for fine-grained spatial referring and grounding, capable of understanding and responding to any-form referring expressions within images.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    multimodal-large-language-model, mllm, visual-grounding, object-referring, computer-vision, deep-learning, ai-model, research
  • mediumreadme#3
    Explicitly state the project's primary license(s) in the README

    Why:

    CURRENT
    Usage and License Notices: The data, and code is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna and GPT-4.
    COPY-PASTE FIX
    Usage and License Notices: The Ferret code and data are licensed under [Insert Specific License Name/Link Here, e.g., a custom research license]. They are intended for research use only and are also restricted to uses that follow the license agreements of LLaMA, Vicuna, and GPT-4.

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/ml-ferret
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. 🤗 Datasets · recommended 1×
  3. MDETR · recommended 1×
  4. OFA · recommended 1×
  5. GLIP · recommended 1×
  • CATEGORY QUERY
    How to build an AI model for fine-grained object referring and visual grounding?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. 🤗 Datasets
    3. MDETR
    4. OFA
    5. GLIP
    6. PyTorch Lightning
    7. Detectron2
    8. OpenMMLab
    9. MMDetection
    10. MMEngine
    11. MMYOLO
    12. TensorFlow
    13. Keras
    14. TensorFlow Hub
    15. Pytorch Geometric

    AI recommended 15 alternatives but never named apple/ml-ferret. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What MLLMs are available for detailed visual region understanding and multimodal reasoning?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. LLaVA
    4. CogVLM
    5. Qwen-VL-Max / Qwen-VL-Chat
    6. Fuyu-8B

    AI recommended 6 alternatives but never named apple/ml-ferret. 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/ml-ferret?
    pass
    AI did not name apple/ml-ferret — 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 apple/ml-ferret in production, what risks or prerequisites should they evaluate first?
    pass
    AI named apple/ml-ferret 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/ml-ferret solve, and who is the primary audience?
    pass
    AI named apple/ml-ferret 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 apple/ml-ferret. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/apple/ml-ferret.svg)](https://repogeo.com/en/r/apple/ml-ferret)
HTML
<a href="https://repogeo.com/en/r/apple/ml-ferret"><img src="https://repogeo.com/badge/apple/ml-ferret.svg" alt="RepoGEO" /></a>
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apple/ml-ferret — 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