RRepoGEO

REPOGEO REPORT · LITE

apple/ml-fastvlm

Default branch main · commit 592b4add · scanned 5/20/2026, 3:43:15 PM

GitHub: 7,345 stars · 553 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
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 apple/ml-fastvlm, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    vision-language-models, vlm, efficient-ai, mobile-ai, on-device-ai, computer-vision, deep-learning, pytorch, llm
  • highreadme#2
    Reposition the README's opening statement to clarify niche

    Why:

    CURRENT
    This is the official repository of **FastVLM: Efficient Vision Encoding for Vision Language Models. (CVPR 2025)**
    COPY-PASTE FIX
    This is the official repository for **FastVLM: Efficient Vision Encoding for Vision Language Models (CVPR 2025)**, specifically designed to accelerate high-resolution image processing for on-device and mobile VLM applications.
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a section to the README, perhaps under 'Getting Started' or 'Legal', stating: 'This project is licensed under the terms found in the [LICENSE](LICENSE) file, which outlines the specific conditions for use and distribution.'

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-fastvlm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MobileNetV3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MobileNetV3 · recommended 1×
  2. EfficientNetV2 · recommended 1×
  3. YOLOv5 · recommended 1×
  4. MediaPipe · recommended 1×
  5. TensorFlow Lite Converter · recommended 1×
  • CATEGORY QUERY
    How to achieve faster vision encoding for high-resolution images on mobile devices?
    you: not recommended
    AI recommended (in order):
    1. MobileNetV3
    2. EfficientNetV2
    3. YOLOv5
    4. MediaPipe
    5. TensorFlow Lite Converter
    6. PyTorch Mobile
    7. TensorFlow Lite

    AI recommended 7 alternatives but never named apple/ml-fastvlm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking vision encoders that reduce token count for efficient vision language model inference.
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. OpenCLIP
    3. DINOv2
    4. EVA-CLIP / EVA-02
    5. SigLIP
    6. BLIP-2
    7. CoCa

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