RRepoGEO

REPOGEO REPORT · LITE

merveenoyan/smol-vision

Default branch main · commit 44ff4dfc · scanned 5/10/2026, 7:19:09 PM

GitHub: 1,913 stars · 146 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 merveenoyan/smol-vision, 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
  • highreadme#1
    Reposition the README's opening statement to clarify differentiator

    Why:

    CURRENT
    # Smol Vision 🐣

Recipes for shrinking, optimizing, customizing cutting edge vision and multimodal AI models.
    COPY-PASTE FIX
    # Smol Vision 🐣

Practical recipes and hands-on examples for shrinking, optimizing, and customizing cutting-edge vision and multimodal AI models. This repository focuses on advanced techniques like quantization, knowledge distillation, and fine-tuning state-of-the-art models (e.g., Kosmos2.5, Florence-2, PaliGemma) for specific tasks, rather than providing a minimal runtime for local LLMs.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the URL to the external location where the full notebooks with rich outputs live (as mentioned in the README).

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 merveenoyan/smol-vision
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO Toolkit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO Toolkit · recommended 1×
  2. TensorRT · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. PyTorch Mobile · recommended 1×
  5. TensorFlow Lite · recommended 1×
  • CATEGORY QUERY
    How to make large vision models smaller and faster for deployment?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. TensorRT
    3. ONNX Runtime
    4. PyTorch Mobile
    5. TensorFlow Lite
    6. NVIDIA TAO Toolkit
    7. DeepSparse

    AI recommended 7 alternatives but never named merveenoyan/smol-vision. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking guidance for fine-tuning state-of-the-art multimodal AI models on custom datasets.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Accelerate (huggingface/accelerate)
    3. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    4. OpenAI API
    5. Keras (keras-team/keras)
    6. MMDetection (open-mmlab/mmdetection)
    7. MMEngine (open-mmlab/mmengine)
    8. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 8 alternatives but never named merveenoyan/smol-vision. 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 merveenoyan/smol-vision?
    pass
    AI did not name merveenoyan/smol-vision — 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 merveenoyan/smol-vision in production, what risks or prerequisites should they evaluate first?
    pass
    AI named merveenoyan/smol-vision 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 merveenoyan/smol-vision solve, and who is the primary audience?
    pass
    AI named merveenoyan/smol-vision 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 merveenoyan/smol-vision. 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/merveenoyan/smol-vision.svg)](https://repogeo.com/en/r/merveenoyan/smol-vision)
HTML
<a href="https://repogeo.com/en/r/merveenoyan/smol-vision"><img src="https://repogeo.com/badge/merveenoyan/smol-vision.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

merveenoyan/smol-vision — 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