RRepoGEO

REPOGEO REPORT · LITE

DirtyHarryLYL/LLM-in-Vision

Default branch main · commit 268b0fcc · scanned 6/9/2026, 11:52:52 PM

GitHub: 868 stars · 39 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
17 /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
1 / 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 DirtyHarryLYL/LLM-in-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 README H1 and opening sentence to clarify it's a curated list

    Why:

    CURRENT
    # LLM-in-Vision
Recent LLM (Large Language Models)-based CV and multi-modal works. Welcome to comment/contribute!
    COPY-PASTE FIX
    # Awesome LLM-in-Vision: A Curated List of Recent Research Papers on LLM-based Computer Vision and Multi-modal Works.
Welcome to comment/contribute!
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or GPL-3.0) in the root of the repository to clearly state the terms of use and contribution.

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 DirtyHarryLYL/LLM-in-Vision
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4V (Vision)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4V (Vision) · recommended 1×
  2. Google Gemini (Pro Vision / Ultra) · recommended 1×
  3. llava-vl/llava · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. openai/CLIP · recommended 1×
  • CATEGORY QUERY
    How can I integrate large language models with computer vision tasks effectively?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (Vision)
    2. Google Gemini (Pro Vision / Ultra)
    3. Llava (Large Language and Vision Assistant) (llava-vl/llava)
    4. Hugging Face Transformers (huggingface/transformers)
    5. CLIP (Contrastive Language-Image Pre-training) by OpenAI (openai/CLIP)
    6. Microsoft Florence-2 (microsoft/Florence-2)
    7. LangChain (langchain-ai/langchain)

    AI recommended 7 alternatives but never named DirtyHarryLYL/LLM-in-Vision. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking research on advanced multi-modal generation techniques combining vision and language models.
    you: not recommended
    AI recommended (in order):
    1. DALL-E 3
    2. Stable Diffusion
    3. Midjourney
    4. Google Imagen
    5. Flamingo
    6. BLIP-2
    7. InstructPix2Pix

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

Embed your GEO score

Drop this badge into the README of DirtyHarryLYL/LLM-in-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/DirtyHarryLYL/LLM-in-Vision.svg)](https://repogeo.com/en/r/DirtyHarryLYL/LLM-in-Vision)
HTML
<a href="https://repogeo.com/en/r/DirtyHarryLYL/LLM-in-Vision"><img src="https://repogeo.com/badge/DirtyHarryLYL/LLM-in-Vision.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

DirtyHarryLYL/LLM-in-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