RRepoGEO

REPOGEO REPORT · LITE

OpenGVLab/VisionLLM

Default branch main · commit 148cc93b · scanned 5/23/2026, 3:42:50 PM

GitHub: 1,146 stars · 64 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 OpenGVLab/VisionLLM, 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
    Update the repository description to be more specific

    Why:

    CURRENT
    VisionLLM Series
    COPY-PASTE FIX
    VisionLLM Series: Generalist Multimodal Large Language Models for hundreds of Vision-Language Tasks.
  • highreadme#2
    Reposition the README H1 to clearly state the project's core identity

    Why:

    CURRENT
    <h1> VisionLLM Series </h1>
    COPY-PASTE FIX
    <h1> VisionLLM Series: Generalist Multimodal Large Language Models </h1>
  • mediumtopics#3
    Expand repository topics to include 'multimodal' and 'vision-language'

    Why:

    CURRENT
    generalist-model, large-language-models, object-detection
    COPY-PASTE FIX
    generalist-model, large-language-models, object-detection, multimodal-llm, vision-language-model, computer-vision

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 OpenGVLab/VisionLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4o
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4o · recommended 1×
  2. Google Gemini · recommended 1×
  3. Llama 3 · recommended 1×
  4. LLaVA · recommended 1×
  5. Fuyu-8B · recommended 1×
  • CATEGORY QUERY
    Seeking a generalist multimodal AI model for comprehensive visual understanding and generation.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Google Gemini
    3. Llama 3
    4. LLaVA
    5. Fuyu-8B
    6. DALL-E 3
    7. Stable Diffusion XL
    8. ControlNet
    9. CogVLM

    AI recommended 9 alternatives but never named OpenGVLab/VisionLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What unified models support hundreds of vision-language tasks, including object detection and perception?
    you: not recommended
    AI recommended (in order):
    1. OWL-ViT
    2. CLIP
    3. DINOv2
    4. Florence-2
    5. Grounding DINO
    6. SEEM
    7. YOLO-World

    AI recommended 7 alternatives but never named OpenGVLab/VisionLLM. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 OpenGVLab/VisionLLM?
    pass
    AI named OpenGVLab/VisionLLM explicitly

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

  • If a team adopts OpenGVLab/VisionLLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenGVLab/VisionLLM 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 OpenGVLab/VisionLLM solve, and who is the primary audience?
    pass
    AI named OpenGVLab/VisionLLM 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 OpenGVLab/VisionLLM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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OpenGVLab/VisionLLM — 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