RRepoGEO

REPOGEO REPORT · LITE

SkyworkAI/Vitron

Default branch main · commit b17ebc6d · scanned 6/1/2026, 2:48:13 AM

GitHub: 576 stars · 34 forks

AI VISIBILITY SCORE
35 /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
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 SkyworkAI/Vitron, 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
  • highlicense#1
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highabout#2
    Update the GitHub repository description for clarity

    Why:

    CURRENT
    NeurIPS 2024 Paper: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
    COPY-PASTE FIX
    Vitron: A universal pixel-level Vision LLM for comprehensive image and video understanding, generation, segmentation, and editing. Official code for NeurIPS 2024.
  • mediumtopics#3
    Add more specific and descriptive GitHub topics

    Why:

    CURRENT
    mllm, multimodal-large-language-models, segmentation
    COPY-PASTE FIX
    mllm, multimodal-large-language-models, segmentation, vision-llm, image-generation, image-editing, video-understanding, pixel-level, neurips-2024

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 SkyworkAI/Vitron
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-V · recommended 1×
  4. CoCa · recommended 1×
  5. Flamingo · recommended 1×
  • CATEGORY QUERY
    Seeking a unified multimodal LLM capable of image understanding, generation, and pixel-level segmentation.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Google Gemini
    3. Llama 3-V
    4. CoCa
    5. Flamingo
    6. BLIP-2

    AI recommended 6 alternatives but never named SkyworkAI/Vitron. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which vision language models offer advanced capabilities for image editing and fine-grained segmentation?
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
    2. Grounding DINO (IDEA-Research/GroundingDINO)
    3. OWL-ViT (google-research/owlvit)
    4. SEEM (facebookresearch/SEEM)
    5. GLIGEN (gligen/GLIGEN)
    6. InstructPix2Pix (timothybrooks/instruct-pix2pix)
    7. BLIP-2 (salesforce/LAVIS)
    8. LLaVA (haotian-liu/LLaVA)

    AI recommended 8 alternatives but never named SkyworkAI/Vitron. 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 SkyworkAI/Vitron?
    pass
    AI named SkyworkAI/Vitron explicitly

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

  • If a team adopts SkyworkAI/Vitron in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SkyworkAI/Vitron 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 SkyworkAI/Vitron solve, and who is the primary audience?
    pass
    AI named SkyworkAI/Vitron 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 SkyworkAI/Vitron. 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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SkyworkAI/Vitron — RepoGEO report