RRepoGEO

REPOGEO REPORT · LITE

bytedance/Sa2VA

Default branch main · commit 16ef345c · scanned 5/9/2026, 6:32:20 PM

GitHub: 1,598 stars · 117 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 bytedance/Sa2VA, 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
  • highreadme#1
    Add a concise problem/solution statement to the README introduction

    Why:

    CURRENT
    The README currently starts with the title, links, and author list, without an immediate, explicit problem/solution statement.
    COPY-PASTE FIX
    Add the following paragraph immediately after the initial links and before the author list:
"Sa2VA is a cutting-edge multimodal large language model (MLLM) designed for dense grounded understanding of both images and videos. By marrying advanced segmentation capabilities (like SAM2) with powerful language models (like LLaVA), Sa2VA enables precise visual reasoning and integrates image segmentation with LLMs, addressing key challenges in multimodal AI."
  • mediumhomepage#2
    Populate the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://lxtgh.github.io/project/sa2va
  • lowtopics#3
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    computer-vision, large-language-models, mllm
    COPY-PASTE FIX
    computer-vision, large-language-models, mllm, multimodal-llm, visual-reasoning, image-segmentation, video-understanding

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 bytedance/Sa2VA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. GPT-4o · recommended 1×
  3. Gemini · recommended 1×
  4. haotian-liu/LLaVA · recommended 1×
  5. AdeptAILabs/fuyu-8b · recommended 1×
  • CATEGORY QUERY
    How can I achieve dense grounded understanding of images and videos using multimodal LLMs?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini
    3. LLaVA (haotian-liu/LLaVA)
    4. Fuyu-8B (AdeptAILabs/fuyu-8b)
    5. InternVL (OpenGVLab/InternVL)
    6. CogVLM (THUDM/CogVLM)
    7. BLIP-2 (salesforce/BLIP2)

    AI recommended 7 alternatives but never named bytedance/Sa2VA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to integrate image segmentation capabilities with large language models for visual reasoning.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. 🤗 Transformers Agents (huggingface/transformers)
    3. Diffusers (huggingface/diffusers)
    4. LangChain (langchain-ai/langchain)
    5. OpenAI GPT models
    6. Google Gemini
    7. Anthropic Claude
    8. segment-anything (facebookresearch/segment-anything)
    9. detectron2 (facebookresearch/detectron2)
    10. OpenAI's Function Calling
    11. GPT-4V
    12. LlamaIndex (run-llama/llama_index)
    13. YOLO
    14. PyTorch (pytorch/pytorch)
    15. Mask R-CNN
    16. U-Net
    17. Microsoft's Florence-2 (microsoft/Florence)

    AI recommended 17 alternatives but never named bytedance/Sa2VA. 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 bytedance/Sa2VA?
    pass
    AI named bytedance/Sa2VA explicitly

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

  • If a team adopts bytedance/Sa2VA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bytedance/Sa2VA 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 bytedance/Sa2VA solve, and who is the primary audience?
    pass
    AI named bytedance/Sa2VA explicitly

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

Embed your GEO score

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MARKDOWN (README)
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bytedance/Sa2VA — 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