RRepoGEO

REPOGEO REPORT · LITE

cpystan/SD-VLM

Default branch main · commit 4023c5c8 · scanned 6/7/2026, 10:28:24 AM

GitHub: 503 stars · 5 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 cpystan/SD-VLM, 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 value proposition statement immediately after the README title

    Why:

    CURRENT
    The README immediately jumps to installation instructions after the H1 title.
    COPY-PASTE FIX
    Insert a concise sentence or two directly after the `# 👷SD-VLM...` title, such as: 'SD-VLM is a novel Vision-Language Model specifically designed to enhance spatial reasoning and understanding by integrating depth information, enabling precise spatial measurements and contextual comprehension from images. This work was accepted by NeurIPS 2025.'
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["vision-language-models", "spatial-reasoning", "depth-estimation", "neurips", "computer-vision", "ai", "machine-learning", "llm"]
  • mediumhomepage#3
    Add the project page URL to the repository homepage field

    Why:

    COPY-PASTE FIX
    Locate the URL for the '[Project Page]' linked in your README and add it to the repository's 'Website' field in the 'About' section.

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 cpystan/SD-VLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PyTorch Lightning · recommended 1×
  3. Detectron2 · recommended 1×
  4. Open3D · recommended 1×
  5. MMSegmentation / MMDetection · recommended 1×
  • CATEGORY QUERY
    How can I build a vision language model for spatial reasoning using depth data?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. Detectron2
    4. Open3D
    5. MMSegmentation / MMDetection
    6. TensorFlow / Keras

    AI recommended 6 alternatives but never named cpystan/SD-VLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help interpret 3D spatial relationships from images with language descriptions?
    you: not recommended
    AI recommended (in order):
    1. PyTorch3D (facebookresearch/pytorch3d)
    2. Open3D (isl-org/Open3D)
    3. Hugging Face Transformers (huggingface/transformers)
    4. CLIP (openai/CLIP)
    5. BLIP (salesforce/BLIP)
    6. ViLT (dandelin/vilt)
    7. Flamingo
    8. Detectron2 (facebookresearch/detectron2)
    9. COLMAP (colmap/colmap)
    10. instant-ngp (NVlabs/instant-ngp)
    11. nerfstudio (nerfstudio-project/nerfstudio)
    12. Matterport3D
    13. ScanNet

    AI recommended 13 alternatives but never named cpystan/SD-VLM. 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 cpystan/SD-VLM?
    pass
    AI named cpystan/SD-VLM explicitly

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

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

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

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cpystan/SD-VLM — 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
cpystan/SD-VLM — RepoGEO report