RRepoGEO

REPOGEO REPORT · LITE

OpenGVLab/SAM-Med2D

Default branch main · commit bfd2b93b · scanned 5/18/2026, 6:27:43 AM

GitHub: 1,117 stars · 111 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
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 OpenGVLab/SAM-Med2D, 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
  • mediumreadme#1
    Add a concise value proposition to the README's opening

    Why:

    CURRENT
    The current README starts with `# SAM-Med2D [Paper]` followed by badges and highlights.
    COPY-PASTE FIX
    Add this sentence directly after the `# SAM-Med2D [Paper]` title: `SAM-Med2D is the official implementation of a lightweight, plug-and-play Medical Adapter module that efficiently fine-tunes the Segment Anything Model (SAM) for accurate 2D medical image segmentation.`
  • lowhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://openxlab.org.cn/apps/detail/GMAI/SAM-Med2D

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/SAM-Med2D
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MONAI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MONAI · recommended 2×
  2. nnUNet · recommended 1×
  3. PyTorch · recommended 1×
  4. Albumentations · recommended 1×
  5. TorchIO · recommended 1×
  • CATEGORY QUERY
    What are the best tools for segmenting medical images using deep learning?
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. nnUNet
    3. PyTorch
    4. Albumentations
    5. TorchIO
    6. TensorFlow/Keras
    7. MONAI Label
    8. 3D Slicer
    9. DeepInfer
    10. ITK-SNAP

    AI recommended 10 alternatives but never named OpenGVLab/SAM-Med2D. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for fine-tuning foundation models for medical imaging segmentation?
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. PyTorch Lightning
    3. Hugging Face Transformers
    4. NVIDIA's FLARE
    5. JAX
    6. Flax
    7. Haiku
    8. Optax
    9. Keras
    10. TensorFlow
    11. KerasCV
    12. MMDetection
    13. MMSegmentation
    14. OpenMMLab
    15. FastAI

    AI recommended 15 alternatives but never named OpenGVLab/SAM-Med2D. 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 OpenGVLab/SAM-Med2D?
    pass
    AI named OpenGVLab/SAM-Med2D 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/SAM-Med2D in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenGVLab/SAM-Med2D 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/SAM-Med2D solve, and who is the primary audience?
    pass
    AI named OpenGVLab/SAM-Med2D 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/SAM-Med2D. 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
<a href="https://repogeo.com/en/r/OpenGVLab/SAM-Med2D"><img src="https://repogeo.com/badge/OpenGVLab/SAM-Med2D.svg" alt="RepoGEO" /></a>
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OpenGVLab/SAM-Med2D — 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