RRepoGEO

REPOGEO REPORT · LITE

open-mmlab/mmsegmentation

Default branch main · commit b040e147 · scanned 5/11/2026, 3:21:57 AM

GitHub: 9,783 stars · 2,842 forks

AI VISIBILITY SCORE
71 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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 open-mmlab/mmsegmentation, 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, application-focused opening statement to the README

    Why:

    COPY-PASTE FIX
    MMSegmentation is OpenMMLab's comprehensive semantic segmentation toolbox and benchmark, providing a modular framework for developing, training, and evaluating state-of-the-art models, including those for **real-time and medical image segmentation**.
  • mediumreadme#2
    Highlight modularity and model zoo in the README's introductory section

    Why:

    COPY-PASTE FIX
    As a highly modular and extensible framework, MMSegmentation offers a vast, up-to-date model zoo, simplifying the development and evaluation of state-of-the-art image segmentation models.
  • lowreadme#3
    Clean up extraneous HTML/markdown at the top of the README

    Why:

    CURRENT
    <div align="center"> ... </div> and [](https://pypi.org/project/mmsegmentation/)
    COPY-PASTE FIX
    Remove extraneous `div` tags and empty markdown link placeholders from the very beginning of the README, ensuring the primary descriptive text is the first meaningful content.

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
1 / 2
50% of queries surface open-mmlab/mmsegmentation
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
segmentation_models.pytorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. segmentation_models.pytorch · recommended 1×
  2. PyTorch-Ignite · recommended 1×
  3. Albumentations · recommended 1×
  4. torchvision · recommended 1×
  5. Catalyst · recommended 1×
  • CATEGORY QUERY
    What are the best deep learning tools for semantic image segmentation in PyTorch?
    you: #2
    AI recommended (in order):
    1. segmentation_models.pytorch
    2. MMSegmentation ← you
    3. PyTorch-Ignite
    4. Albumentations
    5. torchvision
    6. Catalyst
    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust framework for real-time and medical image segmentation tasks.
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. PyTorch Lightning
    3. Keras
    4. TensorFlow
    5. Detectron2

    AI recommended 5 alternatives but never named open-mmlab/mmsegmentation. 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 open-mmlab/mmsegmentation?
    pass
    AI named open-mmlab/mmsegmentation explicitly

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

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

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

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