RRepoGEO

REPOGEO REPORT · LITE

microsoft/MMdnn

Default branch master · commit 3eb8db13 · scanned 5/21/2026, 11:11:18 PM

GitHub: 5,810 stars · 957 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
59 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 microsoft/MMdnn, 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
    Emphasize model architectural visualization and management in README introduction

    Why:

    CURRENT
    MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models.
    COPY-PASTE FIX
    MMdnn is a comprehensive and cross-framework tool for deep learning (DL) model lifecycle management, enabling conversion, architectural visualization, and diagnosis. It helps users inter-operate among different frameworks, intuitively display network architectures, and diagnose model issues, streamlining the path from training to deployment.
  • highreadme#2
    Add a clear statement of MMdnn's core differentiator to the README

    Why:

    COPY-PASTE FIX
    Unlike single-purpose converters or experiment tracking tools, MMdnn uniquely combines comprehensive cross-framework model conversion with intuitive architectural visualization and diagnosis capabilities, offering a complete solution for deep learning model interoperability and management.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/microsoft/MMdnn

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 microsoft/MMdnn
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
ONNX
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX · recommended 1×
  2. OpenVINO Model Optimizer · recommended 1×
  3. TensorFlow Lite Converter · recommended 1×
  4. PyTorch JIT (TorchScript) · recommended 1×
  5. Keras `model.save()` / `tf.keras.models.save_model()` · recommended 1×
  • CATEGORY QUERY
    Need a tool to convert deep learning models between different neural network frameworks.
    you: #2
    AI recommended (in order):
    1. ONNX
    2. MMdnn ← you
    3. OpenVINO Model Optimizer
    4. TensorFlow Lite Converter
    5. PyTorch JIT (TorchScript)
    6. Keras `model.save()` / `tf.keras.models.save_model()`
    Show full AI answer
  • CATEGORY QUERY
    How to visualize and manage deep learning models across various training environments?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B) (wandb/wandb)
    2. MLflow (mlflow/mlflow)
    3. TensorBoard (tensorflow/tensorboard)
    4. Comet ML (comet-ml/comet-python-sdk)
    5. Neptune.ai (neptune-ai/neptune-client)
    6. ClearML (allegroai/clearml)

    AI recommended 6 alternatives but never named microsoft/MMdnn. 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 microsoft/MMdnn?
    pass
    AI did not name microsoft/MMdnn — likely talking about a different project

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

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