RRepoGEO

REPOGEO REPORT · LITE

onnx/tutorials

Default branch main · commit 3a0d50a0 · scanned 6/18/2026, 6:17:35 PM

GitHub: 3,678 stars · 654 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
28 /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
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 onnx/tutorials, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    onnx, machine-learning, deep-learning, model-conversion, framework-interoperability, tutorials, examples, ai, neural-networks
  • highreadme#2
    Reposition README's opening to clarify repo's purpose as a tutorial hub

    Why:

    CURRENT
    # ONNX Tutorials

Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools.
    COPY-PASTE FIX
    # ONNX Tutorials

This repository provides a comprehensive collection of tutorials and examples for creating, converting, and using machine learning models with the Open Neural Network Exchange (ONNX) format. ONNX is an open standard for representing machine learning models, enabling cross-framework interoperability and efficient deployment.
  • mediumhomepage#3
    Add a homepage link to the main ONNX project

    Why:

    COPY-PASTE FIX
    https://onnx.ai/

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 onnx/tutorials
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX · recommended 1×
  2. MMdnn · recommended 1×
  3. MLeap · recommended 1×
  4. skl2onnx · recommended 1×
  5. TensorFlow Lite Converter · recommended 1×
  • CATEGORY QUERY
    How can I convert machine learning models from one framework to another effectively?
    you: not recommended
    AI recommended (in order):
    1. ONNX
    2. MMdnn
    3. MLeap
    4. skl2onnx
    5. TensorFlow Lite Converter
    6. Core ML Tools
    7. XGBoost
    8. LightGBM

    AI recommended 8 alternatives but never named onnx/tutorials. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an open standard to represent deep learning models for interoperability.
    you: not recommended
    AI recommended (in order):
    1. ONNX (Open Neural Network Exchange) (onnx/onnx)
    2. PMML (Predictive Model Markup Language)
    3. Keras H5 (keras-team/keras)
    4. SavedModel (TensorFlow) (tensorflow/tensorflow)
    5. TorchScript (pytorch/pytorch)
    6. Core ML Model Format (Apple) (apple/coremltools)
    7. OpenVINO IR (Intel) (openvinotoolkit/openvino)

    AI recommended 7 alternatives but never named onnx/tutorials. 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 onnx/tutorials?
    pass
    AI did not name onnx/tutorials — 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 onnx/tutorials in production, what risks or prerequisites should they evaluate first?
    pass
    AI named onnx/tutorials 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 onnx/tutorials solve, and who is the primary audience?
    pass
    AI named onnx/tutorials explicitly

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

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onnx/tutorials — 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