RRepoGEO

REPOGEO REPORT · LITE

ENOT-AutoDL/onnx2torch

Default branch main · commit a5958fac · scanned 6/6/2026, 1:07:08 PM

GitHub: 735 stars · 92 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 ENOT-AutoDL/onnx2torch, 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
    Refine repository topics for better categorization

    Why:

    CURRENT
    convert, export, onnx, pytorch
    COPY-PASTE FIX
    convert, onnx, pytorch, migration, deep-learning, model-conversion, interoperability
  • highreadme#2
    Strengthen README opening to highlight core value and differentiator

    Why:

    CURRENT
    onnx2torch is an ONNX to PyTorch converter.
    COPY-PASTE FIX
    onnx2torch is a robust ONNX to PyTorch converter, designed to seamlessly migrate and integrate ONNX deep learning models into the PyTorch ecosystem by transforming them into native PyTorch nn.Module instances.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/ENOT-AutoDL/onnx2torch

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 ENOT-AutoDL/onnx2torch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
onnxruntime
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. onnxruntime · recommended 1×
  2. onnx-simplifier · recommended 1×
  3. Netron · recommended 1×
  4. onnx · recommended 1×
  5. onnx2pytorch · recommended 1×
  • CATEGORY QUERY
    How can I convert a pre-trained ONNX deep learning model to a PyTorch format?
    you: not recommended
    AI recommended (in order):
    1. onnxruntime
    2. onnx-simplifier
    3. Netron
    4. onnx
    5. onnx2pytorch
    6. pytorch-onnx

    AI recommended 6 alternatives but never named ENOT-AutoDL/onnx2torch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help migrate existing ONNX neural network models for use in PyTorch projects?
    you: not recommended
    AI recommended (in order):
    1. onnx-pytorch
    2. torch.onnx
    3. ONNX Runtime
    4. onnxruntime-training
    5. mmdnn

    AI recommended 5 alternatives but never named ENOT-AutoDL/onnx2torch. 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 ENOT-AutoDL/onnx2torch?
    pass
    AI named ENOT-AutoDL/onnx2torch explicitly

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

  • If a team adopts ENOT-AutoDL/onnx2torch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ENOT-AutoDL/onnx2torch 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 ENOT-AutoDL/onnx2torch solve, and who is the primary audience?
    pass
    AI named ENOT-AutoDL/onnx2torch explicitly

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

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ENOT-AutoDL/onnx2torch — 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