REPOGEO REPORT · LITE
onnx/tutorials
Default branch main · commit 3a0d50a0 · scanned 5/8/2026, 11:33:30 PM
GitHub: 3,673 stars · 655 forks
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.
- hightopics#1Add specific topics to improve categorization
Why:
COPY-PASTE FIXonnx, machine-learning, deep-learning, model-conversion, model-deployment, tutorials, examples, how-to
- highreadme#2Reposition README opening to clarify purpose as practical guides
Why:
CURRENTOpen 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 FIXThis repository provides practical, hands-on tutorials and examples for working with the Open Neural Network Exchange (ONNX) format. Learn how to convert models from various machine learning frameworks to ONNX, and how to use ONNX models for deployment.
- mediumhomepage#3Add a homepage link to the ONNX website
Why:
COPY-PASTE FIXhttps://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.
- ONNX · recommended 1×
- MMdnn · recommended 1×
- TensorFlow SavedModel / Keras H5 · recommended 1×
- PyTorch JIT (TorchScript) · recommended 1×
- Core ML Tools · recommended 1×
- CATEGORY QUERYHow to convert trained deep learning models between different machine learning frameworks?you: not recommendedAI recommended (in order):
- ONNX
- MMdnn
- TensorFlow SavedModel / Keras H5
- PyTorch JIT (TorchScript)
- Core ML Tools
AI recommended 5 alternatives but never named onnx/tutorials. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the best practices for standardizing machine learning model deployment across platforms?you: not recommendedAI recommended (in order):
- Docker
- Podman
- Kubernetes
- OpenShift
- Amazon ECS (Elastic Container Service)
- MLflow (MLflow Model Serving)
- TensorFlow Serving
- TorchServe
- KServe (formerly KFServing)
- GitHub Actions
- GitLab CI/CD
- Jenkins
- MLflow Model Registry
- Amazon SageMaker Model Registry
AI recommended 14 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 completenesswarn
Suggestion:
- README presencepass
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?passAI named onnx/tutorials explicitly
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?passAI 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?passAI named onnx/tutorials 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 onnx/tutorials. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/onnx/tutorials)<a href="https://repogeo.com/en/r/onnx/tutorials"><img src="https://repogeo.com/badge/onnx/tutorials.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
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