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.
- Choose a pre-trained ONNX model from the ONNX Model Zoo. A lot of pre-trained ONNX models are provided for common scenarios.
- Convert models from mainstream frameworks. More tutorials are below.
- Use services like Azure Custom Vision service that generate customized ONNX models for your data
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For preparation
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For serving
- Serving ONNX models with MXNet Model Server
- ONNX model hosting with AWS SageMaker and MXNet
- Serving ONNX models with ONNX Runtime on Azure ML - FER Facial Expression Recognition, MNIST Handwritten Digits, Resnet50 Image Classification
- Inferencing ONNX models using ONNX Runtime Python API
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For conversion
For direct conversion to/from ONNX format, see the "Exporting" and "Importing" columns in the table under Getting ONNX Models
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From conversion to deployment
- Converting SuperResolution model from PyTorch to Caffe2 with ONNX and deploying on mobile device
- Transferring SqueezeNet from PyTorch to Caffe2 with ONNX and to Android app
- Converting Style Transfer model from PyTorch to CoreML with ONNX and deploying to an iPhone
- Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX
- MXNet to ONNX to ML.NET with SageMaker, ECS and ECR - external link
- Convert CoreML YOLO model to ONNX, score with ONNX Runtime, and deploy in Azure
- Verifying correctness and comparing performance
- Visualizing an ONNX model (useful for debugging)
- Netron: a viewer for ONNX models
- Example of operating on ONNX protobuf
- Float16 <-> Float32 converter
We welcome improvements to the convertor tools and contributions of new ONNX bindings. Check out contributor guide to get started.
Use ONNX for something cool? Send the tutorial to this repo by submitting a PR.