Evaluating Deep Learning Models in 10 Different Languages (With Examples)

ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators — the building blocks of machine learning and deep learning models — and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. The following post is a compilation of code samples showing how to evaluate Onnx Models in 10 different programming languages.

#10 R

onnx/onnx-r

#9 C++

microsoft/onnxruntime

#8 Java

microsoft/onnxruntime

#7 .NET Core

Tutorial: Detect objects using an ONNX deep learning model — ML.NET

#6 Ruby

ankane/onnxruntime

#5 Rust

microsoft/onnxruntime-tvm

#4 JavaScript

microsoft/onnxjs

#3 Python

onnx/onnx

#2 Swift

Convert fast.ai trained image classification model to iOS app via ONNX and Apple Core ML

#1 C

microsoft/onnxruntime

Bonus: MATLAB

importONNXNetwork

Next Steps

Tutorial: Detect objects using an ONNX deep learning model - ML.NET

microsoft/OLive

About the Author

Aaron (Ari) Bornstein is an AI researcher with a passion for history, engaging with new technologies and computational medicine. As an Open Source Engineer at Microsoft’s Cloud Developer Advocacy team, he collaborates with Israeli Hi-Tech Community, to solve real world problems with game changing technologies that are then documented, open sourced, and shared with the rest of the world.