ONNX (Open Neural Network Exchange)

 

https://onnx.ai/

ONNX (Open Neural Network Exchange) is an open standard for representing machine learning and deep learning models, allowing for interoperability between various frameworks and tools. Many popular machine learning libraries, frameworks, and tools support ONNX, either natively or through additional packages or converters. Some of these tools include:

1. ONNX Runtime: A cross-platform, high-performance inference engine for ONNX models, developed by Microsoft. It allows you to run ONNX models on various platforms and hardware, including CPUs, GPUs, and edge devices.

2. PyTorch: A popular deep learning framework that natively supports exporting models to the ONNX format using the torch.onnx.export() function. PyTorch also supports importing ONNX models for inference.

3. TensorFlow: TensorFlow supports ONNX through the tf2onnx package, which provides a converter to convert TensorFlow models to the ONNX format. Additionally, the ONNX-TensorFlow project provides an ONNX backend for TensorFlow, enabling the execution of ONNX models within the TensorFlow framework.

4. MXNet: Apache MXNet, another deep learning framework, supports ONNX through the mxnet.contrib.onnx module, which provides utilities to import and export ONNX models.

5. Caffe2: Caffe2, a deep learning framework that has been merged into PyTorch, supports ONNX natively. You can import and export ONNX models using the Caffe2-ONNX converter.

6. Apple Core ML: Apple's Core ML framework for running machine learning models on iOS and macOS devices supports ONNX through the onnx-coreml package, which provides a converter for transforming ONNX models into Core ML models.

7. Microsoft ML.NET: ML.NET, a cross-platform, open-source machine learning framework developed by Microsoft, supports ONNX for model inference, enabling the use of ONNX models in .NET applications.

8. OpenCV: The popular open-source computer vision library OpenCV supports ONNX models for deep learning inference through its cv::dnn module.

These are just a few examples of the tools and frameworks that support ONNX. The ONNX ecosystem is continuously growing, with more tools and libraries adopting the format to promote interoperability and simplify the deployment of machine learning models across different platforms and hardware.

ONNX (Open Neural Network Exchange) files store machine learning and deep learning models in a binary format that represents the model's architecture, learned parameters, and metadata. The files themselves are not human-readable, as they are serialized using Protocol Buffers, a language-agnostic and platform-neutral binary format developed by Google.

An ONNX file contains information about the model's graph, including the nodes, edges, inputs, and outputs. It also stores the model's parameters (weights and biases) as tensors and provides metadata, such as the model's version, producer information, and domain.

Although the binary ONNX files are not human-readable, there are tools available to visualize and explore the content of an ONNX file. Some of these tools include:

1. Netron: An open-source, cross-platform viewer for deep learning models, including ONNX, TensorFlow, PyTorch, and many others. Netron provides a graphical representation of the model's structure and lets you inspect the model's nodes, layers, and parameters.

2. ONNX.js: A JavaScript library for running ONNX models in web browsers. It provides a Web API to load and visualize ONNX models, as well as to perform inference directly in the browser.

   https://microsoft.github.io/onnxjs-demo/#/

   https://github.com/microsoft/onnxjs

3. ONNX Python API: The ONNX Python API allows you to load ONNX files, inspect their content programmatically, and perform various operations on the model, such as simplifying, optimizing, or converting the model to different formats.

In summary, while ONNX files are binary files that are not directly human-readable, there are tools available to visualize, explore, and manipulate the content of these files to better understand the model's architecture and parameters.