The travel guide for getting models out of framework jail

What it does

This repository is a curated index of notebooks, walkthroughs, and documentation links that show how to move models into and out of the ONNX interchange format. It catalogs conversion paths from frameworks like PyTorch, TensorFlow, scikit-learn, CoreML, MATLAB, and even LibSVM, then lists runtimes and deployment targets ranging from ONNX Runtime and TensorRT to Windows ML and mobile apps. In short, it is a meta-directory for the ONNX ecosystem.

The interesting bit

The breadth is the feature. Rather than deep-diving on one stack, the repo treats ONNX as a universal adapter, collecting recipes for niche scenarios like exporting PyTorch custom operators, quantizing HuggingFace BERT, or using ONNX as a bridge to convert a PyTorch model straight into TensorFlow. It even points to visualization tools such as Netron and Zetane so you can inspect the protobuf guts of the resulting graph.

Key highlights

• Conversion paths cataloged for over a dozen frameworks, including PyTorch, TensorFlow, CoreML, MATLAB, and LibSVM
• Deployment targets ranging from ONNX Runtime and TensorRT to Windows ML, mobile devices, and Vespa.ai
• End-to-end examples covering mobile style-transfer, BERT quantization, and using ONNX as an intermediary format between frameworks
• Pointers to utilities for model visualization, float16/32 conversion, version migration, and correctness verification
• Most entries are links to external notebooks and repositories rather than original tutorials hosted here

Verdict

Worth bookmarking if you are trying to move a model between frameworks or squeeze one onto an edge device; skip it if you are looking for a single, self-contained codebase to clone and run.