Hello there 👋, This is a showcase of a project I’ve been working on for the past weeks. It’s a simple cross-platform desktop application that allows you to tag images using a machine learning model that I’ve trained. It is written in C# using Avalonia UI, and it uses the FastAI library for the machine learning part, the model is then converted to ONNX. The application is available for Windows, Linux and MacOS. ...
Hello there 👋, In this post, I will show you how to convert a FastAI model to ONNX and CoreML. Convert a FastAI model to Pytorch Before we can convert the model to ONNX format or CoreML, we need to convert it to Pytorch first. This is a simple process. Assuming that the model is trained, we need to call torch.save to export the model: 1 2 3 4 5 from fastai.vision.all import * # learn is your FastAI Learner torch.save(learn.model, "/models/model.pth") Convert a FastAI model to ONNX ONNX is a standard for machine learning models. It allows you to convert a model from a framework like PyTorch or TensorFlow to its format in order to be used in different runtimes, for exampl Windows, Android, Linux, MacOS and iOS and from various programming languages like C#, C++, Java, JavaScript and Python. ...