ONNX

In the first article of this series, I explained how to load, evaluate and convert a PyTorch-trained BERT QnA model to an ONNX-compatible format. In the second article, I explained how to load an ONNX Model, perform processing tasks on both the input and output and connect the prediction pipeline to the frontend UI. In this article, I will give a high-level overview of how to load the converted ONNX BERT model trained using PyTorch. Getting Started The BERT QnA model aims to recognize the context in each text input by looking in both forward & backward directions and provide a text-based human-readable answer as output. This code base provides a more interactive feel by allowing users to input text as a question and context, and the BERT ONNX model will reply with a text-based answer. NuGet Packages Used The following NuGet packages were installed in the Uno platform application project for this code base. Microsoft ML OnnxRuntime Skia Sharp BERT Tokenizers INFERENCING WITH PyTorchBertQnA The TensorFlowMNISTClassifier C# file houses the class-based code for loading, processing, and prediction tasks. STEP 1: Load Embedded Resources In the 1st article of this series, we mentioned the large .onnx file that was created

This article will explore loading a pre-trained ONNX model, trained on the popular MNIST dataset, into an application built with the Uno Platform. By loading a pre-trained ONNX model, we can leverage the power of machine learning to add advanced functionality to our mobile, .NET,  and Uno Platform-built applications, such as image classification or handwriting recognition. We will provide a step-by-step guide on how to load an ONNX model into an Uno Platform application and explain how to use the model to make predictions on new data. By the end of this article, you will have a solid understanding of how to integrate machine learning into your Uno Platform applications. To get a better understanding of training, evaluating and converting an ML model trained with the MNIST dataset to an ONNX-compatible format or how to load an ONNX Model, perform processing tasks on both the input and output and connect the prediction pipeline to the frontend UI, check out Part 1 and Part 2 of this series. Sample App Getting Started The objective of the MNIST classifier model is to recognise handwritten numerical digits from 0-9. The model accepts as input an image of size 14 * 14 and outputs

The previous article introduced the ONNX, an open standard for exchanging and sharing deep learning models which can allow developers to facilitate on-device inference. The article showcased the conversion of 2 none ONNX models (the first built and trained from scratch and another pretrained) to the ONNX format. It went through a high-level overview of how to train, evaluate, convert and test an ML model in TensorFlow formats, PyTorch format & ONNX format.  This article will go through the key steps from a C# Uno Platform developer’s perspective on leveraging ONNX runtime to achieve local (on-device) inference on an Uno platform application. A bonus is readers will see how to set up and use a control from Uno Platforms Material Toolkit.  Introduction In this article’s sample code base, which is an Uno Platform application, the ML task achieved is image classification using an open-source ONNX format model called Mobile NET. What is MOBILENET MobileNet is a collection of Convolutional Neural Networks designed for efficient on-device image classification and other tasks. Google researchers developed the architecture to enable deep learning models to run efficiently on mobile and edge devices with limited computing resources. MobileNet models are trained to recognize a wide

One of the most recent and exciting areas in mobile application development is the integration of machine learning models to add intelligent capabilities. With the ability to process large amounts of data, identify patterns, and make predictions, ML models can enhance the functionality and usability of mobile applications in various ways, such as image recognition, natural language processing, and personalized recommendations. Furthermore, with frameworks such as ONNX, it has become easier to integrate machine learning models into mobile .NET applications, making it a viable option for developers who want to create smart and personalized user experiences. This 4 part series will outline how to consume your pre-trained models (Trained using TensorFlow & PyTorch) in a .NET, Uno Platform built application. Each article will explore one of the following machine-learning tasks:  Part 1: Prepping ONNX Models for Uno Platform Part 2: Image classification using ONNX format model called Mobile NET. Part 3: Loading the ONNX model trained with the MNIST dataset. Part 4: How to add Text-based Q&A with ONNX BERT model trained using PyTorch. This series’ primary objective is to illustrate, in steps, how to consume an ML Model in an Uno Platform Application and not to teach you how