Easily Build Image Classifiers with Azure Custom Vision | The Xamarin Show

Easily Build Image Classifiers with Azure Custom Vision | The Xamarin Show

Table of Contents

1. Introduction
2. What is Image Classification?
3. The Azure Custom Vision Service
4. Building an Image Classifier
5. Using the Custom Vision Service in a Xamarin App
6. Exporting the Model for Offline Use
7. Using Core ML on iOS
8. Using TensorFlow on Android
9. Offline vs Online Strategy
10. Conclusion

Introduction

In this article, we will be exploring the exciting world of image classification and how to build an image classifier using the Azure Custom Vision Service. We will also see how to integrate the image classifier into a Xamarin app and export the model for offline use. Additionally, we will dive into using Core ML on iOS and TensorFlow on Android for image classification. Finally, we will discuss the pros and cons of using an offline or online strategy for image classification.

What is Image Classification?

Image classification is the process of categorizing objects or scenes in images based on their visual content. It involves teaching a machine learning model to identify and classify images into predefined categories or labels. This has numerous applications in various fields, such as medical diagnostics, autonomous vehicles, and content filtering.

The Azure Custom Vision Service

The Azure Custom Vision Service is an AI Tool provided by Microsoft as part of the Azure Cognitive Services. It allows developers to train a neural network to identify and classify images with just a small number of training samples. The service offers pre-trained models for various domains like general objects, food items, landmarks, retail, and adult content filtering.

Building an Image Classifier

To build an image classifier using the Azure Custom Vision Service, you can access the service through the customvision.ai portal. You can create a new project, provide a name, description, and choose a domain for your classifier. The domain selection determines the type of images the model will be trained to classify. You can then upload training images and tag them with their corresponding labels. The service will automatically generate a model based on the training images, saving you the hassle of training the model from scratch.

Using the Custom Vision Service in a Xamarin App

Integrating the image classifier into a Xamarin app is made easy with the Microsoft.Cognitive.CustomVision.Prediction NuGet Package. This package provides a prediction endpoint that allows you to make predictions using the trained image classifier. By passing an image stream to the prediction endpoint, the Custom Vision Service will classify the image and return the most probable tags and their probabilities. The results can then be displayed in the app to provide a seamless user experience.

Exporting the Model for Offline Use

If you want to use the image classifier offline, the Azure Custom Vision Service allows you to export the model. You can export the model in formats compatible with Core ML on iOS and TensorFlow on Android. This enables you to leverage the power of on-device machine learning capabilities. By downloading and compiling the exported model, you can use it in your Xamarin app without the need for an internet connection. This is particularly useful in scenarios where network connectivity is limited or unreliable.

Using Core ML on iOS

Core ML is a framework provided by Apple for integrating machine learning models into iOS apps. With the Core ML framework, you can load the exported image classifier model and make predictions directly on the device. The Core ML API provides a simple interface for running Image Recognition requests and obtaining the result. By converting the image to a CVPixelBuffer and passing it to the Core ML request, you can get the predicted tags and their probabilities. This allows you to create powerful image classification features in your iOS app.

Using TensorFlow on Android

On the Android platform, you can leverage the TensorFlow Lite library for integrating machine learning models into your Xamarin app. TensorFlow Lite is a lightweight version of the popular TensorFlow framework optimized for mobile and embedded devices. By using the Xamarin bindings for TensorFlow Lite, you can load the exported image classifier model and perform inference on the device. This allows you to classify images in real-time without the need for an internet connection. The TensorFlow Lite API provides easy-to-use methods for running the model and getting the predicted labels and probabilities.

Offline vs Online Strategy

Choosing between an offline or online strategy for image classification depends on the specific requirements of your app. If real-time updates and continuous improvement of the model are crucial, an online strategy using the Azure Custom Vision Service is recommended. This allows you to iterate on the model, add new images, and retrain it to improve accuracy. On the other HAND, if you need offline functionality or have limited connectivity, an offline strategy using Core ML on iOS or TensorFlow on Android is more suitable. This allows you to run the image classifier on the device without relying on an internet connection.

Conclusion

Image classification is an exciting field with numerous applications. With the Azure Custom Vision Service, you can easily build and deploy image classifiers in your Xamarin apps. By exporting the models for offline use, you can take advantage of on-device machine learning capabilities using Core ML on iOS and TensorFlow on Android. Whether you choose an online or offline strategy, image classification opens up possibilities for creating intelligent and interactive experiences in your apps.

Highlights

• Image classification allows categorizing objects or scenes in images based on their visual content.
• The Azure Custom Vision Service offers pre-trained models and enables training custom models with a small number of images.
• Integrating the Custom Vision Service into a Xamarin app is made easy with the Microsoft.Cognitive.CustomVision.Prediction NuGet package.
• The exported image classifier models can be used offline with Core ML on iOS and TensorFlow on Android.
• Choosing between online and offline strategies for image classification depends on specific app requirements.

FAQ

Q: What is image classification? A: Image classification is the process of categorizing objects or scenes in images based on their visual content.

Q: How does the Azure Custom Vision Service work? A: The Azure Custom Vision Service allows developers to train a neural network to identify and classify images with just a small number of training samples. It offers pre-trained models for various domains and allows exporting the models for offline use.

Q: Can I use the Custom Vision Service in a Xamarin app? A: Yes, the Microsoft.Cognitive.CustomVision.Prediction NuGet package provides integration with the Custom Vision Service in Xamarin apps, allowing you to make predictions using the trained image classifier.

Q: Can I use the image classifier offline? A: Yes, the exported image classifier models can be used offline with Core ML on iOS and TensorFlow on Android. This allows you to perform image classification on the device without an internet connection.

Q: How do I choose between online and offline strategies for image classification? A: The choice between an online or offline strategy depends on your app's specific requirements. If real-time updates and continuous improvement of the model are crucial, an online strategy using the Custom Vision Service is recommended. If offline functionality or limited connectivity is a concern, an offline strategy using Core ML or TensorFlow is more suitable.

Q: Where can I find the source code for building an image classifier with the Custom Vision Service in Xamarin? A: The source code for building an image classifier is available on GitHub. Please refer to the provided link in the resources section below.

Resources

• Azure Custom Vision
• Custom Vision AI - GitHub
• Core ML - Apple Developer
• TensorFlow Lite - TensorFlow