Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Mobile Vision - Face Detection Using On-Device Models

In this chapter, we will build a Flutter application that is capable of detecting faces from media uploaded from the gallery of a device or directly from the camera using the ML Kit's Firebase Vision Face Detection API. The API leverages the power of pre-trained models hosted on Firebase and provides the application, the ability to identify the key features of a face, detect the expression, and get the contours of the detected faces. As the face detection is performed in real time by the API, it can also be used to track faces in a video sequence, in a video chat, or in games that respond to the user's expression. The application, coded in Dart, will work efficiently on Android and iOS devices.

In this chapter, we will be covering the following topics:

Introduction to image processing
Developing a...

Key benefits

Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing

Cover interesting deep learning solutions for mobile

Build your confidence in training models, performance tuning, memory optimization, and neural network deployment through every project

Description

Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.

Who is this book for?

This book is for data scientists, deep learning and computer vision engineers, and natural language processing (NLP) engineers who want to build smart mobile apps using deep learning methods. You will also find this book useful if you want to improve your mobile app’s user interface (UI) by harnessing the potential of deep learning. Basic knowledge of neural networks and coding experience in Python will be beneficial to get started with this book.

What you will learn

Create your own customized chatbot by extending the functionality of Google Assistant

Improve learning accuracy with the help of features available on mobile devices

Perform visual recognition tasks using image processing

Use augmented reality to generate captions for a camera feed

Authenticate users and create a mechanism to identify rare and suspicious user interactions

Develop a chess engine based on deep reinforcement learning

Explore the concepts and methods involved in rolling out production-ready deep learning iOS and Android applications

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