Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By : Anubhav Singh, Rimjhim Bhadani
Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By: Anubhav Singh, Rimjhim Bhadani

Overview of this book

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.
Table of Contents (13 chapters)

Developing a face detection application using Flutter

With the basic understanding of how a CNN works from Chapter 1Introduction to Deep Learning for Mobile, and how image processing is done at the most basic level, we are ready to proceed with using the pre-trained models from Firebase ML Kit to detect faces from the given images.

We will be using the Firebase ML Kit Face Detection API to detect the faces in an image. The key features of the Firebase Vision Face Detection API are as follows:

  • Recognize and return the coordinates of facial features such as the eyes, ears, cheeks, nose, and mouth of every face detected.
  • Get the contours of detected faces and facial features.
  • Detect facial expressions, such as whether a person is smiling or has one eye closed.
  • Get an identifier for each individual face detected in a video frame. This identifier is consistent across invocations...