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)

Integrating a hosted custom model on Flutter

In this section, we will make a POST request to the hosted model and pass it in the image selected by the user. The server will respond with a NetworkImage in PNG format. Then, we'll update the image widget we added earlier to show the enhanced image that's returned by the model.

Let's start integrating the hosted model into the application:

  1. First of all, we will need two more external libraries to make a successful POST request. Therefore, we'll add the following libraries as dependencies to the pubspec.yaml file:
: 0.12.0+4
mime: 0.9.6+3

The http dependency contains a set of classes and functions that make consuming HTTP resources very convenient. The mime dependency is used for processing streams of MIME multipart media types.

Now, we need to run flutter...