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)

Running image recognition

Now, the image chosen from the gallery can be used as an input for the two prediction methods of the Cloud Vision API and TensorFlow Lite model. Next, let's define methods for running both of them.

Using the Cloud Vision API

In this section, we simply define a visionAPICall method that is used to make an http Post request to the CloudVision API, passing in the request string encoded as json, which returns a json response that is parsed to get the values from the desired labels:

  1. First of all, we define an http plugin dependency in the pubspec.yaml file, as follows:
http: ^0.12.0+2
  1. Import the plugin in PlantSpeciesRecognition.dart to assist in making http requests, like...