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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter
Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

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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Profile Icon Anubhav Singh Profile Icon Bhadani
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Paperback Apr 2020 380 pages 1st Edition
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Arrow left icon
Profile Icon Anubhav Singh Profile Icon Bhadani
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€18.99 per month
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1 (1 Ratings)
Paperback Apr 2020 380 pages 1st Edition
eBook
€17.99 €26.99
Paperback
€32.99
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Free Trial
Renews at €18.99p/m
eBook
€17.99 €26.99
Paperback
€32.99
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Renews at €18.99p/m

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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

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...

Technical requirements

Introduction to image processing

In this chapter, we shall be detecting faces in images. In the context of artificial intelligence, the action of processing an image for the purpose of extracting information about the visual content of that image is called image processing.

Image processing is an emerging field, thanks to the surge in the number of better AI-powered cameras, medical imagery-based machine learning, self-driving vehicles, analysis of people's emotions from images, and many other applications.

Consider the use of image processing by a self-driving vehicle. The vehicle needs to make decisions in as close to real time as possible to ensure the best possible accident-free driving. A delay in the response of the AI model running the car could lead to catastrophic consequences. Several techniques and algorithms have been developed for fast and accurate...

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...

Summary

In this chapter, we examined the concept behind image processing and how we can integrate it with our Android- or iOS-based application made using Flutter to perform face detection. The chapter started with adding relevant dependencies to support the functionalities of Firebase ML Kit and the image_picker library. The required UI components with the necessary functionalities were added. The implementation mainly covered image file selection using the Flutter plugin and how images can be processed once they are selected. An example of on-device Face Detector model usage was presented, along with an in-depth discussion of the method by which the implementation was carried out. 

In the next chapter, we will be discussing how you can create your own AI-powered chatbot that can double-up as a virtual assistant using the Actions on Google platform.&...

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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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Publication date : Apr 06, 2020
Length: 380 pages
Edition : 1st
Language : English
ISBN-13 : 9781789611212
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Product Details

Publication date : Apr 06, 2020
Length: 380 pages
Edition : 1st
Language : English
ISBN-13 : 9781789611212
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Languages :
Concepts :
Tools :

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Table of Contents

11 Chapters
Introduction to Deep Learning for Mobile Chevron down icon Chevron up icon
Mobile Vision - Face Detection Using On-Device Models Chevron down icon Chevron up icon
Chatbot Using Actions on Google Chevron down icon Chevron up icon
Recognizing Plant Species Chevron down icon Chevron up icon
Generating Live Captions from a Camera Feed Chevron down icon Chevron up icon
Building an Artificial Intelligence Authentication System Chevron down icon Chevron up icon
Speech/Multimedia Processing - Generating Music Using AI Chevron down icon Chevron up icon
Reinforced Neural Network-Based Chess Engine Chevron down icon Chevron up icon
Building an Image Super-Resolution Application Chevron down icon Chevron up icon
Road Ahead Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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The flutter App code is confuse, missing variables and incomplete.
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