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)

Creating a custom TensorFlow Lite model for image recognition

Once you have had a fair go at Colaboratory, we're all set up to build the custom TensorFlow Lite model for the task of recognizing plant species. To do so, we will begin with a new Colaboratory notebook and perform the following steps:

  1. Import the necessary modules for the project. Firstly, we import TensorFlow and NumPy. NumPy will be useful for handling the image arrays, and TensorFlow will be used to build the CNN. The code to import the modules can be seen in the following snippet:
!pip install tf-nightly-gpu-2.0-preview
import tensorflow as tf
import numpy as np
import os

Notice the !pip install <package-name> command used on the first line. This is used to install packages in a running Colaboratory notebook, which, in this case, installs the latest TensorFlow release that internally implements the...