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)

Generating image captions from the camera feed

Now that we have a clear idea about the image caption generator and have an application with a camera feed, we are ready to generate captions for the images from the camera feed. The logic to be followed is very simple. Images are captured from the live camera feed at a specific time interval and are stored in the device's local storage. Next, the stored pictures are retrieved and an HTTP POST request is created for the hosted model, passing in the retrieved image to fetch the generated captions, parsing the response, and displaying it on the screen.

Let's now look at the detailed steps, as follows: 

  1. We will first add an http dependency to the pubspec.yaml file to make http requests, as follows:
http: ^0.12.0

Install the dependency to the project using flutter pub get.

  1. To use the http package...