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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   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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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Building the UI for the application

Now that we understand the basic functionality of the image super-resolution model and have created a model for it, let's do a deep dive into building the Flutter application. In this section, we will build the UI of the app.

The UI of the app will be very simple: it will contain two image widgets and button widgets. When the user clicks on the button widget, they will be able to pick an image from the device's gallery. The same image will be sent as input to the server hosting the model. The server will return an enhanced image. The two image widgets that will be placed on the screen will be used to display the input to the server and the output from the server.

The following images illustrate the basic structure and the final flow of the application:

The three primary widgets of the application can be simply arranged in a column...

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