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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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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

Designing the project architecture

In this project, we will be building a mobile app, which, when pointed at any scenery, will be able to create captions describing that scenery. Such an app is highly beneficial for people with visual defects, as it can be used both as an assistive technology on the web and as a day-to-day app if paired with a voice interface such as Alexa or Google Home. The app will be calling a hosted API that will produce captions for any given image passed to it. The API returns three best possible captions for the image, and the app then displays them right below the camera view in the app.

From a bird's-eye view, the project architecture can be illustrated by means of the following diagram:

The input will be the camera feed obtained in a smartphone, which is sent to an image caption generation model hosted as a web API. The model is hosted as a Docker...

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