You're reading from TinyML Cookbook - Second Edition

Product type Book

Published in Nov 2023

Publisher Packt

ISBN-13 9781837637362

Pages 664 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Gian Marco Iodice

Table of Contents (16) Chapters

Preface

1. Getting Ready to Unlock ML on Microcontrollers

2. Unleashing Your Creativity with Microcontrollers

3. Building a Weather Station with TensorFlow Lite for Microcontrollers

4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands

5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 1

6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 2

7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico

8. Classifying Desk Objects with TensorFlow and the Arduino Nano

9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico

10. Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU

11. Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM

12. Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and Raspberry Pi Pico

13. Conclusion

14. Other Books You May Enjoy

15. Index

Building the dataset to classify desk objects

In this recipe, we will build the dataset by collecting images of the mug and book, with the OV7670 camera and the Arduino Nano. The image files will then be uploaded to Google Drive to train the ML model in the next recipe.

Getting ready

Training a deep neural network from scratch for image classification commonly requires a dataset with 1,000 images per class. As you might guess, collecting such a vast number of pictures would be time-consuming. To overcome this challenge, we will employ the technique we applied in the previous Chapter 7, Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico: transfer learning.

This ML technique, which we will exploit in the following recipe, allows us to build a dataset with just 20 samples per class.