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TinyML Cookbook - Second Edition

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
Author (1):
Gian Marco Iodice Gian Marco Iodice
Profile icon Gian Marco Iodice
Toc

Table of Contents (16) Chapters close

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

Designing the Impulse’s pre-processing block

Now that we have the dataset ready, let’s delve into the Impulse preparation.

In this recipe, our initial focus will be on the design of the pre-processing block responsible for preparing the input image feeding the ML model.

Getting ready

The object detection pipeline we are going to build with the help of Edge Impulse is the following:

Figure 7.8: The object detection pipeline

The image preparation block reported in the preceding figure encompasses all the essential operations required to transform the camera frame into the expected input format for the Learning block.

In this project, we won’t use any camera module with the microcontroller. Instead, we will emulate the functionality of an actual camera by transmitting the frames captured with the webcam from the computer over the serial connection.

To avoid the image preparation stage reported in Figure 7.8 and make the implementation...

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