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TinyML Cookbook

You're reading from   TinyML Cookbook Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801814973
Length 344 pages
Edition 1st Edition
Tools
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Author (1):
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Gian Marco Iodice Gian Marco Iodice
Author Profile Icon Gian Marco Iodice
Gian Marco Iodice
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Table of Contents (10) Chapters Close

Preface 1. Chapter 1: Getting Started with TinyML 2. Chapter 2: Prototyping with Microcontrollers FREE CHAPTER 3. Chapter 3: Building a Weather Station with TensorFlow Lite for Microcontrollers 4. Chapter 4: Voice Controlling LEDs with Edge Impulse 5. Chapter 5: Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano 6. Chapter 6: Building a Gesture-Based Interface for YouTube Playback 7. Chapter 7: Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS 8. Chapter 8: Toward the Next TinyML Generation with microNPU 9. Other Books You May Enjoy

Converting a NumPy image to a C-byte array

Our application will be running on a virtual platform with no access to a camera module. Therefore, we need to supply a valid test input image into our application to check whether the model works as expected.

In this recipe, we will get an image from the test dataset that must return a correct classification for the ship class. The sample will then be converted to an int8_t C array and saved as an input.h file.

The following Colab file (refer to the Converting a NumPy image to a C-byte array section) contains the code referred to in this recipe:

  • prepare_model.ipynb:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter07/ColabNotebooks/prepare_model.ipynb

Getting ready

To get ready for this recipe, we just need to know how to prepare the C file containing the input test image. The structure of this file is quite simple and reported in the following figure:

Figure 7.7 –...

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