Summary
The recipes presented in this chapter demonstrated how to build an object detection application for microcontrollers with the help of Edge Impulse using a pre-trained FOMO model.
Initially, we learned how to prepare the dataset by acquiring camera frames with the webcam. Afterward, we delved into the model design. Here, we discussed how to choose a suitable image resolution and color format for an object detection model based on the FOMO architecture. Then, we explored the FOMO architecture to learn why it is ideal for memory-constrained devices.
After introducing the FOMO architecture, we trained the model and tested its accuracy on the test dataset and live images acquired with the webcam.
Finally, we implemented a Python script to emulate a microcontroller camera module and deployed the object detection model on the Raspberry Pi Pico using the Edge Impulse Inferencing SDK.
In this chapter, we have started discussing how to build a tinyML application with...
