Transfer learning with FOMO
After designing the pre-processing block, it is time to train the ML model.
In this recipe, we will discuss the features that make the FOMO model suitable for highly constrained devices and show how to train it in Edge Impulse.
Getting ready
The design of the FOMO architecture, leveraged in this project to enable object detection on Raspberry Pi Pico, demonstrates that by approaching problems from a different and simple perspective, we can turn the seemingly impossible into reality. tinyML developers should always think this way if they want to unlock novel solutions on microcontrollers, as the computational capabilities of these devices are certainly not the same as those of the cloud, laptops, or smartphones.
In the following subsection, we will dive deep into the technical details of FOMO to learn how this model works and be inspired by its underlying ideas.
