Live classifications with the Edge Impulse data forwarder tool

Deploying a model on microcontrollers is error-prone because the code may contain bugs, the integration could be incorrect, or the model could not work reliably in the field. Consequently, conducting model testing becomes essential to exclude at least the ML model from the source of failures. In this recipe, we will use the Live classification tool of Edge Impulse to carry out this investigation.

Getting ready

The most effective approach to assess the performance of an ML model is to evaluate its behavior directly on the target platform, and the Edge Impulse data forwarder tool provides a straightforward method for doing so.

In our specific case, since the dataset was built using the Raspberry Pi Pico, we have gained initial insights into the model’s accuracy during the training phase.

However, there may be instances where the dataset may not be built on top of sensor data coming from the target...