ShawnHymel / perfect-toast-machine
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
The Perfect Toast Machine
This project attempts to toast bread using odor and temperature data (rather than relying on a simple timer). Using this data, we attempt to predict the "time remaining before burnt" using regression. From this, we can estimate a level of "doneness" e.g. by saying that "toast will be perfect 40 seconds before being burned." By hacking a toaster to cancel the toasting process at this point, we should, in theory, be able to perfectly make toast regardless of starting temperature and bread thickness or composition.
Gas and odor data collected from various types of bread over a Black and Decker simple two-slot toaster. Data was standardized before being uploaded to Edge Impulse. The original dataset, curation script, and inference code can be found here: https://github.com/ShawnHymel/perfect-toast-machine.
A full tutorial showing how to build this AI-powered toaster can be found here: https://www.digikey.com/en/maker/projects/how-to-build-an-ai-powered-toaster/2268be5548e74ceca6830bf35f0f0f9e.
Creating your first impulse (100% complete)
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
Data collected18h 15m 50s