ShawnHymel / perfect-toast-machine Public

ShawnHymel / perfect-toast-machine

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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:

A full tutorial showing how to build this AI-powered toaster can be found here:

Download block output

Title Type Size
Raw data training data NPY file 5260 windows
Raw data training labels NPY file 5260 windows
Raw data testing data NPY file 1315 windows
Raw data testing labels NPY file 1315 windows
Regression model TensorFlow Lite (float32) 71 KB
Regression model TensorFlow Lite (int8 quantized) 20 KB
Regression model TensorFlow SavedModel 72 KB
Regression model Keras h5 model 67 KB

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Data collected
18h 15m 50s

Project info

Project ID 129477
Project version 2
License Apache 2.0