ShawnHymel / perfect-toast-machine Public

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:

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

Creating your first impulse (100% complete)

Acquire data

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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

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 144302
Project version 2
License Apache 2.0