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Machine learning (ML) algorithms come in all shapes and sizes, each with their own trade-offs. We continue our exploration of TinyML on Arduino with a look at the Arduino KNN library.

In addition to powerful deep learning frameworks like TensorFlow for Arduino, there are also classical ML approaches suitable for smaller data sets on embedded devices that are useful and easy to understand — one of the simplest is KNN.

One advantage of KNN is once the Arduino has some example data it is instantly ready to classify! We’ve released a new Arduino library so you can include KNN in your sketches quickly and easily, with no off-device training or additional tools required.

In this article, we’ll take a look at KNN using the color classifier example. We’ve shown the same application with deep learning before — KNN is a faster and lighter weight approach by comparison, but won’t scale as well to larger more complex datasets.

Color classification example sketch
In this tutorial, we’ll run through how to classify objects by color using the Arduino_KNN library on the Arduino Nano 33 BLE Sense.

To set up, you will need the following:

Arduino Nano 33 BLE Sense board
Micro USB cable
Open the Arduino IDE or Arduino Create
Install the Arduino_KNN library
Select ColorClassifier from File > Examples > Arduino_KNN
Compile this sketch and upload to your Arduino board”

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