Have you ever wanted to perform person detection on Arduino or ESP32 boards? Found it difficult to custimize the sketch to suit your needs? Fear no more!
Person detection on Arduino and ESP32 microcontrollers doesn't have to be difficult: with the right library, you only need 3 lines of code to perform state-of-the-art person detection. You use TensorFlow Neural Networks without any boilerplate and verbose code using the EloquentTinyML library.
Are you looking for a project to get started with Machine Learning on Arduino and don't know where to start?
Do most of the tutorials on Arduino gesture recognition you found on the internet look too complicated for you?
You're in the right place!
In this tutorial I'm going to show you one of the easiest possible ways to get started with Machine Learning on Arduino boards while also creating something useful: a gesture recognition system based on an accelerometer.
In this short post we'll take a look at how lo load Tensorflow Lite models exported as a C header file from the filesystem, be it an SD card or the built-in SPIFFS filesystem on ESP32 devices.
Ever wandered how fast are the major microcontroller boards to run Tensorflow Lite neural networks? In this post we'll find it out for the case of Fully Connected networks.
This post is a step by step tutorial on how to train, export and run a Tensorflow Neural Network on an Arduino-compatible microcontroller for the task of classification: in particular, we will classify the Wine dataset.
In this post I will show you how to easily deploy your Tensorflow Lite model to an ESP32 using the Arduino IDE without any compilation stuff.
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