/MakeIT-How-to-deploy-SK-learn-model-on-ESP8266

This repository is dedicated to another tutorial of my youtube channel MakeIT. This project is about tiny machine learning and how to classify orientation of an ESP8266 using a SciKit-learn machine learning model.

Primary LanguageJupyter Notebook

MakeIT-How-to-deploy-SK-learn-model-on-ESP8266

This repository is dedicated to another tutorial of my youtube channel MakeIT. This project is about tiny machine learning and how to classify orientation of an ESP8266 using a SciKit-learn machine learning model. It's available here

You can find below the pipeline to develop a TinyML project :

Pipeline

Python part

This has been designed using SciKit-learn library for machine learning. This network has been trained on dataset created with Datasets Builder my software to create datasets. Then this model has been exported into c code .h file. To convert the model I have used micromlgen python library.

Arduino part

In this part I have used VS code and platformIO IDE to deploy the model onto the ESP8266 WEMOS D1 mini lite. To do it we had to import our model, and the libraries to use I2C protocol and MPU6050 acccelerometer on the ESP8266.

Here is the schema of the wiring :

Wiring schema

To conclude by running the code with get an accuracy about 100% which is quite satisfiying.