TinyMLjs

A set of hardware independent web-page, client-side (No Internet required) programming tools to help bring Machine Learning Proof of Concept's to more students, by using the power of the web with javascript-tensorflowJS and the simplicity of the C/C++ microcontroller serial print command combined together using Google's WebSerial that works on any desktop (or laptop) computer.

Note: WebSerial using a polyfill works on a few Android phones such as some of the Google Pixel phones.

Demos

Skip to basic instructions

Acceleration x,y,z time-series Demos: Now works for any sensor other than Vision and Sound

1. Best acceleration Nano33BleSense Does not have latest features.
2. Best acceleration NiclaVision-Acceleration and Time of Flight
3. Best acceleration RAK2270 Sticker Tracker

Note: This is also for other sensors! Always the best so far x,y,x acceleration page. CSV and Senses work and ML works, classification works, cell phone (Apple & Android) motion works



4. Next Acceleration Draft: for nicla-vision a49-next-acceleration.html - model view working, live edit not
5. Next Acceleration Draft: for nicla-vision a50-next-acceleration.html - adding live model editing WORKS!
6. Next Acceleration Last: for nicla-vision a51-next-acceleration.html - tidy it up and test
7. Next Acceleration Draft: for RAK2270 Sticker Tracker a52-next-acceleration.html- GOOD for RAK, but mouse motion issue
8. Next Acceleration blanked out mouse motion a53-next-acceleration.html
9. Next Acceleration for RAK2270 trying to fix mouse motion a54-next-acceleration.html Since it is good for the classroom, works but decimals
10. Next Acceleration a55-next-acceleration.html fixing mouse motion
11. Next Acceleration a56-next-acceleration.html fixing mouse motion
12. Next Acceleration a57 This is good!
13. 58 RAK Next Acceleration a58
14. 58 Nicla Vision a58
15. 58 Nano 33 Ble Sense a58
16. A59-next-using-nano33ble-sense Working on this nowe

Basic instructions

• This is a hardware independent proof of concept. Use a sensor or two and get a Machine Learning Keras model working with an actuator on any microcontroller board.
• Once you know your model works, slow down and record your data into a CSV file for uploading to a professional cloud Machine Learning service like edgeImpulse.com or Arduino Cloud or just stay client-side with tinyMLjs and build another model.
• If you are staying with tinyMLjs, upload the CSV files to a model designer, train the model and save your tensorflowJS saved model
• If you are still staying with tinyMLjs (wow you are great!) load the saved model onto a webpage with webSerial connection to your actuator and test how well your =model works in real world settings.
• Or use the conversion program either downloaded to online that converts the TensorflowJS saved model into tensorflowLite format and then use xxd -i to convert the binary file into a c-header file that can be installed onto your microcontroller using your IDE or the Arduino IDE.

Made by Jeremy Ellis. Twitter @rocksetta, or @rocksetta@masto.ai or GitHub profile at https://github.com/hpssjellis
This Github at https://github.com/hpssjellis/tinyMLjs Use tinyMLjs at your own risk!

Design By Max S. Github at https://github.com/maxbiz100

Started Jun 4th, 2023 Version 0.1.0-2