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In this repository you will find TinyML course syllabi, assignments/labs, code walkthroughs, links to student projects, and lecture videos (where applicable).
A Python package with command-line utilities and scripts to aid the development of machine learning models for Silicon Lab's embedded platforms
This repository holds the Arduino Library for the EdX TinyML Specialization
TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation
Collection of STM32 projects making use of Tensorflow Lite Micro
Code for IEEE Internet Computing Journal paper 'OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices'
Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables
Tensorflow Lite Micro is a DL inference framework for microcontrollers based on Google Tensorflow Lite
TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning
Sleep state prediction in embedded systems based on sensor data.
TensorFlow Lite for Microcontrollers Python package for Raspberry Pi Zero
This repo contains all the necessary files to build a MNIST TinyML application, that works with an OV7670 camera module and TFT LCD module.
A PlatformIO library with the complete and (As of Aug 20, 2023) up-to-date version of Tensorflow Lite for Microcontrollers.
Web GUI to collect and categorize images from web cam (ESP-CAM, ESP-WHO) for ML training
A holistic device for safer and smarter contactless elevator system using Embedded Machine Learning on an Arduino Nano 33 BLE Sense, complete with a deployable PCB and fire safety system pipeline.
Detect a MokaPot with Tensorflow Lite Micro and an Arduino Portenta or OpenMV cam
MoonMakers-Pinball in a game that works in the browser, where we use our Arduino Nano 33 BLE Sense, as a command to control the imposition of our ball thanks to AI.
flooding on roadways with TinyML
This repository is dedicated to the first tutorial of my YouTube channel.
Tensorflow lite used on a SAMD21 microcontroller in the arduino framework for activity recognition
weights of MobileNetV1 and MobileNetV2 trained on greyscale images. supports 96x96 image inputs only. Useful for developing models for Edge devices like Android, IOS and Microcontrollers.
Optical digit recognition using Tensorflow Lite Micro on the NM180100
tflite_micro_runtime
Northern Mechatronics Software Development Kit V2