tensorflow-lite-micro

In this repository you will find TinyML course syllabi, assignments/labs, code walkthroughs, links to student projects, and lecture videos (where applicable).

This repository holds the Google Colabs for the EdX TinyML Specialization

📦 AI-on-the-Edge Device [SLFork] - Smart AI-on-edge meter digitization based on ESP32 / ESP32-S3, upstream core principle with custom enhancements

TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

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

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'

TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning

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

Sleep state prediction in embedded systems based on sensor data.

TensorFlow Lite for Microcontrollers Python package for Raspberry Pi Zero

Learning Path: RISC-V & Advanced Edge AI on SiFive FE310-G002 SoC | 32-bit RISC-V | 320 MHz | 16KB L1 Instruction Cache | 128Mbit (16MB) QSPI Flash | 4-stage pipeline

TensorFlow Lite for BL602

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.

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.

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.