Afya Bora is a data driven-electronic medical consultation platform that allows patients to directly connect with healthcare practitioners.
This project compares inferencing MobileNet and EfficientNet-Lite on the Coral dev board vs MaaXBoard and Raspberry Pi.
This project compares inferencing MobileNet and EfficientNet-Lite on the Raspberry Pi vs MaaXBoard and Coral dev board.
This project compares inferencing MobileNet and EfficientNet-Lite on the MaaXBoard vs Coral dev board and Raspberry Pi.
TensorFlow Lite makes sense for edge devices like the MaaXBoard.
Detect human face details with the help of an Arduino. Store detection results in into cloud data storage.
Tutorial on object detection with Kendryte K210 and raccoons!
Faster than real-time! Based on Mozilla's DeepSpeech Engine 0.7.*
Get Xilinx Vitis AI hardware accelerated inference up and running with minimal effort using Python and PYNQ!
RB-0 is a hobby sized rover that uses the same suspension concept as NASA's newer differential-bar rovers. It uses a Jetson Nano + camera.
Alexa wants to meet Cortana. Now you can run Amazon Alexa voice service in several platforms like macOS, Linux, Windows and Raspbian.
Read about deep learning XNOR networks on a Raspberry Pi 3 B+ with nice FPS.
Discover seven emotions in an image or video with Azure Cognitive Services.
Learn how to configure and boot your Jetson Nano for the first time.
Add the power of a GPU to your rover.
A candy dispenser using Android Things and TensorFlow Lite.
An introduction to Brainium software and hardware stack.
Man vs. machine. Wait, it's actually Machine vs. Machine
Get machine vision running on your MaaXBoard using OpenCV.
Improving usage of our fridge, by monitoring the most important parameters in order to extend the operation time and reduce food waste.
It is a base project to control the ON/OFF switch an LED in a channel from Cayenne MQTT Server using Node.js.
Cook like best chefs on PotsHub. Get recipes from the PotsHub community. Learn how to cook using PotsHub technology.
How to train your custom CNN model and run it on the microcontroller. Recognize anything! Well, almost.