Popular GitHub Repositories for Raspbian Stretch

Prebuilt binary with Tensorflow Lite enabled. For RaspberryPi / Jetson Nano. Support for custom operations in MediaPipe. XNNPACK, XNNPACK Multi-Threads, FlexDelegate.

[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering

Tensorflow based Fast Pose estimation. OpenVINO, Tensorflow Lite, NCS, NCS2 + Python.

A Raspberry Pi powered, distributed (edge) computing camera setups that runs a Tensorflow object detection model to determine whether a person is on the camera. A plugin model allows actions based on the detection, such as playing audio, turning on lights, or triggering an Arduino.

An "Off-line machine learning" (Offline AI, artificial intelligence) project, in which 2 tensorflow-lite apps are developed to run on Raspberry Pi 3. It hopes to propose a generic method on cross-developing, so that developer could fast follow to get your own apps started on Embedded intelligence (AI embedded system), smart object (AI IoT) and etc.

Fast C++ implementation of ESOLA using KFRLib, can be used for online time-stretch augmentation during SpeechToText training.

Configurations and scripts for building your own voice asisstant based on Rhasspy and Node-RED

Open-source voice assistant platform by MyTja written in Python with :heart:

This is a self-documentation of learning distributed data storage, parallel processing, and Linux OS using Apache Hadoop, Apache Spark and Raspbian OS. In this project, 3-node cluster will be setup using Raspberry Pi 4, install HDFS and run Spark processing jobs via YARN.

A chatbot you can host thats free to use and steal forever

This project demonstrates how to use Azure Cognitive Services with a Raspberry Pi to create a closed captioning system. By following the steps outlined in this repository, users can gain an understanding of how to use Microsoft's Azure Cognitive Services with IoT devices.