nvidia-jetson-nano

[ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding

Open-Source AI Camera. Empower any camera/CCTV with state-of-the-art AI, including facial recognition, person recognition(RE-ID) car detection, fall detection and more

AI assisted motion detection for Home Assistant

Helmut Hoffer von Ankershoffen experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT.

A variety of 3D printed mounts, adapters, and kits for NVIDIA's Jetson Nano.

Social Distancing Detector using deep learning and capable to run on edge AI devices such as NVIDIA Jetson, Google Coral, and more.

OCTproZ is an open source software for optical coherence tomography processing and visualization.

Self-driving AI toy car 🤖🚗.

Predict live chess games into FEN notation.

Installation step of vins-fusion gpu version on Nvidia Jetson TX2 & Nvidia Jetson Nano ( JP 4.2.2)

Edge AI 101 Course using Nvidia Jetson

Dockerfile for hasura/graphql-engine to run on arm64/aarch64 (tested on Nvidia Jetson Nano, Rpi4, Apple M1).

This project uses Deep learning concept in detection of Various Deadly diseases. It can Detect 1) Lung Cancer 2) Covid-19 3)Tuberculosis 4) Pneumonia. It uses CT-Scan and X-ray Images of chest/lung in detecting the disease. It has a Accuracy between 50%-80%. It can take input in any Image format or through Live videos and provide accurate output results.

Yahboom Jetbot AI robot with HD camera coding with Python compatible with 4GB(A02/B01)

Person detection for Hikvision DVRs, uses TensorRT and yolov4

Try Edge Computing devices from scratch --- NVIDIA Jetson Nano

Installing QGroundControl (QGC) on Ubuntu ARM64 using Flatpak

CUDA Programming Practices

Control Nvidia jetbot which is based on Jetson Nano via iOS app

This repository contains a YoloV4/Darknet based image classifier coded to run onboard the Nvidia Jetson Nano platform at approximately 10 FPS. The code includes a frame by frame tracker that keeps IDs of vehicles mostly constant. It display the output to the web browser via flask for debugging/demo.