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Explore and learn from Jetson projects created by us and our community. These projects have been built for Jetson Nano, Jetson AGX Xavier, and Jetson TX2

Have a Jetson project to share? Post it on our forum for a chance to be featured here too. Every month, we’ll award one project with a Jetson AGX Xavier Developer Kit that’s a cut above the rest for its application, inventiveness and creativity.


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JetBot

Jetson Nano

Open-source project for learning AI by building fun applications. It’s easy to set up and use, is compatible with many accessories and includes interactive tutorials showing you how to harness the power of AI to follow objects, avoid collisions and more. The kit includes the complete robot chassis, wheels, and controllers along with a battery and 8MP camera. Supports AI frameworks such as TensorFlow and PyTorch.

Hello AI World

Jetson Nano Jetson TX2 Jetson AGX Xavier

Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection (using pretrained models) on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You also get to code your own easy-to-follow recognition program in C++.

JetRacer

Jetson Nano

Autonomous AI racecar using NVIDIA Jetson Nano. With JetRacer, you will:

  • Go fast - Optimize for high framerates to move at high speeds
  • Have fun - Follow examples and program interactively from your browser
By building and experimenting with JetRacer you will create fast AI pipelines and push the boundaries of speed.

Real-time Human Pose Estimation on Jetson Nano

Jetson Nano

This project features multi-instance pose estimation accelerated by NVIDIA TensorRT. It is ideal for applications where low latency is necessary. It includes:

  • Training scripts to train on any keypoint task data in MSCOCO format
  • A collection of models that may be easily optimized with TensorRT using torch2trt
This project can be used easily for the task of human pose estimation, or extended for something new.

TSM: Temporal Shift Module for Efficient Video Understanding

By Ji Lin, Chuang Gan, Song Han. MIT Han Lab
Project of the Month October 2019
Jetson Nano Jetson TX2

TSM is an efficient and light-weight operator for video recognition [on edge devices]. [...] Conventional methods using 3D convolution for temporal modeling are computationally expensive, making it difficult to be deployed on embedded devices which have a tight power constraint. In this ICCV’19 paper, we propose Temporal Shift Module (TSM) that can achieve the performance of 3D CNN but maintain 2D CNN’s complexity by shifting the channels along the temporal dimension. TSM enables real-time low-latency online video recognition and video object detection. [...] On NVIDIA Jetson Nano, it achieves a low latency of 13ms (76fps) for online video recognition.

ShAIdes

By Nick Bild
Project of the Month September 2019
Jetson Nano

My AI is so bright, I gotta wear shades. Effect change in your surroundings by wearing these AI-enabled glasses. ShAIdes is a transparent UI for the real world. A camera is attached to the frames of a pair of glasses, capturing what the wearer sees. It feeds realtime images to an NVIDIA Jetson Nano, which runs two separate image classification CNN models, one to detect objects, and another to detect gestures made by the wearer. When combinations of known objects and gestures are detected, actions are fired that manipulate the wearer’s environment.

Teaching My Nano to Recognize Sign Language

By Dennis Faucher
Project of the Month August 2019
Jetson Nano

The Jetson Nano caches this model into memory and uses its 128 core GPU to recognize live images at up to 60fps. That high fps live recognition is what sets the Nano apart from other IoT devices. I have been hearing recommendations toward "Train in the cloud, deploy at the edge" and this seemed like a good reason to test that concept. Mission accomplished.

Smart Doorbell Camera

By Adam Geitgey
Project of the Month August 2019
Jetson Nano

We’ll create a simple version of a doorbell camera that tracks everyone that walks up to the front door of your house. With face recognition, it will instantly know whether the person at your door has ever visited you before—even if they were dressed differently. And if they have visited, it can tell you exactly when and how often.

FastDepth: Fast Monocular Depth Estimation on Embedded Systems

By Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze
Jetson TX2

[…] There has been a significant and growing interest in depth estimation from a single RGB image, due to the relatively low cost and size of monocular cameras. [We] explore learning-based monocular depth estimation, targeting real-time inference on embedded systems. We propose an efficient and lightweight encoder-decoder network architecture and apply network pruning to further reduce computational complexity and latency. We deploy our proposed network, FastDepth, on the Jetson TX2 platform, where it runs at 178fps on the GPU and at 27fps on the CPU, with active power consumption under 10W. FastDepth achieves close to state-of-the-art accuracy on the NYU Depth v2 dataset.

SINTEF Self-Driving Truck with Induction Charger

By Jon Eivind Stranden
Jetson TX2

This small-scale self-driving truck using Jetson TX2 and ROS Kinetic was built to demonstrate the principle of a wireless inductive charging system developed by Norwegian research institute SINTEF for road use. Navigate using one of two modes; SLAM/Pure Pursuit path tracking and supervised deep learning based on NVIDIA DAVE-2.

Autonomous drone using ORBSLAM2 on the Jetson Nano

By Autonomous Drones Lab, at Tel Aviv University
Jetson Nano

Run ORBSLAM2 and implement close-loop position control in real time on a Jetson Nano, using recorded rosbags (e.g., EUROC) or live footage from a Bebop2 Drone. Tested with Monocular camera in real time. using the OrbSLAM2 and a Bebop2 Drone. In the Autonomous Drones Lab, at Tel Aviv University, we research, develop and implement solutions for autonomous navigation in GPS-denied environments. In order to validate our solution, we work mainly on prototype drones to achieve a quick integration between the hardware, software and the algorithms.

GPU-enabled Kubernetes Cluster for Machine Learning with Jetson Nano

By Kacper Łęgowski, Witold Bołt. JIT Team
Jetson Nano

Jetson Nano is a fully-featured GPU compatible with NVIDIA CUDA libraries. […] CUDA is the de-facto standard for modern machine learning computation. Having […] a cheap, CUDA-equipped device, we thought — let’s build our own machine learning cluster. If you think “cluster”, you typically think “Kubernetes”, […] commonly used to manage distributed applications running on […] even hundreds of thousands of machines. We were not aiming that far, […] ours is composed of 4; [though] it is applicable to any number Jetson Nanos.

Rock-Paper-Scissors

By Alan Mok
Jetson Nano

This is an implementation for Rock-Paper-Scissors game with a machine. The Jetson Nano developer kit is used for AI recognition of hand gestures.

Jetson-FFmpeg

By Jiang Wei
Jetson Nano

With Jetson-FFMpeg, use FFmpeg on Jetson Nano via the L4T Multimedia API, supporting hardware-accelerated encoding of H.264 and HEVC. FFMpeg is a highly portable multimedia framework, able to decode, encode, transcode, mux, demux, stream, filter and play pretty much any format. It supports the most obscure ancient formats up to the cutting edge.

Jetson Stats

By Raffaello Bonghi
Jetson Nano Jetson TX2 Jetson AGX Xavier

Jetson-Stats is a package for monitoring and controlling your NVIDIA Jetson [Nano, Xavier, TX2i, TX2, TX1] embedded board. When you install jetson-stats, the following are included:

  • jtop
  • jetson-release
  • jetson_variables

Homesecurity

By Jaiyam Sharma
Jetson Nano Jetson TX2

This software was written for monitoring the security of my home using single or multiple Picameras. The cameras perform motion detection and record video. The video is sent in an email. After recording video, an object detection model running on Jetson Nano checks if a person is present in the video. A set of 4 raspi zeros stream video over Wi-Fi to a Jetson TX2, which combines inputs from all sources, performs object detection and displays the results on a monitor.

Tiny YOLO v2 Inference Application with NVIDIA TensorRT

By Tsutomu Furuse
Jetson Nano

This application downloads a tiny YOLO v2 model from Open Neural Network eXchange (ONNX) Model Zoo, converts it to an NVIDIA TensorRT plan and then starts the object detection for camera captured image.

Open DataCam

By moovel lab
Jetson Nano Jetson TX2 Jetson AGX Xavier

Quantify the world—monitor urban landscapes with this offline lightweight DIY solution. The simple setup allows you to become an urban data miner. Install on an NVIDIA Jetson board + Logitech webcam and count cars, pedestrians, and motorbikes from your livestream, running yolo and a tracking software we built. Access via smart devices, define areas to track, count and export data once you're finished. You can use this system for surveying without saving video data—not intruding data privacy of counted objects. Where data goes and what happens during the counting algo is transparent.

Electronically Assisted Astronomy

By Alain Paillou
Jetson Nano

With Electronically Assisted Astronomy, the camera replaces your eye. With a telescope, simply observe the deep sky on a screen or even record videos of your observations, using AI to enhance your images. I wanted to make a fully autonomous system I could control from my computer at home using a VNC client, instead of being outside during very cold nights.

Hey Jetson

By Brice Walker
Jetson Nano Jetson TX2 Jetson AGX Xavier

Build a scalable attention-based speech recognition platform in Keras/Tensorflow for inference on the NVIDIA Jetson Platform for AI at the Edge. This real-world application of automatic speech recognition was inspired by my previous career in mental health. This project begins a journey towards building a platform for real-time therapeutic intervention inference and feedback. The ultimate intent was to build a tool to give therapists real-time feedback on the efficacy of their interventions, but on-device speech recognition has many applications in mobile, robotics, or other areas where cloud-based deep learning is not desirable.

Transfer Learning with JetBot - Fun with Traffic Cones

By Dmitri Villevald
Jetson Nano

[When] driving [around] construction areas, I [think] how challenging it would be for self driving cars to navigate [around] traffic cones. It turns out it's not so difficult with NVIDIA's JetBot-with only a couple hundred images, you can train a state-of-the-art deep learning model to teach your robot how to [navigate] a maze of toy traffic cones using only an onboard camera and no other sensors.

Multi-agent System for non-Holonomic Racing (MuSHR)

By MuSHR RACECAR team
Jetson Nano

The Unversity of Washington's Personal Robotics Lab has recently open-sourced the MuSHR Racecar Project. A robotic racecar equipped with lidar, a D435i Realsense Camera, and an NVIDIA Jetson Nano. The car can be used for machine learning, vision, autonomous driving, and robotics education. Build instructions and tutorials can all be found on the MuSHR website!

OCR Tesseract Docker App on BalenaCloud

By Richard Torzynski
Jetson Nano

Upload images using Flask — a lightweight development-purposes server framework — preprocess and reduce image noise using OpenCV, and perform OCR using Python-tesseract. Originally deployed on a Docker container on AWS, this version is deployed using BalenaCloud to a Jetson Nano.

P.A.N.T.H.E.R.: Powerful Autonomous eNTity High-End Robot

By Raffaello Bonghi
Jetson TX2

Using its two tracks, ZED stereo camera and the NVIDIA Jetson TX2, this robot explores the outdoors and interacts with its surroundings. Weighing 9kg (20lbs), with 7cm (2.7in) of ground clearance, and a track system composed of three different dampers to absorb vibrations when drifting on grass, P.A.N.T.H.E.R. can climb little rocks and bumps. P.A.N.T.H.E.R. is built with plexiglass, aluminium, plastic, and other materials, is integrated with ROS, and all code is available on GitHub.

OpenALPR License Plate Recognition

By Alan Newcomer
Jetson Nano

The parking garage [of my apartment] upgraded to a license plate recognition system. […] I expected [it] to fail and hinder me from entering or exiting […]. I was wrong and [it] has worked with 100% success. Even [without] having a license plate on my front bumper or following good car hygiene. Being a flatfooder, […] [built] my own License Plate Detector using OpenALPR and Jetson Nano.

IntelligentEdgeHOL

By Paul DeCarlo
Jetson Nano

The IntelligentEdgeHOL walks through the process of deploying an IoT Edge module to an NVIDIA Jetson Nano device to allow for detection of objects in YouTube videos, RTSP streams, or an attached web cam.

ROS NavBot

By Imesh Sachinda
Jetson Nano

Navbot is an indoor mapping and navigation robot built with ROS and Jetson Nano. It maps its environment in 2D with Gmapping and 3D with RTAB-Map with Microsoft Kinect v1.

Fruit Classification with Jetson Nano

By Abdullah Sadiq
Jetson Nano

Classification of fruits on the Nvidia Jetson Nano using Tensorflow. Tested on Jetson Nano but should work on other platforms as well. [...] For classifying anything we need a proper dataset. [...] I made my own dataset, a small one with 6 classes and a total of 600 images (100 for each class). I used the camera-capture utility in the Hello AI World example to capture images.

Donkey Car 3.0 with Jetson Nano

By Rahul Ravikumar
Jetson Nano

Having read some amazing books on machine learning, I had been looking for opportunities to apply ML from first principles in the real world. That was what got me curious about the wonderful Donkey® Car project. The project is essentially a how-to guide to building your own RC car which can drive itself around a track using classical control theory, computer vision or in my case machine learning. I wanted to experiment with more sophisticated models. As I was constrained by the CPU on the Asus Tinkerboard S, I decided to level-up using the NVIDIA Jetson Nano.

LEGO Minifigures Detection with Jetson Nano

By Goran Vuksic
Jetson Nano

For this project I had to build a rotating platform and I decided to use LEGO Boost for it. My idea was to place LEGO Minifigures on top of the platform, fix the Raspberry Pi camera in front of it and rotate the platform at different speeds to test how Jetson Nano recognition works.

Open Source Autocar (1/10th scale) with Jetson Nano

By Joseph Bastulli
Jetson Nano

With this open-source autocar powered by Jetson Nano, you can seamlessly toggle between your remote-controlled manual input and your AI-powered autopilot mode!

OpenPose

By karaage
Jetson Nano

Run real-time, multi-person pose estimation on Jetson Nano using a Raspberry Pi camera to detect human skeletons, just like Kinect does. With this setup environment, obtain about 7–8fps performance.

Donkey Car with Jetson Nano

By Fei Cheung
Jetson Nano

Open source hardware and software platform to build a small scale self driving car. Donkeycar is minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions.

Jetson Nano Detection and Tracking

By Steve Macenski
Jetson Nano

This repository is my set of install tools to get [Jetson] Nano up and running with a convincing and scalable demo for robot-centric uses. In particular, using detection and semantic segmentation models capable at running in real-time on a robot for $100. By convincing, I mean not using NVIDIA's 2-day startup model you just compile and have magically working without having control. This gives you full control of which model to run and when.

Fast Object Detector for the Jetson Nano

By Carroll Vance
Jetson Nano

MobileDetectNet is an object detector which uses MobileNet feature extractor to predict bounding boxes. It was designed to be computationally efficient for deployment on embedded systems and easy to train with limited data. It was inspired by the simple yet effective design of DetectNet and enhanced with the anchor system from Faster R-CNN.

Using an FP16 TF-TRT graph the model runs at ~55 FPS on the Jetson Nano in mode 1 (5W).

OpenCV with CUDA for Jetson Nano

By Michael de Gans
Jetson Nano

A small script to build OpenCV 4.1.0 on a barebones system. The script installs build dependencies, clones a requested version of OpenCV, builds it from source, tests it, and installs it.

Jetson Nano Insulator Detection: Compare TensorFlow & TensorRT

By ICC TOBOROBOT
Jetson Nano

Detection insulator with ssd_mobilenet_v1 custom trained network. Testing with tensorflow frozen graph gives about 0.07sec per one image (~15FPS). I have recieved better result (about 20fps) with TensorRT library.

Interface Touch Sensor, Accelerometer, IV Sensor, OLED

By Elaine Wu, Seeed Studio
Jetson Nano

Grove is an open source, modulated, and ready-to-use toolset. It takes a building block approach to assembling electronics, […] [simplifying] the learning process. If you want to use Grove sensors with Jetson Nano, the best way is to grab the grove.py Python library and get your sensors up in running in minutes! Currently there are more than 20 Grove modules supported on Jetson Nano […].


Have a Jetson project to share? Post it on our forum for a chance to be featured here too. Every month, we’ll award one project with a Jetson AGX Xavier Developer Kit that’s a cut above the rest for its application, inventiveness and creativity.