dusty-nv/jetson-inference

★ 8,848C++Audience · developerComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((jetson-inference))
    Vision tasks
      Classification
      Object detection
      Segmentation
      Pose estimation
    Tech stack
      TensorRT
      PyTorch
      C++ and Python
    Workflow
      Pre-trained models
      Custom training
      TensorRT deploy
    Integrations
      ROS and ROS2
      WebRTC demos

mindmap root((jetson-inference)) Vision tasks Classification Object detection Segmentation Pose estimation Tech stack TensorRT PyTorch C++ and Python Workflow Pre-trained models Custom training TensorRT deploy Integrations ROS and ROS2 WebRTC demos

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Things people build with this

USE CASE 1

Run real-time object detection on a Jetson camera feed to identify and track people or items on a robot.

USE CASE 2

Train a custom image classifier with your own photos using PyTorch and deploy it to a Jetson board with TensorRT.

USE CASE 3

Build a live semantic segmentation pipeline that labels each camera pixel by category on an embedded device.

USE CASE 4

Stream a computer vision demo from a Jetson to a browser in real time using the built-in WebRTC tools.

Tech stack

C++PythonCUDATensorRTPyTorchROS

Getting it running

Difficulty · hard Time to first run · 1day+

Requires NVIDIA Jetson hardware, there is no CPU or non-NVIDIA GPU fallback.

In plain English

NVIDIA Jetson is a family of small, energy-efficient computing boards designed to run AI tasks at the edge, meaning on a physical device rather than in a remote data center. jetson-inference is a guided library and tutorial series, called Hello AI World, that shows you how to run computer vision AI models on Jetson hardware. It is aimed at developers who want to add visual AI to robots, cameras, or embedded systems without sending data to the cloud. The library is built around TensorRT, NVIDIA's software for running AI models efficiently on their GPUs. It provides ready-to-use modules for several common vision tasks: identifying what is in an image (classification), drawing boxes around specific objects (detection), coloring each pixel by what it belongs to (segmentation), estimating where a person's joints are positioned (pose estimation), identifying what action a person is performing (action recognition), removing background from images, and estimating depth from a single camera. These modules can be called from either C++ or Python code. The Hello AI World tutorial walks through the full workflow: setting up a Jetson device, running pre-trained AI models on live camera input, collecting your own image datasets, training your own classification or detection models using PyTorch, and then deploying those custom models back to the device with TensorRT. Video walkthroughs from an NVIDIA AI certification course are linked in the README for people who prefer watching over reading. The library also includes tools for building web-based demos using WebRTC, which allows streaming video from the Jetson to a browser in real time. It works with ROS and ROS2, the standard software frameworks used in robotics. A companion resource called Jetson AI Lab covers newer topics such as large language models, vision language models, and vision transformers running on the more powerful Jetson Orin hardware. The two resources share the same community but Jetson AI Lab is a separate site focused on generative AI workloads rather than traditional computer vision.

Copy-paste prompts

Prompt 1

Help me set up jetson-inference on a Jetson Nano to run real-time object detection with detectNet on a USB camera feed.

Prompt 2

Show me how to collect my own image dataset with the camera-capture tool and train a custom classifier using PyTorch on Jetson.

Prompt 3

Write a Python script using the jetson-inference segNet module to run semantic segmentation on a live camera stream and display the output.

Prompt 4

Help me integrate jetson-inference pose estimation into a ROS2 node that publishes detected joint positions as a topic.

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