@Jetson YOLO: Introduction

· 2 min read

Using Jetson Orin Nano to deploy YOLOv8 is one of the cleanest ways to learn edge AI from the systems side instead of only from notebooks.

This folder is organized as a guided series rather than a single long article. The goal is to help you build a working Jetson YOLO environment step by step, from checking the base system all the way to running segmentation with a CSI camera.

What You Need

  • An NVIDIA Jetson Orin Nano module, 4 GB or 8 GB
  • Optional CSI camera for the segmentation chapter
  • Ubuntu on the device with a compatible JetPack version
  • Patience for environment setup, because ARM compatibility matters

Why Jetson Orin Nano

Jetson Orin Nano offers strong edge inference performance in a compact form factor. For tutorials like this, it sits in a useful middle ground: capable enough to run modern YOLO models, but constrained enough that environment choices still matter.

That makes it a good teaching platform for:

  • CUDA and TensorRT compatibility
  • ARM64 package constraints
  • GPU-aware Python environment management
  • Real-time inference on local hardware

Why YOLOv8

YOLO has become the default practical entry point for real-time object detection because the deployment path is relatively approachable and the model family covers detection, segmentation, pose, and more.

In this series, the focus is on:

  • Static image detection with TensorRT export
  • Environment setup that actually works on Jetson
  • CSI camera segmentation with GStreamer and OpenCV

Reading Order

  1. Basic Commands
  2. Ubuntu Desktop and JetPack
  3. Downloads Applications
  4. Setup the Environment Using Anaconda
  5. Code and Run YOLOv8
  6. YOLOv8 Segmentation With CSI Camera

Practical Scope

This series combines official documentation with direct setup notes from a real Jetson workflow. The emphasis is not only on the final demo, but on the compatibility details that usually cause the first round of failures:

  • matching JetPack with PyTorch wheels
  • choosing the right Python version
  • keeping TensorRT visible inside the conda environment
  • rebuilding or reinstalling pieces when ARM-specific issues show up

If you are starting from zero, follow the posts in order. If your device is already flashed and your environment is partly configured, you can jump directly to the later chapters.