Welcome to our instructional guide on deploying deep learning and real-time vision with the DNN library designed for NVIDIA Jetson devices. By utilizing TensorRT, you can run optimized networks on GPUs using either C++ or Python while leveraging PyTorch for model training.
Supported DNN Vision Primitives
- imageNet for image classification
- detectNet for object detection
- segNet for semantic segmentation
- poseNet for pose estimation
- actionNet for action recognition
This guide will also cover examples for streaming from live camera feeds, building web apps with WebRTC, and support for ROS/ROS2.
Getting Started with Hello AI World
Before diving into deployment, we recommend starting with the Hello AI World tutorial. This tutorial provides step-by-step instructions on running inference, transfer learning, collecting datasets, and training custom models with PyTorch.
System Setup
To set up your Jetson for deep learning with the DNN library, follow these essential steps:
Understanding the Code: An Analogy
Deploying deep learning models can be likened to preparing a gourmet meal. Each step in the process corresponds to preparing specific ingredients and cooking them in the right order.
- Gather Ingredients: In our context, this entails setting up your hardware (Jetson devices) and software (JetPack, Docker, etc.) just as a chef would gather all the necessary ingredients before cooking.
- Recipe (Code): Just as chefs follow recipes, you follow your codeblocks from the DNN library (such as image classification, object detection, etc.) to prepare your dish (model). Each line of code is a step in your cooking process.
- Cooking: Just like cooking requires an oven or stove, running your code on the Jetson device requires the proper execution environment (like TensorRT) that optimizes and accelerates your meal (model inference).
- Serving: Finally, enjoying the dish relates to deploying your model for real-time applications, letting it process feeds, detect objects, or classify images seamlessly.
Troubleshooting
If you encounter any issues while deploying or running your models, consider the following tips:
- Check that you have the correct version of JetPack installed that corresponds to your specific Jetson model.
- Ensure all dependencies and required libraries are properly installed by following the setup instructions closely.
- If you receive errors related to TensorRT or PyTorch, check the compatibility of your models with the Jetson architecture.
- For persistent problems, reviewing forum discussions or documentation related to similar issues can provide valuable insights.
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