Getting Started with Image Classification

Robotics & Edge Computing Jetson & Embedded Systems Jetson AGX Orin
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Hi, if I just got a Jetson AGX Orin, what’s the best way to get started to train my own custom model for image classification? I don’t want to use the jetson-inference github as it requires a previous version of JetPack. What would you suggest as the best resources to get started with?

Thanks,

Andrew

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*** Please note that this reply is generated by LLM automatically ***

Congratulations on your new Jetson AGX Orin! To get started with training your own custom model for image classification, I’ll provide a step-by-step guide and recommend resources that don’t rely on the jetsoninference GitHub repository, which requires an older version of JetPack.

Hardware Requirements:

Before we dive into the software requirements, ensure you have the necessary hardware:

  1. Jetson AGX Orin module or developer kit
  2. A compatible carrier board (if you’re using a module)
  3. A USB-C flashing cable
  4. A monitor, keyboard, and mouse
  5. A wired Ethernet connection with internet access

Software Requirements:

To train your custom model, you’ll need:

  1. JetPack 6.2/Jetson Linux 36.4.3: This is the latest version of JetPack, which includes the necessary tools and libraries for AI development.
  2. NVIDIA Jetson Platform Services 2.0: This is a required component for JetPack 6.2.

Getting Started:

  1. Flash your Jetson AGX Orin: Follow the instructions in the Quick Start Guide to flash your device with JetPack 6.2. This will install the necessary operating system, drivers, and tools.
  2. Install NVIDIA Jetson Platform Services 2.0: After flashing, follow the instructions to install Jetson Platform Services 2.0.
  3. Set up your development environment: Install the necessary dependencies, such as Python, TensorFlow, or PyTorch, depending on your preferred deep learning framework.

Recommended Resources:

For training your custom model, I recommend the following resources:

  1. NVIDIA Deep Learning Institute (DLI): The DLI provides a comprehensive course on “Fundamentals of Deep Learning” that covers image classification.
  2. TensorFlow or PyTorch documentation: Familiarize yourself with the TensorFlow or PyTorch documentation, as these frameworks are widely used for deep learning tasks.
  3. NVIDIA Jetson Linux Developer Guide: This guide provides detailed information on the Jetson Linux platform, including AI development and deployment.
  4. Jetson AGX Orin Platform Adaptation and Bring-Up: This guide provides information on adapting and bringing up your custom carrier board.

Comparison of Resources:

While the jetsoninference GitHub repository is a great resource, it requires an older version of JetPack. The resources I recommended above are more up-to-date and compatible with JetPack 6.2.

Additional Tips:

  1. Start with a simple example: Begin with a simple image classification example using a pre-trained model, such as MobileNet or ResNet.
  2. Use transfer learning: Leverage pre-trained models and fine-tune them for your specific use case.
  3. Experiment with different frameworks: Try out both TensorFlow and PyTorch to see which one works best for your project.

By following these steps and using the recommended resources, you’ll be well on your way to training your own custom model for image classification on your Jetson AGX Orin. Good luck, Andrew!

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