Seeed Ranger Training - Session 1

Quick Overview

The Seeed Studio reComputer Jetson Edge AI Technology Guide AI training concludes with a review of the four-step process for building a custom defect detection solution using YOLO on Jetson devices, covering L1 (Awareness/Selection), L2 (Integration/Development), L3 (Debugging/After-sales), and a final Q&A section.

Key Points: The training objective was to guide attendees on how to become an AI Engineer (AE) using Seeed Studio's reComputer Jetson platform. The process for custom AI deployment involves four main steps: Algorithm Selection (recommending YOLO), Data Collection (10 sample images), Data Annotation (using Roboflow with 'defect' and 'normal' labels), and Model Training/Deployment. The demonstration showed that the pre-trained YOLO model failed to recognize custom stickers, necessitating custom training using the collected and annotated data. Key prerequisites for the training included basic knowledge of Linux and Python, and proficiency in using the Linux system. The reComputer J4012 is positioned as the most powerful computing platform among edge devices, offering better performance than SoC+NPU alternatives, despite higher power consumption. System flashing requires an x86 Linux host (like Ubuntu) and specific commands, explicitly warning against using 'sudo apt upgrade' on the kernel. The final Q&A confirmed that power supplies operate on a range (12-19V) to accommodate varying power requirements and that serial logs are crucial for diagnosing boot failures.

Context: This video serves as an advanced technical training session, specifically Level 1 (L1) through Level 3 (L3), detailing the end-to-end process for deploying custom Edge AI solutions using Seeed Studio's reComputer Jetson hardware, focusing on a practical example of building a defect detection system for custom stickers.

Detailed Analysis

The training covered three levels: L1 focused on Awareness and Selection, comparing Edge AI needs (inference vs. training) and positioning the reComputer Jetson (a GPU platform) as superior for high-performance edge devices over generic CPUs or SoC+NPU solutions due to its unmatched computing power and ecosystem. L2 detailed the practical development workflow using a custom defect detection scenario: 1. Algorithm Selection (recommending YOLO, guided by GPT), 2. Data Collection (10 sample images), 3. Data Annotation (using Roboflow to label 'defect' and 'normal'), and 4. Model Training/Deployment. The demonstration showed the pre-trained model failing, necessitating custom training. L3 covered system maintenance, including using system logs (like dmesg) for fault diagnosis and warning users not to use 'apt upgrade' on the kernel to avoid compatibility issues with customized drivers. The final quiz confirmed that flashing requires an x86 Linux host and that power supplies accommodate variable voltage needs.