Jetson Nano Practice Exam

Jetson Nano Practice Exam

About Jetson Nano Exam

NVIDIA Jetson is a powerful AI computing platform designed for edge AI applications like computer vision, robotics, and real-time object detection. Unlike traditional microcontrollers like the Raspberry Pi, Jetson offers GPU acceleration with CUDA, making it ideal for deep learning and high-performance AI tasks. This course will take you from basic setup to advanced AI model optimization, teaching you how to work with OpenCV, PyTorch, YOLO, TensorRT, and DeepStream SDK. You will build real-world AI applications, such as automatic number plate recognition, vehicle tracking, pose estimation, and face recognition, all optimized for fast and efficient inference on Jetson devices.

Skills Required

• Basic knowledge of Python is recommended.
• Familiarity with AI and deep learning concepts will be helpful but not mandatory.
• Understanding of Linux commands is beneficial for configuring Jetson devices.
• Willingness to experiment with real-time AI applications.

Knowledge Area

This course will help you develop skills in:

• Jetson setup & AI environment configuration for deep learning tasks.
• Computer vision techniques using OpenCV and PyTorch.
• Real-time object detection with YOLO models and TensorRT acceleration.
• Video analytics & multi-camera synchronization using DeepStream SDK.
• Deploying AI applications for vehicle tracking, number plate recognition, and face detection.

Who should take This Course?

• AI engineers and researchers exploring deep learning on edge devices.
• Software developers interested in real-time computer vision applications.
• Robotics engineers looking to integrate AI into embedded systems.
• Students and professionals who want to build AI-powered security and surveillance systems.

Course Outline

The Jetson Nano Exam covers the following topics - 

Domain 1 - Introduction to Jetson and Course Overview

• Understand what Jetson devices are and how they differ from traditional microcontrollers.
• Explore the capabilities of Jetson compared to the Raspberry Pi.
• Learn about different Jetson models and their specifications.
• Set up an SD card for Jetson, choosing the right storage type for performance.
• Boot Jetson for the first time and complete the initial configuration.

Domain 2 - Installing Libraries & Setting Up AI Environment

• Learn about essential AI libraries such as OpenCV, PyTorch, and CUDA.
• Install OpenCV from scratch, ensuring CUDA acceleration is enabled.
• Set up PyTorch and TorchVision for deep learning tasks on Jetson.

Domain 3 - Computer Vision with OpenCV & PyTorch on Jetson

• Perform basic image processing like reading, displaying, and modifying images.
• Convert images between different color spaces for analysis.
• Apply filters, edge detection, and morphological operations for feature extraction.
• Explore corner detection techniques for object tracking.
• Combine OpenCV and PyTorch for real-world computer vision tasks.

Domain 4 - Object Detection with YOLO

• Understand the concept of object detection and how it is implemented.
• Learn about different YOLO variants and their applications.
• Train a custom object detection model for license plate recognition.
• Annotate datasets and prepare them for training a YOLO model.
• Perform object detection using pre-trained YOLO models.

Domain 5 - Optimizing AI Models with TensorRT

• Learn what TensorRT is and how it improves AI model performance.
• Install TensorRT dependencies and configure the Jetson environment.
• Convert a YOLOX model to TensorRT for faster inference.
• Compare the performance of standard vs. optimized models.

Domain 6 - Introduction to DeepStream SDK

• Understand how DeepStream enables real-time video analytics.
• Explore its applications in security, surveillance, and industrial automation.
• Set up the DeepStream environment on Jetson.
• Test DeepStream SDK with sample video analytics models.

Domain 7 - Running DeepStream & Multi-Camera Synchronization

• Learn about RTSP (Real-Time Streaming Protocol) and ONVIF for multi-camera streaming.
• Set up RTSP streams and test them using VLC media player.
• Synchronize multiple camera feeds for real-time AI applications.
• Modify configuration files to enable object detection across multiple cameras.
• Analyze camera outputs to detect objects in real time.

Domain 8 - Real-World AI Applications & Projects

• Application 1: Vehicle Detection, Tracking & Counting
  • Set up vehicle tracking and counting using DeepStream.
  • Learn how to download and implement tracking models.
  • Watch real-time video processing and counting in action.
• Application 2: Automatic Number Plate Recognition (ANPR) with PaddleOCR
  • Learn about Roboflow and how to annotate datasets in YOLO format.
  • Train a custom YOLOR and YOLOv7 model for license plate detection.
  • Detect license plates in real-time with PaddleOCR.
• Application 3: Pose Estimation with PoseNet
  • Introduction to human pose estimation and keypoint detection.
  • Implement PoseNet on Jetson to track human movement.
  • Experiment with Darknet and Mediapipe for pose estimation.
• Application 4: DeepFake Detection
  • Understand DeepFake technology and its impact.
  • Implement a DeepFake classification model on Jetson.
• Application 5: Face Recognition for Attendance Systems
• Build a face recognition system to track clock-in and clock-out times.
• Train a model for real-time facial recognition using DeepStream.
• Deploy an AI-powered attendance tracking system