Jetson Nano Online Course
Jetson Nano Online Course
This comprehensive course is designed to teach you how to set up, configure, and deploy AI applications on NVIDIA Jetson. From basic setup and software installation to advanced deep learning model optimization, you will gain hands-on experience in real-time object detection, multi-camera processing, and AI-powered automation. The course covers computer vision, deep learning frameworks, and AI model optimization, giving you the skills to build and deploy high-performance AI applications for security, surveillance, robotics, and automation.
Key Benefits
- End-to-End Learning Path – Learn everything from Jetson setup to advanced AI applications.
- Hands-on Projects – Work on real-world AI applications like vehicle tracking, pose estimation, and face recognition.
- Performance Optimization – Boost AI model performance with TensorRT and DeepStream SDK.
- Industry-Relevant AI Skills – Learn multi-camera synchronization, object detection, and real-time video processing.
- Cutting-Edge Technologies – Master the use of OpenCV, PyTorch, YOLO models, and DeepStream on Jetson.
Target Audience
- AI Engineers & Researchers looking to optimize deep learning models for edge computing.
- Software Developers interested in real-time AI applications and object detection.
- Robotics Engineers aiming to integrate AI into robotics and automation projects.
- Computer Vision Enthusiasts who want to build smart video analytics and security applications.
Learning Objectives
By completing this course, you will:
- Set up and configure NVIDIA Jetson for AI applications.
- Install and optimize deep learning libraries such as OpenCV, PyTorch, and TensorRT.
- Implement object detection using YOLO models on custom datasets.
- Optimize deep learning models for faster inference on Jetson devices.
- Deploy AI-powered surveillance applications using DeepStream SDK.
- Build real-world AI solutions, including face recognition, pose estimation, and vehicle tracking.
Course Outline
The Jetson Nano Exam covers the following topics -
Module 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.
Module 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.
Module 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.
Module 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.
Module 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.
Module 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.
Module 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.
Module 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
