Jetson Nano Online Course

Jetson Nano Online Course

This course introduces you to Jetson, showcasing its advantages over traditional microcontrollers and guiding you through setup, configuration, and installing key AI libraries like OpenCV and PyTorch with CUDA support. You’ll learn computer vision techniques, object detection with YOLO, custom model training, and optimization with TensorRT. With hands-on projects in DeepStream SDK, pose estimation, vehicle tracking, and face recognition, you’ll gain practical expertise to build advanced AI applications and harness Jetson’s full potential for real-world use.

Who should take this course?

This course is ideal for AI enthusiasts, developers, and hobbyists who want to explore edge AI and deep learning using the NVIDIA Jetson Nano. It’s also perfect for students, researchers, and makers interested in computer vision, robotics, and IoT projects powered by AI.

What you will learn

• Configure and initialize NVIDIA Jetson platforms
• Compare Jetson with Raspberry Pi for technological advantages
• Install and utilize key libraries like OpenCV and PyTorch on Jetson
• Execute basic to advanced computer vision operations using OpenCV
• Implement YOLO object detection on custom datasets
• Integrate multiple camera inputs using RTSP and ONVIF protocols

Course Outline

Introduction to Jetson and Course Overview

• Jetson Introduction
• Explanation On How To Use It
• Course Overview

Comparison of Jetson and Its Variants Along with RPi+SD Card Flashing

• How Jetson Is Better Than Raspberry Pi
• Comparison Among Different Variants
• SD Card Flashing
• Which Card To Buy
• Running Jetson For The First Time

Installing Libraries and Setting Up AI Computer - Explain Dependencies and Their Use

• Various Libraries and Their Usage e.g., OpenCV, PyTorch, etc.
• Installing Supportive Libraries
• Installing OpenCV From Scratch With CUDA Support
• Installing Other Supportive Libraries
• Installing PyTorch and TorchVision

Computer Vision OpenCV Basics on Jetson + Pytorch

• Perform Some Basic Image Operations Using OpenCV
• Import Libraries, Image Read and Display
• Color Conversion
• Basic Filtration
• Transformation
• Edge Detection
• Morphological Operations
• Corner Detection
• Basics About PyTorch
• Basics About TorchVision
• Combining OpenCV and Torch To Perform Basic Image Operations

What is Object Detection + Yolo Object Detection

• Introduction to Object Detection and How It Is Performed
• YOLO Variants

YOLO Object Detection on Custom Dataset (Number Plate Dataset)

• About The Dataset and Its Annotation for Object Detection
• Train The Model On Some Existing Dataset
• Perform Object Detection Using Pre-trained Model

What is TensorRT? Setting Up Jetson for TensorRT

• Brief about TensorRT and Its Benefits
• Installing Dependencies and Setting Up Environment for TensorRT

Optimizing YOLOX Model for Object Detection Using TensorRT

• Converting the YOLOX model to TensorRT
• Testing TensorRT (TRT) model
• Comparing results

What is DeepStream and Theory?

• Introduction to DeepStream and how it works?
• Use of DeepStream in Different Applications
• Setting up environment for DeepStream SDK
• Setting up environment for DeepStream SDK
• Setting up environment for DeepStream SDK
• DeepStream Deep Dive
• Testing the DeepStream SDK on Jetson – Part 1
• Testing the DeepStream SDK on Jetson – Part 2
• Testing the DeepStream SDK on Jetson – Part 3

Running DeepStream SDK and Setting up Multiple Cameras

• Introduction to RTSP (Real Time Streaming Protocol) and ONVIF
• RTSP Structure
• Testing RTSP using VLC
• Performing Multiple Camera Synchronization Using DeepStream
• Performing object detection on Multiple Cameras: Running the Model on the Jetson
• Performing object detection on Multiple Cameras: Changes to Config File
• Performing object detection on Multiple Cameras: Camera Output
• Final Remarks

App 1 Car detection + Tracking + Counting

• Vehicle Counting & Tracking Introduction
• Setting up the Implementation: How to Download Files
• Implementation Video: Short Version
• Implementation Video: Extended Version
• Implementation Output

Automatic Number Plate Recognition with Paddle OCR

• Brief about Roboflow and how to use it
• How to annotate data in yolo format on Roboflow?
• Brief about Google Colab and Setting Environment for Training Data
• How to Train YOLOR on a custom Dataset
• Training YOLOR on Google Colab
• Training YOLOv7 on Google Colab
• Vehicle Number Plate Detection
• Vehicle Number Plate Detection Demo: ANPR

Pose Estimation Method 1: PoseNet

• Introduction to Pose Estimation
• Importing properties for PoseNet
• Performing PoseNet on Jetson
• Demonstration of the Final Result
• Darknet
• Running Mediapipe

Pose Estimation Method 2: PoseNet

• Introduction to Pose Estimation
• Implementation of PoseNet

DeepFake face classification

• What is DeepFake?
• Implementation of DeepFake Detection

Face Recognition and Attendance: Clock in, clock out.

• Introduction to Face Recognition and Attendance
• Implementation of Face Recognition and Attendance