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.
