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Home > Data > Artificial Intelligence > Jetson Nano Starter to Pro - A Computer Vision Course
Jetson Nano Starter to Pro - A Computer Vision Course
Jetson Nano Starter to Pro - A Computer Vision Course

Jetson Nano Starter to Pro - A Computer Vision Course: Empowering AI Innovations with NVIDIA Jetson

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Profile Icon Ritesh Kanjee
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NZ$423.99
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Video May 2024 5hrs 42mins 1st Edition
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Profile Icon Ritesh Kanjee
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NZ$423.99
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Video May 2024 5hrs 42mins 1st Edition
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NZ$423.99
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Key benefits

  • Comprehensive coverage from basic setup to advanced AI implementations
  • Learn to optimize AI models with TensorRT for enhanced performance
  • Develop real-time AI applications using the DeepStream SDK

Description

This course begins with a thorough introduction to Jetson, highlighting its advantages over traditional microcontrollers like the Raspberry Pi. You'll learn how to select the right SD card, flash it effectively, and perform initial configurations to set the stage for advanced developments. As you progress, delve into installing key AI libraries like OpenCV and PyTorch. Understand their roles in crafting robust AI solutions and enhance their performance with CUDA support. The course offers guidance on fundamental computer vision techniques, enabling seamless image operations, color conversions, and edge detections. Explore object detection with YOLO and its variants through practical examples. Learn to train custom models like number plate recognition and optimize AI models using NVIDIA's TensorRT for enhanced performance. Dive deep into the DeepStream SDK for real-time video analysis and multi-camera synchronization, vital for security and surveillance applications. Explore advanced topics like pose estimation, vehicle tracking, and face recognition, all with hands-on projects to reinforce learning. By the end, you will have mastered how to use this powerful platform to push the boundaries of what's possible in AI applications, making you a valuable asset in the tech industry.

Who is this book for?

This course is ideal for AI researchers, robotics engineers, and software developers who have a basic understanding of AI principles and familiarity with programming concepts. Learners should be comfortable with Python and have an interest in deep learning and computer vision applications.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 08, 2024
Length: 5hrs 42mins
Edition : 1st
Language : English
ISBN-13 : 9781836202455
Category :
Data
Concepts :
Artificial Intelligence
Tools :
OpenCV

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Product Details

Publication date : May 08, 2024
Length: 5hrs 42mins
Edition : 1st
Language : English
ISBN-13 : 9781836202455
Category :
Data
Concepts :
Artificial Intelligence
Tools :
OpenCV

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Table of Contents

16 Chapters
Introduction to Jetson and Course Overview Chevron down icon Chevron up icon
Jetson Introduction
Explanation On How To Use It
Course Overview
Comparison of Jetson and Its Variants Along with RPi+SD Card Flashing Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Introduction to Object Detection and How It Is Performed
YOLO Variants
YOLO Object Detection on Custom Dataset (Number Plate Dataset) Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Brief about TensorRT and Its Benefits
Installing Dependencies and Setting Up Environment for TensorRT
Optimizing YOLOX Model for Object Detection Using TensorRT Chevron down icon Chevron up icon
Converting the YOLOX model to TensorRT
Testing TensorRT (TRT) model
Comparing results
What is DeepStream and Theory? Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Introduction to Pose Estimation
Implementation of PoseNet
DeepFake face classification Chevron down icon Chevron up icon
What is DeepFake?
Implementation of DeepFake Detection
Face Recognition and Attendance: Clock in, clock out. Chevron down icon Chevron up icon
Introduction to Face Recognition and Attendance
Implementation of Face Recognition and Attendance

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About the author

Profile icon Ritesh Kanjee
Ritesh Kanjee
Augmented Startups have over 8 years experience in Printed Circuit Board (PCB) design as well in image processing and embedded control. Author Ritesh Kanjee has completed his Masters Degree in Electronic engineering and published two papers on the IEEE Database with one called "Vision-based adaptive Cruise Control using Pattern Matching" and the other called "A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera" (on Google Scholar). His work was implemented in LabVIEW. He works as an embedded electronic engineer in defence research and has experience in FPGA design with programming in both VHDL and Verilog. He also has expertise in augmented reality and machine learning in which he shall be introducing new technologies through the medium of video
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