Full YOLOv4 Pro Online Course Bundle

Price: 5.00 USD | Size: 4.04 GB | Duration : 4.42 Hours |51 Video Lessons | ★★★★★ 4.5

BRAND : Expert TRAINING | ENGLISH | INSTANT DOWNLOAD

Video description

Learn how you can implement and train your own custom YOLOv4 object detection models in computer vision

About This Video

Social distancing app to calculate the distance between people to determine if they are at risk

Object counting app for counting cars in a parking lot and DeepSORT to track vehicles in traffic

Mask detection app to detect whether or not a person is wearing a mask; if not, flagging an alert

In Detail

This course is a perfect fit if you want to natively train your own YOLOv4 neural network. You’ll start off with a gentle introduction to the world of computer vision with YOLOv4, install darknet, and build libraries for YOLOv4 to implement YOLOv4 on images and videos in real-time.

You’ll even solve current and relevant real-world problems by building your own social distancing monitoring app and implementing vehicle tracking using the robust DeepSORT algorithm.

After that, you’ll learn more techniques and best practices/rules of how to take your Python implementations and develop GUIs for your YOLOv4 apps using PyQT.

Then, you’ll be labeling your own dataset from scratch, converting standard datasets into YOLOv4 format, amplifying your dataset 10x, and employing data augmentation to significantly increase the diversity of available data for training models, without collecting new data.

Table of contents

Chapter 1 : Introduction to the Course

Introduction

How to Excel in this Course

YOLOv4 Theory

Installation of YOLOv4 Dependencies such as CUDA, Python, OpenCV

Chapter 2 : Object Detection with YOLOv4

YOLOv4 Object Detection on Image and Video

YOLOv4 Darknet Explanation with Code and Webcam Implementation

Social Distancing Monitoring App

Social Distancing Monitoring Coaching Session

Count Parked Cars

DeepSORT Intuition - How DeepSORT Object Tracking Works

Robust Tracking with YOLOv4 and DeepSORT

Chapter 3 : YOLOv4 Starter Summary

Evolution of YOLOv1 to YOLOv3

YOLOv5 Chess Piece Detection

Bernie Sanders Detector

Chapter 4 : Labelling a New Dataset in YOLOv4 Format

Introduction to Data Annotation

YOLOv4 Format for Image Labelling

YOLOv4 Labelling Tools

Web-Scaping Data

Annotating Images with LabelImg

Labelling on Video Using LabelImg

Labelling on Video Using Darklabel

Label Objects on this Video

Annotation Summary

Data Annotation Key Takeaway

Chapter 5 : Creating Custom Dataset in YOLOv4 Format

Introduction: How to Create Custom Dataset

Toolkit for Downloading Image Datasets

Downloading Images from Specific Classes

Converting Downloaded Files to YOLOv4 format

Data Augmentation Using Rotational Transform

Summary - Key Takeaways for Custom Datasets

Chapter 6 : Training YOLOv4 Using Darknet Framework

Introduction to Training YOLOV4 with Darknet Framework

Step 1 - Configuring the Files for Training

Step 2 - Creating the obj.names File

Step 3 - Dataset Placement for Training

Step 4 - Train Test Metafiles

Step 5 - Training YOLOv4

Trained YOLOv4 Execution on Image and Video for Mask Detection

Activity: Train on Your Own Dataset

When to Stop Training

Summary - Key Takeaways

Chapter 7 : PyQT User Interface for Object Detection with YOLOv4

Introduction to Object Detection with PyQt

Installing PyQt

GUI Layout Using PyQt Designer

Integrating PyQt with YOLOv4

Code Explanation

Adding GUI Widgets - Counting Objects

Adding Widgets - Slider Threshold

Adding Widgets - Class Filter Using Checkbox Widget

Adding Widgets - Real-Time Live Plot Graph Widget

Social Distancing in PyQt Activity

Conclusion

Finally, you’ll develop your own Mask Detection app to detect whether a person is wearing their mask and to flag an alert.

By the end of this course, you’d be able to implement and train your own custom CNNs with YOLOv4. It will help you in solving real-world problems, freelancing AI projects, getting that opportunity in AI, and tackling your research work by saving time and money. The world is your oyster; just start exploring the world once you have skills in AI.

Who this book is for

This course is for developers, researchers, and students who have at least some programming experience and want to become proficient in AI for computer vision and visual recognition. An individual with machine learning knowledge and who wants to break into neural networks or AI for visual understanding, a scientist looking to apply deep learning + computer vision algorithms, individuals looking to utilize computer vision algorithms in their own projects will highly benefit from this course.

A high-range PC/laptop, Windows 10, and CUDA Nvidia GPU graphics card are pre-requisites.

Table of contents

Chapter 1 : Introduction to the Course

Introduction

How to Excel in this Course

YOLOv4 Theory

Installation of YOLOv4 Dependencies such as CUDA, Python, OpenCV

Chapter 2 : Object Detection with YOLOv4

YOLOv4 Object Detection on Image and Video

YOLOv4 Darknet Explanation with Code and Webcam Implementation

Social Distancing Monitoring App

Social Distancing Monitoring Coaching Session

Count Parked Cars

DeepSORT Intuition - How DeepSORT Object Tracking Works

Robust Tracking with YOLOv4 and DeepSORT

Chapter 3 : YOLOv4 Starter Summary

Evolution of YOLOv1 to YOLOv3

YOLOv5 Chess Piece Detection

Bernie Sanders Detector

Chapter 4 : Labelling a New Dataset in YOLOv4 Format

Introduction to Data Annotation

YOLOv4 Format for Image Labelling

YOLOv4 Labelling Tools

Web-Scaping Data

Annotating Images with LabelImg

Labelling on Video Using LabelImg

Labelling on Video Using Darklabel

Label Objects on this Video

Annotation Summary

Data Annotation Key Takeaway

Chapter 5 : Creating Custom Dataset in YOLOv4 Format

Introduction: How to Create Custom Dataset

Toolkit for Downloading Image Datasets

Downloading Images from Specific Classes

Converting Downloaded Files to YOLOv4 format

Data Augmentation Using Rotational Transform

Summary - Key Takeaways for Custom Datasets

Chapter 6 : Training YOLOv4 Using Darknet Framework

Introduction to Training YOLOV4 with Darknet Framework

Step 1 - Configuring the Files for Training

Step 2 - Creating the obj.names File

Step 3 - Dataset Placement for Training

Step 4 - Train Test Metafiles

Step 5 - Training YOLOv4

Trained YOLOv4 Execution on Image and Video for Mask Detection

Activity: Train on Your Own Dataset

When to Stop Training

Summary - Key Takeaways

Chapter 7 : PyQT User Interface for Object Detection with YOLOv4

Introduction to Object Detection with PyQt

Installing PyQt

GUI Layout Using PyQt Designer

Integrating PyQt with YOLOv4

Code Explanation

Adding GUI Widgets - Counting Objects

Adding Widgets - Slider Threshold

Adding Widgets - Class Filter Using Checkbox Widget

Adding Widgets - Real-Time Live Plot Graph Widget

Social Distancing in PyQt Activity

Conclusion

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