Mastering YOLOv4 Online Course

Mastering YOLOv4 Online Course

This course is designed for anyone eager to train their own YOLOv4 neural network from scratch. You’ll begin with the basics of computer vision and YOLOv4 setup, then move on to real-time image and video detection projects like social distancing monitoring and vehicle tracking with DeepSORT. You’ll also learn to build GUIs with PyQT, label and prepare datasets, and use data augmentation to boost training performance. Finally, you’ll develop a Mask Detection app, applying your skills to real-world problems. By the end, you’ll be able to train and deploy custom YOLOv4 models for research, freelancing, or professional AI projects.

Who should take this course?

This course is designed for data scientists, AI enthusiasts, researchers, and developers who want to specialize in computer vision and object detection using YOLOv4. It’s well-suited for those with a background in Python and deep learning who are looking to apply YOLOv4 to real-world projects such as image recognition, surveillance, autonomous systems, or AI-powered applications. Whether you’re a student exploring advanced AI techniques or a professional aiming to enhance your expertise in computer vision, this course equips you with the skills to master YOLOv4 effectively.

What you will learn

• YOLOv4 detection on images
• Execute YOLOv4 detection on videos and webcam
• How to natively train your own custom YOLOv4 detector
• Prepare files to train and set up configuration files
• Integrate YOLOv4 with PyQT
• Social distancing GUI with PyQT

Course Outline

Introduction to the Course

• Introduction
• How to Excel in this Course
• YOLOv4 Theory

Installation of YOLOv4 Dependencies such as CUDA, Python, OpenCV

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

YOLOv4 Starter Summary

• Evolution of YOLOv1 to YOLOv3
• YOLOv5 Chess Piece Detection
• Bernie Sanders Detector

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

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

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

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