Full YOLOv4 Pro Course Bundle

Video description

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.

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.

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


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.

About The Author

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

Publisher resources

Download Example Code

Table of contents

  1. Chapter 1 : Introduction to the Course
    1. Introduction
    2. How to Excel in this Course
    3. YOLOv4 Theory
    4. Installation of YOLOv4 Dependencies such as CUDA, Python, OpenCV
  2. Chapter 2 : Object Detection with YOLOv4
    1. YOLOv4 Object Detection on Image and Video
    2. YOLOv4 Darknet Explanation with Code and Webcam Implementation
    3. Social Distancing Monitoring App
    4. Social Distancing Monitoring Coaching Session
    5. Count Parked Cars
    6. DeepSORT Intuition - How DeepSORT Object Tracking Works
    7. Robust Tracking with YOLOv4 and DeepSORT
  3. Chapter 3 : YOLOv4 Starter Summary
    1. Evolution of YOLOv1 to YOLOv3
    2. YOLOv5 Chess Piece Detection
    3. Bernie Sanders Detector
  4. Chapter 4 : Labelling a New Dataset in YOLOv4 Format
    1. Introduction to Data Annotation
    2. YOLOv4 Format for Image Labelling
    3. YOLOv4 Labelling Tools
    4. Web-Scaping Data
    5. Annotating Images with LabelImg
    6. Labelling on Video Using LabelImg
    7. Labelling on Video Using Darklabel
    8. Label Objects on this Video
    9. Annotation Summary
    10. Data Annotation Key Takeaway
  5. Chapter 5 : Creating Custom Dataset in YOLOv4 Format
    1. Introduction: How to Create Custom Dataset
    2. Toolkit for Downloading Image Datasets
    3. Downloading Images from Specific Classes
    4. Converting Downloaded Files to YOLOv4 format
    5. Data Augmentation Using Rotational Transform
    6. Summary - Key Takeaways for Custom Datasets
  6. Chapter 6 : Training YOLOv4 Using Darknet Framework
    1. Introduction to Training YOLOV4 with Darknet Framework
    2. Step 1 - Configuring the Files for Training
    3. Step 2 - Creating the obj.names File
    4. Step 3 - Dataset Placement for Training
    5. Step 4 - Train Test Metafiles
    6. Step 5 - Training YOLOv4
    7. Trained YOLOv4 Execution on Image and Video for Mask Detection
    8. Activity: Train on Your Own Dataset
    9. When to Stop Training
    10. Summary - Key Takeaways
  7. Chapter 7 : PyQT User Interface for Object Detection with YOLOv4
    1. Introduction to Object Detection with PyQt
    2. Installing PyQt
    3. GUI Layout Using PyQt Designer
    4. Integrating PyQt with YOLOv4
    5. Code Explanation
    6. Adding GUI Widgets - Counting Objects
    7. Adding Widgets - Slider Threshold
    8. Adding Widgets - Class Filter Using Checkbox Widget
    9. Adding Widgets - Real-Time Live Plot Graph Widget
    10. Social Distancing in PyQt Activity
    11. Conclusion

Product information

  • Title: Full YOLOv4 Pro Course Bundle
  • Author(s): Ritesh Kanjee, Augmented Startups
  • Release date: October 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781803236780