Full YOLOv4 Pro Course Bundle

person icon Packt Publishing

Full YOLOv4 Pro Course Bundle

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

updated on icon Updated on Nov, 2023

language icon Language - English

person icon Packt Publishing

architecture icon Development,Data Science,Python

Lectures -51

Duration -4.5 hours


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Course 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.

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Full-YOLOv4-Pro-Course-Bundle

Audience :

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.


What will you learn in this course:

  • 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.


What are the prerequisites for this course?

  • A high-range PC/laptop, Windows 10, and CUDA Nvidia GPU graphics card are pre-requisites.
Full YOLOv4 Pro Course Bundle


Check out the detailed breakdown of what’s inside the course

Introduction to the Course
4 Lectures
  • play icon Introduction 03:51 03:51
  • play icon How to Excel in this Course 02:56 02:56
  • play icon YOLOv4 Theory 11:48 11:48
  • play icon Installation of YOLOv4 Dependencies such as CUDA, Python, OpenCV 13:23 13:23
Object Detection with YOLOv4
7 Lectures
YOLOv4 Starter Summary
3 Lectures
Labelling a New Dataset in YOLOv4 Format
10 Lectures
Creating Custom Dataset in YOLOv4 Format
6 Lectures
Training YOLOv4 Using Darknet Framework
10 Lectures
PyQT User Interface for Object Detection with YOLOv4
11 Lectures

Instructor Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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