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
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.
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
- Chapter 2 : Object Detection with YOLOv4
- Chapter 3 : YOLOv4 Starter Summary
- Chapter 4 : Labelling a New Dataset in YOLOv4 Format
- Chapter 5 : Creating Custom Dataset in YOLOv4 Format
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
- Title: Full YOLOv4 Pro Course Bundle
- Release date: October 2021
- Publisher(s): Packt Publishing
- ISBN: 9781803236780
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