YOLOv4 Object Detection Course

YOLOv4 Object Detection Course, available at $59.99, has an average rating of 4.5, with 57 lectures, 1 quizzes, based on 77 reviews, and has 4402 subscribers.

You will learn about The basics about YOLOv4 Installing all the pre-requisites including Python, OpenCV, CUDA and Darknet You will be able to detect objects on images Implement YOLOv4 Object detection on videos Creating your own social distancing monitoring app This course is ideal for individuals who are Are a computer vision developer that utilizes AI and are eager to level-up your skills. or Have experience with machine learning and want to break into neural networks or AI for visual understanding. or Are a scientist looking to apply deep learning + computer vision algorithms to your research. or Are a university student and want more than your university offers (or want to get ahead of your class). or Utilize computer vision algorithms in your own projects but have yet to try deep learning. or Used AI in projects before, but never in the context of analysis of visual perception. or Write Python/ML code at your day job and are motivated to stand out from your coworkers. or Are a AI hobbyist who knows how to program and wants to tinker with DIY projects using computer vision. or You understand that this requires hard work and patience to get the right skills. You understand that you’re going to get any results overnight. or You’re someone that believes in taking action. You watch the material and then you actually APPLY it. It is particularly useful for Are a computer vision developer that utilizes AI and are eager to level-up your skills. or Have experience with machine learning and want to break into neural networks or AI for visual understanding. or Are a scientist looking to apply deep learning + computer vision algorithms to your research. or Are a university student and want more than your university offers (or want to get ahead of your class). or Utilize computer vision algorithms in your own projects but have yet to try deep learning. or Used AI in projects before, but never in the context of analysis of visual perception. or Write Python/ML code at your day job and are motivated to stand out from your coworkers. or Are a AI hobbyist who knows how to program and wants to tinker with DIY projects using computer vision. or You understand that this requires hard work and patience to get the right skills. You understand that you’re going to get any results overnight. or You’re someone that believes in taking action. You watch the material and then you actually APPLY it.

Enroll now: YOLOv4 Object Detection Course

Summary

Title: YOLOv4 Object Detection Course

Price: $59.99

Average Rating: 4.5

Number of Lectures: 57

Number of Quizzes: 1

Number of Published Lectures: 57

Number of Published Quizzes: 1

Number of Curriculum Items: 61

Number of Published Curriculum Objects: 60

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

The basics about YOLOv4

Installing all the pre-requisites including Python, OpenCV, CUDA and Darknet

You will be able to detect objects on images

Implement YOLOv4 Object detection on videos

Creating your own social distancing monitoring app

Who Should Attend

Are a computer vision developer that utilizes AI and are eager to level-up your skills.

Have experience with machine learning and want to break into neural networks or AI for visual understanding.

Are a scientist looking to apply deep learning + computer vision algorithms to your research.

Are a university student and want more than your university offers (or want to get ahead of your class).

Utilize computer vision algorithms in your own projects but have yet to try deep learning.

Used AI in projects before, but never in the context of analysis of visual perception.

Write Python/ML code at your day job and are motivated to stand out from your coworkers.

Are a AI hobbyist who knows how to program and wants to tinker with DIY projects using computer vision.

You understand that this requires hard work and patience to get the right skills. You understand that you’re going to get any results overnight.

You’re someone that believes in taking action. You watch the material and then you actually APPLY it.

Target Audiences

Are a computer vision developer that utilizes AI and are eager to level-up your skills.

Have experience with machine learning and want to break into neural networks or AI for visual understanding.

Are a scientist looking to apply deep learning + computer vision algorithms to your research.

Are a university student and want more than your university offers (or want to get ahead of your class).

Utilize computer vision algorithms in your own projects but have yet to try deep learning.

Used AI in projects before, but never in the context of analysis of visual perception.

Write Python/ML code at your day job and are motivated to stand out from your coworkers.

Are a AI hobbyist who knows how to program and wants to tinker with DIY projects using computer vision.

You understand that this requires hard work and patience to get the right skills. You understand that you’re going to get any results overnight.

You’re someone that believes in taking action. You watch the material and then you actually APPLY it.

I started out wanting to learn AI Object Detection in Computer Vision

I used to check a lot of GitHub repos, they were very vague and required for me to be competent in software development/programming and understand all of the jargon –

Now even though I have a masters degree in electronic engineering (M.Eng). It was still challenging for me to figure out. I had a lot of questions like

What to do to get my code working?

Do I have the right hardware

Windows or Linux – If linux, do I use Ubuntu, Red Hat, CentOS, ROS

If Ubuntu, what version 16.04, 18.04, What kernel do I need?

If I am training, what format does my dataset need to be in?

Do I use Python or C++

If python What dependencies do I need?

Which frameworks do I use? PyTorch, TensorFlow 1.0 or 2.0

What commands do I type to infer or train a convolutional neural network

How big my dataset needs to be?

How do I run on GPU, and does my GPU support the framework?

How to train YOLOv4

How create cross platform apps using Yolov4 and PyQt

I was unsure of what to do. Sometimes I would look at the instructions and because the instructions were so vague, I would skip to the next repo and the next, until I found one that resonates with me or one that had a clear set of instructions that I could understand and follow, or had a video tutorial on it. And video tutorials on this particular topic are very scarce.

The other problem was, I would follow the instructions, but I would run in trivial issues, like not having the correct dependencies or I did not have the correct hardware or OS etc. When things don’t work. This would beat me down and make me loose confidence of whether or not this repository would work. Now I had 2 options, I could either spend tons of hours searching the web to debug the issue or move on to the next repo which also may or may not work.

Then, I thought, if me with a masters degree in electronic engineering had all these issues with getting started in AI, surely other people would be having this same issue as me. People such as:

non-programmers/non computer science ,

Hobbyists, Students, researcher, employees.

People starting out in AI.

The YOLOv4 Object Detection Course

When YOLOv4 was released in April 2020, my team and I worked effortlessly to create a course in which will help you implement YOLOv4 with ease. We created this Nano course in which you will learn the basics and get started with YOLOv4. This is all about getting object detection working with YOLOv4 in your windows 10 PC. 

You will learn how to install all the dependencies, including Python, CUDA and OpenCV. Once you’ve managed to compile it successfully, we go on to execute YOLOv4 on images and videos. Then to ensure that you understand whats going on, we delve deeper into the darknet python script and show you how to also run YOLOv4 on a webcam.

Within this nano-course, we shall also create our first weapon against COVID-19 which is our social distancing monitoring app. Which essentially monitors the physical distance between people to ensure that they’re keeping safe distancing from each other. It also displays the number of people at risk at any given time

The YOLOv4  Course provides you with a gentle introduction to the world of computer vision with YOLOv4, first by learning how to install darknet, building libraries for YOLOv4 all the way to implementing YOLOv4 on images and videos in real-time.

From here you will even solve current and relevant real-world problems by building your own social-distancing monitoring app.

Requirements

Please ensure that you have the following:

Basic understanding of Computer Vision

Python Programming Skills

Mid to high range PC/ Laptop

Windows 10

CUDA-enabled GPU – Important*

Forward Thinking

Imagine, if a week from now, once you have completed this course, that you are able to implement and implement your own Convolutional Neural Networks (CNN’s) with YOLOv4object detection pre-trained model. Imagine all the applications you could do with these skills!

You could be take your new found expertise and be:

Solving real world problems,

Freelancing AI projects,

Getting that job/opportunity in AI,

Tackling your research guns blazing!

Saving time, money, &

Wishing you had done this course sooner.

The world is your oyster Ask yourselfWhat cool things would you do once you have skills in AI?

So what are you waiting for?

Course Curriculum

Chapter 1: YOLOv4 Starter Course – Introduction

Lecture 1: Introduction to the Course

Lecture 2: How to take this course & Join the Private Facebook Group

Lecture 3: YOLOv4 Theory

Lecture 4: YOLOv4 Prerequisites: Installations of Anaconda Python, Open CV etc.

Lecture 5: YOLOv4 Object Detection on Image and Video

Lecture 6: Darknet Code Explanation YOLOv4 on Webcam

Lecture 7: Social Distancing Monitoring App

Lecture 8: Lecture 8: Count Parked Cars

Lecture 9: Lecture 9: DeepSORT Intuition – How DeepSORT Object Tracking Works

Lecture 10: Lecture 10: Robust Tracking with YOLOv4 and DeepSORT

Lecture 11: [Bonus] YOLOv5 Chess Piece Detection – Video

Lecture 12: [Bonus] Bernie Sanders Detector

Lecture 13: [Bonus] YOLOV4 on Ubuntu

Lecture 14: [ADDITIONAL LECTURE] YOLOv5 Controversy – Is YOLOv5 Real?

Lecture 15: [ADDITIONAL LECTURE] YOLOv1 – YOLOv3 Evolution

Lecture 16: Bonus Lecture

Chapter 2: YOLOv4 Trainers Course

Lecture 1: Lecture 1: Introduction to Data Annotation – Video

Lecture 2: Lecture 2: YOLOv4 format for Image Labelling

Lecture 3: Lecture 3: YOLOv4 Labelling Tools

Lecture 4: Lecture 4: Web-scaping Data

Lecture 5: Lecture 5: Annotating Images with LabelImg

Lecture 6: Activity 1: Label Objects on this image

Lecture 7: Lecture 6: Labelling on Video using LabelImg

Lecture 8: Lecture 7: Labelling on Video Using Darklabel

Lecture 9: Activity 2: Label Objects on this Video

Lecture 10: Lecture 8: Annotation Summary

Lecture 11: Lecture 9: Data Annotation Key Take-away

Lecture 12: Lecture 9: Introduction How to Create Custom Dataset

Lecture 13: Lecture 10: Toolkit for Downloading Image Datasets

Lecture 14: Lecture 11: Downloading Images from Specific Classes

Lecture 15: Activity 3: Download Images for your Classes

Lecture 16: Lecture 12: Converting Downloaded Files to YOLOv4 format

Lecture 17: Lecture 13: Data Augmentation using Rotational Transform

Lecture 18: Lecture 14: Summary – Key Takeaways for Custom Datasets

Lecture 19: Lecture 15: Introduction to Training YOLOV4 with DarkNet Framework

Lecture 20: Lecture 16: Step 1 – Configuring the files for Training

Lecture 21: Lecture 17: Step 2 – Creating the obj.names file

Lecture 22: Lecture 18: Step 3 – Dataset Placement for Training

Lecture 23: Lecture 19: Step 4 – Train Test metafiles

Lecture 24: Lecture 20: Step 5 – Training YOLOv4

Lecture 25: Lecture 21: Trained YOLOv4 Execution on Image and Video for Mask Detection

Lecture 26: Activity 5: Train on your own dataset

Lecture 27: Lecture 22: When to Stop Training

Lecture 28: Lecture 23: Summary – Key Takeaways

Chapter 3: YOLOv4 PyQT Course

Lecture 1: Lecture 1: Introduction to Object Detection with PyQt

Lecture 2: Lecture 2: Installing PyQt

Lecture 3: Lecture 3: GUI Layout using PyQt Designer

Lecture 4: Lecture 4: Integrating PyQt with YOLOv4

Lecture 5: Lecture 5: Code Explanation

Lecture 6: Lecture 6: Adding GUI Widgets – Counting Objects

Lecture 7: Lecture 7: Adding Widgets – Slider Threshold

Lecture 8: Lecture 8: Adding Widgets – Class Filter using Checkbox Widget

Lecture 9: Lecture 9: Adding Widgets – Real-Time Live Plot Graph Widget

Lecture 10: Lecture 10: Social Distancing in PyQt Activity

Lecture 11: Lecture 11: Conclusion

Lecture 12: Bonus Section: Facial Recognition Attendance GUI – PyQt_Course

Lecture 13: Bonus Lecture – Where to from here – YOLOR

Instructors

Augmented Startups
M(Eng) AI Instructor 100k+ Subs on YouTube & 60k+ students

Geeky Bee AI Private Limited
The AI Solution Provider

Rating Distribution

1 stars: 8 votes

2 stars: 5 votes

3 stars: 12 votes

4 stars: 20 votes

5 stars: 31 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!