Mô tả

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 YOLOv4 object 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 yourself...What cool things would you do once you have skills in AI?

So what are you waiting for?

Bạn sẽ học được gì

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

Yêu cầu

  • Basic python programming skills
  • Mid to high range PC or laptop with Windows 10 operating system
  • Enthusiasm to learn AI
  • CUDA enabled GPU (Graphics Card)
  • Basic Understanding of Computer Vision

Nội dung khoá học

3 sections

YOLOv4 Starter Course - Introduction

19 lectures
Introduction to the Course
00:48
How to take this course & Join the Private Facebook Group
03:04
YOLOv4 Theory
11:45
YOLOv4 Theory Quiz -
3 questions
YOLOv4 Prerequisites: Installations of Anaconda Python, Open CV etc.
13:23
YOLOv4 Object Detection on Image and Video
10:07
Detect Objects on Images and Video
1 question
Darknet Code Explanation YOLOv4 on Webcam
05:53
Social Distancing Monitoring App
11:19
Social Distancing Monitoring Exercise
1 question
Lecture 8: Count Parked Cars
07:05
Lecture 9: DeepSORT Intuition - How DeepSORT Object Tracking Works
15:53
Lecture 10: Robust Tracking with YOLOv4 and DeepSORT
08:14
[Bonus] YOLOv5 Chess Piece Detection - Video
20:34
[Bonus] Bernie Sanders Detector
25:39
[Bonus] YOLOV4 on Ubuntu
00:42
[ADDITIONAL LECTURE] YOLOv5 Controversy - Is YOLOv5 Real?
14:44
[ADDITIONAL LECTURE] YOLOv1 - YOLOv3 Evolution
12:58
Bonus Lecture
00:11

YOLOv4 Trainers Course

28 lectures
Lecture 1: Introduction to Data Annotation - Video
01:32
Lecture 2: YOLOv4 format for Image Labelling
01:34
Lecture 3: YOLOv4 Labelling Tools
03:13
Lecture 4: Web-scaping Data
02:52
Lecture 5: Annotating Images with LabelImg
02:42
Activity 1: Label Objects on this image
00:31
Lecture 6: Labelling on Video using LabelImg
03:10
Lecture 7: Labelling on Video Using Darklabel
04:01
Activity 2: Label Objects on this Video
00:57
Lecture 8: Annotation Summary
01:32
Lecture 9: Data Annotation Key Take-away
01:32
Lecture 9: Introduction How to Create Custom Dataset
00:56
Lecture 10: Toolkit for Downloading Image Datasets
03:09
Lecture 11: Downloading Images from Specific Classes
05:15
Activity 3: Download Images for your Classes
00:52
Lecture 12: Converting Downloaded Files to YOLOv4 format
19:26
Lecture 13: Data Augmentation using Rotational Transform
05:23
Lecture 14: Summary - Key Takeaways for Custom Datasets
00:50
Lecture 15: Introduction to Training YOLOV4 with DarkNet Framework
01:07
Lecture 16: Step 1 - Configuring the files for Training
03:31
Lecture 17: Step 2 - Creating the obj.names file
00:43
Lecture 18: Step 3 - Dataset Placement for Training
00:47
Lecture 19: Step 4 - Train Test metafiles
01:41
Lecture 20: Step 5 - Training YOLOv4
04:22
Lecture 21: Trained YOLOv4 Execution on Image and Video for Mask Detection
02:22
Activity 5: Train on your own dataset
00:46
Lecture 22: When to Stop Training
03:50
Lecture 23: Summary - Key Takeaways
00:35

YOLOv4 PyQT Course

13 lectures
Lecture 1: Introduction to Object Detection with PyQt
01:05
Lecture 2: Installing PyQt
01:33
Lecture 3: GUI Layout using PyQt Designer
05:42
Lecture 4: Integrating PyQt with YOLOv4
03:08
Lecture 5: Code Explanation
04:50
Lecture 6: Adding GUI Widgets - Counting Objects
03:55
Lecture 7: Adding Widgets - Slider Threshold
03:40
Lecture 8: Adding Widgets - Class Filter using Checkbox Widget
04:02
Lecture 9: Adding Widgets - Real-Time Live Plot Graph Widget
04:02
Lecture 10: Social Distancing in PyQt Activity
02:25
Lecture 11: Conclusion
00:56
Bonus Section: Facial Recognition Attendance GUI - PyQt_Course
01:00:22
Bonus Lecture - Where to from here - YOLOR
00:09

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