YOLOv4 Object Detection Course

How to Implement & Train YOLOv4 for Object Detection

3.85 (78 reviews)
Udemy
platform
English
language
Data Science
category
YOLOv4 Object Detection Course
4β€―412
students
5.5 hours
content
Dec 2021
last update
$59.99
regular price

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

Why take this course?

πŸš€ Course Title: YOLOv4 Object Detection Course

πŸŽ“ Course Headline: Master AI Object Detection with YOLOv4 - A Comprehensive Nano-Course for Windows 10!


Introduction: As someone with a master's degree in electronic engineering, I know firsthand the challenges of diving into AI Object Detection. The journey was fraught with unclear GitHub repositories, ambiguous instructions, and a myriad of technical questions. Is my hardware up to snuff? Do I need Linux or Windows for this task? What version of Ubuntu should I use, and which dependencies are crucial for my Python environment? With so many uncertainties, it was like navigating through a labyrinth without a map.


Challenges Faced:

  • What to do to get my code working? πŸ€”
  • Choosing the right hardware for the task πŸ–₯️
  • Deciding between Windows and Linux, and if Linux, which distribution and kernel version to use 🐧
  • Understanding dataset formats for training πŸ“Š
  • Choosing between Python and C++ 🐍 vs. Cpp
  • Identifying the necessary Python dependencies πŸ”—
  • Selecting the right frameworks: PyTorch or TensorFlow (1.0 or 2.0)? πŸ€–
  • Typing the correct commands to infer or train a CNN πŸ€“
  • Determining the size and quality of datasets required πŸ“ˆ
  • Running on GPU, with concerns about compatibility πŸš€
  • Training YOLOv4 from scratch πŸ’ͺ
  • Creating cross-platform applications using Yolov4 and PyQt πŸ’»

Solution: Our course addresses these challenges head-on. We've crafted a curriculum that's clear, concise, and tailored for beginners as well as seasoned programmers. It covers everything from setting up your environment to executing YOLOv4 on images and videos, including a deep dive into the Darknet framework.


Course Highlights:

  • Real-World Application: We'll build a COVID-19 social distancing monitoring app to apply what you've learned in a practical, impactful way. 😷
  • Step-by-Step Learning: From understanding the basics of computer vision to implementing YOLOv4 in real-time, we guide you through every step. 🎬
  • Hands-On Practice: You'll get hands-on experience with one of the most powerful object detection models available today. ✈️
  • Real Impact: Address current and pressing problems with AI, like social distancing monitoring. 🌍

Requirements for Success:

  • A basic understanding of Computer Vision concepts 🧠
  • Python programming skills 🐍
  • A mid to high range PC/Laptop to handle the computational tasks πŸ’»
  • Windows 10 operating system to ensure compatibility with our tools and examples 🎫
  • A CUDA-enabled GPU for efficient training of YOLOv4 models (highly recommended) πŸš€

Forward-Thinking Future: By completing this course, you'll unlock a plethora of opportunities. Whether it's solving real-world problems, freelancing in AI projects, landing your dream job, or pushing the boundaries of your research, your newfound expertise with YOLOv4 object detection will open doors. πŸšͺ


Conclusion: Embark on this journey to master AI Object Detection with YOLOv4. Imagine where you could be a week from now with these skills in hand. The world is waiting for your innovative solutions and groundbreaking applications. Are you ready to take the leap? πŸš€πŸ’«

Sign up today, and let's turn your AI aspirations into reality! 🎯

Screenshots

YOLOv4 Object Detection Course - Screenshot_01YOLOv4 Object Detection Course - Screenshot_02YOLOv4 Object Detection Course - Screenshot_03YOLOv4 Object Detection Course - Screenshot_04

Coupons

DateDiscountStatus
02/07/2020100% OFF
expired
3247268
udemy ID
18/06/2020
course created date
02/07/2020
course indexed date
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course submited by