|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/yolov4-object-detection-nano-course/
课程评论：没有评论
**课程名称：YOLOv4 物体检测课程** **课程概述：** 本课程旨在帮助初学者轻松掌握 YOLOv4 物体检测技术。作者深知初学者在学习AI物体检测过程中遇到的诸多挑战，包括环境配置、依赖项安装、硬件选择（Windows/Linux）、数据集格式、编程语言（Python/C++）、深度学习框架（PyTorch/TensorFlow）以及模型训练和部署等。作者结合自身拥有电子工程硕士学位的学习经历，发现即使是具备一定技术背景的人也常常感到困惑，更不用说非编程或非计算机科学背景的爱好者、学生、研究人员和新入行者。 YOLOv4 于2020年4月发布后，作者团队便致力于开发这门“纳米课程”，以便让学习者能够轻松上手。课程主要针对 Windows 10 PC 用户，内容涵盖： * **基础安装：** 详细指导如何安装所有必要的依赖项，包括 Python、CUDA 和 OpenCV。 * **模型应用：** 成功编译后，演示如何在图像和视频上运行 YOLOv4。 * **代码解析：** 深入讲解 Darknet Python 脚本，展示如何在网络摄像头上运行 YOLOv4。 * **实际项目：** 通过构建一个“社交距离监测”应用程序，直观展示 YOLOv4 的应用，该应用能够监测两人之间的距离，确保安全社交距离，并实时显示处于风险中的人数。 本课程为计算机视觉领域提供了一个温和的入门，从 Darknet 的安装、YOLOv4 库的构建，到实时化 YOLOv4 在图像和视频上的实现。通过实际项目的构建，学习者可以解决现实世界中的问题。 **课程要求：** * 对计算机视觉有基本了解。 * 具备 Python 编程技能。 * 拥有一台中等到配置较高的 PC/笔记本电脑。 * Windows 10 操作系统。 * 具备 CUDA 功能的 GPU（重要）。 **课程的价值：** 完成本课程后，学习者将能够使用 YOLOv4 预训练模型实现卷积神经网络（CNNs），从而拥有强大的AI技能，可以： * 解决现实世界的问题。 * 承接自由职业的AI项目。 * 获得AI领域的工作机会。 * 在研究中取得突破。 * 节省时间和金钱。 课程激发学习者思考利用AI技能可以实现的创意应用，强调了掌握AI技能带来的广阔前景。
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 hardwareWindows or Linux - If linux, do I use Ubuntu, Red Hat, CentOS, ROSIf 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 2.0What commands do I type to infer or train a convolutional neural networkHow big my dataset needs to be?How do I run on GPU, and does my GPU support the framework?How to train YOLOv4How create cross platform apps using Yolov4 and PyQtI 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 CourseWhen 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 timeThe 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.RequirementsPlease ensure that you have the following:Basic understanding of Computer VisionPython Programming SkillsMid to high range PC/ LaptopWindows 10CUDA-enabled GPU - Important*Forward ThinkingImagine, 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?
