YOLOv8 for Beginners: A Complete Tutorial on Windows 11

In this tutorial, we will be covering how to run YOLOv8 on Windows 11. YOLOv8 is one of the most popular object detection algorithms used in the field of AI and machine learning. It is based on deep learning techniques, which enable it to accurately recognize and detect objects in images. This tutorial is designed to be a step-by-step guide, making it easy for beginners to understand and follow.
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Starting with an introduction to YOLOv8 and its capabilities, we will then move on to installing the necessary tools and libraries. From there, we will dive into the implementation process and demonstrate how to run YOLOv8 on Windows 11. The tutorial covers everything from setting up the environment to the actual detection process.
This tutorial is ideal for those who are interested in learning about object detection, AI, and machine learning, or for those who want to get started with YOLOv8 and take their skills to the next level. Whether you're a beginner or have some prior knowledge, this tutorial will provide you with the necessary information and guidance to start using YOLOv8 on Windows 11. We use the AS-One Library which allows you to access any version of YOLO, like YOLOv7,v6,v5,R,X and beyond.
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[@ic6ic8]

its fake and the github link is different from the one that exist in video and if you type the link also don't work

[@allea-zb7kl]

hi, I have a question. if I divide the dataset into training and testing only, is it necessary to run the validation part? and if not, during inference how to find out the mAP?

[@marvinalone]

Hi I have a question regarding how you made the cartoon on the bottom right?
Is that converted according to the real person? what kind of tool can be used to achieve this? I'd appreciate if you can provide me some guidance.

[@lucafritz4860]

It should be apples imojis on a mac or MacBook

[@НикитаСафронов-ю7б]

why do you prefer to use AS-One and not ultralytics?

[@Augmented_AI]

Because we seamlessly integrate prior versions of yolo with tracking. It’s future proof as well

[@iMJKhan]

why not with macOS?

[@glorified3142]

You did not specify the python version you used to replicate this. Difficult to follow your turorial on Ubuntu 23.04.

[@adamazmy6408]

do u know what version he used