You can watch this short video course to familiarize yourself with all required machine learning theory. To follow this tutorial, you should be familiar with Python and have a basic understanding of machine learning, neural networks, and their application in object detection. Finally, we will create a web application to detect objects on images right in a web browser using the custom trained model. Then, I will show how to train your own model to detect specific object types that you select, and how to prepare the data for this process. First, we will use a pre-trained model to detect common object classes like cats and dogs. Here, I will show you the main features of this network for object detection. The newest release is YOLOv8, which we are going to use in this tutorial. Recent releases can do even more than object detection. Since that time, there have been quite a few versions of YOLO. One of the most popular neural networks for this task is YOLO, created in 2015 by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi in their famous research paper "You Only Look Once: Unified, Real-Time Object Detection". The best quality in performing these tasks comes from using convolutional neural networks. Over the years, many methods and algorithms have been developed to find objects in images and their positions. It is an important part of many applications, such as self-driving cars, robotics, and video surveillance. Object detection is a computer vision task that involves identifying and locating objects in images or videos.
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