YOLOv8 Object Detection | Computer Vision | Deep Learning | Ultralytics YOLOv8 Detection

YOLO (You Only Look Once) is a popular real-time object detection system used in computer vision. YOLOv8 is the latest version of the YOLO system, which was introduced in 2021. It is designed to accurately detect objects within an image or video frame, while also being fast and efficient enough to run on devices with limited computational resources.

YOLOv8 uses a convolutional neural network (CNN) to perform object detection. The network is trained on large datasets of annotated images, allowing it to learn the patterns and features that are characteristic of different objects. During inference, the network processes an input image or video frame and outputs a set of bounding boxes that surround the detected objects, along with their corresponding class labels and confidence scores.

YOLOv8 is designed to be highly scalable, making it suitable for a wide range of applications, from small devices to large-scale data centers. It also supports transfer learning, allowing users to fine-tune the model on their own datasets to improve its accuracy and performance for specific use cases.

Overall, YOLOv8 is a powerful and widely used tool for object detection, offering high accuracy and real-time performance, along with the ability to run on a variety of devices and platforms.

In this video, I'll take you through a step-by-step tutorial on Google Colab, and show you how to run YOLOv8 object detection model on some images and video files.
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