Let’s run a sample image detection: This code loads a pre-trained YOLO 11.0 model and performs object detection on an image. Run the code to see YOLO in action as it identifies objects in the image. If you want YOLO to detect specific objects, you’ll need a custom dataset. Here’s how to prepare it:

Welcome to the Ultralytics YOLO Python Usage documentation! This guide is designed to help you seamlessly integrate Ultralytics YOLO into your Python projects for object detection, segmentation, and classification. Here, you'll learn how to load and use pretrained models, train new models, and perform predictions on images.

YOLO is known for its speed and efficiency in detecting objects in a single forward pass through the network. The latest YOLO models, such as YOLOv5 and YOLOv8, have made the framework even faster and more accurate, providing more accessible ways for developers to implement object detection.

With advancements in deep learning, models like YOLO (You Only Look Once) allow us to detect multiple objects in real time, making it ideal for use cases such as security surveillance, autonomous driving, and interactive applications. In this guide, we will walk through building a real-time object detection system using Python and the YOLO model.