The Next Evolution in Object Detection: Introducing YOLO11

Object detection has seen significant advancements over the years, with Ultralytics YOLO11 standing out as the latest cutting-edge model in the YOLO (You Only Look Once) family. Building upon the success of its predecessors, YOLO11 introduces new features, enhanced performance, and flexibility, making it suitable for a wide range of computer vision tasks, including object detection, tracking, instance segmentation, image classification, and even pose estimation.

In this blog, we’ll explore the key features of YOLO11 and walk through the steps to install, train, evaluate, and export a model using this state-of-the-art system.

Object Detection Overview

Object detection is a core task in computer vision, focused on identifying and localizing objects in images or videos by drawing bounding boxes around them. Unlike image classification, which only detects the presence of an object, object detection provides precise spatial locations of objects. Key metrics like Intersection Over Union (IoU) are used to evaluate accuracy by comparing predicted and actual object locations, while Mean Average Precision (mAP) measures performance across multiple object classes.

There are two main approaches to object detection:

• One-stage detectors…