YOLOv5 (You Only Look Once version 5) is a popular object detection model that focuses on both speed and accuracy. It is part of the YOLO family, designed for real-time object detection in images and videos. YOLOv5 is known for being lightweight, easy to train, and highly efficient, making it ideal for a wide range of applications such as autonomous driving, security, and robotics. With its modular design and support for custom datasets, YOLOv5 is widely used by developers and researchers for tasks like detecting multiple objects with high precision in real-time scenarios.
Object detection is a computer vision technique in machine learning that involves identifying and locating objects within an image or video. Unlike image classification, which assigns a label to an entire image, object detection not only classifies objects but also draws bounding boxes around them to specify their exact locations. This task is crucial for applications like autonomous driving, surveillance, and image analysis, where understanding the context and position of objects is essential.
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