Object Detection: Tailoring Anchors with K-Means
Finding the Perfect Fit: Using K-Means Clustering for Anchor Box Selection in Object Detection Object detection, the ability of a computer to identify and locate objects within an image, is a fundamental task in computer vision. Many modern object detectors rely on a clever technique called "anchor boxes" – predefined bounding boxes with various sizes and aspect ratios that serve as initial guesses for potential objects. But choosing the right anchor boxes is crucial! Poorly chosen anchors can lead to inaccurate detections and lower overall performance. This is where K-Means clustering comes in, offering a powerful tool to automatically select optimal anchor boxes tailored to your specific dataset. Understanding Anchor Boxes: Imagine trying to find a needle in a haystack....