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Scaling Up Performance: Adaptive Anchor Boxes in Object Detection Object detection is the cornerstone of many computer vision applications, from self-driving cars to medical image analysis. One key challenge in this field lies in accurately localizing objects within an image. While convolutional neural networks (CNNs) have revolutionized object detection, traditional methods often rely on predefined "anchor boxes" - small bounding boxes with specific sizes and aspect ratios - to represent potential object locations. However, the world is diverse. Objects come in all shapes and sizes, rendering fixed-size anchor boxes inadequate for capturing this variability. This is where adaptive anchor box scaling techniques step in, dynamically adjusting anchor box dimensions to better match the objects they aim to detect. The Problem...