At a specific scale represented by a feature map of shape $h imes w$, a convolutional neural network generates $hw$ sets of anchor boxes, with each set containing $a$ anchor boxes sharing the same center. Once each anchor box is labeled with a ground-truth class and offset, the object detection model must predict these classes and offsets for all $hw$ sets of anchor boxes across the input image.