To illustrate anchor box labeling, consider an image containing ground-truth bounding boxes for a dog and a cat, alongside a set of five generated anchor boxes, denoted as $A_0, \ldots, A_4$. The labeling process evaluates the pairs of anchor boxes and ground-truth bounding boxes based on Intersection over Union (IoU). For example, if the IoU between anchor box $A_4$ and the cat's ground-truth bounding box is the largest among all pairs, $A_4$ is labeled as the cat. Removing all pairs containing $A_4$ or the cat's bounding box, if the remaining pair with the largest IoU is $A_1$ and the dog's bounding box, $A_1$ is labeled as the dog. For the remaining unlabeled anchor boxes (e.g., $A_0, A_2, A_3$), they are assigned to the ground-truth bounding box with the highest IoU only if that IoU exceeds a predefined threshold (e.g., $0.5$). Thus, if $A_2$ has an IoU with the cat exceeding the threshold, it is labeled as the cat; conversely, if $A_0$ and $A_3$ have maximum IoUs below the threshold, they are labeled as the background class.