The performance of an object detection model is evaluated using two separate metrics that parallel its loss components. The classification results of anchor boxes are evaluated using accuracy. Meanwhile, the predicted bounding box offsets are evaluated using the mean absolute error, aligning with the $\ell_1$ norm loss used during training.