increases the coordinate-loss weight, while decreases the confidence-loss weight for boxes without objects.

decreases the confidence-loss contribution from boxes without objects.

Taking the square roots of and makes a fixed absolute size error more costly for a large box than for a small box.

increases the weight of the classification error.