L<sub>1</sub> loss function and Intersection over Union (IoU) are commonly used in object detection. However, minimizing the loss function through training process does not necessarily amount to maximizing IoUs. L<sub>1</sub> loss simply assigns equal weights to difference of the width, height, and center point between a prediction box and a ground truth box but pays less attention to the contribution of each shape property. Observing this, we propose scaling loss which can be easily embedded in convolutional neural networks for mitigating the gap between IoU and loss function. The key insight is to add in the loss function the adaptive weights for width, height, and center point that encode the shape properties of the bounding box. The contribution of each shape property will be adaptively adjusted according to the difference between a prediction box and a ground truth box, i.e. increasing the weight assigned to the bad-regressed shape property. By this means, the scaling loss is able to obtain more accurate prediction box. The proposed scaling loss was embedded in Faster R-CNN and SSD, and was validated on PASCAL VOC 2007. Experimental results verify that the proposed scaling loss can improve the detection accuracy over the smooth L<sub>1</sub> loss and Softer-NMS.