This function is readily invertibl

 This function is readily invertible, i.e., given the regression coefficients and coordinates of the top left corner and the width and height of the original bounding box, the top left corner and width and height of the target box can be easily calculated. Note the regression coefficients are invariant to an affine transformation with no shear. This is an important point as while calculating the classification loss, the target regression coefficients are calculated in the original aspect ratio while the classification network output regression coefficients are calculated after the ROI pooling step on square feature maps (1:1 aspect ratio). This will become clearer when we discuss classification loss below.