You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In parallel with the object classification subnet, we attach another small FCN to each pyramid level for the purpose of regressing the offset from each anchor box to a nearby ground-truth object, if one exists. The design of the box regression subnet is identical to the classification subnet except that it terminates in 4A linear outputs per spatial location, see Figure 3 (d). For each of the A anchors per spatial location, these 4 outputs predict the relative offset between the anchor and the ground-truth box (we use the standard box parameterization from R-CNN [11]). We note that unlike most recent work, we use a class-agnostic bounding box regressor which uses fewer parameters and we found to be equally effective. The object classification subnet and the box regression subnet, though sharing a common structure, use separate parameters.

Question: How classification subnet is different from regression subnet?
Even though the design of box regression subnet is similar to classification subnet, the design terminates in 4A linear outputs per spatial location. Also both the subnets use separate parameters.