In this paper, we explore the idea of distilling small networks
for object detection task. More specifically, we propose a twostage approach to learn more compact and efficient detectors
under the single-shot object detection framework by leveraging knowledge distillation. During the 1st stage, we learn the
feature maps of the student model for each of the prediction
head from the teacher model. Instead of fitting the whole feature map directly, here we propose the mask guided structure
including not only the entire feature map (i.e. global features)
but also region features covered by the object (i.e. local features), which can significantly improve the performance of
the student network. For the 2nd stage, the ground-truth is
used to further refine the performance. Experimental results
on PASCAL VOC and KITTI dataset demonstrate the effectiveness of our proposed approach. We achieve 56.88% mAP
on VOC2007 at 143 FPS with the backbone of 1/8 VGG16.