prior maps is 10, as there are 10 default boxes at each location.

Figure \[pipline\] shows the multi-scale objectness prior generated from a specific image. For visualization, the objectness prior maps are averaged along the channel dimension. We see that the objectness prior maps could explicitly reflect the existence of an object. Thus the searching space of the objects could be dramatically reduced. Objects of various scales will respond at their corresponding feature maps, and we enable this by appropriate matching and end-to-end training. More results could be seen in the experiment section.

Detection and Bounding Box Regression {#detection_sec}
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Different from objectness prior, the detection module needs to classify regions into $K$$+1$ categories ($K$$=20$ for PASCAL VOC dataset and $K$$=80$ for MS COCO dataset, plus 1 for background). We employ the inception module [@googlenet] on the feature maps generated in Section \[multiscale\] to perform detection. Concretely, we add two inception blocks (one block is shown in Figure \[incept\]) on feature maps, and classify the final inception outputs. There are many inception choices as shown in [@googlenet]. In this paper, we just use the most simple structure.

![One inception block.[]{data-label="incept"}](incept.pdf){width="0.38\linewidth"}

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With Softmax, the sub-network outputs the 