and height of each box are computed with respect to the aspect ratio [@fasterrcnn]. In total, there are 2 scales and 5 aspect ratios at each feature map location. The $s_{min}$ is $\frac{1}{10}$ of the input size (e.g., 32 pixels for 320$\times$320 model). By combining predictions for all default boxes with different scales and aspect ratios, we have a diverse set of predictions, covering various object sizes and shapes.

Objectness Prior {#objprior}
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As shown in Section \[refboxes\], we consider default boxes with different scales and aspect ratios from many feature maps. However, only a tiny fraction of boxes covers objects. In other words, the ratio between object and non-object samples is seriously imbalanced. The region-based methods overcome this problem by region proposal networks [@hypernet][@fasterrcnn]. However, the region proposals will bring translation variance compared with the default boxes. So the Fast R-CNN pipeline usually uses region-wise networks for post detection, which brings repeated computation [@rfcn]. In contrast, we add an objectness prior for guiding the search of objects, without generating new region proposals. Concretely, we add a $3$$\times3\times$$2$ convolutional layer followed by a Softmax function to indicate the existence of an object in each box. The channel number of objectness 