over the 2$\times A$ outputs of objectness prior ($A$ = 10 in this paper, as there are 10 types of default boxes). We denote the objectness loss with $L_{obj}$. The second branch outputs the bounding-box regression loss, denoted by $L_{loc}$. It targets at minimizing the smoothed $L_1$ loss [@frcnn] between the predicted location offsets $t = (t_{x},t_{y},t_{w},t_{h})$ and the target offsets $t^* = (t^*_{x},t^*_{y},t^*_{w},t^*_{h})$. Different from Fast R-CNN [@frcnn] that regresses the offsets for each of $K$ classes, we just regress the location one time with no class-specific information. The third branch outputs the classification loss $L_{cls|obj}$ for each box, over $K$$+1$ categories. Given the objectness confidence score $p^{obj}$, the branch first excludes regions whose scores are lower than the threshold $o_{p}$. Then like $L_{obj}$, $L_{cls|obj}$ is computed by a Softmax over $K$$+1$ outputs for each location $p^{cls|obj} = \{p_{0}^{cls|obj}, p_{1}^{cls|obj}, \dots, p_{K}^{cls|obj}\}$. We use a multi-task loss $L$ to jointly train the networks end-to-end for objectness prior, classification and bounding-box regression: $$L = \alpha \frac{1}{N_{obj}}L_{obj} + \beta  \frac{1}{N_{loc}} L_{loc} + (1-\alpha-\beta) \frac{1}{N_{cls|obj}} L_{cls|obj}.
\label{loss}$$ The hyper-parameters $\alpha$ and $\beta$ in Equation \[loss\] control the balance between the three losses. We normalize each loss term with its input number. Under 