per-class score that indicates the presence of a class-specific instance. For bounding box regression, we predict the offsets relative to the default boxes in the cell (see Figure \[det\_module\]).

![Object detection and bounding box regression modules. Top: bounding box regression; Bottom: object classification.[]{data-label="det_module"}](net_det.pdf){width="0.8\linewidth"}

-0.1 in

-0.2 in

Combining Objectness Prior with Detection {#combine}
-----------------------------------------

In this section, we explain how RON combines objectness prior with object detection. We assist object detection with objectness prior both in training and testing phases. For training the network, we firstly assign a binary class label to each candidate region generated from Section \[refboxes\]. Then if the region covers object, we also assign a class-specific label to it. For each ground truth box, we (i) match it with the candidate region with most jaccard overlap; (ii) match candidate regions to any ground truth with jaccard overlap higher than 0.5. This matching strategy guarantees that each ground truth box has at least one region box assigned to it. We assign negative labels to the boxes with jaccard overlap lower than 0.3.

By now, each box has its objectness label and class-specific label. The network will update the class-specific labels dynamically for assisting object detection with objectness 