predictions. The class-specific confidence score for each box is defined as Equation \[det\_score\]: $$p^{cls} = p^{obj} \cdot p^{cls|obj}.
\label{det_score}$$ The scores encode both the probability of the class appearing in the box and how well the predicted box fits the object. After the final score of each box is generated, we adjust the boxes according to the bounding box regression outputs. Finally, non-maximum suppression is applied to get the final detection results.

Results
=======

We train and evaluate our models on three major datasets: PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO. For fair comparison, all experiments are based on the VGG-16 networks. We train all our models on a single Nvidia TitanX GPU, and demonstrate state-of-the-art results on all three datasets.

PASCAL VOC 2007
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  Method                      mAP        aero       bike       bird       boat       bottle     bus        car        cat        chair      cow        table      dog        horse      mbike      person     plant      sheep      sofa       train          tv
  --------------------------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ----------
  Fast R-CNN[@frcnn]          68.4       82.3       78.4       70.8       52.3       38.7       77.8       71.6       89.3       44.2       73.0       55.0       87.5       80.5       80.8       72.0       35.1       68.3       65.7       80.4          64.2
  OHEM[@ohem]                 71.9       83.0       81.3       72.5       