  We report on the methods used in our recent DeepEnsembleCoco submission to
the PASCAL VOC 2012 challenge, which achieves state-of-the-art performance on
the object detection task. Our method is a variant of the R-CNN model proposed
Girshick:CVPR14 with two key improvements to training and evaluation. First,
our method constructs an ensemble of deep CNN models with different
architectures that are complementary to each other. Second, we augment the
PASCAL VOC training set with images from the Microsoft COCO dataset to
significantly enlarge the amount training data. Importantly, we select a subset
of the Microsoft COCO images to be consistent with the PASCAL VOC task. Results
on the PASCAL VOC evaluation server show that our proposed method outperform
all previous methods on the PASCAL VOC 2012 detection task at time of
submission.
