  Deep region-based object detector consists of a region proposal step and a
deep object recognition step. In this paper, we make significant improvements
on both of the two steps. For region proposal we propose a novel lightweight
cascade structure which can effectively improve RPN proposal quality. For
object recognition we re-implement global context modeling with a few
modications and obtain a performance boost (4.2% mAP gain on the ILSVRC 2016
validation set). Besides, we apply the idea of pre-training extensively and
show its importance in both steps. Together with common training and testing
tricks, we improve Faster R-CNN baseline by a large margin. In particular, we
obtain 87.9% mAP on the PASCAL VOC 2012 test set, 65.3% on the ILSVRC 2016 test
set and 36.8% on the COCO test-std set.

Computer Vision and Pattern Recognition