  In object detection, reducing computational cost is as important as improving
accuracy for most practical usages. This paper proposes a novel network
structure, which is an order of magnitude lighter than other state-of-the-art
networks while maintaining the accuracy. Based on the basic principle of more
layers with less channels, this new deep neural network minimizes its
redundancy by adopting recent innovations including C.ReLU and Inception
structure. We also show that this network can be trained efficiently to achieve
solid results on well-known object detection benchmarks: 84.9% and 84.2% mAP on
VOC2007 and VOC2012 while the required compute is less than 10% of the recent
ResNet-101.
