  This paper presents how we can achieve the state-of-the-art accuracy in
multi-category object detection task while minimizing the computational cost by
adapting and combining recent technical innovations. Following the common
pipeline of "CNN feature extraction + region proposal + RoI classification", we
mainly redesign the feature extraction part, since region proposal part is not
computationally expensive and classification part can be efficiently compressed
with common techniques like truncated SVD. Our design principle is "less
channels with more layers" and adoption of some building blocks including
concatenated ReLU, Inception, and HyperNet. The designed network is deep and
thin and trained with the help of batch normalization, residual connections,
and learning rate scheduling based on plateau detection. We obtained solid
results on well-known object detection benchmarks: 83.8% mAP (mean average
precision) on VOC2007 and 82.5% mAP on VOC2012 (2nd place), while taking only
750ms/image on Intel i7-6700K CPU with a single core and 46ms/image on NVIDIA
Titan X GPU. Theoretically, our network requires only 12.3% of the
computational cost compared to ResNet-101, the winner on VOC2012.
