  Scale-sensitive object detection remains a challenging task, where most of
the existing methods could not learn it explicitly and are not robust to scale
variance. In addition, the most existing methods are less efficient during
training or slow during inference, which are not friendly to real-time
applications. In this paper, we propose a practical object detection method
with scale-sensitive network.Our method first predicts a global continuous
scale ,which is shared by all position, for each convolution filter of each
network stage. To effectively learn the scale, we average the spatial features
and distill the scale from channels. For fast-deployment, we propose a scale
decomposition method that transfers the robust fractional scale into
combination of fixed integral scales for each convolution filter, which
exploits the dilated convolution. We demonstrate it on one-stage and two-stage
algorithms under different configurations. For practical applications, training
of our method is of efficiency and simplicity which gets rid of complex data
sampling or optimize strategy. During test-ing, the proposed method requires no
extra operation and is very supportive of hardware acceleration like TensorRT
and TVM. On the COCO test-dev, our model could achieve a 41.5 mAP on one-stage
detector and 42.1 mAP on two-stage detectors based on ResNet-101, outperforming
base-lines by 2.4 and 2.1 respectively without extra FLOPS.
