  Object detection has gained great progress driven by the development of deep
learning. Compared with a widely studied task -- classification, generally
speaking, object detection even need one or two orders of magnitude more FLOPs
(floating point operations) in processing the inference task. To enable a
practical application, it is essential to explore effective runtime and
accuracy trade-off scheme. Recently, a growing number of studies are intended
for object detection on resource constraint devices, such as YOLOv1, YOLOv2,
SSD, MobileNetv2-SSDLite, whose accuracy on COCO test-dev detection results are
yield to mAP around 22-25% (mAP-20-tier). On the contrary, very few studies
discuss the computation and accuracy trade-off scheme for mAP-30-tier detection
networks. In this paper, we illustrate the insights of why RetinaNet gives
effective computation and accuracy trade-off for object detection and how to
build a light-weight RetinaNet. We propose to only reduce FLOPs in
computational intensive layers and keep other layer the same. Compared with
most common way -- input image scaling for FLOPs-accuracy trade-off, the
proposed solution shows a constantly better FLOPs-mAP trade-off line.
Quantitatively, the proposed method result in 0.1% mAP improvement at 1.15x
FLOPs reduction and 0.3% mAP improvement at 1.8x FLOPs reduction.
