Light Weight RetinaNet for Object Detection
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.
ArXiv