2019/05/24 by Yixing Li, Fengbo Ren, Li, Yixing +1 · 27 citations
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Algorithm #Artificial intelligence #CCD and CMOS Imaging Sensors #Computation #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #Engineering #FLOPS #FOS: Computer and information sciences #Inference #Layer (electronics) #Mathematics #Object (grammar) #Object detection #Parallel computing #Pattern recognition (psychology) #Reduction (mathematics) #Task (project management) #cs.CV
paper · pdf · doi:10.48550/arxiv.1905.10011
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/05/24 · openalex publication_date 2019/05/24 · arxiv updated 2019/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.