Abstract
Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we question the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We achieve substantial improvements in the latency-accuracy trade-off by incorporating regular convolutions in the search space, and effectively placing them in the network via neural architecture search. We obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators. On the COCO object detection task, MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by 1.9 mAP on mobile CPUs, 3.7 mAP on EdgeTPUs and 3.4 mAP on DSPs while running equally fast. Moreover, MobileDets are comparable with the state-of-the-art MnasFPN on mobile CPUs even without using the feature pyramid, and achieve better mAP scores on both EdgeTPUs and DSPs with up to 2X speedup.
Abstract (translated)
URL
https://arxiv.org/abs/2004.14525
