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文件名称:MobileNetV2: Inverted Residuals and Linear Bottlenecks
文件大小:1.47MB
文件格式:PDF
更新时间:2021-12-23 09:00:25
MobileNetV2 Bottlenecks
Abstract
In this paper we describe a new mobile architecture,
MobileNetV2, that improves the state of the art perfor-
mance of mobile models on multiple tasks and bench-
marks as well as across a spectrum of different model
sizes. We also describe efficient ways of applying these
mobile models to object detection in a novel framework
we call SSDLite. Additionally, we demonstrate how
to build mobile semantic segmentation models through
a reduced form of DeepLabv3 which we call Mobile
DeepLabv3.
is based on an inverted residual structure where
the shortcut connections are between the thin bottle-
neck layers. The intermediate expansion layer uses
lightweight depthwise convolutions to filter features as
a source of non-linearity. Additionally, we find that it is
important to remove non-linearities in the narrow layers
in order to maintain representational power. We demon-
strate that this improves performance and provide an in-
tuition that led to this design.
Finally, our approach allows decoupling of the in-
put/output domains from the expressiveness of the trans-
formation, which provides a convenient framework for
further analysis. We measure our performance on
ImageNet [1] classification, COCO object detection [2],
VOC image segmentation [3]. We evaluate the trade-offs
between accuracy, and number of operations measured
by multiply-adds (MAdd), as well as actual latency, and
the number of parameters.