公开(公告)号：US11157814B2

公开(公告)日：2021-10-26

申请号：US15707064

申请日：2017-09-18

Applicant: Google Inc.

Inventor： Andrew Gerald Howard ,  Bo Chen ,  Dmitry Kalenichenko ,  Tobias Christoph Weyand ,  Menglong Zhu ,  Marco Andreetto ,  Weijun Wang

IPC: G06N3/08 ,  G06N3/04

Abstract: The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.

公开(公告)号：US20180137406A1

公开(公告)日：2018-05-17

申请号：US15707064

申请日：2017-09-18

Applicant: Google Inc.

Inventor： Andrew Gerald Howard ,  Bo Chen ,  Dmitry Kalenichenko ,  Tobias Christoph Weyand ,  Menglong Zhu ,  Marco Andreetto ,  Weijun Wang

IPC: G06N3/04 ,  G06N3/08 ,  G06T7/32

CPC classification number: G06N3/04 ,  G06N3/0454 ,  G06N3/082 ,  G06N3/084 ,  G06T7/32 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.