Shufflenet: An extremely efficient convolutional neural network for mobile devices
Proceedings of the IEEE conference on computer vision and …, 2018•openaccess.thecvf.com
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which
is designed specially for mobile devices with very limited computing power (eg, 10-150
MFLOPs). The new architecture utilizes two new operations, pointwise group convolution
and channel shuffle, to greatly reduce computation cost while maintaining accuracy.
Experiments on ImageNet classification and MS COCO object detection demonstrate the
superior performance of ShuffleNet over other structures, eg lower top-1 error (absolute …
is designed specially for mobile devices with very limited computing power (eg, 10-150
MFLOPs). The new architecture utilizes two new operations, pointwise group convolution
and channel shuffle, to greatly reduce computation cost while maintaining accuracy.
Experiments on ImageNet classification and MS COCO object detection demonstrate the
superior performance of ShuffleNet over other structures, eg lower top-1 error (absolute …
Abstract
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (eg, 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, eg lower top-1 error (absolute 7.8%) than recent MobileNet~ cite {howard2017mobilenets} on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves $ sim $13 $ imes $ actual speedup over AlexNet while maintaining comparable accuracy.
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