ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

CVPR 2018  ·  Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun ·

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 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, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Person Re-Identification DukeMTMC-reID ShuffleNetV2 [zhang2018shufflenet] mAP 48.09 # 71
Image Classification ImageNet ShuffleNet Top 1 Accuracy 70.9% # 569
Top 5 Accuracy 89.8% # 213