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ShuffleNet

swMATH ID: 39585
Software Authors: Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun
Description: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. 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
Homepage: https://paperswithcode.com/paper/shufflenet-an-extremely-efficient
Keywords: Computer Vision; Pattern Recognition; arXiv_cs.CV; ShuffleNet; Convolutional Neural Network; Mobile Device; CNN
Related Software: ImageNet; AlexNet; MobileNets; Xception; MS-COCO; MobileNetV2; SqueezeNet; Adam; DeepLab; CIFAR; Inception-v4; Caffe; PyTorch; XNOR-Net; EfficientNet; PointNet; YOLO; DARTS; ArcFace; SSD
Cited in: 17 Publications

Standard Articles

1 Publication describing the Software Year
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun
2017

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