ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Abstract
Current network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, such as speed, also depends on the other factors such as memory access cost and platform characterics. Taking these factors into account, this work proposes practical guidelines for efficient network de- sign. Accordingly, a new architecture called ShuffleNet V2 is presented. Comprehensive experiments verify that it is the state-of-the-art in both speed and accuracy.
Cite
Text
Ma et al. "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01264-9_8Markdown
[Ma et al. "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/ma2018eccv-shufflenet/) doi:10.1007/978-3-030-01264-9_8BibTeX
@inproceedings{ma2018eccv-shufflenet,
title = {{ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design}},
author = {Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01264-9_8},
url = {https://mlanthology.org/eccv/2018/ma2018eccv-shufflenet/}
}