Methods › Computer Vision › Image Model Blocks › ShuffleNet V2 Block
ShuffleNet V2 Block
Introduced by Ningning Ma et al. in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
ShuffleNet V2 Block is an image model block used in the ShuffleNet V2 architecture, where speed is the metric optimized for (instead of indirect ones like FLOPs). It utilizes a simple operator called channel split. At the beginning of each unit, the input of c feature channels are split into two branches with c - c′ and c′ channels, respectively. Following G3, one branch remains as identity. The other branch consists of three convolutions with the same input and output channels to satisfy G1. The two 1×1 convolutions are no longer group-wise, unlike the original ShuffleNet. This is partially to follow G2, and partially because the split operation already produces two groups. After convolution, the two branches are concatenated. So, the number of channels keeps the same (G1). The same “channel shuffle” operation as in ShuffleNet is then used to enable information communication between the two branches.
The motivation behind channel split is that alternative architectures, where pointwise group convolutions and bottleneck structures are used, lead to increased memory access cost. Additionally more network fragmentation with group convolutions reduces parallelism (less friendly for GPU), and the element-wise addition operation, while they have low FLOPs, have high memory access cost. Channel split is an alternative where we can maintain a large number of equally wide channels (equally wide minimizes memory access cost) without having dense convolutions or too many groups.
Papers archive 2025-07-28
21 shown of 21, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets 28 Mar 2024 · 0 repositories · arXiv:2403.19177
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Fragility, Robustness and Antifragility in Deep Learning 15 Dec 2023 · 0 repositories · arXiv:2312.09821
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A Non-monotonic Smooth Activation Function 16 Oct 2023 · 0 repositories · arXiv:2310.10126
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Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality 4 Jul 2022 · 0 repositories · arXiv:2207.01296
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SMU: smooth activation function for deep networks using smoothing maximum technique 8 Nov 2021 · 6 repositories · arXiv:2111.04682
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SAU: Smooth activation function using convolution with approximate identities 27 Sep 2021 · 0 repositories · arXiv:2109.13210
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ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions 9 Sep 2021 · 0 repositories · arXiv:2109.04386
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Rethinking Image Deraining via Rain Streaks and Vapors 3 Aug 2020 · 1 repository · arXiv:2008.00823Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
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Egocentric Human Segmentation for Mixed Reality 25 May 2020 · 0 repositories · arXiv:2005.12074
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DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks 22 Apr 2020 · 0 repositories · arXiv:2004.10694
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CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images 23 Jan 2020 · 1 repository · arXiv:2001.08593
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Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks 21 Oct 2019 · 0 repositories · arXiv:1910.09455
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ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices 1 Oct 2019 · 0 repositories
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Mish: A Self Regularized Non-Monotonic Activation Function 23 Aug 2019 · 9 repositories · arXiv:1908.08681Syntology ran 3 of 12 samples · 9 unverified
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DiCENet: Dimension-wise Convolutions for Efficient Networks 8 Jun 2019 · 2 repositories · arXiv:1906.03516Syntology ran 0 of 1 samples · 1 unverified
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Butterfly Transform: An Efficient FFT Based Neural Architecture Design 5 Jun 2019 · 1 repository · arXiv:1906.02256
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ThunderNet: Towards Real-time Generic Object Detection 28 Mar 2019 · 3 repositories · arXiv:1903.11752
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DetNAS: Backbone Search for Object Detection 26 Mar 2019 · 2 repositories · arXiv:1903.10979Syntology ran 0 of 8 samples · 8 unverified
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ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search 7 Dec 2018 · 0 repositories · arXiv:1812.02975
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DSNet for Real-Time Driving Scene Semantic Segmentation 6 Dec 2018 · 0 repositories · arXiv:1812.07049
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ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design 30 Jul 2018 · 35 repositories · arXiv:1807.11164Syntology ran 1 of 30 samples · 29 unverified
Tasks archive 2025-07-28
20 shown of 24 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections