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. 2023 Feb 11;23:32. doi: 10.1186/s12880-023-00974-y

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© The Author(s) 2023

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — The mobile inverted bottleneck convolution (MBConv) block. (a) An initial 1x1 conv block expands the number of input channels according to the expansion factor hyper-parameter. (b) Depth-wise 3x3 conv block over channels. (c) Global average pooling shrinks the tensor along its spatial dimensions. (d, e) A squeeze conv (1x1 conv + swish) and an excitation conv (1x1 conv + sigmoid) first squeeze the channel dimension by a factor of 0.25, then expand it back to its original shape. The output is multiplied by the output tensor from step (b). (f) A final 1x1 conv block with a linear activation maps the tensor to the desired number of output channels, followed by a dropout layer for stochastic depth (dropout rate 0.2)