首页MobileNetV2中的Inverted Bottleneck模块的作用

MobileNetV2中的Inverted Bottleneck模块的作用

时间: 2023-06-03 21:03:43 浏览: 148

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Abstract In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art perfor- mance of mobile models on multiple tasks and bench- marks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottle- neck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demon- strate that this improves performance and provide an in- tuition that led to this design. Finally, our approach allows decoupling of the in- put/output domains from the expressiveness of the trans- formation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters.

Inverted Bottleneck模块是MobileNetV2的核心，它的作用是通过中间的扩张卷积层来增加通道数，然后通过1x1卷积来减少通道数，起到提高精度的作用。同时，Inverted Bottleneck模块还能降低计算量和内存占用，提高模型的效率和速度。

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