这两个层之间有什么区别：Conv和MBConv？
我正在研究一个机器学习项目，以了解有关该领域的更多信息。该项目是关于图像分类的。我想使用EFFNETB0架构，他们在以下一层中在FISRT阶段使用的此架构中提到：“ Conv3x3”以及以下层使用“ MBCONV1”。 我试图了解这两层之间的区别，但我似乎找不到答案。这两个层都是卷积层吗？
但是，“ Conv”和“ MBConv”到底有什么区别？
谢谢您的帮助！
I am working on a machine learning project to learn more about this field. The project is about image classification. I want to use the EffnetB0 architecure and they mention in this architecure they use in the fisrt stage the following layer: "Conv3X3" and the following layers they use "MBConv1".
I tried to understand the difference between these two layers but I can't seem to find the answer. These two layers are both convolutional layers right ?
But what exactly is the difference between "Conv" and "MBConv"?
Thank you for helping me!
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一个说话意味着有一个卷积核心可以按线和卷积扫描与目标图像相对应的矩阵，每个卷积的结果构成了输出矩阵的值。
关于MBCONV，我认为您的意思是移动倒置瓶颈卷积，它更像是一个封装的模块，而不是单个Conv层。 MBCONV的结构可以表示如下：
，您可能会注意到新名称 depthiswise卷积 和 senet ，这是也是一种模块（老实说，这就像一个嵌套娃娃），
如果您只想使用它，则不一定需要完全理解它，直到您需要改善模型结构为止。所以我对你的问题的回答
IS：前者是一个简单的层，后者是由许多简单层组成的复杂模块
A conv means that there is a convolution core to scan the matrix corresponding to the target image line by line and convolution, the result of each convolution constitutes a value of the output matrix.
About the MBConv,i think you means mobile inverted bottleneck convolution，it's more of an encapsulated module than a single conv layer. A MBConv's structure can be expressed as follows:
By the way, you may notice the new names Depthwise Convolution and SENet, which are also a kind of modules(honestly, it's like a nesting doll)
If you just want to use it, you don't necessarily need to fully understand it until you need to improve your model structure. So my answer to your question
is : the former is a simple layer, and the latter is a complex module made up of many simple layers