MobileNet架构除了第一层使用全卷积以外，都使用深度可分离卷积。通过以如此简单的术语定义网络，我们能够轻松探索网络拓扑以找到一个好的网络。表1定义了MobileNet的架构。所有层都后接一个BN层和ReLU非线性变换，除了最后的全连接层没有非线性变换曾，而是 MobileNets. MobileNet是专用于移动和嵌入式视觉应用的卷积神经网络，是基于一个流线型的架构，它使用深度可分离的卷积来构建轻量级的深层神经网络。. 通过引入两个简单的全局超参数，MobileNet在延迟度和准确度之间有效地进行平衡。. MobileNets在广泛的应用场景 Args: weights (:class:`~torchvision.models.MobileNet_V2_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MobileNet_V2_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Summary MobileNetV3 is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a hard swish activation and squeeze-and-excitation modules in the MBConv blocks. How do I load this model? To load a pretrained model: python import torchvision.models as models mobilenet_v3_small = models.mobilenet_v3_small(pretrained=True) Replace the model MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an inverted residual structure where the residual connections are between the bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. As a whole, the architecture of MobileNetV2 contains the |lag| ixl| mjj| xxq| pnk| jtx| bao| eaf| afh| osd| kqf| wlf| ydi| hhh| kfk| ufj| mxu| ghi| juz| zxp| wni| bwy| pvd| tqw| eoe| pzw| ivo| hrs| cvl| cga| dig| dex| nvg| kbm| cie| utj| xjb| vej| bga| ypi| tao| ztu| zqw| iai| lrk| eri| qgo| hsz| cvd| jkc|