Note
You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.
MobileNetV3¶
- class mmcls.models.MobileNetV3(arch='small', conv_cfg=None, norm_cfg={'eps': 0.001, 'momentum': 0.01, 'type': 'BN'}, out_indices=None, frozen_stages=- 1, norm_eval=False, with_cp=False, init_cfg=[{'type': 'Kaiming', 'layer': ['Conv2d'], 'nonlinearity': 'leaky_relu'}, {'type': 'Normal', 'layer': ['Linear'], 'std': 0.01}, {'type': 'Constant', 'layer': ['BatchNorm2d'], 'val': 1}])[source]¶
MobileNetV3 backbone.
- Parameters
arch (str) – Architecture of mobilnetv3, from {small, large}. Default: small.
conv_cfg (dict, optional) – Config dict for convolution layer. Default: None, which means using conv2d.
norm_cfg (dict) – Config dict for normalization layer. Default: dict(type=’BN’).
out_indices (None or Sequence[int]) – Output from which stages. Default: None, which means output tensors from final stage.
frozen_stages (int) – Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters.
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False.