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.
EfficientNet¶
- class mmcls.models.EfficientNet(arch='b0', drop_path_rate=0.0, out_indices=(6,), frozen_stages=0, conv_cfg={'type': 'Conv2dAdaptivePadding'}, norm_cfg={'eps': 0.001, 'type': 'BN'}, act_cfg={'type': 'Swish'}, norm_eval=False, with_cp=False, init_cfg=[{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'layer': ['_BatchNorm', 'GroupNorm'], 'val': 1}])[source]¶
EfficientNet backbone.
- Parameters
arch (str) – Architecture of efficientnet. Defaults to b0.
out_indices (Sequence[int]) – Output from which stages. Defaults to (6, ).
frozen_stages (int) – Stages to be frozen (all param fixed). Defaults to 0, which means not freezing any parameters.
conv_cfg (dict) – Config dict for convolution layer. Defaults to None, which means using conv2d.
norm_cfg (dict) – Config dict for normalization layer. Defaults to dict(type=’BN’).
act_cfg (dict) – Config dict for activation layer. Defaults to dict(type=’Swish’).
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. Defaults to False.
with_cp (bool) – Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Defaults to False.