mmcls.models.ConvNeXt

class mmcls.models.ConvNeXt(arch='tiny', in_channels=3, stem_patch_size=4, norm_cfg={'eps': 1e-06, 'type': 'LN2d'}, act_cfg={'type': 'GELU'}, linear_pw_conv=True, drop_path_rate=0.0, layer_scale_init_value=1e-06, out_indices=- 1, frozen_stages=0, gap_before_final_norm=True, init_cfg=None)

ConvNeXt.

A PyTorch implementation of : A ConvNet for the 2020s

Modified from the official repo and timm.

Parameters

• arch (str | dict) –

  The model’s architecture. If string, it should be one of architecture in ConvNeXt.arch_settings. And if dict, it should include the following two keys:

  • depths (list[int]): Number of blocks at each stage.

  • channels (list[int]): The number of channels at each stage.

  Defaults to ‘tiny’.

• in_channels (int) – Number of input image channels. Defaults to 3.

• stem_patch_size (int) – The size of one patch in the stem layer. Defaults to 4.

• norm_cfg (dict) – The config dict for norm layers. Defaults to dict(type='LN2d', eps=1e-6).

• act_cfg (dict) – The config dict for activation between pointwise convolution. Defaults to dict(type='GELU').

• linear_pw_conv (bool) – Whether to use linear layer to do pointwise convolution. Defaults to True.

• drop_path_rate (float) – Stochastic depth rate. Defaults to 0.

• layer_scale_init_value (float) – Init value for Layer Scale. Defaults to 1e-6.

• out_indices (Sequence | int) – Output from which stages. Defaults to -1, means the last stage.

• frozen_stages (int) – Stages to be frozen (all param fixed). Defaults to 0, which means not freezing any parameters.

• gap_before_final_norm (bool) – Whether to globally average the feature map before the final norm layer. In the official repo, it’s only used in classification task. Defaults to True.

• init_cfg (dict, optional) – Initialization config dict