super-gradients/src/super_gradients/recipes/arch_params/unet_default_arch_params.yaml

backbone_params:
  in_channels: 3
  strides_list: [2, 2, 2, 2, 2]   #  list of stride per stage.
  width_list: [32, 64, 128, 256, 512]   # list of num channels per stage.
  num_blocks_list: [2, 3, 3, 2, 2]    # list of num blocks per stage.
  block_types_list: [REPVGG, REPVGG, REPVGG, REPVGG, REPVGG]   # list of block types per stage. See unet_encoder.DownBlockType for options.
  is_out_feature_list: [ True, True, True, True, True ]   # list of flags whether stage features should be an output.
  block_params:
    anti_alias: True                # RepVGG stage param
    droppath_prob: 0.               # XBlock stage param
    bottleneck_ratio: 1.            # XBlock stage param
    group_width: 16                 # XBlock stage param
    se_ratio:                       # XBlock stage param
    steps: 4                        # STDC stage params
    stdc_downsample_mode: dw_conv   # STDC stage params


context_module:
  ASPP:
    in_channels: ${last:${backbone_params.width_list}}
    dilation_list: [2, 4, 6]
    in_out_ratio: 1.
    # legacy parameter to support old trained checkpoints that were trained by mistake with extra redundant
    # biases before batchnorm operators. should be set to `False` for new training processes.
    use_bias: False

decoder_params:
  # skip expansion ratio value, before fusing the skip features from the encoder with the decoder features, a projection
  # convolution is applied upon the encoder features to project the num_channels by skip_expansion.
  skip_expansion: 0.25
  decoder_scale: 0.25   # num_channels width ratio between encoder stages and decoder stages.
  up_block_types: [UP_CAT, UP_CAT, UP_CAT, UP_CAT]    # See unet_decoder.UpBlockType for options.
  up_block_repeat_list: [ 1, 1, 1, 1]   # num of blocks per decoder stage, the `block` implementation depends on the up-block type.
  mode: bilinear
  align_corners: False
  up_factor: 2
  is_skip_list: [True, True, True, True]    # List of flags whether to use feature-map from encoder stage as skip connection or not.
  min_decoder_channels: 1                   # The minimum num_channels of decoder stages. Useful i.e if we want to keep the width above the num of classes.

dropout: 0.
final_upsample_factor: 2    # Final upsample scale factor after the segmentation head.
head_upsample_mode: bilinear
align_corners: False
head_hidden_channels:   #  num channels before the last classification layer. see `mid_channels` in `SegmentationHead` class.

use_aux_heads: False
aux_heads_params:
  use_aux_list: [False, False, True, True, True]    # whether to append to auxiliary head per encoder stage.
  aux_heads_factor: [2, 4, 8, 16, 32]   # Upsample factor per encoder stage.
  aux_hidden_channels: [32, 32, 64, 64, 64]   # Hidden num channels before last classification layer, per encoder stage.
  aux_out_channels: [1, 1, 19, 19, 19]    # Output channels, can be refers as num_classes, of auxiliary head per encoder stage.

_convert_: all