data
ResNeStConfig
extends
ModelConfigResNeStConfig(num_classes: int = 1000, in_channels: int = 3, layers: tuple[int, ...] = (3, 4, 6, 3), radix: int = 2, groups: int = 1, avg_down: bool = True, avd: bool = True, avd_first: bool = False, stem_width: int = 32, deep_stem: bool = True, dropout: float = 0.0, zero_init_residual: bool = False)Configuration for ResNeSt.
layers — per-stage block repetition counts.
radix — number of split branches in SplitAttn conv.
groups — cardinality (number of convolution groups).
avg_down — use AvgPool + 1×1 Conv for downsampling shortcuts.
avd — use averaged downsampling (AvgPool) around SplitAttn.
avd_first — place the AvgPool before (True) or after (False) SplitAttn.
stem_width — channel width of each deep-stem conv (output = stem_width*2).
deep_stem — use a 3-convolution deep stem instead of a single 7×7 conv.
Used by 3
Constructors
1dunder
__init__
→None__init__(num_classes: int = 1000, in_channels: int = 3, layers: tuple[int, ...] = (3, 4, 6, 3), radix: int = 2, groups: int = 1, avg_down: bool = True, avd: bool = True, avd_first: bool = False, stem_width: int = 32, deep_stem: bool = True, dropout: float = 0.0, zero_init_residual: bool = False)