fcn_resnet_50¶

lucid.models.fcn_resnet_50(num_classes: int = 21, in_channels: int = 3, aux_loss: bool = True, **kwargs) → FCN¶

The fcn_resnet_50 function builds an FCN model with a ResNet-50 backbone preset for semantic segmentation.

Total Parameters (num_classes=21): 35,322,218

Function Signature¶

@register_model
def fcn_resnet_50(
    num_classes: int = 21,
    in_channels: int = 3,
    aux_loss: bool = True,
    **kwargs
) -> FCN

Parameters¶

num_classes (int): Number of segmentation classes.
in_channels (int): Number of input channels.
aux_loss (bool): If True, enables the auxiliary classifier head.
kwargs (dict, optional): Additional overrides applied to FCNConfig.

Returns¶

FCN: FCN model configured with a ResNet-50 backbone.

Example Usage¶

from lucid.models import fcn_resnet_50
import lucid

model = fcn_resnet_50(num_classes=21)
x = lucid.random.randn(1, 3, 224, 224)
logits = model(x)
print(logits.shape)