wide_resnet_50¶
The wide_resnet_50 function is a registered model that creates a Wide ResNet-50 architecture. This is a variant of the ResNet-50 model with a wider bottleneck configuration, often used to enhance the representational capacity of the network.
Total Parameters: 78,973,224
Function Signature¶
def wide_resnet_50(num_classes: int = 1000, **kwargs) -> ResNet
Parameters¶
num_classes (int, optional): The number of output classes for the final fully connected layer. Default is 1000, suitable for ImageNet classification.
kwargs (dict, optional): Additional keyword arguments passed to the ResNet constructor. These can include options for customization such as initialization or model-specific settings.
Returns¶
ResNet: A Wide ResNet-50 model configured with the specified number of output classes and additional options.
Architecture Details¶
Wide ResNet-50 is characterized by its wider bottleneck layers compared to the standard ResNet-50. Specifically, the base_width parameter determines the width of the bottleneck layers, with a default value of 128 in this implementation.
Note
The architecture comprises 4 stages with layers [3, 4, 6, 3], corresponding to the number of residual blocks in each stage.
Examples¶
Creating a Wide ResNet-50 model for ImageNet classification:
import lucid.models as models
# Create the model
model = models.wide_resnet_50(num_classes=1000)
# Print model summary
models.summarize(model)