se_resnet_18

lucid.models.se_resnet_18(num_classes: int = 1000, **kwargs) → SENet

Overview

The se_resnet_18 function constructs an SE-ResNet-18 model, a lightweight residual network augmented with Squeeze-and-Excitation (SE) blocks for adaptive recalibration of feature maps. It is suitable for image classification tasks.

Total Parameters: 11,778,592

Function Signature

@register_model
def se_resnet_18(num_classes: int = 1000, **kwargs) -> SENet:

Parameters

  • num_classes (int, optional): Number of output classes for the classification task. Default is 1000.

  • kwargs: Additional keyword arguments to customize the model.

Returns

  • SENet: An instance of the SE-ResNet-18 model.

Examples

Creating an SE-ResNet-18 model for 1000 classes:

model = se_resnet_18(num_classes=1000)
print(model)

Note

  • SE-ResNet-18 uses a configuration of [2, 2, 2, 2] for its layers.

  • Incorporates _SEResNetModule for SE operations.

  • Initializes weights internally unless specified otherwise through kwargs.