efficientnet_v2_l

lucid.models.efficientnet_v2_l(num_classes: int = 1000, **kwargs) → EfficientNet_V2

The efficientnet_v2_l function instantiates a large variant of the EfficientNet-v2 model, specifically designed for lightweight tasks while maintaining high performance.

Total Parameters: 120,617,032

Function Signature

@register_model
def efficientnet_v2_l(num_classes: int = 1000, **kwargs) -> EfficientNet_V2:

Parameters

  • num_classes (int, optional): The number of output classes for the model. Default is 1000.

  • kwargs (dict, optional): Additional keyword arguments for configuring the EfficientNet_V2 model. These parameters are passed directly to the underlying constructor of EfficientNet_V2.

Returns

  • EfficientNet_V2: An instance of the EfficientNet_V2 model pre-configured for the small variant.

Usage

The efficientnet_v2_l function simplifies the creation of an EfficientNet-v2 model for lightweight tasks such as image classification on smaller datasets. The configuration ensures an optimized balance between efficiency and accuracy.

Examples

Creating a EfficientNet-v2-L model:

import lucid
import lucid.models as models

# Instantiate the large variant of EfficientNet_V2
model = models.efficientnet_v2_l(num_classes=100, dropout=0.2)

# Create a sample input tensor
input_tensor = lucid.random.randn(1, 3, 224, 224)

# Perform a forward pass
output = model(input_tensor)
print(output)