inception_resnet_v2

lucid.models.inception_resnet_v2(num_classes: int = 1000, **kwargs) → InceptionResNet

Overview

The inception_resnet_v2 function implements the Inception-ResNet v2 architecture, which builds on Inception-ResNet v1 with deeper layers and improved efficiency. This model leverages the advantages of Inception modules and residual connections for enhanced performance on complex image classification tasks.

This function returns a preconfigured InceptionResNet model optimized for use in various applications, while allowing for flexibility in the number of output classes and other customizations.

Total Parameters: 35,847,512

Function Signature

@register_model
def inception_resnet_v2(
    num_classes: int = 1000,
    **kwargs,
) -> InceptionResNet

Parameters

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

  • kwargs (dict, optional): Additional arguments passed to the underlying InceptionResNet base class or components.

Returns

  • InceptionResNet: An instance of the InceptionResNet model configured for the v2 architecture.

Example Usage

Below is an example of defining and using the inception_resnet_v2 function:

import lucid.models as models

# Create an Inception-ResNet v2 model with default parameters
model = models.inception_resnet_v2(num_classes=1000)

# Sample input tensor (e.g., batch of 299x299 RGB images)
input_tensor = lucid.Tensor([...])

# Forward pass
output = model(input_tensor)
print(output)