inception_resnet_v1

lucid.models.inception_resnet_v1(num_classes: int = 1000, **kwargs) → InceptionResNet

Overview

The inception_resnet_v1 function implements the Inception-ResNet v1 architecture. This model combines the multi-scale processing of Inception modules with the residual connections of ResNet, enabling efficient optimization and enhanced performance for image classification tasks.

This function returns a preconfigured InceptionResNet model for use in various applications, with the flexibility to adjust the number of output classes or include additional customizations.

Total Parameters: 22,739,128

Function Signature

@register_model
def inception_resnet_v1(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 v1 architecture.

Example Usage

Below is an example of defining and using the inception_resnet_v1 function:

import lucid.models as models

# Create an Inception-ResNet v1 model with default parameters
model = models.inception_resnet_v1(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)