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. It builds an InceptionResNetConfig preset internally and forwards extra keyword arguments to that config.

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 keyword arguments forwarded to InceptionResNetConfig, such as in_channels or dropout_prob.

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)

model = models.inception_resnet_v1(
    num_classes=10,
    in_channels=1,
    dropout_prob=0.25,
)

print(model.config)