convnext_xlarge¶
The convnext_xlarge function creates a ConvNeXt variant with an expansive configuration, offering the highest model capacity in the ConvNeXt family. This model is optimized for large-scale applications and highly complex tasks, featuring the largest depths and dimensions for superior performance.
Total Parameters: 350,196,968
Function Signature¶
@register_model
def convnext_xlarge(num_classes: int = 1000, **kwargs) -> ConvNeXt
Parameters¶
num_classes (int, optional): The number of output classes for classification. Default is 1000.
kwargs (dict, optional): Additional keyword arguments for further customization of the ConvNeXt model.
Returns¶
ConvNeXt: An instance of the ConvNeXt model with an extra-large configuration.
Examples¶
Basic Usage
from lucid.models import convnext_xlarge
# Create a ConvNeXt-XLarge model with default 1000 classes
model = convnext_xlarge(num_classes=1000)
# Input tensor with shape (1, 3, 224, 224)
input_ = lucid.random.randn(1, 3, 224, 224)
# Perform forward pass
output = model(input_)
print(output.shape) # Shape: (1, 1000)
Custom Number of Classes
# Create a ConvNeXt-XLarge model with 21841 classes
model = convnext_xlarge(num_classes=21841)
input_ = lucid.random.randn(1, 3, 224, 224)
output = model(input_)
print(output.shape) # Shape: (1, 21841)