convnext_base¶
The convnext_base function creates a ConvNeXt variant with a standard configuration, providing a robust model capacity suitable for general-purpose applications. This variant has larger depths and dimensions, offering enhanced performance on image classification tasks.
Total Parameters: 88,591,464
Function Signature¶
@register_model
def convnext_base(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 a base configuration.
Examples¶
Basic Usage
from lucid.models import convnext_base
# Create a ConvNeXt-Base model with default 1000 classes
model = convnext_base(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)