convnext_small¶
The convnext_small function creates a ConvNeXt variant with a balanced configuration, offering a trade-off between computational efficiency and model capacity. This model variant features moderately increased depths and dimensions compared to the tiny variant, making it suitable for a wider range of applications.
Total Parameters: 46,884,148
Function Signature¶
@register_model
def convnext_small(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 small configuration.
Examples¶
Basic Usage
from lucid.models import convnext_small
# Create a ConvNeXt-Small model with default 1000 classes
model = convnext_small(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)