keras/src/applications/convnext.py/ConvNeXtSmall
def ConvNeXtSmall(
    include_top=True,
    include_preprocessing=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    name="convnext_small",
):
    return ConvNeXt(
        weights_name="convnext_small",
        depths=MODEL_CONFIGS["small"]["depths"],
        projection_dims=MODEL_CONFIGS["small"]["projection_dims"],
        drop_path_rate=0.0,
        layer_scale_init_value=1e-6,
        default_size=MODEL_CONFIGS["small"]["default_size"],
        name=name,
        include_top=include_top,
        include_preprocessing=include_preprocessing,
        weights=weights,
        input_tensor=input_tensor,
        input_shape=input_shape,
        pooling=pooling,
        classes=classes,
        classifier_activation=classifier_activation,
    )