vit_base¶

lucid.models.vit_base(image_size: int = 224, patch_size: int = 16, num_classes: int = 1000, **kwargs) → ViT¶

The vit_base function instantiates a Vision Transformer (ViT-B) model with a predefined architecture. This model offers a strong balance between accuracy and efficiency, making it well-suited for large-scale vision tasks.

Total Parameters: 86,567,656

Function Signature¶

@register_model
def vit_base(
    image_size: int = 224,
    patch_size: int = 16,
    num_classes: int = 1000,
    **kwargs
) -> ViT

Parameters¶

image_size (int, optional): The size of the input image (assumes square images). Default is 224.
patch_size (int, optional): The size of the patches the image is divided into. Default is 16.
num_classes (int, optional): The number of output classes for classification. Default is 1000.
kwargs (dict, optional): Additional parameters for customization.

Returns¶

ViT: An instance of the ViT class configured as a base vision transformer.

Examples¶

>>> import lucid.models as models
>>> model = models.vit_base()
>>> print(model)
ViT(img_size=224, patch_size=16, num_classes=1000, ...)