maxvit_base¶

lucid.models.maxvit_large(in_channels: int = 3, num_classes: int = 1000, **kwargs) → MaxViT¶

The maxvit_base function constructs a high-capacity MaxViT model variant designed for more complex tasks, such as large-scale image classification. It offers deeper layers in the middle stages for enhanced expressiveness.

Total Parameters: 171,187,880

Function Signature¶

def maxvit_base(
    in_channels: int = 3,
    num_classes: int = 1000,
    **kwargs
) -> MaxViT

Parameters¶

in_channels (int, optional): Number of input image channels. Default is 3.
num_classes (int, optional): Number of classification categories. Default is 1000.
kwargs (any): Additional keyword arguments passed to the MaxViT constructor.

Model Configuration¶

This base preset uses the following configuration:

depths: (2, 6, 14, 2) — More blocks in the deeper stages.
channels: (96, 192, 384, 768)
embed_dim: 64

Example¶

import lucid
from lucid.models.transformer import maxvit_base

model = maxvit_base()
input_tensor = lucid.randn(1, 3, 224, 224)
output = model(input_tensor)
print(output.shape)  # (1, 1000)

Note

The maxvit_base variant is suitable for high-resolution datasets and larger model capacity benchmarks.