swin_v2_huge¶

lucid.models.swin_v2_huge(img_size: int = 224, num_classes: int = 1000, **kwargs) → SwinTransformer_V2¶

The swin_v2_huge function initializes an extra-large Swin Transformer V2 model with a predefined architecture. This model extends the Swin Transformer V2 family by incorporating deeper layers, a larger number of attention heads, and more advanced normalization techniques. It is designed for high-performance vision tasks such as image classification, object detection, and segmentation.

Total Parameters: 657,796,668

Function Signature¶

@register_model
def swin_v2_huge(img_size: int = 224, num_classes: int = 1000, **kwargs) -> SwinTransformer_V2

Parameters¶

img_size (int, optional): The size of the input image (assumes square images). Default is 224.
num_classes (int, optional): The number of output classes for classification. Default is 1000.
kwargs (dict, optional): Additional parameters for customization, including:
- depths (list[int]): A list specifying the number of transformer blocks in each stage. The typical default configuration is [2, 2, 18, 2], indicating that the model has 4 stages with 2, 2, 18, and 2 blocks respectively.
- num_heads (list[int]): A list specifying the number of attention heads in each stage. The common default is [6, 12, 24, 48], allowing for significantly improved multi-scale feature extraction and global context understanding.

Returns¶

SwinTransformer_V2: An instance of the SwinTransformer_V2 class configured as a high-capacity vision transformer.

Examples¶

>>> import lucid.models as models
>>> model = models.swin_v2_huge()
>>> print(model)
SwinTransformer_V2(img_size=224, num_classes=1000, ...)