pvt_v2_b2

lucid.models.pvt_v2_b2(img_size: int = 224, num_classes: int = 1000, in_channels: int = 3, **kwargs) → PVT_V2

The pvt_v2_b2 function instantiates the PVTv2-B2 model, a stronger configuration in the Pyramid Vision Transformer V2 series. Compared to B1, it introduces deeper transformer depths in each stage while maintaining the same embedding size and attention configuration, offering better feature modeling for more complex visual tasks.

Total Parameters: 25,362,856

Function Signature

@register_model
def pvt_v2_b2(img_size: int = 224, num_classes: int = 1000, in_channels: int = 3, **kwargs) -> PVT_V2

Parameters

  • img_size (int, optional): The input image size. Default is 224.

  • num_classes (int, optional): The number of output classes for classification. Default is 1000.

  • in_channels (int, optional): Number of input channels. Default is 3.

  • kwargs (dict, optional): Additional parameters for customization, including:

  • embed_dims (list[int]): Embedding dimensions across four stages. Default is [64, 128, 320, 512].

  • depths (list[int]): Number of transformer blocks per stage. Default is [3, 4, 6, 3].

  • num_heads (list[int]): Multi-head self-attention head counts per stage. Default is [1, 2, 5, 8].

  • mlp_ratios (list[int]): MLP expansion ratios per stage. Default is [8, 8, 4, 4].

  • sr_ratios (list[int]): Spatial reduction ratios for attention projection. Default is [8, 4, 2, 1].

  • qkv_bias (bool): Whether to include bias terms in query/key/value. Default is True.

  • norm_layer (Callable): Normalization layer used in the model. Default is nn.LayerNorm(eps=1e-6).

Returns

  • PVT_V2: An instance of the PVT_V2 class configured as a PVTv2-B2 vision transformer.

Examples

>>> import lucid.models as models
>>> model = models.pvt_v2_b2()
>>> print(model)
PVT_V2(img_size=224, num_classes=1000, patch_size=7, embed_dims=[64, 128, 320, 512],
       num_heads=[1, 2, 5, 8], depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], linear=False, ...)