diff --git a/vision_toolbox/backbones/swin.py b/vision_toolbox/backbones/swin.py
index 4ca7e7d..04a1d65 100644
--- a/vision_toolbox/backbones/swin.py
+++ b/vision_toolbox/backbones/swin.py
@@ -134,8 +134,8 @@ def __init__(
         d_model: int,
         n_heads: int,
         depths: tuple[int, ...],
+        window_sizes: tuple[int, ...],
         patch_size: int = 4,
-        window_size: int = 7,
         mlp_ratio: float = 4.0,
         bias: bool = True,
         dropout: float = 0.0,
@@ -143,7 +143,6 @@ def __init__(
         act: _act = nn.GELU,
     ) -> None:
         assert img_size % patch_size == 0
-        assert img_size % window_size == 0
         assert d_model % n_heads == 0
         super().__init__()
         self.patch_embed = nn.Conv2d(3, d_model, patch_size, patch_size)
@@ -152,7 +151,7 @@ def __init__(
 
         input_size = img_size // patch_size
         self.stages = nn.Sequential()
-        for i, depth in enumerate(depths):
+        for i, (depth, window_size) in enumerate(zip(depths, window_sizes)):
             stage = nn.Sequential()
             for i in range(depth):
                 blk = SwinBlock(input_size, d_model, n_heads, window_size, i % 2, mlp_ratio, bias, dropout, norm, act)
@@ -176,12 +175,17 @@ def forward(self, x: Tensor) -> Tensor:
 
     @staticmethod
     def from_config(variant: str, img_size: int, pretrained: bool = False) -> SwinTransformer:
-        d_model, n_heads, depths = dict(
-            T=(96, 3, (2, 2, 6, 2)),
-            S=(96, 3, (2, 2, 18, 2)),
-            B=(128, 4, (2, 2, 18, 2)),
-            L=(192, 6, (2, 2, 18, 2)),
-        )[variant]
-        m = SwinTransformer(img_size, d_model, n_heads, depths)
+        d_model, n_heads, depths, window_sizes = {
+            # Sub-section 3.3 in https://arxiv.org/pdf/2103.14030.pdf
+            "T": (96, 3, (2, 2, 6, 2), (7, 7, 7, 7)),
+            "S": (96, 3, (2, 2, 18, 2), (7, 7, 7, 7)),
+            "B": (128, 4, (2, 2, 18, 2), (7, 7, 7, 7)),
+            "L": (192, 6, (2, 2, 18, 2), (7, 7, 7, 7)),
+            # https://github.com/microsoft/Cream/blob/main/AutoFormerV2/configs
+            "S3-T": (96, 3, (2, 2, 6, 2), (7, 7, 14, 7)),
+            "S3-S": (96, 3, (2, 2, 18, 2), (14, 14, 14, 14)),
+            "S3-B": (96, 3, (2, 2, 30, 2), (7, 7, 14, 7)),
+        }[variant]
+        m = SwinTransformer(img_size, d_model, n_heads, depths, window_sizes)
 
         return m