diff --git a/timm/models/layers/conv_bn_act.py b/timm/models/layers/conv_bn_act.py
index f5c94720..d7835320 100644
--- a/timm/models/layers/conv_bn_act.py
+++ b/timm/models/layers/conv_bn_act.py
@@ -9,13 +9,15 @@ from timm.models.layers import get_padding
 
 class ConvBnAct(nn.Module):
     def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, dilation=1, groups=1,
-                 drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
+                 drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None):
         super(ConvBnAct, self).__init__()
         padding = get_padding(kernel_size, stride, dilation)  # assuming PyTorch style padding for this block
+        use_aa = aa_layer is not None
         self.conv = nn.Conv2d(
-            in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
+            in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1 if use_aa else stride,
             padding=padding, dilation=dilation, groups=groups, bias=False)
         self.bn = norm_layer(out_channels)
+        self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else None
         self.drop_block = drop_block
         if act_layer is not None:
             self.act = act_layer(inplace=True)
@@ -29,4 +31,6 @@ class ConvBnAct(nn.Module):
             x = self.drop_block(x)
         if self.act is not None:
             x = self.act(x)
+        if self.aa is not None:
+            x = self.aa(x)
         return x
diff --git a/timm/models/layers/selective_kernel.py b/timm/models/layers/selective_kernel.py
index fcb26947..ed9132de 100644
--- a/timm/models/layers/selective_kernel.py
+++ b/timm/models/layers/selective_kernel.py
@@ -52,7 +52,7 @@ class SelectiveKernelConv(nn.Module):
 
     def __init__(self, in_channels, out_channels, kernel_size=None, stride=1, dilation=1, groups=1,
                  attn_reduction=16, min_attn_channels=32, keep_3x3=True, split_input=False,
-                 drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
+                 drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None):
         """ Selective Kernel Convolution Module
 
         As described in Selective Kernel Networks (https://arxiv.org/abs/1903.06586) with some modifications.
@@ -98,7 +98,8 @@ class SelectiveKernelConv(nn.Module):
         groups = min(out_channels, groups)
 
         conv_kwargs = dict(
-            stride=stride, groups=groups, drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer)
+            stride=stride, groups=groups, drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer,
+            aa_layer=aa_layer)
         self.paths = nn.ModuleList([
             ConvBnAct(in_channels, out_channels, kernel_size=k, dilation=d, **conv_kwargs)
             for k, d in zip(kernel_size, dilation)])
diff --git a/timm/models/sknet.py b/timm/models/sknet.py
index d9657352..2ba1b772 100644
--- a/timm/models/sknet.py
+++ b/timm/models/sknet.py
@@ -46,12 +46,12 @@ class SelectiveKernelBasic(nn.Module):
     expansion = 1
 
     def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
-                 sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None,
-                 drop_block=None, drop_path=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None):
+                 sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU,
+                 norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
         super(SelectiveKernelBasic, self).__init__()
 
         sk_kwargs = sk_kwargs or {}
-        conv_kwargs = dict(drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer)
+        conv_kwargs = dict(drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer)
         assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
         assert base_width == 64, 'BasicBlock doest not support changing base width'
         first_planes = planes // reduce_first
@@ -94,11 +94,12 @@ class SelectiveKernelBottleneck(nn.Module):
 
     def __init__(self, inplanes, planes, stride=1, downsample=None,
                  cardinality=1, base_width=64, sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None,
-                 drop_block=None, drop_path=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None):
+                 act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None,
+                 drop_block=None, drop_path=None):
         super(SelectiveKernelBottleneck, self).__init__()
 
         sk_kwargs = sk_kwargs or {}
-        conv_kwargs = dict(drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer)
+        conv_kwargs = dict(drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer)
         width = int(math.floor(planes * (base_width / 64)) * cardinality)
         first_planes = width // reduce_first
         outplanes = planes * self.expansion
diff --git a/timm/version.py b/timm/version.py
index cdf16bc4..641e1a8b 100644
--- a/timm/version.py
+++ b/timm/version.py
@@ -1 +1 @@
-__version__ = '0.1.24'
+__version__ = '0.1.26'