diff --git a/timm/models/efficientnet_builder.py b/timm/models/efficientnet_builder.py
index db6f54f9..ca2060c4 100644
--- a/timm/models/efficientnet_builder.py
+++ b/timm/models/efficientnet_builder.py
@@ -358,15 +358,24 @@ class EfficientNetBuilder:
         return stages
 
 
-def _init_weight_goog(m, n=''):
+def _init_weight_goog(m, n='', fix_group_fanout=False):
     """ Weight initialization as per Tensorflow official implementations.
 
+    Args:
+        m (nn.Module): module to init
+        n (str): module name
+        fix_group_fanout (bool): enable correct fanout calculation w/ group convs
+
+    FIXME change fix_group_fanout to default to True if experiments show better training results
+
     Handles layers in EfficientNet, EfficientNet-CondConv, MixNet, MnasNet, MobileNetV3, etc:
     * https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py
     * https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
     """
     if isinstance(m, CondConv2d):
         fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+        if fix_group_fanout:
+            fan_out //= m.groups
         init_weight_fn = get_condconv_initializer(
             lambda w: w.data.normal_(0, math.sqrt(2.0 / fan_out)), m.num_experts, m.weight_shape)
         init_weight_fn(m.weight)
@@ -374,6 +383,8 @@ def _init_weight_goog(m, n=''):
             m.bias.data.zero_()
     elif isinstance(m, nn.Conv2d):
         fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+        if fix_group_fanout:
+            fan_out //= m.groups
         m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
         if m.bias is not None:
             m.bias.data.zero_()
@@ -390,21 +401,6 @@ def _init_weight_goog(m, n=''):
         m.bias.data.zero_()
 
 
-def _init_weight_default(m, n=''):
-    """ Basic ResNet (Kaiming) style weight init"""
-    if isinstance(m, CondConv2d):
-        init_fn = get_condconv_initializer(partial(
-            nn.init.kaiming_normal_, mode='fan_out', nonlinearity='relu'), m.num_experts, m.weight_shape)
-        init_fn(m.weight)
-    elif isinstance(m, nn.Conv2d):
-        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-    elif isinstance(m, nn.BatchNorm2d):
-        m.weight.data.fill_(1.0)
-        m.bias.data.zero_()
-    elif isinstance(m, nn.Linear):
-        nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='linear')
-
-
 def efficientnet_init_weights(model: nn.Module, init_fn=None):
     init_fn = init_fn or _init_weight_goog
     for n, m in model.named_modules():