diff --git a/timm/models/mobilenetv3.py b/timm/models/mobilenetv3.py
index 668341d64..984d6462a 100644
--- a/timm/models/mobilenetv3.py
+++ b/timm/models/mobilenetv3.py
@@ -453,7 +453,8 @@ def _gen_mobilenet_v3_rw(
 
 
 def _gen_mobilenet_v3(
-        variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs
+        variant: str, channel_multiplier: float = 1.0, depth_multiplier: float = 1.0,
+        group_size=None, pretrained: bool = False, **kwargs
 ) -> MobileNetV3:
     """Creates a MobileNet-V3 model.
 
@@ -537,7 +538,7 @@ def _gen_mobilenet_v3(
             ]
     se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, group_size=group_size),
+        block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, group_size=group_size),
         num_features=num_features,
         stem_size=16,
         fix_stem=channel_multiplier < 0.75,
@@ -927,6 +928,9 @@ def _cfg(url: str = '', **kwargs):
         origin_url='https://github.com/Alibaba-MIIL/ImageNet21K',
         paper_ids='arXiv:2104.10972v4',
         interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), num_classes=11221),
+    'mobilenetv3_large_150d.untrained': _cfg(
+        #hf_hub_id='timm/',
+    ),
 
     'mobilenetv3_small_050.lamb_in1k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth',
@@ -1099,6 +1103,11 @@ def mobilenetv3_large_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
     model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
     return model
 
+@register_model
+def mobilenetv3_large_150d(pretrained: bool = False, **kwargs) -> MobileNetV3:
+    """ MobileNet V3 """
+    model = _gen_mobilenet_v3('mobilenetv3_large_150d', 1.5, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
+    return model
 
 @register_model
 def mobilenetv3_small_050(pretrained: bool = False, **kwargs) -> MobileNetV3: