diff --git a/tests/test_models.py b/tests/test_models.py
index d8ac8d64..e75b17f9 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -41,7 +41,7 @@ NON_STD_FILTERS = [
     'vit_*', 'tnt_*', 'pit_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
     'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*',
     'poolformer_*', 'volo_*', 'sequencer2d_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*',
-    'eva_*', 'flexivit*', 'eva02*', 'samvit_*'
+    'eva_*', 'flexivit*', 'eva02*', 'samvit_*', 'efficientvit_m*'
 ]
 NUM_NON_STD = len(NON_STD_FILTERS)
 
diff --git a/timm/models/efficientvit_mit.py b/timm/models/efficientvit_mit.py
index d7c01a0f..674e960c 100644
--- a/timm/models/efficientvit_mit.py
+++ b/timm/models/efficientvit_mit.py
@@ -661,14 +661,14 @@ def efficientvit_b0(pretrained=False, **kwargs):
 @register_model
 def efficientvit_b1(pretrained=False, **kwargs):
     model_args = dict(width_list=[16, 32, 64, 128, 256], depth_list=[1, 2, 3, 3, 4], dim=16, head_width_list=[1536, 1600])
-    return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
+    return _create_efficientvit('efficientvit_b1', pretrained=pretrained, **dict(model_args, **kwargs))
 
 @register_model
 def efficientvit_b2(pretrained=False, **kwargs):
     model_args = dict(width_list=[24, 48, 96, 192, 384], depth_list=[1, 3, 4, 4, 6], dim=32, head_width_list=[2304, 2560])
-    return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
+    return _create_efficientvit('efficientvit_b2', pretrained=pretrained, **dict(model_args, **kwargs))
 
 @register_model
 def efficientvit_b3(pretrained=False, **kwargs):
     model_args = dict(width_list=[32, 64, 128, 256, 512], depth_list=[1, 4, 6, 6, 9], dim=32, head_width_list=[2304, 2560])
-    return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
+    return _create_efficientvit('efficientvit_b3', pretrained=pretrained, **dict(model_args, **kwargs))
diff --git a/timm/models/efficientvit_msra.py b/timm/models/efficientvit_msra.py
index 57d5cf6d..ca93283e 100644
--- a/timm/models/efficientvit_msra.py
+++ b/timm/models/efficientvit_msra.py
@@ -373,8 +373,8 @@ class EfficientViTMSRA(nn.Module):
         self.stages = nn.Sequential(*stages)
 
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
-        self.out_dims = embed_dim[-1]
-        self.head = BNLinear(self.out_dims, num_classes) if num_classes > 0 else torch.nn.Identity()
+        self.num_features = embed_dim[-1]
+        self.head = BNLinear(self.num_features, num_classes) if num_classes > 0 else torch.nn.Identity()
 
     @torch.jit.ignore
     def group_matcher(self, coarse=False):
@@ -396,7 +396,7 @@ class EfficientViTMSRA(nn.Module):
         self.num_classes = num_classes
         if global_pool is not None:
             self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
-        self.head = BNLinear(self.out_dims, num_classes) if num_classes > 0 else torch.nn.Identity()
+        self.head = BNLinear(self.num_features, num_classes) if num_classes > 0 else torch.nn.Identity()
 
     def forward_features(self, x):
         x = self.patch_embed(x)