diff --git a/hubconf.py b/hubconf.py
index 760436c0..0d638be5 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -6,9 +6,9 @@
 from midas.midas_net import MidasNet
 from midas.midas_net_custom import MidasNet_small
 
-def DPT_BEit_L_512(pretrained=True, **kwargs):
+def DPT_BEiT_L_512(pretrained=True, **kwargs):
     """ # This docstring shows up in hub.help()
-    MiDaS DPT_BEit_L_512 model for monocular depth estimation
+    MiDaS DPT_BEiT_L_512 model for monocular depth estimation
     pretrained (bool): load pretrained weights into model
     """
 
@@ -29,9 +29,9 @@ def DPT_BEit_L_512(pretrained=True, **kwargs):
 
     return model
 
-def DPT_BEit_L_384(pretrained=True, **kwargs):
+def DPT_BEiT_L_384(pretrained=True, **kwargs):
     """ # This docstring shows up in hub.help()
-    MiDaS DPT_BEit_L_384 model for monocular depth estimation
+    MiDaS DPT_BEiT_L_384 model for monocular depth estimation
     pretrained (bool): load pretrained weights into model
     """
 
@@ -52,6 +52,29 @@ def DPT_BEit_L_384(pretrained=True, **kwargs):
 
     return model
 
+def DPT_BEiT_B_384(pretrained=True, **kwargs):
+    """ # This docstring shows up in hub.help()
+    MiDaS DPT_BEiT_B_384 model for monocular depth estimation
+    pretrained (bool): load pretrained weights into model
+    """
+
+    model = DPTDepthModel(
+            path=None,
+            backbone="beitb16_384",
+            non_negative=True,
+        )
+
+    if pretrained:
+        checkpoint = (
+            "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_base_384.pt"
+        )
+        state_dict = torch.hub.load_state_dict_from_url(
+            checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
+        )
+        model.load_state_dict(state_dict)
+
+    return model
+
 def DPT_SwinV2_L_384(pretrained=True, **kwargs):
     """ # This docstring shows up in hub.help()
     MiDaS DPT_SwinV2_L_384 model for monocular depth estimation
@@ -75,6 +98,29 @@ def DPT_SwinV2_L_384(pretrained=True, **kwargs):
 
     return model
 
+def DPT_SwinV2_B_384(pretrained=True, **kwargs):
+    """ # This docstring shows up in hub.help()
+    MiDaS DPT_SwinV2_B_384 model for monocular depth estimation
+    pretrained (bool): load pretrained weights into model
+    """
+
+    model = DPTDepthModel(
+            path=None,
+            backbone="swin2b24_384",
+            non_negative=True,
+        )
+
+    if pretrained:
+        checkpoint = (
+            "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_base_384.pt"
+        )
+        state_dict = torch.hub.load_state_dict_from_url(
+            checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
+        )
+        model.load_state_dict(state_dict)
+
+    return model
+
 def DPT_SwinV2_T_256(pretrained=True, **kwargs):
     """ # This docstring shows up in hub.help()
     MiDaS DPT_SwinV2_T_256 model for monocular depth estimation
@@ -98,6 +144,29 @@ def DPT_SwinV2_T_256(pretrained=True, **kwargs):
 
     return model
 
+def DPT_Swin_L_384(pretrained=True, **kwargs):
+    """ # This docstring shows up in hub.help()
+    MiDaS DPT_Swin_L_384 model for monocular depth estimation
+    pretrained (bool): load pretrained weights into model
+    """
+
+    model = DPTDepthModel(
+            path=None,
+            backbone="swinl12_384",
+            non_negative=True,
+        )
+
+    if pretrained:
+        checkpoint = (
+            "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin_large_384.pt"
+        )
+        state_dict = torch.hub.load_state_dict_from_url(
+            checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
+        )
+        model.load_state_dict(state_dict)
+
+    return model
+
 def DPT_Next_ViT_L_384(pretrained=True, **kwargs):
     """ # This docstring shows up in hub.help()
     MiDaS DPT_Next_ViT_L_384 model for monocular depth estimation
@@ -131,6 +200,8 @@ def DPT_LeViT_224(pretrained=True, **kwargs):
             path=None,
             backbone="levit_384",
             non_negative=True,
+            head_features_1=64,
+            head_features_2=8,
         )
 
     if pretrained:
@@ -289,4 +360,76 @@ def transforms():
         ]
     )
 
+    transforms.beit512_transform = Compose(
+        [
+            lambda img: {"image": img / 255.0},
+            Resize(
+                512,
+                512,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method="minimal",
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            PrepareForNet(),
+            lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0),
+        ]
+    )
+
+    transforms.swin384_transform = Compose(
+        [
+            lambda img: {"image": img / 255.0},
+            Resize(
+                384,
+                384,
+                resize_target=None,
+                keep_aspect_ratio=False,
+                ensure_multiple_of=32,
+                resize_method="minimal",
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            PrepareForNet(),
+            lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0),
+        ]
+    )
+
+    transforms.swin256_transform = Compose(
+        [
+            lambda img: {"image": img / 255.0},
+            Resize(
+                256,
+                256,
+                resize_target=None,
+                keep_aspect_ratio=False,
+                ensure_multiple_of=32,
+                resize_method="minimal",
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            PrepareForNet(),
+            lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0),
+        ]
+    )
+
+    transforms.levit_transform = Compose(
+        [
+            lambda img: {"image": img / 255.0},
+            Resize(
+                224,
+                224,
+                resize_target=None,
+                keep_aspect_ratio=False,
+                ensure_multiple_of=32,
+                resize_method="minimal",
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            PrepareForNet(),
+            lambda sample: torch.from_numpy(sample["image"]).unsqueeze(0),
+        ]
+    )
+
     return transforms