diff --git a/mypy.ini b/mypy.ini
index 07b9c75c516..f58ab3d9919 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -47,10 +47,6 @@ ignore_errors = True
 
 ignore_errors = True
 
-[mypy-torchvision.models.densenet.*]
-
-ignore_errors=True
-
 [mypy-torchvision.models.detection.anchor_utils]
 
 ignore_errors = True
diff --git a/torchvision/models/densenet.py b/torchvision/models/densenet.py
index 3b42807cc96..569ed0ad3d0 100644
--- a/torchvision/models/densenet.py
+++ b/torchvision/models/densenet.py
@@ -1,7 +1,7 @@
 import re
 from collections import OrderedDict
 from functools import partial
-from typing import Any, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, cast
 
 import torch
 import torch.nn as nn
@@ -116,7 +116,7 @@ def __init__(
             )
             self.add_module("denselayer%d" % (i + 1), layer)
 
-    def forward(self, init_features: Tensor) -> Tensor:
+    def forward(self, init_features: Tensor) -> Tensor:  # type: ignore[override]
         features = [init_features]
         for name, layer in self.items():
             new_features = layer(features)
@@ -227,7 +227,7 @@ def _load_state_dict(model: nn.Module, weights: WeightsEnum, progress: bool) ->
         r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
     )
 
-    state_dict = weights.get_state_dict(progress=progress, check_hash=True)
+    state_dict = cast(Dict[str, Tensor], weights.get_state_dict(progress=progress, check_hash=True))
     for key in list(state_dict.keys()):
         res = pattern.match(key)
         if res: