diff --git a/torchvision/models/mnasnet.py b/torchvision/models/mnasnet.py
index 5deb87c2ad1..ba815206be2 100644
--- a/torchvision/models/mnasnet.py
+++ b/torchvision/models/mnasnet.py
@@ -8,10 +8,10 @@
 
 _MODEL_URLS = {
     "mnasnet0_5":
-    "https://github.com/1e100/mnasnet_trainer/releases/download/v0.1/mnasnet0.5_top1_67.592-7c6cb539b9.pth",
+    "https://download.pytorch.org/models/mnasnet0.5_top1_67.592-7c6cb539b9.pth",
     "mnasnet0_75": None,
     "mnasnet1_0":
-    "https://github.com/1e100/mnasnet_trainer/releases/download/v0.1/mnasnet1.0_top1_73.512-f206786ef8.pth",
+    "https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
     "mnasnet1_3": None
 }
 
@@ -143,41 +143,41 @@ def _initialize_weights(self):
                 nn.init.zeros_(m.bias)
 
 
-def _load_pretrained(model_name, model):
+def _load_pretrained(model_name, model, progress):
     if model_name not in _MODEL_URLS or _MODEL_URLS[model_name] is None:
         raise ValueError(
             "No checkpoint is available for model type {}".format(model_name))
     checkpoint_url = _MODEL_URLS[model_name]
-    model.load_state_dict(load_state_dict_from_url(checkpoint_url))
+    model.load_state_dict(load_state_dict_from_url(checkpoint_url, progress=progress))
 
 
-def mnasnet0_5(pretrained=False, **kwargs):
+def mnasnet0_5(pretrained=False, progress=True, **kwargs):
     """ MNASNet with depth multiplier of 0.5. """
     model = MNASNet(0.5, **kwargs)
     if pretrained:
-        _load_pretrained("mnasnet0_5", model)
+        _load_pretrained("mnasnet0_5", model, progress)
     return model
 
 
-def mnasnet0_75(pretrained=False, **kwargs):
+def mnasnet0_75(pretrained=False, progress=True, **kwargs):
     """ MNASNet with depth multiplier of 0.75. """
     model = MNASNet(0.75, **kwargs)
     if pretrained:
-        _load_pretrained("mnasnet0_75", model)
+        _load_pretrained("mnasnet0_75", model, progress)
     return model
 
 
-def mnasnet1_0(pretrained=False, **kwargs):
+def mnasnet1_0(pretrained=False, progress=True, **kwargs):
     """ MNASNet with depth multiplier of 1.0. """
     model = MNASNet(1.0, **kwargs)
     if pretrained:
-        _load_pretrained("mnasnet1_0", model)
+        _load_pretrained("mnasnet1_0", model, progress)
     return model
 
 
-def mnasnet1_3(pretrained=False, **kwargs):
+def mnasnet1_3(pretrained=False, progress=True, **kwargs):
     """ MNASNet with depth multiplier of 1.3. """
     model = MNASNet(1.3, **kwargs)
     if pretrained:
-        _load_pretrained("mnasnet1_3", model)
+        _load_pretrained("mnasnet1_3", model, progress)
     return model