diff --git a/torchvision/models/densenet.py b/torchvision/models/densenet.py
index 43f8e7c3eba..beb5857bbe8 100644
--- a/torchvision/models/densenet.py
+++ b/torchvision/models/densenet.py
@@ -69,10 +69,12 @@ class DenseNet(nn.Module):
     """
 
     def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
-                 num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000):
+                 num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, transform_input=False):
 
         super(DenseNet, self).__init__()
 
+        self.transform_input = transform_input
+
         # First convolution
         self.features = nn.Sequential(OrderedDict([
             ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
@@ -110,6 +112,14 @@ def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                 nn.init.constant_(m.bias, 0)
 
     def forward(self, x):
+
+        # imagenet normalisation
+        if self.transform_input:
+            x_ch0 = (torch.unsqueeze(x[:, 0], 1) - 0.485) / 0.229
+            x_ch1 = (torch.unsqueeze(x[:, 1], 1) - 0.456) / 0.224
+            x_ch2 = (torch.unsqueeze(x[:, 2], 1) - 0.406) / 0.225
+            x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
+
         features = self.features(x)
         out = F.relu(features, inplace=True)
         out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
@@ -123,6 +133,8 @@ def densenet121(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        transform_input (bool): If True, preprocesses the input according to the method with which it
+        was trained on ImageNet. Default: *False*
     """
     model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
                      **kwargs)
@@ -150,6 +162,8 @@ def densenet169(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        transform_input (bool): If True, preprocesses the input according to the method with which it
+        was trained on ImageNet. Default: *False*
     """
     model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
                      **kwargs)
@@ -177,6 +191,8 @@ def densenet201(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        transform_input (bool): If True, preprocesses the input according to the method with which it
+        was trained on ImageNet. Default: *False*
     """
     model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
                      **kwargs)
@@ -204,6 +220,8 @@ def densenet161(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        transform_input (bool): If True, preprocesses the input according to the method with which it
+        was trained on ImageNet. Default: *False*
     """
     model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
                      **kwargs)