diff --git a/torchvision/models/segmentation/segmentation.py b/torchvision/models/segmentation/segmentation.py
index 8e1d2573068..15df4d8ae39 100644
--- a/torchvision/models/segmentation/segmentation.py
+++ b/torchvision/models/segmentation/segmentation.py
@@ -32,7 +32,7 @@ def _segm_resnet(name, backbone_name, num_classes, aux, pretrained_backbone=True
         aux_classifier = FCNHead(inplanes, num_classes)
 
     model_map = {
-        'deeplab': (DeepLabHead, DeepLabV3),
+        'deeplabv3': (DeepLabHead, DeepLabV3),
         'fcn': (FCNHead, FCN),
     }
     inplanes = 2048
@@ -43,20 +43,12 @@ def _segm_resnet(name, backbone_name, num_classes, aux, pretrained_backbone=True
     return model
 
 
-def fcn_resnet50(pretrained=False, progress=True,
-                 num_classes=21, aux_loss=None, **kwargs):
-    """Constructs a Fully-Convolutional Network model with a ResNet-50 backbone.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on COCO train2017 which
-            contains the same classes as Pascal VOC
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
+def _load_model(arch_type, backbone, pretrained, progress, num_classes, aux_loss, **kwargs):
     if pretrained:
         aux_loss = True
-    model = _segm_resnet("fcn", "resnet50", num_classes, aux_loss, **kwargs)
+    model = _segm_resnet(arch_type, backbone, num_classes, aux_loss, **kwargs)
     if pretrained:
-        arch = 'fcn_resnet50_coco'
+        arch = arch_type + '_' + backbone + '_coco'
         model_url = model_urls[arch]
         if model_url is None:
             raise NotImplementedError('pretrained {} is not supported as of now'.format(arch))
@@ -66,6 +58,18 @@ def fcn_resnet50(pretrained=False, progress=True,
     return model
 
 
+def fcn_resnet50(pretrained=False, progress=True,
+                 num_classes=21, aux_loss=None, **kwargs):
+    """Constructs a Fully-Convolutional Network model with a ResNet-50 backbone.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on COCO train2017 which
+            contains the same classes as Pascal VOC
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _load_model('fcn', 'resnet50', pretrained, progress, num_classes, aux_loss, **kwargs)
+
+
 def fcn_resnet101(pretrained=False, progress=True,
                   num_classes=21, aux_loss=None, **kwargs):
     """Constructs a Fully-Convolutional Network model with a ResNet-101 backbone.
@@ -75,18 +79,7 @@ def fcn_resnet101(pretrained=False, progress=True,
             contains the same classes as Pascal VOC
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    if pretrained:
-        aux_loss = True
-    model = _segm_resnet("fcn", "resnet101", num_classes, aux_loss, **kwargs)
-    if pretrained:
-        arch = 'fcn_resnet101_coco'
-        model_url = model_urls[arch]
-        if model_url is None:
-            raise NotImplementedError('pretrained {} is not supported as of now'.format(arch))
-        else:
-            state_dict = load_state_dict_from_url(model_url, progress=progress)
-            model.load_state_dict(state_dict)
-    return model
+    return _load_model('fcn', 'resnet101', pretrained, progress, num_classes, aux_loss, **kwargs)
 
 
 def deeplabv3_resnet50(pretrained=False, progress=True,
@@ -98,18 +91,7 @@ def deeplabv3_resnet50(pretrained=False, progress=True,
             contains the same classes as Pascal VOC
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    if pretrained:
-        aux_loss = True
-    model = _segm_resnet("deeplab", "resnet50", num_classes, aux_loss, **kwargs)
-    if pretrained:
-        arch = 'deeplabv3_resnet50_coco'
-        model_url = model_urls[arch]
-        if model_url is None:
-            raise NotImplementedError('pretrained {} is not supported as of now'.format(arch))
-        else:
-            state_dict = load_state_dict_from_url(model_url, progress=progress)
-            model.load_state_dict(state_dict)
-    return model
+    return _load_model('deeplabv3', 'resnet50', pretrained, progress, num_classes, aux_loss, **kwargs)
 
 
 def deeplabv3_resnet101(pretrained=False, progress=True,
@@ -121,15 +103,4 @@ def deeplabv3_resnet101(pretrained=False, progress=True,
             contains the same classes as Pascal VOC
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    if pretrained:
-        aux_loss = True
-    model = _segm_resnet("deeplab", "resnet101", num_classes, aux_loss, **kwargs)
-    if pretrained:
-        arch = 'deeplabv3_resnet101_coco'
-        model_url = model_urls[arch]
-        if model_url is None:
-            raise NotImplementedError('pretrained {} is not supported as of now'.format(arch))
-        else:
-            state_dict = load_state_dict_from_url(model_url, progress=progress)
-            model.load_state_dict(state_dict)
-    return model
+    return _load_model('deeplabv3', 'resnet101', pretrained, progress, num_classes, aux_loss, **kwargs)