diff --git a/models/resnet_in.py b/models/resnet_in.py
index 31c0c7d..fa2d950 100644
--- a/models/resnet_in.py
+++ b/models/resnet_in.py
@@ -107,7 +107,7 @@ def forward(self, x):
 
 class ResNet(nn.Module):
 
-    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
+    def __init__(self, block, layers, initial_kernel_size=7, num_classes=1000, zero_init_residual=False,
                  groups=1, width_per_group=64, replace_stride_with_dilation=None,
                  norm_layer=None):
         super(ResNet, self).__init__()
@@ -116,6 +116,8 @@ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
         self._norm_layer = norm_layer
 
         self.inplanes = 64
+        self.initial_kernel_size = initial_kernel_size
+        self.num_classes = num_classes
         self.dilation = 1
         if replace_stride_with_dilation is None:
             # each element in the tuple indicates if we should replace
@@ -126,7 +128,7 @@ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                              "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
         self.groups = groups
         self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=self.initial_kernel_size, stride=2, padding=3,
                                bias=False)
         self.bn1 = norm_layer(self.inplanes)
         self.relu = nn.ReLU(inplace=True)
@@ -139,7 +141,7 @@ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
         self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                        dilate=replace_stride_with_dilation[2])
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
+        self.fc = nn.Linear(512 * block.expansion, self.num_classes)
 
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
@@ -209,7 +211,7 @@ def _resnet(arch, block, layers, pretrained, progress, **kwargs):
     return model
 
 
-def resnet18(pretrained=False, progress=True, **kwargs):
+def ResNet18(pretrained=False, progress=True, **kwargs):
     r"""ResNet-18 model from
     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
     Args:
@@ -220,7 +222,7 @@ def resnet18(pretrained=False, progress=True, **kwargs):
                    **kwargs)
 
 
-def resnet34(pretrained=False, progress=True, **kwargs):
+def ResNet34(pretrained=False, progress=True, **kwargs):
     r"""ResNet-34 model from
     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
     Args:
@@ -231,7 +233,7 @@ def resnet34(pretrained=False, progress=True, **kwargs):
                    **kwargs)
 
 
-def resnet50(pretrained=False, progress=True, **kwargs):
+def ResNet50(pretrained=False, progress=True, **kwargs):
     r"""ResNet-50 model from
     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
     Args:
@@ -242,7 +244,7 @@ def resnet50(pretrained=False, progress=True, **kwargs):
                    **kwargs)
 
 
-def resnet101(pretrained=False, progress=True, **kwargs):
+def ResNet101(pretrained=False, progress=True, **kwargs):
     r"""ResNet-101 model from
     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
     Args: