diff --git a/unet/unet_model.py b/unet/unet_model.py
index e5d2388..40291c5 100644
--- a/unet/unet_model.py
+++ b/unet/unet_model.py
@@ -18,10 +18,10 @@ class UNet(nn.Module):
         self.down3 = Down(256, 512)
         factor = 2 if bilinear else 1
         self.down4 = Down(512, 1024 // factor)
-        self.up1 = Up(1024, 512, bilinear)
-        self.up2 = Up(512, 256, bilinear)
-        self.up3 = Up(256, 128, bilinear)
-        self.up4 = Up(128, 64 * factor, bilinear)
+        self.up1 = Up(1024, 512 // factor, bilinear)
+        self.up2 = Up(512, 256 // factor, bilinear)
+        self.up3 = Up(256, 128 // factor, bilinear)
+        self.up4 = Up(128, 64, bilinear)
         self.outc = OutConv(64, n_classes)
 
     def forward(self, x):
diff --git a/unet/unet_parts.py b/unet/unet_parts.py
index daaa2da..7c64dc1 100644
--- a/unet/unet_parts.py
+++ b/unet/unet_parts.py
@@ -48,7 +48,7 @@ class Up(nn.Module):
         # if bilinear, use the normal convolutions to reduce the number of channels
         if bilinear:
             self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
-            self.conv = DoubleConv(in_channels, out_channels // 2, in_channels // 2)
+            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
         else:
             self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
             self.conv = DoubleConv(in_channels, out_channels)