diff --git a/codes/models/classifiers/cifar_resnet.py b/codes/models/classifiers/cifar_resnet.py
index 79fac29e..ceb78064 100644
--- a/codes/models/classifiers/cifar_resnet.py
+++ b/codes/models/classifiers/cifar_resnet.py
@@ -85,20 +85,20 @@ class ResNet(nn.Module):
     def __init__(self, block, num_block, num_classes=100):
         super().__init__()
 
-        self.in_channels = 64
+        self.in_channels = 32
 
         self.conv1 = nn.Sequential(
-            nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
-            nn.BatchNorm2d(64),
+            nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(32),
             nn.ReLU(inplace=True))
         #we use a different inputsize than the original paper
         #so conv2_x's stride is 1
-        self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
-        self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
-        self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
-        self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
-        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
+        self.conv2_x = self._make_layer(block, 32, num_block[0], 1)
+        self.conv3_x = self._make_layer(block, 64, num_block[1], 2)
+        self.conv4_x = self._make_layer(block, 128, num_block[2], 2)
+        self.conv5_x = self._make_layer(block, 256, num_block[3], 2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(256 * block.expansion, num_classes)
 
     def _make_layer(self, block, out_channels, num_blocks, stride):
         """make resnet layers(by layer i didnt mean this 'layer' was the
@@ -131,7 +131,7 @@ class ResNet(nn.Module):
         output = self.conv3_x(output)
         output = self.conv4_x(output)
         output = self.conv5_x(output)
-        output = self.avg_pool(output)
+        output = self.avgpool(output)
         output = output.view(output.size(0), -1)
         output = self.fc(output)