diff --git a/models/convnext.py b/models/convnext.py
index 94d42384d..8d57b904b 100644
--- a/models/convnext.py
+++ b/models/convnext.py
@@ -75,7 +75,7 @@ def __init__(self, in_chans=3, num_classes=1000,
             LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
         )
         self.downsample_layers.append(stem)
-        for i in range(3):
+        for i in range(len(dims) - 1):
             downsample_layer = nn.Sequential(
                     LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
                     nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
@@ -85,7 +85,7 @@ def __init__(self, in_chans=3, num_classes=1000,
         self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
         dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] 
         cur = 0
-        for i in range(4):
+        for i in range(len(dims)):
             stage = nn.Sequential(
                 *[Block(dim=dims[i], drop_path=dp_rates[cur + j], 
                 layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
@@ -106,7 +106,7 @@ def _init_weights(self, m):
             nn.init.constant_(m.bias, 0)
 
     def forward_features(self, x):
-        for i in range(4):
+        for i in range(len(self.stages)):
             x = self.downsample_layers[i](x)
             x = self.stages[i](x)
         return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)