diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index d87b736d339f33ead2ae77b38c4b6195bf3aff33..2967dd95dd99c69799eef06f0d07eeefa53060d3 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -352,6 +352,20 @@ class ResNet(nn.Module):
             memory while slowing down the training speed.
         zero_init_residual (bool): whether to use zero init for last norm layer
             in resblocks to let them behave as identity.
+
+    Example:
+        >>> from mmdet.models import ResNet
+        >>> import torch
+        >>> self = ResNet(depth=18)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 64, 8, 8)
+        (1, 128, 4, 4)
+        (1, 256, 2, 2)
+        (1, 512, 1, 1)
     """
 
     arch_settings = {
diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py
index c5feaa48c2bf6529f168277edecb01adfd142a13..be2897686bfb719bcaa6d5582db9c429264514bf 100644
--- a/mmdet/models/backbones/resnext.py
+++ b/mmdet/models/backbones/resnext.py
@@ -179,6 +179,20 @@ class ResNeXt(ResNet):
             memory while slowing down the training speed.
         zero_init_residual (bool): whether to use zero init for last norm layer
             in resblocks to let them behave as identity.
+
+    Example:
+        >>> from mmdet.models import ResNeXt
+        >>> import torch
+        >>> self = ResNeXt(depth=50)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 256, 8, 8)
+        (1, 512, 4, 4)
+        (1, 1024, 2, 2)
+        (1, 2048, 1, 1)
     """
 
     arch_settings = {