diff --git a/configs/_base_/models/fast_scnn.py b/configs/_base_/models/fast_scnn.py
index 0aefda274..67ee0d39a 100644
--- a/configs/_base_/models/fast_scnn.py
+++ b/configs/_base_/models/fast_scnn.py
@@ -7,7 +7,7 @@ model = dict(
         downsample_dw_channels=(32, 48),
         global_in_channels=64,
         global_block_channels=(64, 96, 128),
-        global_block_downsample_factors=(2, 2, 1),
+        global_block_strides=(2, 2, 1),
         global_out_channels=128,
         higher_in_channels=64,
         lower_in_channels=128,
diff --git a/mmseg/models/backbones/fast_scnn.py b/mmseg/models/backbones/fast_scnn.py
index c3531baa4..f1c465771 100644
--- a/mmseg/models/backbones/fast_scnn.py
+++ b/mmseg/models/backbones/fast_scnn.py
@@ -79,13 +79,14 @@ class GlobalFeatureExtractor(nn.Module):
             Default: (64, 96, 128)
         out_channels(int): Number of output channels of the GFE module.
             Default: 128
-        expand_ratio (int): Upsampling factor of each Inverted Residual
-            module. Default: 6
+        expand_ratio (int): Adjusts number of channels of the hidden layer
+            in InvertedResidual by this amount.
+            Default: 6
         num_blocks (tuple[int]): Tuple of ints. Each int specifies the
             number of times each Inverted Residual module is repeated.
             The repeated Inverted Residual modules are called a 'group'.
             Default: (3, 3, 3)
-        downsample_factors (tuple[int]): Tuple of ints. Each int specifies
+        strides (tuple[int]): Tuple of ints. Each int specifies
             the downsampling factor of each 'group'.
             Default: (2, 2, 1)
         pool_scales (tuple[int]): Tuple of ints. Each int specifies
@@ -106,7 +107,7 @@ class GlobalFeatureExtractor(nn.Module):
                  out_channels=128,
                  expand_ratio=6,
                  num_blocks=(3, 3, 3),
-                 downsample_factors=(2, 2, 1),
+                 strides=(2, 2, 1),
                  pool_scales=(1, 2, 3, 6),
                  conv_cfg=None,
                  norm_cfg=dict(type='BN'),
@@ -118,17 +119,14 @@ class GlobalFeatureExtractor(nn.Module):
         self.act_cfg = act_cfg
         assert len(block_channels) == len(num_blocks) == 3
         self.bottleneck1 = self._make_layer(in_channels, block_channels[0],
-                                            num_blocks[0],
-                                            downsample_factors[0],
+                                            num_blocks[0], strides[0],
                                             expand_ratio)
         self.bottleneck2 = self._make_layer(block_channels[0],
                                             block_channels[1], num_blocks[1],
-                                            downsample_factors[1],
-                                            expand_ratio)
+                                            strides[1], expand_ratio)
         self.bottleneck3 = self._make_layer(block_channels[1],
                                             block_channels[2], num_blocks[2],
-                                            downsample_factors[2],
-                                            expand_ratio)
+                                            strides[2], expand_ratio)
         self.ppm = PPM(
             pool_scales,
             block_channels[2],
@@ -269,8 +267,8 @@ class FastSCNN(nn.Module):
             the output channels for each of the MobileNet-v2 bottleneck
             residual blocks in GFE.
             Default: (64, 96, 128).
-        global_block_downsample_factors (tuple[int]): Tuple of integers
-            that describe the downsampling factors for each of the
+        global_block_strides (tuple[int]): Tuple of integers
+            that describe the strides (downsampling factors) for each of the
             MobileNet-v2 bottleneck residual blocks in GFE.
             Default: (2, 2, 1).
         global_out_channels (int): Number of output channels of GFE.
@@ -303,7 +301,7 @@ class FastSCNN(nn.Module):
                  downsample_dw_channels=(32, 48),
                  global_in_channels=64,
                  global_block_channels=(64, 96, 128),
-                 global_block_downsample_factors=(2, 2, 1),
+                 global_block_strides=(2, 2, 1),
                  global_out_channels=128,
                  higher_in_channels=64,
                  lower_in_channels=128,
@@ -324,7 +322,7 @@ class FastSCNN(nn.Module):
 
         # Calculate scale factor used in FFM.
         self.scale_factor = 1
-        for factor in global_block_downsample_factors:
+        for factor in global_block_strides:
             self.scale_factor *= factor
 
         self.in_channels = in_channels
@@ -332,7 +330,7 @@ class FastSCNN(nn.Module):
         self.downsample_dw_channels2 = downsample_dw_channels[1]
         self.global_in_channels = global_in_channels
         self.global_block_channels = global_block_channels
-        self.global_block_downsample_factors = global_block_downsample_factors
+        self.global_block_strides = global_block_strides
         self.global_out_channels = global_out_channels
         self.higher_in_channels = higher_in_channels
         self.lower_in_channels = lower_in_channels
@@ -353,7 +351,7 @@ class FastSCNN(nn.Module):
             global_in_channels,
             global_block_channels,
             global_out_channels,
-            downsample_factors=self.global_block_downsample_factors,
+            downsample_factors=self.global_block_strides,
             conv_cfg=self.conv_cfg,
             norm_cfg=self.norm_cfg,
             act_cfg=self.act_cfg,