diff --git a/mmseg/models/backbones/fast_scnn.py b/mmseg/models/backbones/fast_scnn.py
index 3acf541c8..fb9df6383 100644
--- a/mmseg/models/backbones/fast_scnn.py
+++ b/mmseg/models/backbones/fast_scnn.py
@@ -188,6 +188,44 @@ class FastSCNN(nn.Module):
                  norm_cfg=dict(type='BN'),
                  act_cfg=dict(type='ReLU'),
                  align_corners=False):
+
+        """Fast-SCNN Backbone.
+        Args:
+            in_channels(int): Number of input image channels. Default=3 (RGB)
+
+            downsample_dw_channels1(int): Number of output channels after
+                the first conv layer in Learning-To-Downsample (LTD) module.
+
+            downsample_dw_channels2(int): Number of output channels after the second conv layer in LTD.
+
+            global_in_channels(int): Number of input channels of Global Feature Extractor(GFE).
+                Equal to number of output channels of LTD.
+
+            global_block_channels(tuple): Tuple of integers that describe the output channels for
+                each of the MobileNet-v2 bottleneck residual blocks in GFE.
+
+            global_out_channels(int): Number of output channels of GFE.
+
+            higher_in_channels(int): Number of input channels of the higher resolution branch in FFM.
+                Equal to global_in_channels.
+
+            lower_in_channels(int): Number of input channels of  the lower resolution branch in FFM.
+                Equal to global_out_channels.
+
+            fusion_out_channels(int): Number of output channels of FFM.
+
+            scale_factor(int): The upsampling factor of the higher resolution branch in FFM.
+                Equal to the downsampling factor in GFE.
+
+            out_indices(tuple): Tuple of indices of list [higher_res_features, lower_res_features, fusion_output].
+                Often set to (0,1,2) to enable aux. heads.
+
+            conv_cfg (dict|None): Config of conv layers.
+            norm_cfg (dict|None): Config of norm layers.
+            act_cfg (dict): Config of activation layers.
+            align_corners (bool): align_corners argument of F.interpolate.
+        """
+
         super(FastSCNN, self).__init__()
         self.in_channels = in_channels
         self.downsample_dw_channels1 = downsample_dw_channels1
diff --git a/mmseg/models/decode_heads/sep_fcn_head.py b/mmseg/models/decode_heads/sep_fcn_head.py
index e93dfab9b..d93c246f7 100644
--- a/mmseg/models/decode_heads/sep_fcn_head.py
+++ b/mmseg/models/decode_heads/sep_fcn_head.py
@@ -5,6 +5,27 @@ from .fcn_head import FCNHead
 
 @HEADS.register_module()
 class SepFCNHead(FCNHead):
+    """Depthwise-Separable Fully Convolutional Network for Semantic Segmentation
+
+    This head is implemented according to Fast-SCNN.
+    Args:
+        in_channels(int): Number of output channels of FFM.
+
+        channels(int): Number of middle-stage channels in the decode head.
+
+        concat_input(bool): Whether to concatenate original decode input into
+            the result of consecutive convolution layers.
+
+        num_classes(int): Used to determine the dimension of final prediction tensor.
+
+        in_index(int): Correspond with 'out_indices' in FastSCNN backbone.
+
+        norm_cfg (dict|None): Config of norm layers.
+
+        align_corners (bool): align_corners argument of F.interpolate.
+
+        loss_decode(dict): Config of loss type and some relevant additional options.
+    """
 
     def __init__(self, **kwargs):
         super(SepFCNHead, self).__init__(**kwargs)