diff --git a/mmseg/datasets/isprs.py b/mmseg/datasets/isprs.py
index 9306b4847..888ea4762 100644
--- a/mmseg/datasets/isprs.py
+++ b/mmseg/datasets/isprs.py
@@ -7,7 +7,7 @@ from .custom import CustomDataset
 class ISPRSDataset(CustomDataset):
     """ISPRS dataset.
 
-    In segmentation map annotation for LoveDA, 0 is the ignore index.
+    In segmentation map annotation for ISPRS, 0 is the ignore index.
     ``reduce_zero_label`` should be set to True. The ``img_suffix`` and
     ``seg_map_suffix`` are both fixed to '.png'.
     """
diff --git a/mmseg/metrics/iou_metric.py b/mmseg/metrics/iou_metric.py
index 4486e1213..c5bf28d6f 100644
--- a/mmseg/metrics/iou_metric.py
+++ b/mmseg/metrics/iou_metric.py
@@ -129,7 +129,7 @@ class IoUMetric(BaseMetric):
     @staticmethod
     def intersect_and_union(pred_label: torch.tensor, label: torch.tensor,
                             num_classes: int, ignore_index: int):
-        """Calculate intersection and Union.
+        """Calculate Intersection and Union.
 
         Args:
             pred_label (torch.tensor): Prediction segmentation map
@@ -175,22 +175,22 @@ class IoUMetric(BaseMetric):
                               beta: int = 1):
         """Calculate evaluation metrics
         Args:
-            total_area_intersect (ndarray): The intersection of prediction and
-                ground truth histogram on all classes.
-            total_area_union (ndarray): The union of prediction and ground
+            total_area_intersect (np.ndarray): The intersection of prediction
+                and ground truth histogram on all classes.
+            total_area_union (np.ndarray): The union of prediction and ground
                 truth histogram on all classes.
-            total_area_pred_label (ndarray): The prediction histogram on all
-                classes.
-            total_area_label (ndarray): The ground truth histogram on
+            total_area_pred_label (np.ndarray): The prediction histogram on
                 all classes.
-            metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and
+            total_area_label (np.ndarray): The ground truth histogram on
+                all classes.
+            metrics (List[str] | str): Metrics to be evaluated, 'mIoU' and
                 'mDice'.
             nan_to_num (int, optional): If specified, NaN values will be
                 replaced by the numbers defined by the user. Default: None.
             beta (int): Determines the weight of recall in the combined score.
                 Default: 1.
         Returns:
-            Dict[str, ndarray]: per category evaluation metrics,
+            Dict[str, np.ndarray]: per category evaluation metrics,
                 shape (num_classes, ).
         """
 
diff --git a/mmseg/models/segmentors/encoder_decoder.py b/mmseg/models/segmentors/encoder_decoder.py
index bb6b80691..135e29528 100644
--- a/mmseg/models/segmentors/encoder_decoder.py
+++ b/mmseg/models/segmentors/encoder_decoder.py
@@ -44,7 +44,7 @@ class EncoderDecoder(BaseSegmentor):
      whole_inference()/slide_inference(): encoder_decoder()
      encoder_decoder(): extract_feat() -> decode_head.predict()
 
-    4 The ``_forward`` method is used to output the tensor by running the model,
+    3. The ``_forward`` method is used to output the tensor by running the model,
     which includes two steps: (1) Extracts features to obtain the feature maps
     (2)Call the decode head forward function to forward decode head model.