diff --git a/docs/source/image_classification.mdx b/docs/source/image_classification.mdx
index a4ee939b4fb..e1b3e059266 100644
--- a/docs/source/image_classification.mdx
+++ b/docs/source/image_classification.mdx
@@ -38,13 +38,13 @@ Now apply some augmentations with `albumentations`. You'll randomly crop the ima
 
 ```py
 >>> import cv2
->>> import albumentations as A
+>>> import albumentations
 >>> import numpy as np
 
->>> transform = A.Compose([
-...     A.RandomCrop(width=256, height=256),
-...     A.HorizontalFlip(p=0.5),
-...     A.RandomBrightnessContrast(p=0.2),
+>>> transform = albumentations.Compose([
+...     albumentations.RandomCrop(width=256, height=256),
+...     albumentations.HorizontalFlip(p=0.5),
+...     albumentations.RandomBrightnessContrast(p=0.2),
 ... ])
 ```
 
diff --git a/docs/source/object_detection.mdx b/docs/source/object_detection.mdx
index 14bdc313346..97b600578ba 100644
--- a/docs/source/object_detection.mdx
+++ b/docs/source/object_detection.mdx
@@ -77,14 +77,14 @@ With `albumentations`, you can apply transforms that will affect the image while
 `albumentations` expects the image to be in BGR format, not RGB, so you'll have to convert the image before applying the transform.
 
 ```py
->>> import albumentations as A
+>>> import albumentations
 >>> import numpy as np
 
->>> transform = A.Compose([
-...     A.Resize(480, 480),
-...     A.HorizontalFlip(p=1.0),
-...     A.RandomBrightnessContrast(p=1.0),
-... ], bbox_params=A.BboxParams(format='coco',  label_fields=['category']))
+>>> transform = albumentations.Compose([
+...     albumentations.Resize(480, 480),
+...     albumentations.HorizontalFlip(p=1.0),
+...     albumentations.RandomBrightnessContrast(p=1.0),
+... ], bbox_params=albumentations.BboxParams(format='coco',  label_fields=['category']))
 
 >>> # RGB PIL Image -> BGR Numpy array
 >>> image = np.flip(np.array(example['image']), -1)
diff --git a/docs/source/semantic_segmentation.mdx b/docs/source/semantic_segmentation.mdx
index 1499428674c..b7ee935b79a 100644
--- a/docs/source/semantic_segmentation.mdx
+++ b/docs/source/semantic_segmentation.mdx
@@ -94,12 +94,12 @@ After defining the color palette, you should be ready to visualize some overlays
 Now apply some augmentations with `albumentations`. You’ll first resize the image and adjust its brightness.
 
 ```py
->>> import albumentations as A
+>>> import albumentations
 
->>> transform = A.Compose(
+>>> transform = albumentations.Compose(
 ...     [
-...         A.Resize(256, 256),
-...         A.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, p=0.5),
+...         albumentations.Resize(256, 256),
+...         albumentations.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, p=0.5),
 ...     ]
 ... )
 ```