diff --git a/pl_examples/domain_templates/semantic_segmentation.py b/pl_examples/domain_templates/semantic_segmentation.py
index 7bcad597a9..2e718a37ac 100644
--- a/pl_examples/domain_templates/semantic_segmentation.py
+++ b/pl_examples/domain_templates/semantic_segmentation.py
@@ -32,6 +32,19 @@ DEFAULT_VOID_LABELS = (0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1)
 DEFAULT_VALID_LABELS = (7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33)
 
 
+def _create_synth_kitti_dataset(path_dir: str, image_dims: tuple = (1024, 512)):
+    """Create synthetic dataset with random images, just to simulate that the dataset have been already downloaded."""
+    path_dir_images = os.path.join(path_dir, KITTI.IMAGE_PATH)
+    path_dir_masks = os.path.join(path_dir, KITTI.MASK_PATH)
+    for p_dir in (path_dir_images, path_dir_masks):
+        os.makedirs(p_dir, exist_ok=True)
+    for i in range(3):
+        path_img = os.path.join(path_dir_images, f'dummy_kitti_{i}.png')
+        Image.new('RGB', image_dims).save(path_img)
+        path_mask = os.path.join(path_dir_masks, f'dummy_kitti_{i}.png')
+        Image.new('L', image_dims).save(path_mask)
+
+
 class KITTI(Dataset):
     """
     Class for KITTI Semantic Segmentation Benchmark dataset
@@ -53,6 +66,12 @@ class KITTI(Dataset):
     In the `get_item` function, images and masks are resized to the given `img_size`, masks are
     encoded using `encode_segmap`, and given `transform` (if any) are applied to the image only
     (mask does not usually require transforms, but they can be implemented in a similar way).
+
+    >>> from pl_examples import DATASETS_PATH
+    >>> dataset_path = os.path.join(DATASETS_PATH, "Kitti")
+    >>> _create_synth_kitti_dataset(dataset_path, image_dims=(1024, 512))
+    >>> KITTI(dataset_path, 'train')  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    <...semantic_segmentation.KITTI object at ...>
     """
     IMAGE_PATH = os.path.join('training', 'image_2')
     MASK_PATH = os.path.join('training', 'semantic')
@@ -141,6 +160,23 @@ class SegModel(pl.LightningModule):
     It uses the FCN ResNet50 model as an example.
 
     Adam optimizer is used along with Cosine Annealing learning rate scheduler.
+
+    >>> from pl_examples import DATASETS_PATH
+    >>> dataset_path = os.path.join(DATASETS_PATH, "Kitti")
+    >>> _create_synth_kitti_dataset(dataset_path, image_dims=(1024, 512))
+    >>> SegModel(dataset_path)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    SegModel(
+      (net): UNet(
+        (layers): ModuleList(
+          (0): DoubleConv(...)
+          (1): Down(...)
+          (2): Down(...)
+          (3): Up(...)
+          (4): Up(...)
+          (5): Conv2d(64, 19, kernel_size=(1, 1), stride=(1, 1))
+        )
+      )
+    )
     """
     def __init__(
             self,
diff --git a/pl_examples/pytorch_ecosystem/__init__.py b/pl_examples/pytorch_ecosystem/__init__.py
deleted file mode 100644
index d7aa17d7f8..0000000000
--- a/pl_examples/pytorch_ecosystem/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright The PyTorch Lightning team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.