diff --git a/lightly/data/_helpers.py b/lightly/data/_helpers.py
index 00ab4fe09..b73bd4283 100644
--- a/lightly/data/_helpers.py
+++ b/lightly/data/_helpers.py
@@ -4,7 +4,7 @@
 # All Rights Reserved
 
 import os
-from typing import Any, Callable, Dict, List, Optional, Set
+from typing import Any, Callable, Dict, Optional, Tuple
 
 from torchvision import datasets
 
@@ -34,32 +34,29 @@
 VIDEO_EXTENSIONS = (".mp4", ".mov", ".avi", ".mpg", ".hevc", ".m4v", ".webm", ".mpeg")
 
 
-def _dir_contains_videos(root: str, extensions: tuple):
-    """Checks whether directory contains video files.
+def _dir_contains_videos(root: str, extensions: Tuple[str, ...]) -> bool:
+    """Checks whether the directory contains video files.
 
     Args:
         root: Root directory path.
+        extensions: Tuple of valid video file extensions.
 
     Returns:
-        True if root contains video files.
-
+        True if the root directory contains video files, False otherwise.
     """
     with os.scandir(root) as scan_dir:
         return any(f.name.lower().endswith(extensions) for f in scan_dir)
 
 
-def _contains_videos(root: str, extensions: tuple):
-    """Checks whether directory or any subdirectory contains video files.
-
-    Iterates over all subdirectories of "root" recursively and returns True
-    if any of the subdirectories contains a file with a VIDEO_EXTENSION.
+def _contains_videos(root: str, extensions: Tuple[str, ...]) -> bool:
+    """Checks whether the directory or any subdirectory contains video files.
 
     Args:
         root: Root directory path.
+        extensions: Tuple of valid video file extensions.
 
     Returns:
-        True if "root" or any subdir contains video files.
-
+        True if the root directory or any subdirectory contains video files, False otherwise.
     """
     for subdir, _, _ in os.walk(root):
         if _dir_contains_videos(subdir, extensions):
@@ -67,28 +64,26 @@ def _contains_videos(root: str, extensions: tuple):
     return False
 
 
-def _is_lightly_output_dir(dirname: str):
+def _is_lightly_output_dir(dirname: str) -> bool:
     """Checks whether the directory is a lightly_output directory.
 
     Args:
-        dirname: Directory to check.
+        dirname: Directory name to check.
 
     Returns:
-        True if dirname is "lightly_outputs" else false.
-
+        True if the directory name is "lightly_outputs", False otherwise.
     """
     return "lightly_outputs" in dirname
 
 
-def _contains_subdirs(root: str):
-    """Checks whether directory contains subdirectories.
+def _contains_subdirs(root: str) -> bool:
+    """Checks whether the directory contains subdirectories.
 
     Args:
         root: Root directory path.
 
     Returns:
-        True if root contains subdirectories else false.
-
+        True if the root directory contains subdirectories (excluding "lightly_outputs"), False otherwise.
     """
     with os.scandir(root) as scan_dir:
         return any(not _is_lightly_output_dir(f.name) for f in scan_dir if f.is_dir())
@@ -96,42 +91,45 @@ def _contains_subdirs(root: str):
 
 def _load_dataset_from_folder(
     root: str,
-    transform,
+    transform: Callable[[Any], Any],
     is_valid_file: Optional[Callable[[str], bool]] = None,
-    tqdm_args: Dict[str, Any] = None,
+    tqdm_args: Optional[Dict[str, Any]] = None,
     num_workers_video_frame_counting: int = 0,
-):
-    """Initializes dataset from folder.
+) -> datasets.VisionDataset:
+    """Initializes a dataset from a folder.
+
+    This function determines the appropriate dataset type based on the contents of the root directory
+    and returns the corresponding dataset object.
 
     Args:
-        root: (str) Root directory path
-        transform: (torchvision.transforms.Compose) image transformations
+        root: Root directory path.
+        transform: Composed image transformations to be applied to the dataset.
+        is_valid_file: Optional function to determine valid files.
+        tqdm_args: Optional dictionary of arguments for tqdm progress bar.
+        num_workers_video_frame_counting: Number of workers for video frame counting.
 
     Returns:
-        Dataset consisting of images/videos in the root directory.
+        A dataset object (VideoDataset, ImageFolder, or DatasetFolder) based on the directory contents.
 
     Raises:
-        ValueError: If the specified dataset doesn't exist
-
+        ValueError: If the specified dataset directory doesn't exist or if videos are present
+                but VideoDataset is not available.
     """
     if not os.path.exists(root):
         raise ValueError(f"The input directory {root} does not exist!")
 
-    # if there is a video in the input directory but we do not have
-    # the right dependencies, raise a ValueError
     contains_videos = _contains_videos(root, VIDEO_EXTENSIONS)
     if contains_videos and not VIDEO_DATASET_AVAILABLE:
         raise ValueError(
             f"The input directory {root} contains videos "
-            "but the VideoDataset is not available. \n"
+            "but the VideoDataset is not available. "
             "Make sure you have installed the right "
             "dependencies. The error from the imported "
             f"module was: {VIDEO_DATASET_ERRORMSG}"
         )
 
     if contains_videos:
-        # root contains videos -> create a video dataset
-        dataset = VideoDataset(
+        return VideoDataset(
             root,
             extensions=VIDEO_EXTENSIONS,
             transform=transform,
@@ -140,17 +138,13 @@ def _load_dataset_from_folder(
             num_workers=num_workers_video_frame_counting,
         )
     elif _contains_subdirs(root):
-        # root contains subdirectories -> create an image folder dataset
-        dataset = datasets.ImageFolder(
+        return datasets.ImageFolder(
             root, transform=transform, is_valid_file=is_valid_file
         )
     else:
-        # root contains plain images -> create a folder dataset
-        dataset = DatasetFolder(
+        return DatasetFolder(
             root,
             extensions=IMG_EXTENSIONS,
             transform=transform,
             is_valid_file=is_valid_file,
         )
-
-    return dataset