open-edge-platform
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-"""Anomalib dataclasses."""
+"""Anomalib dataclasses.
+
+This module provides a collection of dataclasses used throughout the Anomalib library
+for representing and managing various types of data related to anomaly detection tasks.
+
+The dataclasses are organized into two main categories:
+1. Numpy-based dataclasses for handling numpy array data.
+2. Torch-based dataclasses for handling PyTorch tensor data.
+
+Key components:
+
+Numpy Dataclasses:
+    ``NumpyImageItem``: Represents a single image item as numpy arrays.
+    ``NumpyImageBatch``: Represents a batch of image data as numpy arrays.
+    ``NumpyVideoItem``: Represents a single video item as numpy arrays.
+    ``NumpyVideoBatch``: Represents a batch of video data as numpy arrays.
+
+Torch Dataclasses:
+    ``Batch``: Base class for torch-based batch data.
+    ``DatasetItem``: Base class for torch-based dataset items.
+    ``DepthItem``: Represents a single depth data item.
+    ``DepthBatch``: Represents a batch of depth data.
+    ``ImageItem``: Represents a single image item as torch tensors.
+    ``ImageBatch``: Represents a batch of image data as torch tensors.
+    ``VideoItem``: Represents a single video item as torch tensors.
+    ``VideoBatch``: Represents a batch of video data as torch tensors.
+    ``InferenceBatch``: Specialized batch class for inference results.
+
+These dataclasses provide a structured way to handle various types of data
+in anomaly detection tasks, ensuring type consistency and easy data manipulation
+across different components of the Anomalib library.
+"""
 
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0