diff --git a/src/twomartens/masterthesis/data.py b/src/twomartens/masterthesis/data.py
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+++ b/src/twomartens/masterthesis/data.py
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+#  -*- coding: utf-8 -*-
+#
+#  Copyright 2019 Jim Martens
+#
+#  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.
+
+"""
+Functionality to load COCO data into Tensorflow data sets.
+
+Functions:
+    load_coco(...): loads the COCO data into a Tensorflow data set
+"""
+from typing import Tuple
+
+import tensorflow as tf
+from pycocotools import coco
+
+
+def load_coco(data_path: str, data_type: str, num_epochs: int, batch_size: int = 32) -> tf.data.Dataset:
+    """
+    Loads the COCO data and returns a data set.
+    
+    Args:
+        data_path: path to the COCO data set
+        data_type: type of the COCO data (e.g. 'val2014')
+        num_epochs: number of epochs
+        batch_size: batch size
+    Returns:
+        Tensorflow data set
+    """
+    annotation_file = f"{data_path}/annotations/instances_{data_type}.json"
+    coco_interface = coco.COCO(annotation_file)
+    img_ids = coco_interface.getImgIds()  # return all image IDs
+    images = coco_interface.loadImgs(img_ids)  # load all images
+    annotation_ids = coco_interface.getAnnIds(img_ids)
+    annotations = coco_interface.loadAnns(annotation_ids)  # load all image annotations
+    file_names = [image['file_name'] for image in images]
+    cat_ids = [annotation['category_id'] for annotation in annotations]
+    length_dataset = len(file_names)
+
+    def _load_image(image_data: Tuple[str, int]):
+        path, label = image_data
+        image = tf.read_file(f"{data_path}/images/{path}")
+        image = tf.image.decode_image(image, channels=3)
+    
+        return image, label
+    
+    # build image data set
+    path_dataset = tf.data.Dataset.from_tensor_slices(file_names)
+    label_dataset = tf.data.Dataset.from_tensor_slices(cat_ids)
+    dataset = tf.data.Dataset.zip((path_dataset, label_dataset))
+    dataset = dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=length_dataset, count=num_epochs))
+    dataset = dataset.batch(batch_size=batch_size)
+    dataset = dataset.map(_load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+    
+    return dataset