diff --git a/src/twomartens/masterthesis/data.py b/src/twomartens/masterthesis/data.py
index 98f14c2..2e6e921 100644
--- a/src/twomartens/masterthesis/data.py
+++ b/src/twomartens/masterthesis/data.py
@@ -27,28 +27,42 @@ import tensorflow as tf
 from pycocotools import coco
 
 
-def load_coco(data_path: str, data_type: str, category: int,
+def load_coco(data_path: str, category: int,
               num_epochs: int, batch_size: int = 32) -> tf.data.Dataset:
     """
-    Loads the COCO data and returns a data set.
+    Loads the COCO trainval35k 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')
         category: id of the inlying class
         num_epochs: number of epochs
         batch_size: batch size (default: 32)
     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(catIds=[category])  # return all image IDs belonging to given category
-    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]
+    annotation_file_train = f"{data_path}/annotations/instances_train2014.json"
+    annotation_file_val = f"{data_path}/annotations/instances_valminusminival2014.json"
+    # load training images
+    coco_train = coco.COCO(annotation_file_train)
+    img_ids = coco_train.getImgIds(catIds=[category])  # return all image IDs belonging to given category
+    images = coco_train.loadImgs(img_ids)  # load all images
+    annotation_ids = coco_train.getAnnIds(img_ids)
+    annotations = coco_train.loadAnns(annotation_ids)  # load all image annotations
+    file_names = [f"train2014/{image['file_name']}" for image in images]
     cat_ids = [annotation['category_id'] for annotation in annotations]
+    
+    # load validation images
+    coco_val = coco.COCO(annotation_file_val)
+    img_ids = coco_val.getImgIds(catIds=[category])  # return all image IDs belonging to given category
+    images = coco_val.loadImgs(img_ids)  # load all images
+    annotation_ids = coco_val.getAnnIds(img_ids)
+    annotations = coco_val.loadAnns(annotation_ids)  # load all image annotations
+    file_names_val = [f"val2014/{image['file_name']}" for image in images]
+    cat_ids_val = [annotation['category_id'] for annotation in annotations]
+    
+    file_names.extend(file_names_val)
+    cat_ids.extend(cat_ids_val)
+    
     length_dataset = len(file_names)
 
     def _load_image(image_data: Tuple[str, int]):