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diff --git a/python/openvino/runtime/dla_benchmark/convert_annotations.py b/python/openvino/runtime/dla_benchmark/convert_annotations.py
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+#!/usr/bin/env python3
+# ============================================================================
+# This script takes two paths as input. The first path is to the annotation file
+# in json format. This annotation file is the validation data used in the 2017 COCO
+# competition for object detection, downloaded from https://cocodataset.org/
+# The second path indicates the folder where the user wants to store the converted
+# annotation files in plain text format. Each file in the destination folder contains
+# the true label and the bounding boxes for its corresponding validation image.
+# To use the average precision calculation in the dla_benchmark, you must
+# provide the text-formatted annotation files.
+# Note that 91 classes are used in the mscoco paper https://arxiv.org/pdf/1405.0312.pdf,
+# whereas 80 are used in validation 2014/2017 dataset.
+# ============================================================================
+
+import json
+import sys
+
+
+def cat80(cat: int) -> int:
+    '''
+    The validation dataset omits 11 classes and causes mismatches with
+    the predicted classes in the benckmark_app. This function maps the
+    class id from the json annotation file to those used in the dla_benchmark.
+    '''
+    diff = 1
+    if cat > 11:
+        diff += 1
+    if cat > 25:
+        diff += 1
+    if cat > 28:
+        diff += 2
+    if cat > 44:
+        diff += 1
+    if cat > 65:
+        diff += 1
+    if cat > 67:
+        diff += 2
+    if cat > 70:
+        diff += 1
+    if cat > 82:
+        diff += 1
+    if cat > 90:
+        diff += 1
+    return cat - diff
+
+
+def parse_annotation_file(path_to_annotation: str, destination_folder: str) -> int:
+    fin = open(path_to_annotation)
+    json_data = json.load(fin)
+    per_image_data = dict()
+
+    # Gets all bounding boxes and labels w.r.t. each validation image.
+    for annotation in json_data["annotations"]:
+        image_id = annotation["image_id"]
+        bbox_data = [str(cat80(annotation["category_id"]))] + list(map(str, annotation["bbox"]))
+        if image_id in per_image_data:
+            per_image_data[image_id].append(bbox_data)
+        else:
+            per_image_data[image_id] = [bbox_data]
+    fin.close()
+
+    # Creates and writes to text files.
+    for image_meta in json_data["images"]:
+        file_path = rf'{destination_folder}/{image_meta["file_name"][:-4]}.txt'
+        if image_meta["id"] in per_image_data:
+            bboxes = per_image_data[image_meta["id"]]
+        else:
+            bboxes = []
+        with open(file_path, "w") as fout:
+            fout.write("\n".join([" ".join(bbox) for bbox in bboxes]))
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        sys.exit(
+            ("Usage: {0} "
+             "<path to the validation file in json format> "
+             "<path to the folder to store the annotation text files> "
+             )
+            .format(sys.argv[0]))
+
+    json_instances = sys.argv[1]
+    destination = sys.argv[2]
+
+    try:
+        parse_annotation_file(json_instances, destination)
+    except Exception as err:
+        print(err)
+    else:
+        print("Finished.")