diff --git a/python/pyarmnn/examples/object_detection/run_video_file.py b/python/pyarmnn/examples/object_detection/run_video_file.py
index e31b779..52f19d2 100644
--- a/python/pyarmnn/examples/object_detection/run_video_file.py
+++ b/python/pyarmnn/examples/object_detection/run_video_file.py
@@ -1,83 +1,87 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
-# SPDX-License-Identifier: MIT
-
-"""
-Object detection demo that takes a video file, runs inference on each frame producing
-bounding boxes and labels around detected objects, and saves the processed video.
-"""
-
-import os
-import sys
-script_dir = os.path.dirname(__file__)
-sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
-
-import cv2
-from tqdm import tqdm
-from argparse import ArgumentParser
-
-from ssd import ssd_processing, ssd_resize_factor
-from yolo import yolo_processing, yolo_resize_factor
-from utils import dict_labels
-from cv_utils import init_video_file_capture, preprocess, draw_bounding_boxes
-from network_executor import ArmnnNetworkExecutor
-
-
-def get_model_processing(model_name: str, video: cv2.VideoCapture, input_binding_info: tuple):
-    """
-    Gets model-specific information such as model labels and decoding and processing functions.
-    The user can include their own network and functions by adding another statement.
-
-    Args:
-        model_name: Name of type of supported model.
-        video: Video capture object, contains information about data source.
-        input_binding_info: Contains shape of model input layer, used for scaling bounding boxes.
-
-    Returns:
-        Model labels, decoding and processing functions.
-    """
-    if model_name == 'ssd_mobilenet_v1':
-        return ssd_processing, ssd_resize_factor(video)
-    elif model_name == 'yolo_v3_tiny':
-        return yolo_processing, yolo_resize_factor(video, input_binding_info)
-    else:
-        raise ValueError(f'{model_name} is not a valid model name')
-
-
-def main(args):
-    video, video_writer, frame_count = init_video_file_capture(args.video_file_path, args.output_video_file_path)
-
-    executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
-    process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
-    labels = dict_labels(args.label_path, include_rgb=True)
-
-    for _ in tqdm(frame_count, desc='Processing frames'):
-        frame_present, frame = video.read()
-        if not frame_present:
-            continue
-        input_tensors = preprocess(frame, executor.input_binding_info)
-        output_result = executor.run(input_tensors)
-        detections = process_output(output_result)
-        draw_bounding_boxes(frame, detections, resize_factor, labels)
-        video_writer.write(frame)
-    print('Finished processing frames')
-    video.release(), video_writer.release()
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser()
-    parser.add_argument('--video_file_path', required=True, type=str,
-                        help='Path to the video file to run object detection on')
-    parser.add_argument('--model_file_path', required=True, type=str,
-                        help='Path to the Object Detection model to use')
-    parser.add_argument('--model_name', required=True, type=str,
-                        help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
-    parser.add_argument('--label_path', required=True, type=str,
-                        help='Path to the labelset for the provided model file')
-    parser.add_argument('--output_video_file_path', type=str,
-                        help='Path to the output video file with detections added in')
-    parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
-                        help='Takes the preferred backends in preference order, separated by whitespace, '
-                             'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
-                             'Defaults to [CpuAcc, CpuRef]')
-    args = parser.parse_args()
-    main(args)
+# Copyright © 2020-2021 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+"""
+Object detection demo that takes a video file, runs inference on each frame producing
+bounding boxes and labels around detected objects, and saves the processed video.
+"""
+
+import os
+import sys
+script_dir = os.path.dirname(__file__)
+sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
+
+import cv2
+from tqdm import tqdm
+from argparse import ArgumentParser
+
+from ssd import ssd_processing, ssd_resize_factor
+from yolo import yolo_processing, yolo_resize_factor
+from utils import dict_labels
+from cv_utils import init_video_file_capture, preprocess, draw_bounding_boxes
+from network_executor import ArmnnNetworkExecutor
+
+
+def get_model_processing(model_name: str, video: cv2.VideoCapture, input_binding_info: tuple):
+    """
+    Gets model-specific information such as model labels and decoding and processing functions.
+    The user can include their own network and functions by adding another statement.
+
+    Args:
+        model_name: Name of type of supported model.
+        video: Video capture object, contains information about data source.
+        input_binding_info: Contains shape of model input layer, used for scaling bounding boxes.
+
+    Returns:
+        Model labels, decoding and processing functions.
+    """
+    if model_name == 'ssd_mobilenet_v1':
+        return ssd_processing, ssd_resize_factor(video)
+    elif model_name == 'yolo_v3_tiny':
+        return yolo_processing, yolo_resize_factor(video, input_binding_info)
+    else:
+        raise ValueError(f'{model_name} is not a valid model name')
+
+
+def main(args):
+    video, video_writer, frame_count = init_video_file_capture(args.video_file_path, args.output_video_file_path)
+
+    executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
+    process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
+    labels = dict_labels(args.label_path, include_rgb=True)
+
+    for _ in tqdm(frame_count, desc='Processing frames'):
+        frame_present, frame = video.read()
+        if not frame_present:
+            continue
+        model_name = args.model_name
+        if model_name == "ssd_mobilenet_v1":
+            input_tensors = preprocess(frame, executor.input_binding_info, True)
+        else:
+            input_tensors = preprocess(frame, executor.input_binding_info, False)
+        output_result = executor.run(input_tensors)
+        detections = process_output(output_result)
+        draw_bounding_boxes(frame, detections, resize_factor, labels)
+        video_writer.write(frame)
+    print('Finished processing frames')
+    video.release(), video_writer.release()
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser()
+    parser.add_argument('--video_file_path', required=True, type=str,
+                        help='Path to the video file to run object detection on')
+    parser.add_argument('--model_file_path', required=True, type=str,
+                        help='Path to the Object Detection model to use')
+    parser.add_argument('--model_name', required=True, type=str,
+                        help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
+    parser.add_argument('--label_path', required=True, type=str,
+                        help='Path to the labelset for the provided model file')
+    parser.add_argument('--output_video_file_path', type=str,
+                        help='Path to the output video file with detections added in')
+    parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
+                        help='Takes the preferred backends in preference order, separated by whitespace, '
+                             'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
+                             'Defaults to [CpuAcc, CpuRef]')
+    args = parser.parse_args()
+    main(args)
diff --git a/python/pyarmnn/examples/object_detection/run_video_stream.py b/python/pyarmnn/examples/object_detection/run_video_stream.py
index 8635a40..dba615b 100644
--- a/python/pyarmnn/examples/object_detection/run_video_stream.py
+++ b/python/pyarmnn/examples/object_detection/run_video_stream.py
@@ -1,85 +1,90 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
-# SPDX-License-Identifier: MIT
-
-"""
-Object detection demo that takes a video stream from a device, runs inference
-on each frame producing bounding boxes and labels around detected objects,
-and displays a window with the latest processed frame.
-"""
-
-import os
-import sys
-script_dir = os.path.dirname(__file__)
-sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
-
-import cv2
-from argparse import ArgumentParser
-
-from ssd import ssd_processing, ssd_resize_factor
-from yolo import yolo_processing, yolo_resize_factor
-from utils import dict_labels
-from cv_utils import init_video_stream_capture, preprocess, draw_bounding_boxes
-from network_executor import ArmnnNetworkExecutor
-
-
-def get_model_processing(model_name: str, video: cv2.VideoCapture, input_binding_info: tuple):
-    """
-    Gets model-specific information such as model labels and decoding and processing functions.
-    The user can include their own network and functions by adding another statement.
-
-    Args:
-        model_name: Name of type of supported model.
-        video: Video capture object, contains information about data source.
-        input_binding_info: Contains shape of model input layer, used for scaling bounding boxes.
-
-    Returns:
-        Model labels, decoding and processing functions.
-    """
-    if model_name == 'ssd_mobilenet_v1':
-        return ssd_processing, ssd_resize_factor(video)
-    elif model_name == 'yolo_v3_tiny':
-        return yolo_processing, yolo_resize_factor(video, input_binding_info)
-    else:
-        raise ValueError(f'{model_name} is not a valid model name')
-
-
-def main(args):
-    video = init_video_stream_capture(args.video_source)
-    executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
-
-    process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
-    labels = dict_labels(args.label_path, include_rgb=True)
-
-    while True:
-        frame_present, frame = video.read()
-        frame = cv2.flip(frame, 1)  # Horizontally flip the frame
-        if not frame_present:
-            raise RuntimeError('Error reading frame from video stream')
-        input_tensors = preprocess(frame, executor.input_binding_info)
-        print("Running inference...")
-        output_result = executor.run(input_tensors)
-        detections = process_output(output_result)
-        draw_bounding_boxes(frame, detections, resize_factor, labels)
-        cv2.imshow('PyArmNN Object Detection Demo', frame)
-        if cv2.waitKey(1) == 27:
-            print('\nExit key activated. Closing video...')
-            break
-    video.release(), cv2.destroyAllWindows()
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser()
-    parser.add_argument('--video_source', type=int, default=0,
-                        help='Device index to access video stream. Defaults to primary device camera at index 0')
-    parser.add_argument('--model_file_path', required=True, type=str,
-                        help='Path to the Object Detection model to use')
-    parser.add_argument('--model_name', required=True, type=str,
-                        help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
-    parser.add_argument('--label_path', required=True, type=str,
-                        help='Path to the labelset for the provided model file')
-    parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
-                        help='Takes the preferred backends in preference order, separated by whitespace, '
-                             'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
-                             'Defaults to [CpuAcc, CpuRef]')
-    args = parser.parse_args()
-    main(args)
+# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+"""
+Object detection demo that takes a video stream from a device, runs inference
+on each frame producing bounding boxes and labels around detected objects,
+and displays a window with the latest processed frame.
+"""
+
+import os
+import sys
+script_dir = os.path.dirname(__file__)
+sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
+
+import cv2
+from argparse import ArgumentParser
+
+from ssd import ssd_processing, ssd_resize_factor
+from yolo import yolo_processing, yolo_resize_factor
+from utils import dict_labels
+from cv_utils import init_video_stream_capture, preprocess, draw_bounding_boxes
+from network_executor import ArmnnNetworkExecutor
+
+
+def get_model_processing(model_name: str, video: cv2.VideoCapture, input_binding_info: tuple):
+    """
+    Gets model-specific information such as model labels and decoding and processing functions.
+    The user can include their own network and functions by adding another statement.
+
+    Args:
+        model_name: Name of type of supported model.
+        video: Video capture object, contains information about data source.
+        input_binding_info: Contains shape of model input layer, used for scaling bounding boxes.
+
+    Returns:
+        Model labels, decoding and processing functions.
+    """
+    if model_name == 'ssd_mobilenet_v1':
+        return ssd_processing, ssd_resize_factor(video)
+    elif model_name == 'yolo_v3_tiny':
+        return yolo_processing, yolo_resize_factor(video, input_binding_info)
+    else:
+        raise ValueError(f'{model_name} is not a valid model name')
+
+
+def main(args):
+    video = init_video_stream_capture(args.video_source)
+    executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
+
+    model_name = args.model_name
+    process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
+    labels = dict_labels(args.label_path, include_rgb=True)
+
+    while True:
+        frame_present, frame = video.read()
+        frame = cv2.flip(frame, 1)  # Horizontally flip the frame
+        if not frame_present:
+            raise RuntimeError('Error reading frame from video stream')
+
+        if model_name == "ssd_mobilenet_v1":
+            input_tensors = preprocess(frame, executor.input_binding_info, True)
+        else:
+            input_tensors = preprocess(frame, executor.input_binding_info, False)
+        print("Running inference...")
+        output_result = executor.run(input_tensors)
+        detections = process_output(output_result)
+        draw_bounding_boxes(frame, detections, resize_factor, labels)
+        cv2.imshow('PyArmNN Object Detection Demo', frame)
+        if cv2.waitKey(1) == 27:
+            print('\nExit key activated. Closing video...')
+            break
+    video.release(), cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    parser = ArgumentParser()
+    parser.add_argument('--video_source', type=int, default=0,
+                        help='Device index to access video stream. Defaults to primary device camera at index 0')
+    parser.add_argument('--model_file_path', required=True, type=str,
+                        help='Path to the Object Detection model to use')
+    parser.add_argument('--model_name', required=True, type=str,
+                        help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
+    parser.add_argument('--label_path', required=True, type=str,
+                        help='Path to the labelset for the provided model file')
+    parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
+                        help='Takes the preferred backends in preference order, separated by whitespace, '
+                             'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
+                             'Defaults to [CpuAcc, CpuRef]')
+    args = parser.parse_args()
+    main(args)