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diff --git a/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py b/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py
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+#!/usr/bin/env python3
+# Copyright © 2020 NXP and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+import pyarmnn as ann
+import numpy as np
+import os
+from PIL import Image
+import example_utils as eu
+
+
+def preprocess_onnx(img: Image, width: int, height: int, data_type, scale: float, mean: list,
+                    stddev: list):
+    """Preprocessing function for ONNX imagenet models based on:
+    https://github.com/onnx/models/blob/master/vision/classification/imagenet_inference.ipynb
+
+    Args:
+        img (PIL.Image): Loaded PIL.Image
+        width (int): Target image width
+        height (int): Target image height
+        data_type: Image datatype (np.uint8 or np.float32)
+        scale (float): Scaling factor
+        mean: RGB mean values
+        stddev: RGB standard deviation
+
+    Returns:
+        np.array: Preprocess image as Numpy array
+    """
+    img = img.resize((256, 256), Image.BILINEAR)
+    # first rescale to 256,256 and then center crop
+    left = (256 - width) / 2
+    top = (256 - height) / 2
+    right = (256 + width) / 2
+    bottom = (256 + height) / 2
+    img = img.crop((left, top, right, bottom))
+    img = img.convert('RGB')
+    img = np.array(img)
+    img = np.reshape(img, (-1, 3))  # reshape to [RGB][RGB]...
+    img = ((img / scale) - mean) / stddev
+    # NHWC to NCHW conversion, by default NHWC is expected
+    # image is loaded as [RGB][RGB][RGB]... transposing it makes it [RRR...][GGG...][BBB...]
+    img = np.transpose(img)
+    img = img.flatten().astype(data_type)  # flatten into a 1D tensor and convert to float32
+    return img
+
+
+args = eu.parse_command_line()
+
+model_filename = 'mobilenetv2-1.0.onnx'
+labels_filename = 'synset.txt'
+archive_filename = 'mobilenetv2-1.0.zip'
+labels_url = 'https://s3.amazonaws.com/onnx-model-zoo/' + labels_filename
+model_url = 'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/' + model_filename
+
+# Download resources
+image_filenames = eu.get_images(args.data_dir)
+
+model_filename, labels_filename = eu.get_model_and_labels(args.model_dir, model_filename, labels_filename,
+                                                          archive_filename,
+                                                          [model_url, labels_url])
+
+# all 3 resources must exist to proceed further
+assert os.path.exists(labels_filename)
+assert os.path.exists(model_filename)
+assert image_filenames
+for im in image_filenames:
+    assert (os.path.exists(im))
+
+# Create a network from a model file
+net_id, parser, runtime = eu.create_onnx_network(model_filename)
+
+# Load input information from the model and create input tensors
+input_binding_info = parser.GetNetworkInputBindingInfo("data")
+
+# Load output information from the model and create output tensors
+output_binding_info = parser.GetNetworkOutputBindingInfo("mobilenetv20_output_flatten0_reshape0")
+output_tensors = ann.make_output_tensors([output_binding_info])
+
+# Load labels
+labels = eu.load_labels(labels_filename)
+
+# Load images and resize to expected size
+images = eu.load_images(image_filenames,
+                        224, 224,
+                        np.float32,
+                        255.0,
+                        [0.485, 0.456, 0.406],
+                        [0.229, 0.224, 0.225],
+                        preprocess_onnx)
+
+eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info)