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diff --git a/python/pyarmnn/examples/onnx_mobilenetv2.py b/python/pyarmnn/examples/onnx_mobilenetv2.py
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+# Copyright 2020 NXP
+# SPDX-License-Identifier: MIT
+
+import pyarmnn as ann
+import numpy as np
+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
+
+
+if __name__ == "__main__":
+    # Download resources
+    kitten_filename = eu.download_file('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
+    labels_filename = eu.download_file('https://s3.amazonaws.com/onnx-model-zoo/synset.txt')
+    model_filename = eu.download_file(
+        'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/mobilenetv2-1.0.onnx')
+
+    # 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
+    image_names = [kitten_filename]
+    images = eu.load_images(image_names,
+                            224, 224,
+                            np.float32,
+                            255.0,
+                            [0.485, 0.456, 0.406],
+                            [0.229, 0.224, 0.225],
+                            preprocess_onnx)
+
+    for idx, im in enumerate(images):
+        # Create input tensors
+        input_tensors = ann.make_input_tensors([input_binding_info], [im])
+
+        # Run inference
+        print("Running inference on '{0}' ...".format(image_names[idx]))
+        runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
+
+        # Process output
+        out_tensor = ann.workload_tensors_to_ndarray(output_tensors)[0][0]
+        results = np.argsort(out_tensor)[::-1]
+        eu.print_top_n(5, results, labels, out_tensor)