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diff --git a/python/pyarmnn/examples/tflite_mobilenetv1_quantized.py b/python/pyarmnn/examples/tflite_mobilenetv1_quantized.py
new file mode 100644
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+++ b/python/pyarmnn/examples/tflite_mobilenetv1_quantized.py
@@ -0,0 +1,71 @@
+# Copyright 2020 NXP
+# SPDX-License-Identifier: MIT
+
+from zipfile import ZipFile
+import numpy as np
+import pyarmnn as ann
+import example_utils as eu
+import os
+
+
+def unzip_file(filename):
+    """Unzips a file to its current location.
+
+    Args:
+        filename (str): Name of the archive.
+
+    Returns:
+        str: Directory path of the extracted files.
+    """
+    with ZipFile(filename, 'r') as zip_obj:
+        zip_obj.extractall(os.path.dirname(filename))
+    return os.path.dirname(filename)
+
+
+if __name__ == "__main__":
+    # Download resources
+    archive_filename = eu.download_file(
+        'https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip')
+    dir_path = unzip_file(archive_filename)
+    # names of the files in the archive
+    labels_filename = os.path.join(dir_path, 'labels_mobilenet_quant_v1_224.txt')
+    model_filename = os.path.join(dir_path, 'mobilenet_v1_1.0_224_quant.tflite')
+    kitten_filename = eu.download_file('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
+
+    # Create a network from the model file
+    net_id, graph_id, parser, runtime = eu.create_tflite_network(model_filename)
+
+    # Load input information from the model
+    # tflite has all the need information in the model unlike other formats
+    input_names = parser.GetSubgraphInputTensorNames(graph_id)
+    assert len(input_names) == 1  # there should be 1 input tensor in mobilenet
+
+    input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
+    input_width = input_binding_info[1].GetShape()[1]
+    input_height = input_binding_info[1].GetShape()[2]
+
+    # Load output information from the model and create output tensors
+    output_names = parser.GetSubgraphOutputTensorNames(graph_id)
+    assert len(output_names) == 1  # and only one output tensor
+    output_binding_info = parser.GetNetworkOutputBindingInfo(graph_id, output_names[0])
+    output_tensors = ann.make_output_tensors([output_binding_info])
+
+    # Load labels file
+    labels = eu.load_labels(labels_filename)
+
+    # Load images and resize to expected size
+    image_names = [kitten_filename]
+    images = eu.load_images(image_names, input_width, input_height)
+
+    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)