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diff --git a/python/pyarmnn/test/test_tflite_parser.py b/python/pyarmnn/test/test_tflite_parser.py
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+++ b/python/pyarmnn/test/test_tflite_parser.py
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+# Copyright © 2019 Arm Ltd. All rights reserved.
+# SPDX-License-Identifier: MIT
+import os
+
+import pytest
+import pyarmnn as ann
+import numpy as np
+
+
+@pytest.fixture()
+def parser(shared_data_folder):
+    """
+    Parse and setup the test network (ssd_mobilenetv1) to be used for the tests below
+    """
+    parser = ann.ITfLiteParser()
+    parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'ssd_mobilenetv1.tflite'))
+
+    yield parser
+
+
+def test_tflite_parser_swig_destroy():
+    assert ann.ITfLiteParser.__swig_destroy__, "There is a swig python destructor defined"
+    assert ann.ITfLiteParser.__swig_destroy__.__name__ == "delete_ITfLiteParser"
+
+
+def test_check_tflite_parser_swig_ownership(parser):
+    # Check to see that SWIG has ownership for parser. This instructs SWIG to take
+    # ownership of the return value. This allows the value to be automatically
+    # garbage-collected when it is no longer in use
+    assert parser.thisown
+
+def test_tflite_get_sub_graph_count(parser):
+    graphs_count = parser.GetSubgraphCount()
+    assert graphs_count == 1
+
+
+def test_tflite_get_network_input_binding_info(parser):
+    graphs_count = parser.GetSubgraphCount()
+    graph_id = graphs_count - 1
+
+    input_names = parser.GetSubgraphInputTensorNames(graph_id)
+
+    input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
+
+    tensor = input_binding_info[1]
+    assert tensor.GetDataType() == 2
+    assert tensor.GetNumDimensions() == 4
+    assert tensor.GetNumElements() == 270000
+    assert tensor.GetQuantizationOffset() == 128
+    assert tensor.GetQuantizationScale() == 0.007874015718698502
+
+
+def test_tflite_get_network_output_binding_info(parser):
+    graphs_count = parser.GetSubgraphCount()
+    graph_id = graphs_count - 1
+
+    output_names = parser.GetSubgraphOutputTensorNames(graph_id)
+
+    output_binding_info1 = parser.GetNetworkOutputBindingInfo(graph_id, output_names[0])
+    output_binding_info2 = parser.GetNetworkOutputBindingInfo(graph_id, output_names[1])
+    output_binding_info3 = parser.GetNetworkOutputBindingInfo(graph_id, output_names[2])
+    output_binding_info4 = parser.GetNetworkOutputBindingInfo(graph_id, output_names[3])
+
+    # Check the tensor info retrieved from GetNetworkOutputBindingInfo
+    tensor1 = output_binding_info1[1]
+    tensor2 = output_binding_info2[1]
+    tensor3 = output_binding_info3[1]
+    tensor4 = output_binding_info4[1]
+
+    assert tensor1.GetDataType() == 1
+    assert tensor1.GetNumDimensions() == 3
+    assert tensor1.GetNumElements() == 40
+    assert tensor1.GetQuantizationOffset() == 0
+    assert tensor1.GetQuantizationScale() == 0.0
+
+    assert tensor2.GetDataType() == 1
+    assert tensor2.GetNumDimensions() == 2
+    assert tensor2.GetNumElements() == 10
+    assert tensor2.GetQuantizationOffset() == 0
+    assert tensor2.GetQuantizationScale() == 0.0
+
+    assert tensor3.GetDataType() == 1
+    assert tensor3.GetNumDimensions() == 2
+    assert tensor3.GetNumElements() == 10
+    assert tensor3.GetQuantizationOffset() == 0
+    assert tensor3.GetQuantizationScale() == 0.0
+
+    assert tensor4.GetDataType() == 1
+    assert tensor4.GetNumDimensions() == 1
+    assert tensor4.GetNumElements() == 1
+    assert tensor4.GetQuantizationOffset() == 0
+    assert tensor4.GetQuantizationScale() == 0.0
+
+
+def test_tflite_get_subgraph_input_tensor_names(parser):
+    graphs_count = parser.GetSubgraphCount()
+    graph_id = graphs_count - 1
+
+    input_names = parser.GetSubgraphInputTensorNames(graph_id)
+
+    assert input_names == ('normalized_input_image_tensor',)
+
+
+def test_tflite_get_subgraph_output_tensor_names(parser):
+    graphs_count = parser.GetSubgraphCount()
+    graph_id = graphs_count - 1
+
+    output_names = parser.GetSubgraphOutputTensorNames(graph_id)
+
+    assert output_names[0] == 'TFLite_Detection_PostProcess'
+    assert output_names[1] == 'TFLite_Detection_PostProcess:1'
+    assert output_names[2] == 'TFLite_Detection_PostProcess:2'
+    assert output_names[3] == 'TFLite_Detection_PostProcess:3'
+
+
+def test_tflite_filenotfound_exception(shared_data_folder):
+    parser = ann.ITfLiteParser()
+
+    with pytest.raises(RuntimeError) as err:
+        parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'some_unknown_network.tflite'))
+
+    # Only check for part of the exception since the exception returns
+    # absolute path which will change on different machines.
+    assert 'Cannot find the file' in str(err.value)
+
+
+def test_tflite_parser_end_to_end(shared_data_folder):
+    parser = ann.ITfLiteParser()
+
+    network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder,"inception_v3_quant.tflite"))
+
+    graphs_count = parser.GetSubgraphCount()
+    graph_id = graphs_count - 1
+
+    input_names = parser.GetSubgraphInputTensorNames(graph_id)
+    input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
+
+    output_names = parser.GetSubgraphOutputTensorNames(graph_id)
+
+    preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
+
+    options = ann.CreationOptions()
+    runtime = ann.IRuntime(options)
+
+    opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+    assert 0 == len(messages)
+
+    net_id, messages = runtime.LoadNetwork(opt_network)
+    assert "" == messages
+
+    # Load test image data stored in input.npy
+    input_tensor_data = np.load(os.path.join(shared_data_folder, 'tflite_parser/inceptionv3_golden_input.npy'))
+    input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
+
+    output_tensors = []
+    for index, output_name in enumerate(output_names):
+        out_bind_info = parser.GetNetworkOutputBindingInfo(graph_id, output_name)
+        out_tensor_info = out_bind_info[1]
+        out_tensor_id = out_bind_info[0]
+        output_tensors.append((out_tensor_id,
+                               ann.Tensor(out_tensor_info)))
+
+    runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
+
+    output_vectors = []
+    for index, out_tensor in enumerate(output_tensors):
+        output_vectors.append(out_tensor[1].get_memory_area())
+
+    # Load golden output file to compare the output results with
+    expected_outputs = np.load(os.path.join(shared_data_folder, 'tflite_parser/inceptionv3_golden_output.npy'))
+
+    # Check that output matches golden output
+    np.testing.assert_allclose(output_vectors, expected_outputs, 0.08)