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diff --git a/python/pyarmnn/test/test_caffe_parser.py b/python/pyarmnn/test/test_caffe_parser.py
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+++ b/python/pyarmnn/test/test_caffe_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 (alexnet) to be used for the tests below
+    """
+
+    # Create caffe parser
+    parser = ann.ICaffeParser()
+
+    # Specify path to model
+    path_to_model = os.path.join(shared_data_folder, 'squeezenet_v1.1_armnn.caffemodel')
+
+    # Specify the tensor shape relative to the input [1, 3, 227, 227]
+    tensor_shape = {'data': ann.TensorShape((1, 3, 227, 227))}
+
+    # Specify the requested_outputs
+    requested_outputs = ["prob"]
+
+    # Parse tf binary & create network
+    parser.CreateNetworkFromBinaryFile(path_to_model, tensor_shape, requested_outputs)
+
+    yield parser
+
+
+def test_caffe_parser_swig_destroy():
+    assert ann.ICaffeParser.__swig_destroy__, "There is a swig python destructor defined"
+    assert ann.ICaffeParser.__swig_destroy__.__name__ == "delete_ICaffeParser"
+
+
+def test_check_caffe_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_get_network_input_binding_info(parser):
+    input_binding_info = parser.GetNetworkInputBindingInfo("data")
+
+    tensor = input_binding_info[1]
+    assert tensor.GetDataType() == 1
+    assert tensor.GetNumDimensions() == 4
+    assert tensor.GetNumElements() == 154587
+
+
+def test_get_network_output_binding_info(parser):
+    output_binding_info1 = parser.GetNetworkOutputBindingInfo("prob")
+
+    # Check the tensor info retrieved from GetNetworkOutputBindingInfo
+    tensor1 = output_binding_info1[1]
+
+    assert tensor1.GetDataType() == 1
+    assert tensor1.GetNumDimensions() == 4
+    assert tensor1.GetNumElements() == 1000
+
+
+@pytest.mark.skip("Skipped. Currently there is a bug in armnn (RecordByRecordCaffeParser). To be enabled it once fixed.")
+def test_filenotfound_exception(shared_data_folder):
+    parser = ann.ICaffeParser()
+
+    # path to model
+    path_to_model = os.path.join(shared_data_folder, 'some_unknown_network.caffemodel')
+
+    # generic tensor shape [1, 1, 1, 1]
+    tensor_shape = {'data': ann.TensorShape((1, 1, 1, 1))}
+
+    # requested_outputs
+    requested_outputs = [""]
+
+    with pytest.raises(RuntimeError) as err:
+        parser.CreateNetworkFromBinaryFile(path_to_model, tensor_shape, requested_outputs)
+
+    # Only check for part of the exception since the exception returns
+    # absolute path which will change on different machines.
+    assert 'Failed to open graph file' in str(err.value)
+
+
+def test_caffe_parser_end_to_end(shared_data_folder):
+    parser = ann.ICaffeParser = ann.ICaffeParser()
+
+    # Load the network specifying the inputs and outputs
+    input_name = "data"
+    tensor_shape = {input_name: ann.TensorShape((1, 3, 227, 227))}
+    requested_outputs = ["prob"]
+
+    network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'squeezenet_v1.1_armnn.caffemodel'),
+                                                 tensor_shape, requested_outputs)
+
+    # Specify preferred backend
+    preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
+
+    input_binding_info = parser.GetNetworkInputBindingInfo(input_name)
+
+    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 golden_input.npy
+    input_tensor_data = np.load(os.path.join(shared_data_folder, 'caffe_parser/squeezenet_v1_1_input.npy'))
+    input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
+
+    # Load output binding info and
+    outputs_binding_info = []
+    for output_name in requested_outputs:
+        outputs_binding_info.append(parser.GetNetworkOutputBindingInfo(output_name))
+    output_tensors = ann.make_output_tensors(outputs_binding_info)
+
+    runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
+    output_vectors = []
+
+    output_vectors = ann.workload_tensors_to_ndarray(output_tensors)
+
+    # Load golden output file to compare the output results with
+    expected_output = np.load(os.path.join(shared_data_folder, 'caffe_parser/squeezenet_v1_1_output.npy'))
+
+    # Check that output matches golden output to 4 decimal places (there are slight rounding differences after this)
+    np.testing.assert_almost_equal(output_vectors, expected_output, 4)