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diff --git a/python/pyarmnn/examples/common/network_executor.py b/python/pyarmnn/examples/common/network_executor.py
new file mode 100644
index 0000000000..6e2c53c43d
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+++ b/python/pyarmnn/examples/common/network_executor.py
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+# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+import os
+from typing import List, Tuple
+
+import pyarmnn as ann
+import numpy as np
+
+
+def create_network(model_file: str, backends: list, input_names: Tuple[str] = (), output_names: Tuple[str] = ()):
+    """
+    Creates a network based on the model file and a list of backends.
+
+    Args:
+        model_file: User-specified model file.
+        backends: List of backends to optimize network.
+        input_names:
+        output_names:
+
+    Returns:
+        net_id: Unique ID of the network to run.
+        runtime: Runtime context for executing inference.
+        input_binding_info: Contains essential information about the model input.
+        output_binding_info: Used to map output tensor and its memory.
+    """
+    if not os.path.exists(model_file):
+        raise FileNotFoundError(f'Model file not found for: {model_file}')
+
+    _, ext = os.path.splitext(model_file)
+    if ext == '.tflite':
+        parser = ann.ITfLiteParser()
+    else:
+        raise ValueError("Supplied model file type is not supported. Supported types are [ tflite ]")
+
+    network = parser.CreateNetworkFromBinaryFile(model_file)
+
+    # Specify backends to optimize network
+    preferred_backends = []
+    for b in backends:
+        preferred_backends.append(ann.BackendId(b))
+
+    # Select appropriate device context and optimize the network for that device
+    options = ann.CreationOptions()
+    runtime = ann.IRuntime(options)
+    opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(),
+                                         ann.OptimizerOptions())
+    print(f'Preferred backends: {backends}\n{runtime.GetDeviceSpec()}\n'
+          f'Optimization warnings: {messages}')
+
+    # Load the optimized network onto the Runtime device
+    net_id, _ = runtime.LoadNetwork(opt_network)
+
+    # Get input and output binding information
+    graph_id = parser.GetSubgraphCount() - 1
+    input_names = parser.GetSubgraphInputTensorNames(graph_id)
+    input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
+    output_names = parser.GetSubgraphOutputTensorNames(graph_id)
+    output_binding_info = []
+    for output_name in output_names:
+        out_bind_info = parser.GetNetworkOutputBindingInfo(graph_id, output_name)
+        output_binding_info.append(out_bind_info)
+    return net_id, runtime, input_binding_info, output_binding_info
+
+
+def execute_network(input_tensors: list, output_tensors: list, runtime, net_id: int) -> List[np.ndarray]:
+    """
+    Executes inference for the loaded network.
+
+    Args:
+        input_tensors: The input frame tensor.
+        output_tensors: The output tensor from output node.
+        runtime: Runtime context for executing inference.
+        net_id: Unique ID of the network to run.
+
+    Returns:
+        list: Inference results as a list of ndarrays.
+    """
+    runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
+    output = ann.workload_tensors_to_ndarray(output_tensors)
+    return output
+
+
+class ArmnnNetworkExecutor:
+
+    def __init__(self, model_file: str, backends: list):
+        """
+        Creates an inference executor for a given network and a list of backends.
+
+        Args:
+            model_file: User-specified model file.
+            backends: List of backends to optimize network.
+        """
+        self.network_id, self.runtime, self.input_binding_info, self.output_binding_info = create_network(model_file,
+                                                                                                          backends)
+        self.output_tensors = ann.make_output_tensors(self.output_binding_info)
+
+    def run(self, input_tensors: list) -> List[np.ndarray]:
+        """
+        Executes inference for the loaded network.
+
+        Args:
+            input_tensors: The input frame tensor.
+
+        Returns:
+            list: Inference results as a list of ndarrays.
+        """
+        return execute_network(input_tensors, self.output_tensors, self.runtime, self.network_id)