diff --git a/src/head_pose_estimation.py b/src/head_pose_estimation.py
new file mode 100644
index 0000000..d1ad016
--- /dev/null
+++ b/src/head_pose_estimation.py
@@ -0,0 +1,92 @@
+'''
+This is a sample class for a model. You may choose to use it as-is or make any changes to it.
+This has been provided just to give you an idea of how to structure your model class.
+'''
+import os
+import sys
+import cv2
+import numpy as np
+from openvino.inference_engine import IENetwork, IECore
+
+class Model_X:
+    '''
+    Class for the Face Detection Model.
+    '''
+    def __init__(self, model_name, device='CPU', extensions=None):
+        '''
+        TODO: Use this to set your instance variables.
+        '''
+        self.model_name = model_name
+        self.device = device
+        self.extensions = extensions
+
+    def load_model(self):
+        '''
+        TODO: You will need to complete this method.
+        This method is for loading the model to the device specified by the user.
+        If your model requires any Plugins, this is where you can load them.
+        '''
+        model_bin = self.model_name + '.bin'
+        model_xml = self.model_name + '.xml'
+        self.model = IENetwork(model_xml, model_bin)
+        self.plugin = IECore()
+        if self.extensions:
+            self.plugin.add_extension(self.extensions, self.device)
+        
+        self.exec_net = self.plugin.load_network(self.model, device_name=self.device, num_requests=1)
+        self.check_model()
+
+    def predict(self, image):
+        '''
+        TODO: You will need to complete this method.
+        This method is meant for running predictions on the input image.
+        '''
+
+        frame = self.preprocess_input(image)
+        self.exec_net.start_async(request_id=0, inputs={ self.input_name: frame })
+        if self.exec_net.requests[0].wait(-1) == 0:
+            self.output_coords = [self.exec_net.requests[0].outputs[output_name].astype(float).item()
+                                for output_name in self.output_names]
+            return self.output_coords
+
+    def check_model(self):
+        self.input_name = next(iter(self.model.inputs))
+        self.input_shape = self.model.inputs[self.input_name].shape
+        self.output_names = list(self.model.outputs.keys())
+        self.output_dict = {}
+        for name in self.output_names:
+            self.output_dict[name] = self.model.outputs[name].shape        
+
+    def preprocess_input(self, image):
+        '''
+        Before feeding the data into the model for inference,
+        you might have to preprocess it. This function is where you can do that.
+        '''
+        height, width = self.input_shape[2], self.input_shape[3]
+        frame = cv2.resize(image, (width, height))
+        return frame.transpose((2, 0, 1))
+
+    def preprocess_output(self, outputs):
+        '''
+        Before feeding the output of this model to the next model,
+        you might have to preprocess the output. This function is where you can do that.
+        '''
+        pred = np.array([outputs])
+        return pred
+
+
+def main():
+    CPU_EXTENSION_MAC = '/opt/intel/openvino_2019.3.376/deployment_tools/inference_engine/lib/intel64/libcpu_extension.dylib'
+    model_name = 'models/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001'
+    image = 'bin/test-image1.jpg'
+    model = Model_X(model_name=model_name, device='CPU', extensions=CPU_EXTENSION_MAC)
+    model.load_model()
+    image = cv2.imread(image)
+    pred = model.predict(image)
+    pred = model.preprocess_output(pred)
+    print(pred, pred.shape, 'GGGGGGGGGGGGGGGGGGG')
+
+if __name__ == '__main__':
+    main()
+
+# py src/head_pose_estimation.py