diff --git a/.github/workflows/dockerhub-main-push.yml b/.github/workflows/dockerhub-main-push.yml
index 8f8dc0d..dbec67d 100644
--- a/.github/workflows/dockerhub-main-push.yml
+++ b/.github/workflows/dockerhub-main-push.yml
@@ -1,8 +1,10 @@
 name: DockerHub Main Push
 
 on:
-  push:
-    branches: [ "main" ]
+  workflow_run:
+    workflows: ["Unittest Main Push"]
+    types:
+      - completed
 
 jobs:
   build:
diff --git a/.github/workflows/unittest-dev.yml b/.github/workflows/unittest-dev.yml
new file mode 100644
index 0000000..918ddd3
--- /dev/null
+++ b/.github/workflows/unittest-dev.yml
@@ -0,0 +1,30 @@
+name: Unittest Dev
+
+on:
+  pull_request:
+    branches: [ "dev" ]
+  push:
+    branches: [ "dev" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install python dependencies
+      run: |
+        python -m pip install --upgrade pip
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Test with unittest
+      run: |
+        python -m unittest discover ./webscrapers/unittests/utilities
diff --git a/.github/workflows/unittest-main-pr.yml b/.github/workflows/unittest-main-pr.yml
new file mode 100644
index 0000000..73fe9d1
--- /dev/null
+++ b/.github/workflows/unittest-main-pr.yml
@@ -0,0 +1,28 @@
+name: Unittest Main Pull Request
+
+on:
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install python dependencies
+      run: |
+        python -m pip install --upgrade pip
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Test with unittest
+      run: |
+        python -m unittest discover ./webscrapers/unittests/utilities
diff --git a/.github/workflows/unittest-main-push.yml b/.github/workflows/unittest-main-push.yml
new file mode 100644
index 0000000..5fe6263
--- /dev/null
+++ b/.github/workflows/unittest-main-push.yml
@@ -0,0 +1,28 @@
+name: Unittest Main Push
+
+on:
+  push:
+    branches: [ "main" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install python dependencies
+      run: |
+        python -m pip install --upgrade pip
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Test with unittest
+      run: |
+        python -m unittest discover ./webscrapers/unittests/utilities
diff --git a/model/arch/crop_image.py b/model/arch/crop_image.py
new file mode 100644
index 0000000..03960a4
--- /dev/null
+++ b/model/arch/crop_image.py
@@ -0,0 +1,39 @@
+import numpy as np
+from beartype import beartype
+from typing import Union
+
+@beartype
+def crop_image(
+    image_array:np.array,
+    pt1_wh:Union[tuple,list],
+    pt2_wh:Union[tuple,list]
+    ) -> np.array:
+    """Crops an image array to specified combination of two diagonal points
+
+    Parameters
+    ---------
+    image : numpy.array
+        The numpy image array to crop
+    pt1_wh : list, tuple
+        Diagonal point 1 coordinates for cropping the image
+    pt2_wh : list, tuple
+        Diagonal point 2 coordinates for cropping the image
+    
+    Returns
+    -------
+    numpy.array
+        The cropped image array
+    """
+    # extract out diagonal cropping points
+    (pt1_w, pt1_h) = pt1_wh
+    (pt2_w, pt2_h) = pt2_wh
+    # group height and width points
+    wpts = (pt1_h, pt2_h)
+    hpts = (pt1_w, pt2_w)
+    # extract out image shape
+    n_image_dims = len(image_array.shape)
+    if n_image_dims == 3:
+        crop_image_array = image_array[min(hpts):max(hpts), min(wpts):max(wpts), :] 
+    else:
+        crop_image_array = image_array[min(hpts):max(hpts), min(wpts):max(wpts)] 
+    return crop_image_array
\ No newline at end of file
diff --git a/model/data_prep.py b/model/arch/data_prep.py
similarity index 89%
rename from model/data_prep.py
rename to model/arch/data_prep.py
index 1d66338..f76a54f 100644
--- a/model/data_prep.py
+++ b/model/arch/data_prep.py
@@ -2,6 +2,7 @@
 import os
 import sys
 import pickle
+import logging
 import numpy as np
 import pandas as pd
 from sklearn.model_selection import train_test_split
@@ -9,17 +10,23 @@
 # load custom modules
 sys.path.append(os.getcwd())
 import cons
-from utilities.load_image import load_image
-from utilities.greyscale_image import greyscale_image
-from utilities.pad_image import pad_image
-from utilities.plot_image import plot_image
-from utilities.resize_image import resize_image
+from arch.load_image import load_image
+from arch.greyscale_image import greyscale_image
+from arch.pad_image import pad_image
+from arch.resize_image import resize_image
 
-def data_prep(cons):
+def data_prep():
+    """
+    Data preparation pipeline for generating the model training, testing and validation data.
 
-    """"""
+    Parameters
+    ----------
 
-    print("Generating image file paths and classes ...")
+    Returns
+    -------
+    """
+
+    logging.info("Generating image file paths and classes ...")
 
 
     if False:
@@ -55,7 +62,7 @@ def data_prep(cons):
         # combine train and test files
         image_fpaths = {**train_image_fpaths, **test_image_fpaths}
     
-    print("Creating image dataframe ...")
+    logging.info("Creating image dataframe ...")
 
     # create list to hold image data
     image_data = []
@@ -78,7 +85,7 @@ def data_prep(cons):
     # convert image data object into a pandas dataframe
     image_dataframe = pd.DataFrame(image_data)
 
-    print("Padding images ...")
+    logging.info("Padding images ...")
 
     # find the largest image dimensions
     max_height = image_dataframe['image_shape'].apply(lambda x: x[0]).max() # height
@@ -90,7 +97,7 @@ def data_prep(cons):
     # apply padding to standardize all images shapes
     image_dataframe['pad_image_array'] = image_dataframe['image_array'].apply(lambda x: pad_image(x, pad_shape_wh = pad_shape))
 
-    print('Down sizing image ...')
+    logging.info('Down sizing image ...')
 
     # set down size shape
     downsize_shape = tuple([round(dim * 1/3) for dim in pad_shape])
@@ -98,7 +105,7 @@ def data_prep(cons):
     # apply resizing to downsize image shapes
     image_dataframe['pad_image_array'] = image_dataframe['pad_image_array'].apply(lambda x: resize_image(x, reshape_wh = downsize_shape))
 
-    print('Splitting train set ...')
+    logging.info('Splitting train set ...')
 
     # subset the output image data
     sub_cols = ['image_fpath', 'pad_image_array', 'target', 'dataset']
@@ -136,8 +143,6 @@ def data_prep(cons):
     with open(cons.test_data_pickle_fpath, 'wb') as handle:
         pickle.dump(test_data_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)
 
-    return 0
-
 # if running as main programme
 if __name__ == "__main__":
 
diff --git a/model/arch/greyscale_image.py b/model/arch/greyscale_image.py
new file mode 100644
index 0000000..d6a6a73
--- /dev/null
+++ b/model/arch/greyscale_image.py
@@ -0,0 +1,31 @@
+import numpy as np
+from beartype import beartype
+
+@beartype
+def greyscale_image(
+    rgb_image_array:np.array,
+    keep_3dim:bool=True
+    ) -> np.array:
+    """
+    Transforms an image array to greyscale
+
+    Parameters
+    ----------
+    rgb_image_array : numpy.array
+        The coloured numpy image array to transform to greyscale
+    keep_3dim : bool
+        Whether to keep the third dimension of the image array, default is True
+    
+    Returns
+    -------
+    numpy.array
+        The transformed greyscale numpy image
+    """
+    # apply grey scale transformation
+    grey_image_array =  np.dot(rgb_image_array[:, :, :3], [0.2125, 0.7154, 0.0721])
+    # floor transformed pixel floats
+    gray_img = grey_image_array.astype(np.uint8)
+    # if keeping third dimension
+    if keep_3dim:
+        gray_img = gray_img[..., np.newaxis]
+    return gray_img
diff --git a/model/arch/keras_model.py b/model/arch/keras_model.py
index 566f4ea..c4c04ef 100644
--- a/model/arch/keras_model.py
+++ b/model/arch/keras_model.py
@@ -12,9 +12,9 @@
 sys.path.append(os.getcwd())
 import cons
 from utilities.plot_image import plot_image
-from arch.LeNet5 import LeNet5
+from keras.LeNet5 import LeNet5
 from fit_model import fit_model
-from plot_model import plot_model_fit
+from utilities.plot_model import plot_model_fit
 
 print('Loading data ...')
 
diff --git a/model/arch/load_image.py b/model/arch/load_image.py
new file mode 100644
index 0000000..23d2e16
--- /dev/null
+++ b/model/arch/load_image.py
@@ -0,0 +1,24 @@
+import numpy as np
+import cv2
+from beartype import beartype
+
+@beartype
+def load_image(image_fpath:str) -> np.array:
+    """
+    Loads an image file as an image array from disk
+
+    Parameters
+    ----------
+    image_fpath : str
+        The file path to the image file to load as an image array
+    
+    Returns
+    -------
+    numpy.array
+        The loaded image array
+    """
+    # load image from file path
+    image_imread = cv2.imread(image_fpath)
+    # convert to numpy array
+    rgb_image_array = np.array(image_imread)
+    return rgb_image_array
\ No newline at end of file
diff --git a/model/utilities/pad_image.py b/model/arch/pad_image.py
similarity index 50%
rename from model/utilities/pad_image.py
rename to model/arch/pad_image.py
index 21dbbcf..d471c9c 100644
--- a/model/utilities/pad_image.py
+++ b/model/arch/pad_image.py
@@ -1,7 +1,27 @@
 import numpy as np
+from beartype import beartype
+from typing import Union
 
-def pad_image(image_array, pad_shape_wh):
-    """"""
+@beartype
+def pad_image(
+    image_array:np.array,
+    pad_shape_wh:Union[list,tuple]
+    ) -> np.array:
+    """
+    Pads an image array to a desired width and height
+
+    Parameters
+    ----------
+    image_array: np.array
+        The image array to pad to a specified dimension
+    pad_shape_wh : list, tuple
+        The desired dimensions to pad the input image array to
+
+    Returns
+    -------
+    numpy.array
+        The padded image array
+    """
     image_array_shape = image_array.shape
     (img_h, img_w) = image_array_shape[0:2]
     (pad_img_w, pad_img_h) = pad_shape_wh
diff --git a/model/arch/resize_image.py b/model/arch/resize_image.py
new file mode 100644
index 0000000..e5f80b3
--- /dev/null
+++ b/model/arch/resize_image.py
@@ -0,0 +1,38 @@
+import numpy as np
+import cv2
+from beartype import beartype
+from typing import Union
+
+@beartype
+def resize_image(
+    image_array:np.array,
+    reshape_wh:Union[list,tuple],
+    interpolation=cv2.INTER_LINEAR,
+    keep_3dim:bool=True
+    ) -> np.array:
+    """
+    Resizes a numpy image array to a specified shape width and height
+
+    Parameters
+    ----------
+    image_array : numpy.array
+        The image array to reshape to new dimensions
+    reshape_wh : list, tuple
+        The dimensions to reshape the image array to
+    interpolation : cv2.INTER_LINEAR
+        The interpolation function for reshaping the image array, default is cv2.INTER_LINEAR
+    keep_3dim : bool
+        Whether to maintain the third dimension of the input numpy image array, default is True
+    
+    Returns
+    -------
+    numpy.array
+        The reshaped numpy image array
+    """
+    # rescale the image either by shrinking or expanding
+    res_image_array = cv2.resize(image_array, dsize = reshape_wh, interpolation=interpolation)
+    # keep 3dim; when applying resizing to (:, :, 1) shape images
+    if keep_3dim and len(res_image_array.shape) == 2:
+        res_image_array = res_image_array[..., np.newaxis]
+
+    return res_image_array
\ No newline at end of file
diff --git a/model/exeKerasModel.cmd b/model/exeKerasModel.cmd
new file mode 100644
index 0000000..adaca20
--- /dev/null
+++ b/model/exeKerasModel.cmd
@@ -0,0 +1 @@
+call python -m pdb prg_keras_model.py --run_model_training --run_testset_prediction
\ No newline at end of file
diff --git a/model/exeKerasModel.sh b/model/exeKerasModel.sh
new file mode 100644
index 0000000..4dd185d
--- /dev/null
+++ b/model/exeKerasModel.sh
@@ -0,0 +1 @@
+python -m pdb prg_keras_model.py --run_model_training --run_testset_prediction
\ No newline at end of file
diff --git a/model/exeUnittests.cmd b/model/exeUnittests.cmd
new file mode 100644
index 0000000..780f944
--- /dev/null
+++ b/model/exeUnittests.cmd
@@ -0,0 +1 @@
+call python -m unittest discover unittests\utilities
\ No newline at end of file
diff --git a/model/keras/AlexNet8.py b/model/keras/AlexNet8.py
index 1300584..40b92e9 100644
--- a/model/keras/AlexNet8.py
+++ b/model/keras/AlexNet8.py
@@ -1,14 +1,32 @@
 # load in relevant libraries
+from beartype import beartype
+from typing import Union
 from keras.models import Model
 from keras.layers import Flatten, Dense, Input, Dropout
 from keras.layers import Conv2D, MaxPooling2D
 
-def AlexNet8(input_shape = (227, 227, 3), 
-            n_classes = 1000,
-            output_activation = 'softmax'
-            ):
-    
+@beartype
+def AlexNet8(
+        input_shape:Union[list,tuple]=(227,227,3), 
+        n_classes:int=1000,
+        output_activation:str='softmax'
+        ) -> Model:
     """
+    AlexNet8 Keras model
+    
+    Parameters
+    ----------
+    input_shape : list,tuple
+        The dimensions of the input image arrays, default is (227.227,3)
+    n_classes : int
+        The number of output classes to classify for, default is 1000
+    output_activation : str
+        The type of activation function to use, default is softmax
+    
+    Returns
+    -------
+    Model
+        The keras AlexNet8 model
     """
 
     # set input shapes
diff --git a/model/keras/LeNet5.py b/model/keras/LeNet5.py
index a3b7373..cf08cb4 100644
--- a/model/keras/LeNet5.py
+++ b/model/keras/LeNet5.py
@@ -1,90 +1,31 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Jan 31 15:22:57 2021
-@author: oislen
-"""
-
-# load in relevant libraries
+from beartype import beartype
+from typing import Union
 from keras.models import Model
 from keras.layers import Flatten, Dense, Input, Dropout
 from keras.layers import Conv2D, AveragePooling2D
 
-def LeNet5(input_shape = (28, 28, 1), 
-           n_classes = 10,
-           output_activation = 'softmax'
-           ):
-    
+@beartype
+def LeNet5(
+    input_shape:Union[list,tuple]=(28,28,1), 
+    n_classes:int=10,
+    output_activation:str='softmax'
+    ) -> Model:
     """
-        
-    LeNet5 Documentation
-    
-    Function Overview
-    
-    This function generates a LeNet5 Model architecture:
-        
-         1. Conv2D 
-             - filters: 32  
-             - kernal: 5 x 5 
-             - activation: relu 
-             - padding: same
-             
-            MaxPooling2D 
-             - pool: 2 x 2 
-             
-         2. Conv2D 
-             - filters: 32  
-             - kernal: 5 x 5 
-             - activation: relu 
-             - padding: same
-             
-            MaxPooling2D 
-             - pool: 2 x 2 
-             
-            Flatten
-         
-         3. Dense 
-             - units: 128  
-             - activation: relu 
-             
-            Dropout 
-             - rate: 0.25 
-             
-         4. Dense 
-             - units: 64  
-             - activation: relu 
-             
-            Dropout 
-             - 0.25 rate
-             
-         5. Dense 
-            - units: n classes  
-            - activation: output_activation 
-        
-    Defaults
-    
-    LeNet5(input_shape = (28, 28, 1), 
-           n_classes = 10,
-           output_activation = 'softmax',
-           name = 'LeNet5'
-           )
+    LeNet5 keras model
     
     Parameters
-    
-    input_shape - the input image shape / dimensions
-    n_classes - the number of target classes
+    ----------
+    input_shape : list,tuple
+        the input image shape / dimensions, default is (28,28,1)
+    n_classes : int
+        The number of target classes, default is 10
+    output_activation : str
+        The type of activation function to use, default is softmax
     
     Returns
-    
-    model - keras.Model, the LeNet model
-    
-    Example
-    
-    LeNet5(input_shape = (28, 28, 1), 
-           n_classes = 10, 
-           output_activation = 'softmax', 
-           name = 'LeNet5'
-           )
-    
+    -------
+    Model
+        Keras.Model, the LeNet model
     """
     
     # set input shapes
diff --git a/model/keras/ResNet50_pretrained.py b/model/keras/ResNet50_pretrained.py
index 6d56720..e561bf4 100644
--- a/model/keras/ResNet50_pretrained.py
+++ b/model/keras/ResNet50_pretrained.py
@@ -1,13 +1,34 @@
+from beartype import beartype
+from typing import Union
 from keras.layers import Dropout, Dense, Flatten
 from keras.applications import ResNet50
 from keras.models import Model
 
-def ResNet50_pretrained(input_shape = (224, 224, 3), 
-                        n_classes = 2,
-                        output_activation = 'softmax'
-                        ):
-
+@beartype
+def ResNet50_pretrained(
+    input_shape:Union[list,tuple]=(224,224,3), 
+    n_classes:int=2,
+    output_activation:str='softmax'
+    ) -> Model:
     """
+    Pretrained ResNet50 keras model
+    
+    Parameters
+    ----------
+    input_shape : list,tuple
+        the input image shape / dimensions, default is (224,224,3)
+    n_classes : int
+        The number of target classes, default is 2
+    output_activation : str
+        The type of activation function to use, default is softmax
+    
+    Returns
+    -------
+    Model
+        Keras.Model, the pretrained ResNet50 model
+    
+    Example
+    -------
     model = ResNet50_pretrained(input_shape = (224, 224, 3), 
                                 n_classes = 2,
                                 output_activation = 'softmax',
@@ -41,7 +62,4 @@ def ResNet50_pretrained(input_shape = (224, 224, 3),
 
     model = Model(inputs = ResNet50_pretrained.input, outputs = x, name = 'ResNet50_pretrained')
 
-    return model
-
-
-
+    return model
\ No newline at end of file
diff --git a/model/keras/UNet.py b/model/keras/UNet.py
index e860445..75873d6 100644
--- a/model/keras/UNet.py
+++ b/model/keras/UNet.py
@@ -1,3 +1,8 @@
+from beartype import beartype
+from typing import Union
+from keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose
+from keras.layers.merge import concatenate
+import tensorflow as tf
 
 def conv_block(inputs=None, n_filters=32, dropout_prob=0, max_pooling=True):
     """
diff --git a/model/keras/VGG16.py b/model/keras/VGG16.py
index 05ed56e..dc26bfe 100644
--- a/model/keras/VGG16.py
+++ b/model/keras/VGG16.py
@@ -1,14 +1,30 @@
-# load in relevant libraries
+from beartype import beartype
+from typing import Union
 from keras.models import Model
 from keras.layers import Flatten, Dense, Input, Dropout
 from keras.layers import Conv2D, MaxPooling2D
 
-def VGG16(input_shape = (224, 224, 3), 
-         n_classes = 1000,
-         output_activation = 'softmax'
-         ):
-    
+@beartype
+def VGG16(input_shape:Union[list,tuple]=(224,224,3), 
+         n_classes:int=1000,
+         output_activation:str='softmax'
+         ) -> Model:
     """
+    VGG16 keras model
+    
+    Parameters
+    ----------
+    input_shape : list,tuple
+        the input image shape / dimensions, default is (224,224,3)
+    n_classes : int
+        The number of target classes, default is 1000
+    output_activation : str
+        The type of activation function to use, default is softmax
+    
+    Returns
+    -------
+    Model
+        Keras.Model, the VGG16 model
     """
 
     # set input shapes
diff --git a/model/keras/VGG16_pretrained.py b/model/keras/VGG16_pretrained.py
index 85af564..c7a0729 100644
--- a/model/keras/VGG16_pretrained.py
+++ b/model/keras/VGG16_pretrained.py
@@ -1,12 +1,34 @@
+from beartype import beartype
+from typing import Union
 from keras.layers import Dropout, Dense, GlobalMaxPooling2D
 from keras.applications import VGG16
 from keras.models import Model
 
-def VGG16_pretrained(input_shape = (224, 224, 3), 
-                     n_classes = 1,
-                     output_activation = 'sigmoid'
-                     ):
+@beartype
+def VGG16_pretrained(
+    input_shape:Union[list,tuple]=(224,224,3), 
+    n_classes:int=1,
+    output_activation:str='sigmoid'
+    ) -> Model:
     """
+    Pretrained VGG16 keras model
+    
+    Parameters
+    ----------
+    input_shape : list,tuple
+        the input image shape / dimensions, default is (224,224,3)
+    n_classes : int
+        The number of target classes, default is 1
+    output_activation : str
+        The type of activation function to use, default is sigmoid
+    
+    Returns
+    -------
+    Model
+        Keras.Model, the pretrained VGG16 model
+    
+    Example
+    -------
     model = VGG16_pretrained(input_shape = (224, 224, 3), 
                              n_classes = 1,
                              output_activation = 'sigmoid',
diff --git a/model/prg_keras_model.py b/model/prg_keras_model.py
index 371f790..30570cd 100644
--- a/model/prg_keras_model.py
+++ b/model/prg_keras_model.py
@@ -147,7 +147,7 @@
         
         logging.info("Plot example test set predictions...")
         # plot random sample predictions
-        plot_preds(data=test_df, cons=cons, output_fpath=cons.keras_pred_images_fpath, show_plot=False)
+        plot_preds(data=test_df, output_fpath=cons.keras_pred_images_fpath, show_plot=False)
         timeLogger.logTime(parentKey="Plots", subKey="TestSetPredictions")
         
         logging.info("Generate a sample submission file for kaggle...")
diff --git a/model/prg_torch_model.py b/model/prg_torch_model.py
index 1dc57b1..2cc37d8 100644
--- a/model/prg_torch_model.py
+++ b/model/prg_torch_model.py
@@ -165,7 +165,7 @@
         
         logging.info("Plot example test set predictions...")
         # plot random sample predictions
-        plot_preds(data=test_df, cons=cons, output_fpath=cons.torch_pred_images_fpath, show_plot=False)
+        plot_preds(data=test_df, output_fpath=cons.torch_pred_images_fpath, show_plot=False)
         timeLogger.logTime(parentKey="Plots", subKey="TestSetPredictions")
         
         logging.info("Generate a sample submission file for kaggle...")
diff --git a/model/unittests/utilities/test_TimeIt.py b/model/unittests/utilities/test_TimeIt.py
new file mode 100644
index 0000000..11cfdfa
--- /dev/null
+++ b/model/unittests/utilities/test_TimeIt.py
@@ -0,0 +1,34 @@
+import unittest
+import os
+import sys
+
+sys.path.append(os.path.join(os.getcwd(), "models"))
+
+import cons
+from utilities.TimeIt import TimeIt
+
+parentKey="Test Parent Key"
+subKey="Test Sub Key"
+exp_log_keys = ["parentKey", "subKey", "stepTime", "cumulativeTime"]
+obs_timeit = TimeIt()
+obs_timeit.logTime(parentKey=parentKey, subKey=subKey)
+obs_log_keys = list(obs_timeit.log[0].keys())
+
+class Test_TimeIt(unittest.TestCase):
+    """"""
+
+    def setUp(self):
+        self.exp_log_keys = exp_log_keys
+        self.obs_log_keys = obs_log_keys
+
+    def test_type(self):
+        self.assertEqual(type(self.obs_log_keys), type(self.exp_log_keys))
+
+    def test_len(self):
+        self.assertEqual(len(self.obs_log_keys), len(self.exp_log_keys))
+
+    def test_values(self):
+        self.assertEqual(self.obs_log_keys, self.exp_log_keys)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/model/utilities/TimeIt.py b/model/utilities/TimeIt.py
index 3be6215..c056c13 100644
--- a/model/utilities/TimeIt.py
+++ b/model/utilities/TimeIt.py
@@ -1,12 +1,12 @@
 import logging
+from beartype import beartype
+from typing import Union
 from time import time
 
 class TimeIt():
-    """
-    """
 
     def __init__(self):
-        """
+        """An object for timing code execution times, records time step and cumulative time.
         """
         self.log = []
         self.currentTime = time()
@@ -14,8 +14,20 @@ def __init__(self):
         self.stepTime = None
         self.cumulativeTime = 0.0
     
-    def logTime(self, parentKey, subKey=None):
-        """
+    @beartype
+    def logTime(
+        self,
+        parentKey:str,
+        subKey:Union[str,None]=None
+        ):
+        """Sets a timestamp for code execution step
+
+        Parameters
+        ----------
+        paraentKey : str
+            The parent key to label the code execution step
+        subKey : str
+            The sub key to label the code execution step, default is None
         """
         self.previousTime = self.currentTime
         self.currentTime = time()
@@ -24,4 +36,12 @@ def logTime(self, parentKey, subKey=None):
         logEntry = {"parentKey":parentKey, "subKey":subKey, "stepTime":self.stepTime, "cumulativeTime":self.cumulativeTime}
         logging.info(logEntry)
         self.log.append(logEntry)
-    
\ No newline at end of file
+
+    def reset(self):
+        """Resets the timing object
+        """
+        self.log = []
+        self.currentTime = time()
+        self.previousTime = None
+        self.stepTime = None
+        self.cumulativeTime = 0.0
\ No newline at end of file
diff --git a/model/utilities/crop_image.py b/model/utilities/crop_image.py
deleted file mode 100644
index e09a3c7..0000000
--- a/model/utilities/crop_image.py
+++ /dev/null
@@ -1,15 +0,0 @@
-def crop_image(image_array, pt1_wh, pt2_wh):
-    """"""
-    # extract out diagonal cropping points
-    (pt1_w, pt1_h) = pt1_wh
-    (pt2_w, pt2_h) = pt2_wh
-    # group height and width points
-    wpts = (pt1_h, pt2_h)
-    hpts = (pt1_w, pt2_w)
-    # extract out image shape
-    n_image_dims = len(image_array.shape)
-    if n_image_dims == 3:
-        crop_image_array = image_array[min(hpts):max(hpts), min(wpts):max(wpts), :] 
-    else:
-        crop_image_array = image_array[min(hpts):max(hpts), min(wpts):max(wpts)] 
-    return crop_image_array
\ No newline at end of file
diff --git a/model/utilities/greyscale_image.py b/model/utilities/greyscale_image.py
deleted file mode 100644
index b7a30e4..0000000
--- a/model/utilities/greyscale_image.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import numpy as np
-
-def greyscale_image(rgb_image_array, keep_3dim = True):
-    """"""
-    # apply grey scale transformation
-    grey_image_array =  np.dot(rgb_image_array[:, :, :3], [0.2125, 0.7154, 0.0721])
-    # floor transformed pixel floats
-    gray_img = grey_image_array.astype(np.uint8)
-    # if keeping third dimension
-    if keep_3dim:
-        gray_img = gray_img[..., np.newaxis]
-    return gray_img
diff --git a/model/utilities/load_image.py b/model/utilities/load_image.py
deleted file mode 100644
index a5db503..0000000
--- a/model/utilities/load_image.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import numpy as np
-import cv2
-
-def load_image(image_fpath):
-    """"""
-    # load image from file path
-    image_imread = cv2.imread(image_fpath)
-    # convert to numpy array
-    rgb_image_array = np.array(image_imread)
-    return rgb_image_array
\ No newline at end of file
diff --git a/model/utilities/plot_generator.py b/model/utilities/plot_generator.py
index bf76cca..5e0a7cd 100644
--- a/model/utilities/plot_generator.py
+++ b/model/utilities/plot_generator.py
@@ -3,8 +3,14 @@
 from beartype import beartype
 
 @beartype
-def plot_generator(generator, mode:str='keras', output_fpath:Union[str,None]=None, show_plot:bool=True):
-    """Plots multiple images from a generator.
+def plot_generator(
+    generator,
+    mode:str='keras',
+    output_fpath:Union[str,None]=None,
+    show_plot:bool=True
+    ):
+    """
+    Plots multiple images from a generator.
 
     Parameters
     ----------
diff --git a/model/utilities/plot_image.py b/model/utilities/plot_image.py
index 187cae1..abdd2bf 100644
--- a/model/utilities/plot_image.py
+++ b/model/utilities/plot_image.py
@@ -1,10 +1,16 @@
 import matplotlib.pyplot as plt
+import numpy as np
 from beartype import beartype
 from typing import Union
 
 @beartype
-def plot_image(image_array, output_fpath:Union[str,None]=None, show_plot:bool=True):
-    """Plots an image array.
+def plot_image(
+    image_array,
+    output_fpath:Union[str,None]=None,
+    show_plot:bool=True
+    ):
+    """
+    Plots an image array.
 
     Parameters
     ----------
diff --git a/model/utilities/plot_model.py b/model/utilities/plot_model.py
index 179e280..316cdde 100644
--- a/model/utilities/plot_model.py
+++ b/model/utilities/plot_model.py
@@ -1,43 +1,34 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Feb  2 11:37:53 2021
-@author: oislen
-"""
-
 # load relevant libraries
 import os
+from beartype import beartype
+from typing import Union
 from matplotlib import pyplot as plt
  
-def plot_model_fit(model_fit, output_fdir = None, show_plot:bool=True):
-    
+@beartype
+def plot_model_fit(
+    model_fit,
+    output_fdir:Union[str,None]=None,
+    show_plot:bool=True
+    ):
     """
-    
-    Plot Model Fit Documentation
-    
-    Function Overview
-    
-    This function plots the model's fit during training in relation to a validation set.
-    
-    Defaults
-    
-    plot_model_fit(model_fit)
+    Plots the model's fit during training in relation to a validation set.
     
     Parameters
+    ----------
     
-    model_fit - model.predict(), the Keras model predict object
+    model_fit : model.predict(), 
+        the Keras model predict object
+    output_fdir : str
+        The file path to save the model fit plots to disk, default is None
     show_plot : bool
         Whether to show the generated plot, default is True
     
     Returns
-    
-    0 for successful execution
-    
-    Exmaple
-    
-    plot_model_fit(model_fit = model_fit)
-    
-    Source
-    
+    -------
+
+    Reference
+    ----------
+
     https://github.com/jiadaizhao/Advanced-Machine-Learning-Specialization
     
     """
@@ -88,6 +79,4 @@ def plot_model_fit(model_fit, output_fdir = None, show_plot:bool=True):
         plt.savefig(loss_output_fpath)
     if show_plot:
         plt.show()
-    plt.close()
-    
-    return 0
\ No newline at end of file
+    plt.close()
\ No newline at end of file
diff --git a/model/utilities/plot_preds.py b/model/utilities/plot_preds.py
index 615a726..7850dee 100644
--- a/model/utilities/plot_preds.py
+++ b/model/utilities/plot_preds.py
@@ -1,11 +1,30 @@
 import os
+from beartype import beartype
 import matplotlib.pyplot as plt
+import pandas as pd
 from tensorflow.keras.preprocessing.image import load_img
+import cons
 
-def plot_preds(data, cons, output_fpath = None, show_plot:bool=True):
+@beartype
+def plot_preds(
+    data:pd.DataFrame,
+    output_fpath:str = None,
+    show_plot:bool=True
+    ):
     """
+    Shows model predictions as a grid of images with labels
+
+    Parameters
+    ----------
+    data : pandas.DataFrame
+        A dataframe of image arrays and associated labels
+    output_fpath : str
+        The file path to save the plot of predictions, default is None
     show_plot : bool
         Whether to show the generated plot, default is True
+
+    Returns
+    -------
     """
     sample_test = data.head(18)
     plt.figure(figsize=(12, 24))
@@ -23,5 +42,4 @@ def plot_preds(data, cons, output_fpath = None, show_plot:bool=True):
         plt.savefig(output_fpath)
     if show_plot:
         plt.show()
-    plt.close()
-    return 0
\ No newline at end of file
+    plt.close()
\ No newline at end of file
diff --git a/model/utilities/resize_image.py b/model/utilities/resize_image.py
deleted file mode 100644
index 6c44240..0000000
--- a/model/utilities/resize_image.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import numpy as np
-import cv2
-
-def resize_image(image_array, reshape_wh, interpolation = cv2.INTER_LINEAR, keep_3dim = True):
-    """"""
-    # rescale the image either by shrinking or expanding
-    res_image_array = cv2.resize(image_array, dsize = reshape_wh, interpolation=interpolation)
-    # keep 3dim; when applying resizing to (:, :, 1) shape images
-    if keep_3dim and len(res_image_array.shape) == 2:
-        res_image_array = res_image_array[..., np.newaxis]
-
-    return res_image_array
\ No newline at end of file
diff --git a/webscrapers/playwright/cons.py b/webscrapers/arch/playwright/cons.py
similarity index 100%
rename from webscrapers/playwright/cons.py
rename to webscrapers/arch/playwright/cons.py
diff --git a/webscrapers/playwright/webscraper.py b/webscrapers/arch/playwright/webscraper.py
similarity index 100%
rename from webscrapers/playwright/webscraper.py
rename to webscrapers/arch/playwright/webscraper.py
diff --git a/webscrapers/scrapy/scrapy.cfg b/webscrapers/arch/scrapy/scrapy.cfg
similarity index 100%
rename from webscrapers/scrapy/scrapy.cfg
rename to webscrapers/arch/scrapy/scrapy.cfg
diff --git a/webscrapers/scrapy/scrapy_webscraper/__init__.py b/webscrapers/arch/scrapy/scrapy_webscraper/__init__.py
similarity index 100%
rename from webscrapers/scrapy/scrapy_webscraper/__init__.py
rename to webscrapers/arch/scrapy/scrapy_webscraper/__init__.py
diff --git a/webscrapers/scrapy/scrapy_webscraper/items.py b/webscrapers/arch/scrapy/scrapy_webscraper/items.py
similarity index 100%
rename from webscrapers/scrapy/scrapy_webscraper/items.py
rename to webscrapers/arch/scrapy/scrapy_webscraper/items.py
diff --git a/webscrapers/scrapy/scrapy_webscraper/middlewares.py b/webscrapers/arch/scrapy/scrapy_webscraper/middlewares.py
similarity index 100%
rename from webscrapers/scrapy/scrapy_webscraper/middlewares.py
rename to webscrapers/arch/scrapy/scrapy_webscraper/middlewares.py
diff --git a/webscrapers/scrapy/scrapy_webscraper/pipelines.py b/webscrapers/arch/scrapy/scrapy_webscraper/pipelines.py
similarity index 100%
rename from webscrapers/scrapy/scrapy_webscraper/pipelines.py
rename to webscrapers/arch/scrapy/scrapy_webscraper/pipelines.py
diff --git a/webscrapers/scrapy/scrapy_webscraper/settings.py b/webscrapers/arch/scrapy/scrapy_webscraper/settings.py
similarity index 100%
rename from webscrapers/scrapy/scrapy_webscraper/settings.py
rename to webscrapers/arch/scrapy/scrapy_webscraper/settings.py
diff --git a/webscrapers/scrapy/scrapy_webscraper/spiders/FreeImages.py b/webscrapers/arch/scrapy/scrapy_webscraper/spiders/FreeImages.py
similarity index 100%
rename from webscrapers/scrapy/scrapy_webscraper/spiders/FreeImages.py
rename to webscrapers/arch/scrapy/scrapy_webscraper/spiders/FreeImages.py
diff --git a/webscrapers/scrapy/scrapy_webscraper/spiders/__init__.py b/webscrapers/arch/scrapy/scrapy_webscraper/spiders/__init__.py
similarity index 100%
rename from webscrapers/scrapy/scrapy_webscraper/spiders/__init__.py
rename to webscrapers/arch/scrapy/scrapy_webscraper/spiders/__init__.py
diff --git a/webscrapers/exeUnittests.cmd b/webscrapers/exeUnittests.cmd
new file mode 100644
index 0000000..780f944
--- /dev/null
+++ b/webscrapers/exeUnittests.cmd
@@ -0,0 +1 @@
+call python -m unittest discover unittests\utilities
\ No newline at end of file
diff --git a/webscrapers/prg_scrape_imgs.py b/webscrapers/prg_scrape_imgs.py
index 27acfb0..7d45bc6 100644
--- a/webscrapers/prg_scrape_imgs.py
+++ b/webscrapers/prg_scrape_imgs.py
@@ -1,9 +1,9 @@
 import logging
-import cons
-from commandline_interface import commandline_interface
-from webscrapers.download_comp_data import download_comp_data
-from webscrapers.beautifulsoup.webscraper import webscraper
 from beartype import beartype
+import cons
+from utilities.commandline_interface import commandline_interface
+from utilities.download_comp_data import download_comp_data
+from utilities.webscraper import webscraper
 
 @beartype
 def scrape_imags(
diff --git a/webscrapers/unittests/utilities/test_gen_urls.py b/webscrapers/unittests/utilities/test_gen_urls.py
new file mode 100644
index 0000000..dcf2cb6
--- /dev/null
+++ b/webscrapers/unittests/utilities/test_gen_urls.py
@@ -0,0 +1,30 @@
+import unittest
+import os
+import sys
+
+sys.path.append(os.path.join(os.getcwd(), "webscrapers"))
+
+import cons
+from utilities.webscraper import gen_urls
+
+exp_urls = ['https://free-images.com/search/?q=cat&skip=0', 'https://free-images.com/search/?q=cat&skip=100', 'https://free-images.com/search/?q=cat&skip=200']
+obs_urls = gen_urls(search="cat", n_images=300, home_url=cons.home_url)
+
+class Test_gen_urls(unittest.TestCase):
+    """"""
+
+    def setUp(self):
+        self.obs_urls = obs_urls
+        self.exp_urls = exp_urls
+
+    def test_type(self):
+        self.assertEqual(type(self.obs_urls), type(self.exp_urls))
+
+    def test_len(self):
+        self.assertEqual(len(self.obs_urls), len(self.exp_urls))
+
+    def test_values(self):
+        self.assertEqual(self.obs_urls, self.exp_urls)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/webscrapers/unittests/utilities/test_scrape_srcs.py b/webscrapers/unittests/utilities/test_scrape_srcs.py
new file mode 100644
index 0000000..7d283ff
--- /dev/null
+++ b/webscrapers/unittests/utilities/test_scrape_srcs.py
@@ -0,0 +1,31 @@
+import unittest
+import os
+import sys
+
+sys.path.append(os.path.join(os.getcwd(), "webscrapers"))
+
+import cons
+from utilities.webscraper import scrape_srcs
+
+exp_srcs = ['https://free-images.com/sm/d790/cat_home_cat_looking.jpg', 'https://free-images.com/sm/e396/cat_hangover_red_cute_51.jpg', 'https://free-images.com/sm/bc44/cat_hangover_siamese_cat_2.jpg']
+urls = ['https://free-images.com/search/?q=cat&skip=0']
+obs_srcs = scrape_srcs(urls=urls, n_images=3, home_url=cons.home_url)
+
+class Test_scrape_srcs(unittest.TestCase):
+    """"""
+
+    def setUp(self):
+        self.obs_srcs = obs_srcs
+        self.exp_srcs = exp_srcs
+
+    def test_type(self):
+        self.assertEqual(type(self.obs_srcs), type(self.exp_srcs))
+
+    def test_len(self):
+        self.assertEqual(len(self.obs_srcs), len(self.exp_srcs))
+
+    def test_values(self):
+        self.assertEqual(self.obs_srcs, self.exp_srcs)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/webscrapers/commandline_interface.py b/webscrapers/utilities/commandline_interface.py
similarity index 100%
rename from webscrapers/commandline_interface.py
rename to webscrapers/utilities/commandline_interface.py
diff --git a/webscrapers/download_comp_data.py b/webscrapers/utilities/download_comp_data.py
similarity index 100%
rename from webscrapers/download_comp_data.py
rename to webscrapers/utilities/download_comp_data.py
diff --git a/webscrapers/beautifulsoup/webscraper.py b/webscrapers/utilities/webscraper.py
similarity index 100%
rename from webscrapers/beautifulsoup/webscraper.py
rename to webscrapers/utilities/webscraper.py