diff --git a/mini_proj/Load_Images.py b/mini_proj/Load_Images.py
index 802b394..0b83761 100644
--- a/mini_proj/Load_Images.py
+++ b/mini_proj/Load_Images.py
@@ -19,8 +19,6 @@ def preprocess(img):
     hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
     img = color.hsv2rgb(hsv)
 
-    #img = img/255                           # Scaling images down to values of 0-255
-
     return img
 
 '''
@@ -66,8 +64,7 @@ def gen_data(w_path, n_w_path):
             pic = augment(pic)
             pic_roll = np.rollaxis(pic, -1)                                 # rolls colour axis to 0 
             imgs_raw.append(pic_roll)
-            imgs_lbl.append(1) 
-
+            imgs_lbl.append(1)                          # Value of 1 as Waldo is still present in the transformed image
 
         print('Completed: {0}/{1} Waldo images'.format(w+1, total_w))
         w += 1
@@ -91,7 +88,7 @@ def gen_data(w_path, n_w_path):
     ## Randomise and split data into training and test sets
     # Code was modified from code written by: Kyle O'Brien (medium.com/@kylepob61392)
     n_images = len(imgs_raw)
-    TRAIN_TEST_SPLIT = 0.75
+    TRAIN_TEST_SPLIT = 0.75             # Amount of trainingdata as a percentage of the total
 
     # Split at the given index
     split_index = int(TRAIN_TEST_SPLIT * n_images)
@@ -113,17 +110,6 @@ def gen_data(w_path, n_w_path):
         test_data.append(imgs_raw[index])
         test_lbl.append(imgs_lbl[index])
 
-    # # Calculate what 30% of each set is
-    # third_of_w = math.floor(0.3*total_w)
-    # third_of_nw = math.floor(0.3*total_nw)
-    
-    # # Split data into training and test data (60%/30%)    
-    # train_data = np.append(imgs_raw[(third_of_w+1):total_w], imgs_raw[(total_w + third_of_nw + 1):len(imgs_raw)-1], axis=0)
-    # train_lbl = np.append(imgs_lbl[(third_of_w+1):total_w], imgs_lbl[(total_w + third_of_nw + 1):len(imgs_lbl)-1], axis=0)
-    # # If axis not given, both arrays are flattened before being appended
-    # test_data = np.append(imgs_raw[0:third_of_w], imgs_raw[total_w:(total_w + third_of_nw)], axis=0)
-    # test_lbl = np.append(imgs_lbl[0:third_of_w], imgs_lbl[total_w:(total_w + third_of_nw)], axis=0)
-
     try:
         # Save the data as numpy files
         np.save('Waldo_train_data.npy', train_data)