diff --git a/utils/utils.py b/utils/utils.py
index 17ee5f8..2156be2 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -10,11 +10,10 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 from torchvision.ops import nms
-#import maskrcnn_benchmark.layers.nms as nms
 
-def mkdir_if_missing(d):
-    if not osp.exists(d):
-        os.makedirs(d)
+
+def mkdir_if_missing(dir):
+    os.makedirs(dir, exist_ok=True)
 
 
 def float3(x):  # format floats to 3 decimals
@@ -38,7 +37,10 @@ def load_classes(path):
     return list(filter(None, names))  # filter removes empty strings (such as last line)
 
 
-def model_info(model):  # Plots a line-by-line description of a PyTorch model
+def model_info(model):  
+    """
+    Prints out a line-by-line description of a PyTorch model ending with a summary.
+    """
     n_p = sum(x.numel() for x in model.parameters())  # number parameters
     n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
     print('\n%5s %50s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
@@ -50,7 +52,10 @@ def model_info(model):  # Plots a line-by-line description of a PyTorch model
 
 
 
-def plot_one_box(x, img, color=None, label=None, line_thickness=None):  # Plots one bounding box on image img
+def plot_one_box(x, img, color=None, label=None, line_thickness=None):
+    """
+    Plots one bounding box on image img.
+    """
     tl = line_thickness or round(0.0004 * max(img.shape[0:2])) + 1  # line thickness
     color = color or [random.randint(0, 255) for _ in range(3)]
     c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
@@ -74,21 +79,25 @@ def weights_init_normal(m):
 
 def xyxy2xywh(x):
     # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
-    y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
-    y[:, 0] = (x[:, 0] + x[:, 2]) / 2
-    y[:, 1] = (x[:, 1] + x[:, 3]) / 2
-    y[:, 2] = x[:, 2] - x[:, 0]
-    y[:, 3] = x[:, 3] - x[:, 1]
+    # x, y are coordinates of center 
+    # (x1, y1) and (x2, y2) are coordinates of bottom left and top right respectively. 
+    y = torch.zeros_like(x) if x.dtype is torch.float32 else np.zeros_like(x)
+    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
+    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
+    y[:, 2] = x[:, 2] - x[:, 0]  # width
+    y[:, 3] = x[:, 3] - x[:, 1]  # height
     return y
 
 
 def xywh2xyxy(x):
     # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
-    y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
-    y[:, 0] = (x[:, 0] - x[:, 2] / 2)
-    y[:, 1] = (x[:, 1] - x[:, 3] / 2)
-    y[:, 2] = (x[:, 0] + x[:, 2] / 2)
-    y[:, 3] = (x[:, 1] + x[:, 3] / 2)
+    # x, y are coordinates of center 
+    # (x1, y1) and (x2, y2) are coordinates of bottom left and top right respectively. 
+    y = torch.zeros_like(x) if x.dtype is torch.float32 else np.zeros_like(x)
+    y[:, 0] = (x[:, 0] - x[:, 2] / 2)  # Bottom left x
+    y[:, 1] = (x[:, 1] - x[:, 3] / 2)  # Bottom left y
+    y[:, 2] = (x[:, 0] + x[:, 2] / 2)  # Top right x
+    y[:, 3] = (x[:, 1] + x[:, 3] / 2)  # Top right y
     return y
 
 
@@ -107,7 +116,7 @@ def scale_coords(img_size, coords, img0_shape):
 
 
 def ap_per_class(tp, conf, pred_cls, target_cls):
-    """ Compute the average precision, given the recall and precision curves.
+    """ Computes the average precision, given the recall and precision curves.
     Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
     # Arguments
         tp:    True positives (list).
@@ -161,7 +170,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
 
 
 def compute_ap(recall, precision):
-    """ Compute the average precision, given the recall and precision curves.
+    """ Computes the average precision, given the recall and precision curves.
     Code originally from https://github.com/rbgirshick/py-faster-rcnn.
     # Arguments
         recall:    The recall curve (list).
@@ -542,8 +551,6 @@ def jaccard(box_a, box_b, iscrowd:bool=False):
     return out if use_batch else out.squeeze(0)
 
 
-
-
 def return_torch_unique_index(u, uv):
     n = uv.shape[1]  # number of columns
     first_unique = torch.zeros(n, device=u.device).long()
@@ -555,16 +562,19 @@ def return_torch_unique_index(u, uv):
 
 def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
     # Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
-
     a = torch.load(filename, map_location='cpu')
     a['optimizer'] = []
     torch.save(a, filename.replace('.pt', '_lite.pt'))
 
 
 def plot_results():
-    # Plot YOLO training results file 'results.txt'
-    # import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v1.txt')
-
+    """
+    Plot YOLO training results from the file 'results.txt'
+    Example of what this is trying to plot can be found at: 
+    https://user-images.githubusercontent.com/26833433/63258271-fe9d5300-c27b-11e9-9a15-95038daf4438.png
+    An example results.txt file:
+    import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v1.txt')
+    """
     plt.figure(figsize=(14, 7))
     s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'mAP', 'Recall', 'Precision']
     files = sorted(glob.glob('results*.txt'))