diff --git a/models.py b/models.py
index 0e9d752f..d6e31eaa 100755
--- a/models.py
+++ b/models.py
@@ -1,8 +1,7 @@
+import torch.nn.functional as F
+
 from utils.parse_config import *
 from utils.utils import *
-from pathlib import Path
-
-import torch.nn.functional as F
 
 ONNX_EXPORT = False
 
@@ -71,9 +70,7 @@ def create_modules(module_defs, img_size):
         elif mdef['type'] == 'yolo':
             yolo_index += 1
             mask = [int(x) for x in mdef['mask'].split(',')]  # anchor mask
-            a = [float(x) for x in mdef['anchors'].split(',')]  # anchors
-            a = [(a[i], a[i + 1]) for i in range(0, len(a), 2)]
-            modules = YOLOLayer(anchors=[a[i] for i in mask],  # anchor list
+            modules = YOLOLayer(anchors=mdef['anchors'][mask],  # anchor list
                                 nc=int(mdef['classes']),  # number of classes
                                 img_size=img_size,  # (416, 416)
                                 yolo_index=yolo_index)  # 0, 1 or 2
diff --git a/utils/parse_config.py b/utils/parse_config.py
index a25eca3f..23581a51 100644
--- a/utils/parse_config.py
+++ b/utils/parse_config.py
@@ -1,35 +1,42 @@
+import numpy as np
+
+
 def parse_model_cfg(path):
-    """Parses the yolo-v3 layer configuration file and returns module definitions"""
+    # Parses the yolo-v3 layer configuration file and returns module definitions
     file = open(path, 'r')
     lines = file.read().split('\n')
     lines = [x for x in lines if x and not x.startswith('#')]
     lines = [x.rstrip().lstrip() for x in lines]  # get rid of fringe whitespaces
-    module_defs = []
+    mdefs = []  # module definitions
     for line in lines:
         if line.startswith('['):  # This marks the start of a new block
-            module_defs.append({})
-            module_defs[-1]['type'] = line[1:-1].rstrip()
-            if module_defs[-1]['type'] == 'convolutional':
-                module_defs[-1]['batch_normalize'] = 0  # pre-populate with zeros (may be overwritten later)
+            mdefs.append({})
+            mdefs[-1]['type'] = line[1:-1].rstrip()
+            if mdefs[-1]['type'] == 'convolutional':
+                mdefs[-1]['batch_normalize'] = 0  # pre-populate with zeros (may be overwritten later)
         else:
-            key, value = line.split("=")
-            value = value.strip()
-            module_defs[-1][key.rstrip()] = value.strip()
+            key, val = line.split("=")
+            key = key.rstrip()
 
-    return module_defs
+            if 'anchors' in key:
+                mdefs[-1][key] = np.array([float(x) for x in val.split(',')]).reshape((-1, 2))  # np anchors
+            else:
+                mdefs[-1][key] = val.strip()
+
+    return mdefs
 
 
 def parse_data_cfg(path):
-    """Parses the data configuration file"""
+    # Parses the data configuration file
     options = dict()
-    options['gpus'] = '0,1,2,3'
-    options['num_workers'] = '10'
     with open(path, 'r') as fp:
         lines = fp.readlines()
+
     for line in lines:
         line = line.strip()
         if line == '' or line.startswith('#'):
             continue
-        key, value = line.split('=')
-        options[key.strip()] = value.strip()
+        key, val = line.split('=')
+        options[key.strip()] = val.strip()
+
     return options
diff --git a/utils/utils.py b/utils/utils.py
index 988a5ff1..b6e8a678 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -10,7 +10,6 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 import torch.nn as nn
-from PIL import Image
 from tqdm import tqdm
 
 from . import torch_utils  # , google_utils