diff --git a/models.py b/models.py
index 117ed30f..136c314d 100755
--- a/models.py
+++ b/models.py
@@ -37,18 +37,18 @@ def create_modules(module_defs):
             modules.add_module('upsample_%d' % i, upsample)
 
         elif module_def['type'] == 'route':
-            layers = [int(x) for x in module_def["layers"].split(',')]
+            layers = [int(x) for x in module_def['layers'].split(',')]
             filters = sum([output_filters[layer_i] for layer_i in layers])
             modules.add_module('route_%d' % i, EmptyLayer())
 
         elif module_def['type'] == 'shortcut':
             filters = output_filters[int(module_def['from'])]
-            modules.add_module("shortcut_%d" % i, EmptyLayer())
+            modules.add_module('shortcut_%d' % i, EmptyLayer())
 
-        elif module_def["type"] == "yolo":
-            anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
+        elif module_def['type'] == 'yolo':
+            anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
             # Extract anchors
-            anchors = [float(x) for x in module_def["anchors"].split(",")]
+            anchors = [float(x) for x in module_def['anchors'].split(',')]
             anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
             anchors = [anchors[i] for i in anchor_idxs]
             num_classes = int(module_def['classes'])
@@ -72,7 +72,6 @@ class EmptyLayer(nn.Module):
 
 
 class YOLOLayer(nn.Module):
-    # YOLO Layer 0
 
     def __init__(self, anchors, nC, img_dim, anchor_idxs):
         super(YOLOLayer, self).__init__()
@@ -104,15 +103,15 @@ class YOLOLayer(nn.Module):
     def forward(self, p, targets=None, requestPrecision=False, epoch=None):
         FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
 
-        bs = p.shape[0]
-        nG = p.shape[2]
+        bs = p.shape[0]  # batch size
+        nG = p.shape[2]  # number of grid points
         stride = self.img_dim / nG
 
         if p.is_cuda and not self.grid_x.is_cuda:
             self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
             self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
 
-        # x.view(4, 650, 19, 19) -- > (4, 10, 19, 19, 65)  # (bs, anchors, grid, grid, classes + xywh)
+        # p.view(12, 255, 13, 13) -- > (12, 3, 13, 13, 80)  # (bs, anchors, grid, grid, classes + xywh)
         p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous()  # prediction
 
         # Get outputs
@@ -255,7 +254,7 @@ def load_weights(self, weights_path):
     """Parses and loads the weights stored in 'weights_path'"""
 
     # Open the weights file
-    fp = open(weights_path, "rb")
+    fp = open(weights_path, 'rb')
     header = np.fromfile(fp, dtype=np.int32, count=5)  # First five are header values
 
     # Needed to write header when saving weights