diff --git a/models.py b/models.py
index 2712c943..61c66dc1 100755
--- a/models.py
+++ b/models.py
@@ -123,16 +123,16 @@ class YOLOLayer(nn.Module):
         y = torch.sigmoid(p[..., 1])  # Center y
 
         # Width and height (yolo method)
-        w = p[..., 2]  # Width
-        h = p[..., 3]  # Height
-        width = torch.exp(w.data) * self.anchor_w
-        height = torch.exp(h.data) * self.anchor_h
+        # w = p[..., 2]  # Width
+        # h = p[..., 3]  # Height
+        # width = torch.exp(w.data) * self.anchor_w
+        # height = torch.exp(h.data) * self.anchor_h
 
         # Width and height (power method)
-        # w = torch.sigmoid(p[..., 2])  # Width
-        # h = torch.sigmoid(p[..., 3])  # Height
-        # width = ((w.data * 2) ** 2) * self.anchor_w
-        # height = ((h.data * 2) ** 2) * self.anchor_h
+        w = torch.sigmoid(p[..., 2])  # Width
+        h = torch.sigmoid(p[..., 3])  # Height
+        width = ((w.data * 2) ** 2) * self.anchor_w
+        height = ((h.data * 2) ** 2) * self.anchor_h
 
         # Add offset and scale with anchors (in grid space, i.e. 0-13)
         pred_boxes = FT(bs, self.nA, nG, nG, 4)
@@ -168,13 +168,13 @@ class YOLOLayer(nn.Module):
             if nM > 0:
                 lx = k * MSELoss(x[mask], tx[mask])
                 ly = k * MSELoss(y[mask], ty[mask])
-                lw = (k * 0.7) * MSELoss(w[mask], tw[mask])
-                lh = (k * 0.7) * MSELoss(h[mask], th[mask])
+                lw = (k * 1) * MSELoss(w[mask], tw[mask])
+                lh = (k * 1) * MSELoss(h[mask], th[mask])
 
                 # lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
-                lconf = (k * 5) * BCEWithLogitsLoss(pred_conf, mask.float())
+                lconf = (k * 10) * BCEWithLogitsLoss(pred_conf, mask.float())
 
-                lcls = (k / 20) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
+                lcls = (k / 10) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
                 # lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
             else:
                 lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
diff --git a/utils/utils.py b/utils/utils.py
index 314b12bf..55e7e64a 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -259,12 +259,12 @@ def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG
         ty[b, a, gj, gi] = gy - gj.float()
 
         # Width and height (yolo method)
-        tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0])
-        th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1])
+        # tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0])
+        # th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1])
 
         # Width and height (power method)
-        # tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2
-        # th[b, a, gj, gi] = torch.sqrt(gh / anchor_wh[a, 1]) / 2
+        tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2
+        th[b, a, gj, gi] = torch.sqrt(gh / anchor_wh[a, 1]) / 2
 
         # One-hot encoding of label
         tcls[b, a, gj, gi, tc] = 1
@@ -436,8 +436,9 @@ def plot_results():
     import matplotlib.pyplot as plt
     plt.figure(figsize=(16, 8))
     s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
-    for f in ('results.txt',):
-        results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T
+    for f in ('results.txt',
+              ):
+        results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T  # column 16 is mAP
         for i in range(9):
             plt.subplot(2, 5, i + 1)
             plt.plot(results[i, :250], marker='.', label=f)