diff --git a/models.py b/models.py
index 5cd0b865..8414d4e4 100755
--- a/models.py
+++ b/models.py
@@ -119,16 +119,16 @@ 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)
diff --git a/utils/utils.py b/utils/utils.py
index 011cc17a..8eebdc33 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -263,13 +263,13 @@ def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG
         tx[b, a, gj, gi] = gx - gi.float()
         ty[b, a, gj, gi] = gy - gj.float()
 
-        # 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
+        # Width and height (yolo method)
+        tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0] + 1e-16)
+        th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1] + 1e-16)
 
-        # Width and height (yolov3 method)
-        # tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0] + 1e-16)
-        # th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1] + 1e-16)
+        # 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
 
         # One-hot encoding of label
         tcls[b, a, gj, gi, tc] = 1