diff --git a/utils/utils.py b/utils/utils.py
index a7e67b0c..7448496c 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -391,15 +391,15 @@ def compute_loss(p, targets, model):  # predictions, targets, model
         if nb:  # number of targets
             ng += nb
             ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
-            tobj[b, a, gj, gi] = 1.0  # obj
             # ps[:, 2:4] = torch.sigmoid(ps[:, 2:4])  # wh power loss (uncomment)
 
             # GIoU
             pxy = torch.sigmoid(ps[:, 0:2])  # pxy = pxy * s - (s - 1) / 2,  s = 1.5  (scale_xy)
             pwh = torch.exp(ps[:, 2:4]).clamp(max=1E3) * anchor_vec[i]
             pbox = torch.cat((pxy, pwh), 1)  # predicted box
-            giou = 1.0 - bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)  # giou computation
-            lbox += giou.sum() if red == 'sum' else giou.mean()  # giou loss
+            giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)  # giou computation
+            lbox += (1.0 - giou).sum() if red == 'sum' else (1.0 - giou).mean()  # giou loss
+            tobj[b, a, gj, gi] = 1.0  # giou.type(tobj.dtype)  # obj
 
             if 'default' in arc and model.nc > 1:  # cls loss (only if multiple classes)
                 t = torch.zeros_like(ps[:, 5:])  # targets