diff --git a/train.py b/train.py
index 7b76bb4c..70ae0272 100644
--- a/train.py
+++ b/train.py
@@ -348,7 +348,7 @@ if __name__ == '__main__':
 
             # Mutate
             init_seeds(seed=int(time.time()))
-            s = [.1, .1, .1, .1, .1, .1, .1, .1, .2 * 0, .2 * 0, .05 * 0, .2 * 0]  # fractional sigmas
+            s = [.15, .15, .15, .15, .15, .15, .15, .15, 0, 0, 0, 0]  # fractional sigmas
             for i, k in enumerate(hyp.keys()):
                 x = (np.random.randn(1) * s[i] + 1) ** 2.0  # plt.hist(x.ravel(), 300)
                 hyp[k] *= float(x)  # vary by 20% 1sigma
diff --git a/utils/utils.py b/utils/utils.py
index 5698d6e4..b22d9f67 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -274,24 +274,28 @@ def wh_iou(box1, box2):
 def compute_loss(p, targets, model, giou_loss=True):  # predictions, targets, model
     ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
     lxy, lwh, lcls, lobj = ft([0]), ft([0]), ft([0]), ft([0])
-    txy, twh, tcls, tbox, indices, anchor_vec = build_targets(model, targets)
+    txy, twh, tcls, tbox, indices, anchor_vec, nc = build_targets(model, targets)
     h = model.hyp  # hyperparameters
 
     # Define criteria
-    MSE = nn.MSELoss()
-    BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
-    BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
+    MSE = nn.MSELoss(reduction='sum')
+    BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]), reduction='sum')
+    BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]), reduction='sum')
     # CE = nn.CrossEntropyLoss()  # (weight=model.class_weights)
 
     # Compute losses
     bs = p[0].shape[0]  # batch size
-    k = bs / 64  # loss gain
+    k = 3 * bs / 64  # loss gain
+    nt, ng = 0, 0  # number of targets, number grid points
     for i, pi0 in enumerate(p):  # layer i predictions, i
         b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
         tobj = torch.zeros_like(pi0[..., 0])  # target obj
+        ng += tobj.numel()
+        nb = len(b)
 
         # Compute losses
-        if len(b):  # number of targets
+        if nb:  # number of targets
+            nt += nb
             pi = pi0[b, a, gj, gi]  # predictions closest to anchors
             tobj[b, a, gj, gi] = 1.0  # obj
             # pi[..., 2:4] = torch.sigmoid(pi[..., 2:4])  # wh power loss (uncomment)
@@ -299,21 +303,27 @@ def compute_loss(p, targets, model, giou_loss=True):  # predictions, targets, mo
             if giou_loss:
                 pbox = torch.cat((torch.sigmoid(pi[..., 0:2]), torch.exp(pi[..., 2:4]) * anchor_vec[i]), 1)  # predicted
                 giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)  # giou computation
-                lxy += (k * h['giou']) * (1.0 - giou).mean()  # giou loss
+                lxy += (1.0 - giou).sum()  # giou loss
             else:
                 lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy loss
-                lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i])  # wh yolo loss
+                lwh += MSE(pi[..., 2:4], twh[i])  # wh yolo loss
 
             tclsm = torch.zeros_like(pi[..., 5:])
-            tclsm[range(len(b)), tcls[i]] = 1.0
-            lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm)  # cls loss (BCE)
-            # lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i])  # cls loss (CE)
+            tclsm[range(nb), tcls[i]] = 1.0
+            lcls += BCEcls(pi[..., 5:], tclsm)  # cls loss (BCE)
+            # lcls += CE(pi[..., 5:], tcls[i])  # cls loss (CE)
 
             # Append targets to text file
             # with open('targets.txt', 'a') as file:
             #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
 
-        lobj += (k * h['obj']) * BCEobj(pi0[..., 4], tobj)  # obj loss
+        lobj += BCEobj(pi0[..., 4], tobj)  # obj loss
+
+    lxy *= (k * h['giou']) / nt
+    lwh *= (k * h['wh']) / nt
+    lcls *= (k * h['cls']) / (nt * nc)
+    lobj *= (k * h['obj']) / ng
+
     loss = lxy + lwh + lobj + lcls
 
     return loss, torch.cat((lxy, lwh, lobj, lcls, loss)).detach()
@@ -375,7 +385,7 @@ def build_targets(model, targets):
         if c.shape[0]:
             assert c.max() <= layer.nc, 'Target classes exceed model classes'
 
-    return txy, twh, tcls, tbox, indices, anchor_vec
+    return txy, twh, tcls, tbox, indices, anchor_vec, layer.nc
 
 
 def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5):