diff --git a/models.py b/models.py
index 01151e25..f254b9c8 100755
--- a/models.py
+++ b/models.py
@@ -172,7 +172,7 @@ class YOLOLayer(nn.Module):
                 lh = k * MSELoss(h[mask], th[mask])
 
                 # lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
-                lconf = (k * 1) * BCEWithLogitsLoss(pred_conf, mask.float())
+                lconf = (k * 5) * BCEWithLogitsLoss(pred_conf, mask.float())
 
                 lcls = (k / 10) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
                 # lcls = (k * 10) * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
diff --git a/train.py b/train.py
index 2e862954..cbd78cb7 100644
--- a/train.py
+++ b/train.py
@@ -125,7 +125,8 @@ def main(opt):
                     g['lr'] = lr
 
             # Compute loss, compute gradient, update parameters
-            loss = model(imgs.to(device), targets, requestPrecision=False)
+            precision_per_batch = False
+            loss = model(imgs.to(device), targets, requestPrecision=precision_per_batch)
             loss.backward()
 
             # accumulated_batches = 1  # accumulate gradient for 4 batches before stepping optimizer
@@ -133,24 +134,26 @@ def main(opt):
             optimizer.step()
             optimizer.zero_grad()
 
-            # Compute running epoch-means of tracked metrics
+            # Running epoch-means of tracked metrics
             ui += 1
-            metrics += model.losses['metrics']
-            TP, FP, FN = metrics
             for key, val in model.losses.items():
                 rloss[key] = (rloss[key] * ui + val) / (ui + 1)
 
-            # Precision
-            precision = TP / (TP + FP)
-            k = (TP + FP) > 0
-            if k.sum() > 0:
-                mean_precision = precision[k].mean()
+            if precision_per_batch:
+                TP, FP, FN = metrics
+                metrics += model.losses['metrics']
 
-            # Recall
-            recall = TP / (TP + FN)
-            k = (TP + FN) > 0
-            if k.sum() > 0:
-                mean_recall = recall[k].mean()
+                # Precision
+                precision = TP / (TP + FP)
+                k = (TP + FP) > 0
+                if k.sum() > 0:
+                    mean_precision = precision[k].mean()
+
+                # Recall
+                recall = TP / (TP + FN)
+                k = (TP + FN) > 0
+                if k.sum() > 0:
+                    mean_recall = recall[k].mean()
 
             s = ('%11s%11s' + '%11.3g' * 14) % (
                 '%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
diff --git a/utils/utils.py b/utils/utils.py
index d7231fa6..b4933d96 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -438,7 +438,7 @@ 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 ('results5.txt', 'results_new.txt', 'results3.txt',
+    for f in ('results_d5.txt', 'results_d10.txt', 'results_new.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):