diff --git a/train.py b/train.py
index a1f2fe1a..33adb52e 100644
--- a/train.py
+++ b/train.py
@@ -180,12 +180,12 @@ def train():
                                              collate_fn=dataset.collate_fn)
 
     # Testloader
-    testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size_test, batch_size * 2,
+    testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size_test, batch_size,
                                                                  hyp=hyp,
                                                                  rect=True,
                                                                  cache_images=opt.cache_images,
                                                                  single_cls=opt.single_cls),
-                                             batch_size=batch_size * 2,
+                                             batch_size=batch_size,
                                              num_workers=nw,
                                              pin_memory=True,
                                              collate_fn=dataset.collate_fn)
@@ -311,7 +311,7 @@ def train():
             is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
             results, maps = test.test(cfg,
                                       data,
-                                      batch_size=batch_size * 2,
+                                      batch_size=batch_size,
                                       img_size=img_size_test,
                                       model=ema.ema,
                                       conf_thres=0.001 if final_epoch else 0.01,  # 0.001 for best mAP, 0.01 for speed
diff --git a/utils/utils.py b/utils/utils.py
index 9eee669c..80b25d94 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -543,7 +543,8 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
             x = x[torch.isfinite(x).all(1)]
 
         # If none remain process next image
-        if not x.shape[0]:
+        n = x.shape[0]  # number of boxes
+        if not n:
             continue
 
         # Sort by confidence
@@ -555,10 +556,11 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
         boxes, scores = x[:, :4].clone() + c.view(-1, 1) * max_wh, x[:, 4]  # boxes (offset by class), scores
         if method == 'merge':  # Merge NMS (boxes merged using weighted mean)
             i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
-            iou = box_iou(boxes, boxes).tril_()  # lower triangular iou matrix
-            weights = (iou > iou_thres) * scores.view(-1, 1)
-            weights /= weights.sum(0)
-            x[:, :4] = torch.mm(weights.T, x[:, :4])  # merged_boxes(n,4) = weights(n,n) * boxes(n,4)
+            if n < 1000:  # update boxes
+                iou = box_iou(boxes, boxes).tril_()  # lower triangular iou matrix
+                weights = (iou > iou_thres) * scores.view(-1, 1)
+                weights /= weights.sum(0)
+                x[:, :4] = torch.mm(weights.T, x[:, :4])  # merged_boxes(n,4) = weights(n,n) * boxes(n,4)
         elif method == 'vision':
             i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
         elif method == 'fast':  # FastNMS from https://github.com/dbolya/yolact