diff --git a/utils/utils.py b/utils/utils.py
index d7b99549..134cf6ef 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -558,9 +558,15 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
             boxes += c.view(-1, 1) * max_wh
             if method == 'vision_batch':
                 i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
+            elif method == 'merge_batch':  # Merge NMS
+                i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
+                iou = box_iou(boxes, boxes[i]).tril_()  # upper triangular iou matrix
+                weights = (iou > conf_thres) * scores.view(-1, 1)
+                weights /= weights.sum(0)
+                pred[i, :4] = torch.matmul(weights.T, pred[:, :4])  # merged_boxes(n,4) = weights(n,n) * boxes(n,4)
             elif method == 'fast_batch':  # FastNMS from https://github.com/dbolya/yolact
                 iou = box_iou(boxes, boxes).triu_(diagonal=1)  # upper triangular iou matrix
-                i = iou.max(dim=0)[0] < iou_thres
+                i = iou.max(0)[0] < iou_thres
 
             output[image_i] = pred[i]
             continue
@@ -577,10 +583,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
             elif n > 500:
                 dc = dc[:500]  # limit to first 500 boxes: https://github.com/ultralytics/yolov3/issues/117
 
-            if method == 'vision':
-                det_max.append(dc[torchvision.ops.boxes.nms(dc[:, :4], dc[:, 4], iou_thres)])
-
-            elif method == 'or':  # default
+            if method == 'or':  # default
                 # METHOD1
                 # ind = list(range(len(dc)))
                 # while len(ind):
@@ -629,7 +632,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
 
         if len(det_max):
             det_max = torch.cat(det_max)  # concatenate
-            output[image_i] = det_max[(-det_max[:, 4]).argsort()]  # sort
+            output[image_i] = det_max[det_max[:, 4].argsort(descending=True)]  # sort
 
     return output