diff --git a/utils/utils.py b/utils/utils.py
index f3403120..b2dad445 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -501,7 +501,7 @@ def build_targets(model, targets):
     return tcls, tbox, indices, av
 
 
-def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_cls=True, classes=None, agnostic=False):
+def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=True, classes=None, agnostic=False):
     """
     Removes detections with lower object confidence score than 'conf_thres'
     Non-Maximum Suppression to further filter detections.
@@ -516,7 +516,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_cls=Tru
     method = 'vision_batch'
     batched = 'batch' in method  # run once per image, all classes simultaneously
     nc = prediction[0].shape[1] - 5  # number of classes
-    multi_cls = multi_cls and (nc > 1)  # allow multiple classes per anchor
+    multi_label &= nc > 1  # multiple labels per box
     output = [None] * len(prediction)
     for image_i, pred in enumerate(prediction):
         # Apply conf constraint
@@ -536,7 +536,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_cls=Tru
         box = xywh2xyxy(pred[:, :4])
 
         # Detections matrix nx6 (xyxy, conf, cls)
-        if multi_cls:
+        if multi_label:
             i, j = (pred[:, 5:] > conf_thres).nonzero().t()
             pred = torch.cat((box[i], pred[i, j + 5].unsqueeze(1), j.float().unsqueeze(1)), 1)
         else:  # best class only