diff --git a/detect.py b/detect.py
index b9ddbf0f..09bb7311 100644
--- a/detect.py
+++ b/detect.py
@@ -86,7 +86,7 @@ def detect(save_img=False):
             pred = pred.float()
 
         # Apply NMS
-        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes)
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
 
         # Apply Classifier
         if classify:
@@ -169,6 +169,7 @@ if __name__ == '__main__':
     parser.add_argument('--view-img', action='store_true', help='display results')
     parser.add_argument('--save-txt', action='store_true', help='display results')
     parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
+    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
     opt = parser.parse_args()
     print(opt)
 
diff --git a/utils/utils.py b/utils/utils.py
index 70e40667..91dc62a9 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -499,7 +499,7 @@ def build_targets(model, targets):
     return tcls, tbox, indices, av
 
 
-def non_max_suppression(prediction, conf_thres=0.5, iou_thres=0.5, multi_cls=True, method='vision_batch', classes=None):
+def non_max_suppression(prediction, conf_thres=0.5, iou_thres=0.5, multi_cls=True, classes=None, agnostic=False):
     """
     Removes detections with lower object confidence score than 'conf_thres'
     Non-Maximum Suppression to further filter detections.
@@ -511,6 +511,7 @@ def non_max_suppression(prediction, conf_thres=0.5, iou_thres=0.5, multi_cls=Tru
     # Box constraints
     min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
 
+    method = 'vision_batch'
     output = [None] * len(prediction)
     for image_i, pred in enumerate(prediction):
         # Apply conf constraint
@@ -548,7 +549,8 @@ def non_max_suppression(prediction, conf_thres=0.5, iou_thres=0.5, multi_cls=Tru
 
         # Batched NMS
         if method == 'vision_batch':
-            output[image_i] = pred[torchvision.ops.boxes.batched_nms(pred[:, :4], pred[:, 4], pred[:, 5], iou_thres)]
+            c = j * 0 if agnostic else j  # class-agnostic NMS
+            output[image_i] = pred[torchvision.ops.boxes.batched_nms(pred[:, :4], pred[:, 4], c, iou_thres)]
             continue
 
         # Sort by confidence