diff --git a/detect.py b/detect.py
index 74838a49..da919b37 100644
--- a/detect.py
+++ b/detect.py
@@ -86,7 +86,7 @@ def detect(save_txt=False, save_img=False):
             pred = pred.float()
 
         # Apply NMS
-        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres)
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes)
 
         # Apply Classifier
         if classify:
@@ -110,9 +110,6 @@ def detect(save_txt=False, save_img=False):
                     n = (det[:, -1] == c).sum()  # detections per class
                     s += '%g %ss, ' % (n, names[int(c)])  # add to string
 
-                # Print time (inference + NMS)
-                print('%sDone. (%.3fs)' % (s, time.time() - t))
-
                 # Write results
                 for *xyxy, conf, cls in det:
                     if save_txt:  # Write to file
@@ -123,6 +120,9 @@ def detect(save_txt=False, save_img=False):
                         label = '%s %.2f' % (names[int(cls)], conf)
                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
 
+            # Print time (inference + NMS)
+            print('%sDone. (%.3fs)' % (s, time.time() - t))
+
             # Stream results
             if view_img:
                 cv2.imshow(p, im0)
@@ -167,6 +167,7 @@ if __name__ == '__main__':
     parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
     parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
     parser.add_argument('--view-img', action='store_true', help='display results')
+    parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
     opt = parser.parse_args()
     print(opt)
 
diff --git a/utils/utils.py b/utils/utils.py
index c7da014c..684fbf48 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -498,7 +498,7 @@ 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'):
+def non_max_suppression(prediction, conf_thres=0.5, iou_thres=0.5, multi_cls=True, method='vision_batch', classes=None):
     """
     Removes detections with lower object confidence score than 'conf_thres'
     Non-Maximum Suppression to further filter detections.
@@ -537,6 +537,10 @@ def non_max_suppression(prediction, conf_thres=0.5, iou_thres=0.5, multi_cls=Tru
             conf, j = pred[:, 5:].max(1)
             pred = torch.cat((box, conf.unsqueeze(1), j.float().unsqueeze(1)), 1)
 
+        # Filter by class
+        if classes:
+            pred = pred[(j.view(-1, 1) == torch.Tensor(classes)).any(1)]
+
         # Apply finite constraint
         if not torch.isfinite(pred).all():
             pred = pred[torch.isfinite(pred).all(1)]