diff --git a/detect.py b/detect.py
index 53b63ebf..f6efbeac 100644
--- a/detect.py
+++ b/detect.py
@@ -63,6 +63,9 @@ def detect(save_img=False):
     # Run inference
     if device.type != 'cpu':
         model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
+    old_img_w = old_img_h = imgsz
+    old_img_b = 1
+
     t0 = time.time()
     for path, img, im0s, vid_cap in dataset:
         img = torch.from_numpy(img).to(device)
@@ -71,13 +74,22 @@ def detect(save_img=False):
         if img.ndimension() == 3:
             img = img.unsqueeze(0)
 
+        # Warmup
+        if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
+            old_img_b = img.shape[0]
+            old_img_h = img.shape[2]
+            old_img_w = img.shape[3]
+            for i in range(3):
+                model(img, augment=opt.augment)[0]
+
         # Inference
         t1 = time_synchronized()
         pred = model(img, augment=opt.augment)[0]
+        t2 = time_synchronized()
 
         # Apply NMS
         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
-        t2 = time_synchronized()
+        t3 = time_synchronized()
 
         # Apply Classifier
         if classify:
@@ -93,7 +105,6 @@ def detect(save_img=False):
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # img.jpg
             txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
-            s += '%gx%g ' % img.shape[2:]  # print string
             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
             if len(det):
                 # Rescale boxes from img_size to im0 size
@@ -117,7 +128,7 @@ def detect(save_img=False):
                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
 
             # Print time (inference + NMS)
-            #print(f'{s}Done. ({t2 - t1:.3f}s)')
+            #print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}s) NMS')
 
             # Stream results
             if view_img: