diff --git a/tools/demo.py b/tools/demo.py
index 5799e8a..4c8b8c9 100644
--- a/tools/demo.py
+++ b/tools/demo.py
@@ -6,6 +6,7 @@
 import cv2
 import numpy as np
 import torch
+from tqdm import tqdm
 from loguru import logger
 from PIL import Image
 
@@ -23,7 +24,6 @@
 
 class Infer():
     def __init__(self, config, infer_size=[640,640], device='cuda', output_dir='./', ckpt=None, end2end=False):
-
         self.ckpt_path = ckpt
         suffix = ckpt.split('.')[-1]
         if suffix == 'onnx':
@@ -66,9 +66,7 @@ def _pad_image(self, img, target_size):
 
         return ImageList(pad_imgs, img_sizes, pad_sizes)
 
-
     def _build_engine(self, config, engine_type):
-
         print(f'Inference with {engine_type} engine!')
         if engine_type == 'torch':
             model = build_local_model(config, self.device)
@@ -88,7 +86,6 @@ def _build_engine(self, config, engine_type):
         return model
 
     def build_tensorRT_engine(self, trt_path):
-
         import tensorrt as trt
         from cuda import cuda
         loggert = trt.Logger(trt.Logger.INFO)
@@ -145,11 +142,7 @@ def predict(batch):  # result gets copied into output
 
         return predict
 
-
-
-
     def build_onnx_engine(self, onnx_path):
-
         import onnxruntime
 
         session = onnxruntime.InferenceSession(onnx_path)
@@ -166,7 +159,6 @@ def build_onnx_engine(self, onnx_path):
 
 
     def preprocess(self, origin_img):
-
         img = transform_img(origin_img, 0,
                             **self.config.test.augment.transform,
                             infer_size=self.infer_size)
@@ -178,10 +170,8 @@ def preprocess(self, origin_img):
         return img, (ow, oh)
 
     def postprocess(self, preds, image, origin_shape=None):
-
         if self.engine_type == 'torch':
             output = preds
-
         elif self.engine_type == 'onnx':
             scores = torch.Tensor(preds[0])
             bboxes = torch.Tensor(preds[1])
@@ -225,9 +215,7 @@ def postprocess(self, preds, image, origin_shape=None):
 
         return bboxes,  scores, cls_inds
 
-
     def forward(self, origin_image):
-
         image, origin_shape = self.preprocess(origin_image)
 
         if self.engine_type == 'torch':
@@ -301,7 +289,10 @@ def make_parser():
                         default=True,
                         type=bool,
                         help='whether save visualization results')
-
+    parser.add_argument('--vid_stride',
+                        default=1,
+                        type=int,
+                        help='video frame-rate stride')
 
     return parser
 
@@ -334,22 +325,24 @@ def main():
             vid_writer = cv2.VideoWriter(
                 save_path, cv2.VideoWriter_fourcc(*"mp4v"),
                 fps, (int(width), int(height)))
-        while True:
-            ret_val, frame = cap.read()
-            if ret_val:
-                bboxes, scores, cls_inds = infer_engine.forward(frame)
-                result_frame = infer_engine.visualize(frame, bboxes, scores, cls_inds, conf=args.conf, save_result=False)
-                if args.save_result:
-                    vid_writer.write(result_frame)
+        for i in tqdm(range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))):
+            # ret_val, frame = cap.read()
+            cap.grab()  # .read() = .grab() followed by .retrieve()
+            if i % args.vid_stride == 0:
+                ret_val, frame = cap.retrieve()
+                if ret_val:
+                    bboxes, scores, cls_inds = infer_engine.forward(frame)
+                    result_frame = infer_engine.visualize(frame, bboxes, scores, cls_inds, conf=args.conf, save_result=False)
+                    if args.save_result:
+                        vid_writer.write(result_frame)
+                    else:
+                        cv2.namedWindow("DAMO-YOLO", cv2.WINDOW_NORMAL)
+                        cv2.imshow("DAMO-YOLO", result_frame)
+                    ch = cv2.waitKey(1)
+                    if ch == 27 or ch == ord("q") or ch == ord("Q"):
+                        break
                 else:
-                    cv2.namedWindow("DAMO-YOLO", cv2.WINDOW_NORMAL)
-                    cv2.imshow("DAMO-YOLO", result_frame)
-                ch = cv2.waitKey(1)
-                if ch == 27 or ch == ord("q") or ch == ord("Q"):
                     break
-            else:
-                break
-
 
 
 if __name__ == '__main__':