diff --git a/README.md b/README.md
index 5419464..f6e2cdd 100644
--- a/README.md
+++ b/README.md
@@ -18,38 +18,23 @@ and maps vectors with labels provided by datasets used for pre-training.
 Use the pretrained Omnivore model to classify and generate a vector for the given video path './archery.mp4' 
 ([download](https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4)).
 
- *Write the pipeline in simplified style*:
+*Write a pipeline with explicit inputs/outputs name specifications*:
 
-- Predict labels (default):
 ```python
-import towhee
-
-(
-    towhee.glob('./archery.mp4') 
-          .video_decode.ffmpeg()
-          .action_classification.omnivore(
-            model_name='omnivore_swinT', topk=5)
-          .show()
+from towhee.dc2 import pipe, ops, DataCollection
+
+p = (
+    pipe.input('path')
+        .map('path', 'frames', ops.video_decode.ffmpeg())
+        .map('frames', ('labels', 'scores', 'features'),
+             ops.action_classification.omnivore(model_name='omnivore_swinT'))
+        .output('path', 'labels', 'scores', 'features')
 )
-```
-<img src="./result1.png" height="px"/>
-
-*Write a same pipeline with explicit inputs/outputs name specifications*:
 
-```python
-import towhee
-
-(
-    towhee.glob['path']('./archery.mp4')
-          .video_decode.ffmpeg['path', 'frames']()
-          .action_classification.omnivore['frames', ('labels', 'scores', 'features')](
-                model_name='omnivore_swinT')
-          .select['path', 'labels', 'scores', 'features']()
-          .show(formatter={'path': 'video_path'})
-)
+DataCollection(p('./archery.mp4')).show()
 ```
 
-<img src="./result2.png" height="px"/>
+<img src="./result.png" height="px"/>
 
 <br />
 
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