diff --git a/README.md b/README.md
index 2928a75..cc3000e 100644
--- a/README.md
+++ b/README.md
@@ -19,36 +19,22 @@ and maps vectors with labels.
Use the pretrained VideoSwinTransformer model ('swin_t_k400_1k')
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:*
```python
-import towhee
-
-(
- towhee.glob('./archery.mp4')
- .video_decode.ffmpeg()
- .action_classification.video_swin_transformer(model_name='swin_t_k400_1k')
- .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.video_swin_transformer(model_name='swin_t_k400_1k'))
+ .output('path', 'labels', 'scores', 'features')
)
-```
-
-
-
-*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.video_swin_transformer['frames', ('labels', 'scores', 'features')](model_name='swin_t_k400_1k')
- .select['path', 'labels', 'scores', 'features']()
- .show(formatter={'path': 'video_path'})
-)
+DataCollection(p('./archery.mp4')).show()
```
-
+
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