diff --git a/README.md b/README.md
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--- 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')
)
-```
-
-
-*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()
```
-
+
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