diff --git a/README.md b/README.md
index 15d3d25..872bfa6 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 Movinet model to classify and generate a vector for the given video path './jumpingjack.gif' 
 ([download](https://github.com/tensorflow/models/raw/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/jumpingjack.gif)).
 
- *Write the pipeline in simplified style*:
+*Write a pipeline with explicit inputs/outputs name specifications*:
 
-- Predict labels (default):
 ```python
-import towhee
-
-(
-    towhee.glob('./jumpingjack.gif') 
-          .video_decode.ffmpeg()
-          .action_classification.movinet(
-            model_name='movineta0', 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.movinet(model_name='movineta0'))
+        .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']('./jumpingjack.gif')
-          .video_decode.ffmpeg['path', 'frames']()
-          .action_classification.movinet['frames', ('labels', 'scores', 'features')](
-                model_name='movineta0')
-          .select['path', 'labels', 'scores', 'features']()
-          .show(formatter={'path': 'video_path'})
-)
+DataCollection(p('./jumpingjack.gif')).show()
 ```
 
-<img src="./result2.png" height="px"/>
+<img src="./result.png" height="px"/>
 
 <br />
 
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