Update

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

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')
 )
-```
-<img src="./result1.png" width="800px"/>
-
-<br />
 
-*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()
 ```
-<img src="./result2.png" width="800px"/>
+<img src="./result.png" width="800px"/>
 
 <br />