diff --git a/README.md b/README.md
index 8a698d8..be7853b 100644
--- a/README.md
+++ b/README.md
@@ -22,37 +22,40 @@ This operator uses [PyTorch.yolov5](https://pytorch.org/hub/ultralytics_yolov5/)
 
 ### Code Example
 
+Load an image from path './test.png' and use yolov5 model to detect objects in the image.
 
+*Write a same pipeline with explicit inputs/outputs name specifications:*
 
-- Write the pipeline in the simplified way:
-
+- **option 1: towhee>=0.9.0**
 ```Python
-import towhee
-
-towhee.glob('./test.png') \
-	.image_decode() \
-	.object_detection.yolov5() \
-	.show()
-```
+from towhee.dc2 import pipe, ops, DataCollection
 
-<img src="./results1.png" alt="results1" height="40px"/>
+p = (
+    pipe.input('path')
+        .map('path', 'img', ops.image_decode())
+        .map('img', ('box', 'class', 'score'), ops.object_detection.yolov5())
+        .map(('img', 'box'), 'object', ops.image_crop(clamp=True))
+        .output('img', 'object', 'class')
+)
 
-- Write the same pipeline with explicitly specified inputs and outputs:
+DataCollection(p('./test.png')).show()
+```
 
-```Python
+- **option 2:**
+```python
 import towhee
 
-towhee.glob['path']('./test.png') \
-	.image_decode['path','img']() \
-	.object_detection.yolov5['img', ('box', 'class', 'score')]() \
-	.image_crop[('img', 'box'), 'object'](clamp = True) \
-	.select['img','object']() \
-	.show()
+(
+    towhee.glob['path']('./test.png')
+          .image_decode['path','img']()
+          .object_detection.yolov5['img', ('box', 'class', 'score')]()
+          .image_crop[('img', 'box'), 'object'](clamp = True)
+          .select['img','object', 'class']()
+          .show()
+)
 ```
 
-<img src="./results2.png" alt="results1" height="140px"/>
-
-
+<img src="./result.png" alt="result" height="140px"/>
 
 <br />
 
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