diff --git a/README.md b/README.md
index 028175a..b559dda 100644
--- a/README.md
+++ b/README.md
@@ -18,20 +18,26 @@ who maintains SOTA deep-learning models and tools in computer vision.
Load an image from path './towhee.jpeg'
and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.
- *Write the pipeline in simplified style:*
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+- **option 1:**
```python
-import towhee
+from towhee.dc2 import pipe, ops, DataCollection
-towhee.glob('./towhee.jpeg') \
- .image_decode() \
- .image_embedding.timm(model_name='resnet50') \
- .show()
+p = (
+ pipe.input('path')
+ .map('path', 'img', ops.image_decode())
+ .map('img', 'vec', ops.image_embedding.timm(model_name='resnet50'))
+ .output('img', 'vec')
+)
+
+DataCollection(p('towhee.jpeg')).show()
```
-
-*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+- **option 2:**
```python
import towhee
@@ -41,7 +47,6 @@ towhee.glob['path']('./towhee.jpeg') \
.select['img', 'vec']() \
.show()
```
-
@@ -104,6 +109,13 @@ Save model to local with specified format.
The path where model is saved to. By default, it will save model to the operator directory.
+```python
+from towhee import ops
+
+op = ops.image_embedding.timm(model_name='resnet50').get_op()
+op.save_model('onnx', 'test.onnx')
+```
+
***supported_model_names(format=None)***
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rename to result.png
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