diff --git a/README.md b/README.md
index b3edd6f..c0b46d1 100644
--- a/README.md
+++ b/README.md
@@ -13,35 +13,34 @@ which maintains SOTA deep-learning models and tools in computer vision.
 
 ## Code Example
 
-Load an image from path './dog.jpg'
+Load an image from path './towhee.jpg'
 and use the pretrained ResNet50 model ('resnet50') to generate an image embedding. 
 
  *Write the pipeline in simplified style*:
 
 ```python
-from towhee import dc
+import towhee
 
-dc.glob('./dog.jpg') \
-  .image_decode.cv2() \
-  .image_embedding.timm(model_name='resnet50') \
-  .show()
+towhee.glob('./towhee.jpg') \
+      .image_decode.cv2() \
+      .image_embedding.timm(model_name='resnet50') \
+      .show()
 ```
-    | [0.052790146, 0.0, 0.0, ...] shape=(2048,) |
+<img src="https://towhee.io/image-embedding/timm/src/branch/main/result1.png" alt="result1" style="width:50px;"/>
+
+![result1](result1.png)
 
 *Write a same pipeline with explicit inputs/outputs name specifications:*
 
 ```python
-from towhee import dc
+import towhee
 
-dc.glob['path']('./dog.jpg') \
-  .image_decode.cv2['path', 'img']() \
-  .image_embedding.timm['img', 'vec'](model_name='resnet50') \
-  .select('vec') \
-  .to_list()
+towhee.glob['path']('./dog.jpg') \
+      .image_decode.cv2['path', 'img']() \
+      .image_embedding.timm['img', 'vec'](model_name='resnet50') \
+      .select('img', 'vec') \
+      .show('img', 'vec')
 ```
-    [array([0.05279015, 0.        , 0.        , ..., 0.00239191, 0.06632169,
-        0.        ], dtype=float32)]
-
 
 ## Factory Constructor
 
diff --git a/result1.png b/result1.png
new file mode 100644
index 0000000..b5a03c7
Binary files /dev/null and b/result1.png differ