diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Image-Text Retrieval Embdding with BLIP
+# Image Captioning with BLIP
 
 *author: David Wang*
 
@@ -9,7 +9,7 @@
 
 ## Description
 
-This operator extracts features for image or text with [BLIP](https://arxiv.org/abs/2201.12086) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This is a adaptation from [salesforce/BLIP](https://github.com/salesforce/BLIP).
+This operator generates the caption with [BLIP](https://arxiv.org/abs/2201.12086) which describes the content of the given image. This is an adaptation from [salesforce/BLIP](https://github.com/salesforce/BLIP).
 
 
 <br />
@@ -17,45 +17,33 @@ This operator extracts features for image or text with [BLIP](https://arxiv.org/
 
 ## Code Example
 
-Load an image from path './teddy.jpg' to generate an image embedding. 
-
-Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding. 
+Load an image from path './animals.jpg' to generate the caption. 
 
  *Write the pipeline in simplified style*:
 
 ```python
 import towhee
 
-towhee.glob('./teddy.jpg') \
+towhee.glob('./animals.jpg') \
       .image_decode() \
-      .image_text_embedding.blip(model_name='blip_base', modality='image') \
-      .show()
-
-towhee.dc(["A teddybear on a skateboard in Times Square."]) \
-      .image_text_embedding.blip(model_name='blip_base', modality='text') \
+      .image_captioning.blip(model_name='blip_base') \
+      .select() \
       .show()
 ```
-<img src="https://towhee.io/image-text-embedding/blip/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
-<img src="https://towhee.io/image-text-embedding/blip/raw/branch/main/vec2.png" alt="result2" style="height:20px;"/>
+<img src="./cap.png" alt="result1" style="height:20px;"/>
 
 *Write a same pipeline with explicit inputs/outputs name specifications:*
 
 ```python
 import towhee
 
-towhee.glob['path']('./teddy.jpg') \
+towhee.glob['path']('./animals.jpg') \
       .image_decode['path', 'img']() \
-      .image_text_embedding.blip['img', 'vec'](model_name='blip_base', modality='image') \
-      .select['img', 'vec']() \
-      .show()
-
-towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
-      .image_text_embedding.blip['text','vec'](model_name='blip_base', modality='text') \
-      .select['text', 'vec']() \
+      .image_captioning.blip['img', 'text'](model_name='blip_base') \
+      .select['img', 'text']() \
       .show()
 ```
-<img src="https://towhee.io/image-text-embedding/blip/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
-<img src="https://towhee.io/image-text-embedding/blip/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>
+<img src="./tabular.png" alt="result2" style="height:60px;"/>
 
 
 <br />
@@ -66,7 +54,7 @@ towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
 
 Create the operator via the following factory method
 
-***blip(model_name, modality)***
+***blip(model_name)***
 
 **Parameters:**
 
@@ -75,33 +63,23 @@ Create the operator via the following factory method
 ​   The model name of BLIP. Supported model names: 
 - blip_base     
 
-
-​   ***modality:*** *str*
-
-​   Which modality(*image* or *text*) is used to generate the embedding. 
-
 <br />
 
 
 
 ## Interface
 
-An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.
+An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption.
 
 
 **Parameters:**
 
 ​	***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)*  or *str*
 
-​  The data (image or text based on specified modality) to generate embedding.	
-
-
-
-**Returns:** *numpy.ndarray*
-
-​   The data embedding extracted by model.
-
+​  The image to generate embedding.	
 
 
 
+**Returns:** *str*
 
+​   The caption generated by model.
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