diff --git a/README.md b/README.md
index b60c0b4..21b35f8 100644
--- a/README.md
+++ b/README.md
@@ -4,21 +4,25 @@ Authors: derekdqc, shiyu22
 
 ## Overview
 
-This pipeline is used to **extract the feature vector of the image**. First step is to normalize the image, and then use resnet50 model to generate the vector.
+This pipeline is used to **extract the feature vector of a given image**.
+
+The pipeline performs two major steps. First, normalize the image, and then use resnet50 model to generate the vector.
 
 ## Interface
 
-**Args:**
+**Pipeline Input Arguments:**
+
+- img(`PIL.Image`):
 
-​	img_tensor(`PIL.Image`):
+  The image to be encoded. `PIL.Image` is the currently supported image type. 
 
-​		The image to be encoded.
+**Pipeline Returns:**
 
-**Returns:**
+-  (`Tuple[('cnn', numpy.ndarray)]`)
 
-​	(`Tuple[('cnn', numpy.ndarray)]`)
+  The pipeline returns a tuple.
 
-​		The embedding of the image.
+  - The `feature_vector` field contains the embedding of the image.
 
 ## How to use
 
@@ -43,6 +47,6 @@ $ pip3 install towhee
 
 ## How it works
 
-You can learn the pipeline and operator in [Towhee architecture](https://github.com/towhee-io/towhee#towhee-architecture). This pipeline includes two main functions: [towhee/transform-image](https://hub.towhee.io/towhee/transform-image) and [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding). It is necessary to ensure that the input and output of the four Operators correspond to each other, and the input and output data types can be defined by DataFrame.
+This pipeline includes two main operators: transform image (implemented as [towhee/transform-image](https://hub.towhee.io/towhee/transform-image)) and image embedding (implemented as [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding)). The transform image operator will first convert the original image into a normalized format, such as with 512x512 resolutions. Then, the normalized image will be encoded via image embedding operator, and finally we get a feature vector of the given image. (The basic concepts of pipeline and operator are introduced in [Towhee architecture](https://github.com/towhee-io/towhee#towhee-architecture).)
 
 ![img](./readme_res/pipeline.png)
diff --git a/image_embedding_resnet50.yaml b/image_embedding_resnet50.yaml
index 3ed4a1f..40bbf44 100644
--- a/image_embedding_resnet50.yaml
+++ b/image_embedding_resnet50.yaml
@@ -7,7 +7,7 @@ operators:
         inputs:
             -
                 df: '_start_df'
-                name: 'img_tensor'
+                name: 'img'
                 col: 0
         outputs:
             -
@@ -23,7 +23,7 @@ operators:
         inputs:
             -
                 df: 'image'
-                name: 'img_tensor'
+                name: 'img'
                 col: 0
         outputs:
             -
@@ -53,7 +53,7 @@ operators:
         inputs:
             -
                 df: 'embedding'
-                name: 'cnn'
+                name: 'feature_vector'
                 col: 0
         outputs:
             -
@@ -65,13 +65,13 @@ dataframes:
         name: '_start_df'
         columns:
             -
-                name: 'img_tensor'
+                name: 'img'
                 vtype: 'PIL.Image'
     -
         name: 'image'
         columns:
             -
-                name: 'img_tensor'
+                name: 'img'
                 vtype: 'PIL.Image'
     -
         name: 'image_preproc'
@@ -83,11 +83,11 @@ dataframes:
         name: 'embedding'
         columns:
             -
-                name: 'cnn'
+                name: 'feature_vector'
                 vtype: 'numpy.ndarray'
     -
         name: '_end_df'
         columns:
             -
-                name: 'cnn'
+                name: 'feature_vector'
                 vtype: 'numpy.ndarray'