diff --git a/README.md b/README.md
index 21b35f8..0d9d278 100644
--- a/README.md
+++ b/README.md
@@ -1,28 +1,26 @@
-# Image Embedding Pipeline with Resnet50
+# Pipeline: Image Embedding using Resnet50
 
 Authors: derekdqc, shiyu22
 
 ## Overview
 
-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.
+The pipeline is used to **extract the feature vector of a given image**. It first normalizes the image and then uses Resnet50 model to generate the vector.
 
 ## Interface
 
-**Pipeline Input Arguments:**
-
-- img(`PIL.Image`):
+**Input Arguments:**
 
-  The image to be encoded. `PIL.Image` is the currently supported image type. 
+- image:
+  - the input image to be encoded
+  - supported types: `PIL.Image`
 
-**Pipeline Returns:**
+**Pipeline Output:**
 
--  (`Tuple[('cnn', numpy.ndarray)]`)
+The pipeline returns a tuple `Tuple[('feature_vector', numpy.ndarray)]` containing following fields:
 
-  The pipeline returns a tuple.
-
-  - The `feature_vector` field contains the embedding of the image.
+- feature_vector:
+  - the embedding of input image
+  - data type: `numpy.ndarray`
 
 ## How to use
 
@@ -40,13 +38,15 @@ $ pip3 install towhee
 >>> from towhee import pipeline
 >>> from PIL import Image
 
->>> img = Image.open('path/to/your/image') # for example, './test_data/test.jpg'
+>>> img = Image.open('path/to/your/image') # for example, './test.jpg'
 >>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
 >>> embedding = embedding_pipeline(img)
 ```
 
 ## How it works
 
-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).)
+This pipeline includes two main operators: [transform image](https://hub.towhee.io/towhee/transform-image-operator-class) (implemented as [towhee/transform-image](https://hub.towhee.io/towhee/transform-image)) and [image embedding](https://hub.towhee.io/towhee/image-embedding-operator-class) (implemented as [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding)). The transform image op 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 op, and finally we get a feature vector of the given image.
+
+> Refer [Towhee architecture](https://github.com/towhee-io/towhee#towhee-architecture) for basic concepts in Towhee: pipeline, operator, dataframe.
 
 ![img](./readme_res/pipeline.png)