diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,6 +1,10 @@
 # RetinaFace Face Detection 
 
-*Authors: David Wang*
+*author: David Wang*
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+
+
+<br />
 
 
 
@@ -8,11 +12,15 @@
 
 This operator detects faces in the images by using [RetinaFace](https://arxiv.org/abs/1905.00641) Detector. It will return the bounding box positions and the confidence scores of detected faces. This repo is an adaptaion from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface).
 
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+<br />
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 ## Code Example
 
 Load an image from path './turing.png' and use the pretrained RetinaFace model to generate face bounding boxes and confidence scores. 
 
- *Write the pipeline in simplified style*:
+ *Write the pipeline in simplified style:*
 
 ```python
 import towhee
@@ -33,25 +41,35 @@ import towhee
 towhee.glob['path']('turing.png') \
   .image_decode.cv2['path', 'img']() \
   .face_detection.retinaface['img', ('bbox','score')]() \
-  .select('img', 'bbox', 'score') \
+  .select['img', 'bbox', 'score']() \
   .show()
 ```
 
 <img src="https://towhee.io/face-detection/retinaface/raw/branch/main/result2.png" alt="result2" style="height:60px;"/>
 
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+<br />
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 ## Factory Constructor
 
 Create the operator via the following factory method.
 
 ***face_detection.retinaface()***
 
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+<br />
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 ## Interface
 
 A face detection operator takes an image as input. It generates the bounding box positions and confidence scores back to ndarray.
 
 **Parameters:**
 
-​	***img***: *towhee.types.Image (a sub-class of numpy.ndarray)* 
+​	***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)* 
 
 ​	the image to detect faces.