diff --git a/README.md b/README.md
index 74a512e..4cc414d 100644
--- a/README.md
+++ b/README.md
@@ -4,19 +4,19 @@
 
 <br />
 
-## Desription
+## Description
 
 An image embedding operator generates a vector given an image.
-This operator extracts features for image with pretrained models provided by [Timm](https://github.com/rwightman/pytorch-image-models).
+This operator extracts features for image with pre-trained models provided by [Timm](https://github.com/rwightman/pytorch-image-models).
 Timm is a deep-learning library developed by [Ross Wightman](https://twitter.com/wightmanr),
-which maintains SOTA deep-learning models and tools in computer vision.
+who maintains SOTA deep-learning models and tools in computer vision.
 
 <br />
 
 ## Code Example
 
 Load an image from path './towhee.jpg'
-and use the pretrained ResNet50 model ('resnet50') to generate an image embedding.
+and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.
 
  *Write the pipeline in simplified style:*
 
@@ -47,7 +47,7 @@ towhee.glob['path']('./towhee.jpg') \
 
 ## Factory Constructor
 
-Create the operator via the following factory method
+Create the operator via the following factory method:
 
 ***image_embedding.timm(model_name='resnet34', num_classes=1000, skip_preprocess=False)***
 
@@ -56,7 +56,7 @@ Create the operator via the following factory method
 ***model_name:*** *str*
 
 The model name in string. The default value is "resnet34".
-Refer [Timm Docs](https://fastai.github.io/timmdocs/#List-Models-with-Pretrained-Weights) to get a full list of supported models.
+Refer to [Timm Docs](https://fastai.github.io/timmdocs/#List-Models-with-Pretrained-Weights) to get a full list of supported models.
 
 ***num_classes:*** *int*
 
@@ -65,7 +65,7 @@ It is related to model and dataset.
 
 ***skip_preprocess:*** *bool*
 
-The flag to control whether to skip image preprocess.
+The flag to control whether to skip image pre-process.
 The default value is False.
 If set to True, it will skip image preprocessing steps (transforms).
 In this case, input image data must be prepared in advance in order to properly fit the model.