Modify README

README.md

@@ -18,13 +18,13 @@ which maintains SOTA deep-learning models and tools in computer vision.
 Load an image from path './towhee.jpg'
 and use the pretrained ResNet50 model ('resnet50') to generate an image embedding. 
 
- *Write the pipeline in simplified style*:
+ *Write the pipeline in simplified style:*
 
 ```python
 import towhee
 
 towhee.glob('./towhee.jpg') \
-      .image_decode.cv2() \
+      .image_decode() \
       .image_embedding.timm(model_name='resnet50') \
       .show()
 ```
@@ -36,9 +36,9 @@ towhee.glob('./towhee.jpg') \
 import towhee
 
 towhee.glob['path']('./towhee.jpg') \
-      .image_decode.cv2['path', 'img']() \
+      .image_decode['path', 'img']() \
       .image_embedding.timm['img', 'vec'](model_name='resnet50') \
-      .select('img', 'vec') \
+      .select['img', 'vec']() \
       .show()
 ```
 <img src="./result2.png" height="150px"/>
@@ -53,18 +53,17 @@ Create the operator via the following factory method
 
 **Parameters:**
 
-***model_name***: *str*
+***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.
 
-
-***num_classes***: *int*
+***num_classes:*** *int*
 
 The number of classes. The default value is 1000.
 It is related to model and dataset.
 
-***skip_preprocess***: *bool*
+***skip_preprocess:*** *bool*
 
 The flag to control whether to skip image preprocess.
 The default value is False.
@@ -78,18 +77,15 @@ In this case, input image data must be prepared in advance in order to properly
 An image embedding operator takes a towhee image as input.
 It uses the pre-trained model specified by model name to generate an image embedding in ndarray.
 
-
 **Parameters:**
 
-***img***: *towhee.types.Image (a sub-class of numpy.ndarray)*
+***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)*
 
 The decoded image data in numpy.ndarray.
 
 
 
-**Returns**:
-
-*numpy.ndarray*
+**Returns:** *numpy.ndarray*
 
 The image embedding extracted by model.