Update

Signed-off-by: shiyu22 <shiyu.chen@zilliz.com>

README.md

@@ -1,10 +1,10 @@
-# Pipeline: Image Embedding using Resnet50
+# Pipeline: Image Embedding using Resnet
 
 Authors: derekdqc, shiyu22
 
 ## Overview
 
-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.
+The pipeline is used to **extract the feature vector of a given image**. It first normalizes the image and then uses Resnet model to generate the vector.
 
 ## Interface
 
@@ -39,13 +39,15 @@ $ pip3 install towhee
 >>> from PIL import Image
 
 >>> img = Image.open('path/to/your/image') # for example, './test.jpg'
->>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
+>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet')
 >>> embedding = embedding_pipeline(img)
 ```
 
+> This pipeline uses the resnet50 model, you can also change the parameters `resnet50` in the local file **~/.towhee/image_embedding_resnet50&towhee$main/image_embedding_resnet.yaml** to the `resnet101` model.
+
 ## How it works
 
-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 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.
+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/resnet-image-embedding](https://hub.towhee.io/towhee/resnet-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.
 
 > Refer [Towhee architecture](https://github.com/towhee-io/towhee#towhee-architecture) for basic concepts in Towhee: pipeline, operator, dataframe.
 

image_embedding_resnet50.yaml

@@ -1,4 +1,4 @@
-name: 'image-embedding-resnet50'
+name: 'image-embedding-resnet'
 operators:
     -
         name: '_start_op'
@@ -32,7 +32,7 @@ operators:
             type: map
     -
         name: 'embedding_model'
-        function: 'towhee/resnet50-image-embedding' # same as 'resnet50-image-embedding', default user is towhee
+        function: 'towhee/resnet-image-embedding' # same as 'resnet50-image-embedding', default user is towhee
         tag: 'main' # optional
         init_args:
             model_name: 'resnet50'