Update

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

README.md

@@ -6,32 +6,19 @@ Authors: derekdqc, shiyu22
 
 This pipeline is used to **extract the feature vector of the image**. First step is to normalize the image, and then use resnet50 model to generate the vector.
 
-The pipeline parses [the yaml file](./image_embedding_resnet50.yaml), which declares some components we call **Operator** and **DataFrame**. Next, we will introduce the interface, show how to use it and how it works, have fun with it!
-
 ## Interface
 
-`towhee.pipeline(task: str, fmc: FileManagerConfig = FileManagerConfig(), branch: str = 'main', force_download: bool = False)`  [\[source\]](https://github.com/towhee-io/towhee/blob/main/towhee/__init__.py)
-
-**params:**
-
-- **task**(str): task name or pipeline repo name.
-- **fmc**(FileManagerConfig): optional, file manager config for the local instance, default is a default FileManagerConfig obejct.
-- **branch**(str): optional, which branch to use for operators/pipelines on hub, defaults to 'main'.
-- **force_download**(bool): optional, whether to redownload pipeline and operators, default is False.
-
-**return:**
-
-- **_PipelineWrapper**, an instance of the wrapper class around `Pipeline`.
+**Args:**
 
-When we declare a pipeline object with a specific task, such as `towhee/image-embedding-resnet50` in this repo, it will run according to the Yaml file, and the input and output are:
+- img_tensor(`PIL.Image`), 
 
-**inputs:**
+  The image to be encoded.
 
-- **img_tensor**(PIL.Image), the image to be encoded.
+**Returns:**
 
-**outputs:**
+- (`Tuple[('cnn', numpy.ndarray)]`)
 
-- **cnn**(numpy.ndarray), the embedding of the image.
+  The embedding of the image.
 
 ## How to use
 
@@ -56,14 +43,6 @@ $ pip3 install towhee
 
 ## How it works
 
-First of all, you need to learn the pipeline and operator in Towhee architecture:
-
-- **Pipeline**: A `Pipeline` is a single machine learning task that is composed of several operators. Operators are connected together internally via a directed acyclic graph.
-
-- **Operator**: An `Operator` is a single node within a pipeline. It contains files (e.g. code, configs, models, etc...) and works for reusable operations (e.g., preprocessing an image, inference with a pretrained model).
-
-This pipeline includes four functions: `_start_op`, `towhee/transform-image`, `towhee/resnet50-image-embedding` and `_end_op`. It is necessary to ensure that the input and output of the four Operators correspond to each other, and the input and output data types can be defined by DataFrame.
-
-![img](./pic/pipeline.png)
+You can learn the pipeline and operator in [Towhee architecture](https://github.com/towhee-io/towhee#towhee-architecture). This pipeline includes two main functions: [towhee/transform-image](https://hub.towhee.io/towhee/transform-image) and [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding). It is necessary to ensure that the input and output of the four Operators correspond to each other, and the input and output data types can be defined by DataFrame.
 
-Among the four Operator, `_start_op` and `_end_op` are required in any Pipeline, and they are used to start and end the pipeline in the Towhee system. For the other two Operators, please refer to [towhee/transform-image](https://hub.towhee.io/towhee/transform-image) and [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding).
+![img](./readme_res/pipeline.png)

test_image_embedding_resnet50.py

@@ -1,18 +0,0 @@
-import os
-import unittest
-from towhee import pipeline
-from PIL import Image
-
-
-class TestImageEmbeddingResnet50(unittest.TestCase):
-    def test_image_embedding_resnet50(self):
-        img_src = './test_data/test.jpg'
-        test_img = Image.open(img_src)
-        dimension = 1000
-        embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
-        embedding = embedding_pipeline(test_img)
-        self.assertEqual((1, dimension), embedding[0][0].shape)
-
-
-if __name__ == '__main__':
-    unittest.main()