Update README

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

README.md

@@ -1,20 +1,69 @@
 # Image Embedding Pipeline with Resnet50
 
-Authors: name or github-name(email)
+Authors: Kyle, shiyu22
 
 ## Overview
 
-Introduce the functions of pipeline.
+This pipeline is used to **extract the feature vector of the image**, first to normalize the image , and then use resnet50 model to generate the vector.
+
+In fact, the pipeline runs by parsing [the yaml file](./image_embedding_resnet50.yaml), which declares some functions we call **Operator**, and the **DataFrame** required by each Operator. Next will introduce the interface, how to use it and how it works, have fun with it!
 
 ## Interface
 
-The interface of pipeline.(input & output)
+`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)
+
+**param:**
+
+- **task**(str), task name or pipeline repo name.
+- **fmc**(FileManagerConfig), optional file manager config for the local instance, default is FileManagerConfig().
+- **branch**(str), which branch to use for operators/pipelines on hub, defaults to 'main'.
+- **force_download**(bool), whether to redownload pipeline and operators, default is False.
+
+**return:**
+
+- **_PipelineWrapper**, which is a wrapper class around `Pipeline`.
+
+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 as follows:
+
+**inputs:**
+
+- **img_tensor**(PIL.Image), image to be embedded.
+
+**outputs:**
+
+- **cnn**(numpy.ndarray), the embedding of image.
 
 ## How to use
 
-- Requirements from requirements.txt
-- Run it with Towhee
+1. Install [Towhee](https://github.com/towhee-io/towhee)
+
+```bash
+$ pip3 install towhee
+```
+
+> You can refer to [Getting Started with Towhee](towhee.io) for more details. If you have questions, you can [submit an issue to the towhee repository](https://github.com/towhee-io/towhee/issues).
+
+2. Run it with Towhee
+
+```python
+>>> from towhee import pipeline
+>>> from PIL import Image
+
+>>> img = Image.open('./test_data/test.jpg')
+>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
+>>> embedding = embedding_pipeline(img)
+```
 
 ## How it works
 
-- op1->op2->op3 , and intro all the op used. (auto generate graph)
+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)
+
+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).