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 # Image Embedding Operator with Resnet50
 
-Authors: name or github-name(email)
+Authors: Kyle, shiyu22
 
 ## Overview
 
-Introduce the functions of op and the model used.
+This Operator generates feature vectors from the pytorch pretrained **Resnet50** mode, which is trained on [COCO dataset](https://cocodataset.org/#download).
+
+**Resnet** models were proposed in “[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)”, this model was the winner of ImageNet challenge in 2015.  The fundamental breakthrough with ResNet was it allowed us to train extremely deep neural networks with 150+layers successfully. Prior to ResNet training very deep neural networks was difficult due to the problem of vanishing gradients.
 
 ## Interface
 
-The interface of all the functions in op. (input & output)
+`Class Resnet50ImageEmbedding(Operator)` [source](./resnet50_image_embedding.py)
+
+`__init__(self, model_name: str)`
+
+**param:**
+
+- model_name(str): the model name for embedding, like 'resnet50'.
+
+`__call__(self, img_tensor: torch.Tensor)`
+
+**param:**
+
+- img_tensor(torch.Tensor): the normalized image tensor.
+
+**return:**
+
+- cnn(numpy.ndarray): the embedding of image.
 
 ## How to use
 
-- Requirements from requirements.txt
-- How it works in some typical pipelines and the yaml example.
+### Requirements
+
+You can get the required python package by [requirements.txt](./requirements.txt) and [pytorch/requirements.txt](./pytorch/requirements.txt). In fact, Towhee will automatically install these packages when you first load the Operator Repo, so you don't need to install them manually, here is just a list.
+
+- towhee
+- torch
+- torchvision
+- numpy
+
+### How it works
+
+The towhee/resnet50_image_embedding Operator implements the function of image embedding, which can add to the pipeline, for example, it's the key Operator within [image_embedding_resnet50](https://hub.towhee.io/towhee/image-embedding-resnet50) pipeline, and it is the red box in the picture below.
+
+![img](./pic/operator.png)
+
+When using this Operator to write Pipline's Yaml file, you need to declare the following content according to the interface of resnet50_image_embedding class:
+
+```yaml
+operators:
+    -
+        name: 'embedding_model'
+        function: 'towhee/resnet50-image-embedding'
+        tag: 'main'
+        init_args:
+            model_name: 'resnet50'
+        inputs:
+            -
+                df: 'image_preproc'
+                name: 'img_tensor'
+                col: 0
+        outputs:
+            -
+                df: 'embedding'
+        iter_info:
+            type: map
+dataframes:
+    -
+        name: 'image_preproc'
+        columns:
+            -
+                name: 'img_transformed'
+                vtype: 'torch.Tensor'
+    -
+        name: 'embedding'
+        columns:
+            -
+                name: 'cnn'
+                vtype: 'numpy.ndarray'
+```
+
+We can see that in yaml, the **operator** part declares the `init_args` of the class and the` input` and `output`dataframe, and the **dataframe** declares the parameter `name` and `vtype`.
+
+### File Structure
+
+Here is the main file structure of the `resnet50-image-embedding` Operator. If you want to learn more about the source code or modify it yourself, you can learn from it.
+
+```bash
+├── .gitattributes
+├── .gitignore
+├── README.md
+├── __init__.py
+├── requirements.txt              #General python dependency package
+├── resnet50_image_embedding.py   #The python file for Towhee, it defines the interface of the system and usually does not need to be modified.
+├── resnet50_image_embedding.yaml #The YAML file contains Operator information, such as model frame, input, and output.
+├── pytorch               #The directory of the pytorh
+│   ├── __init__.py
+│   ├── model             #The directory of the pytorch model, which can store data such as weights.
+│   ├── requirements.txt  #The python dependency package for the pytorch model.
+│   └── model.py          #The code of the pytorch model, including the initialization model and prediction.
+├── test_data/   #The directory of test data, including test.jpg
+└── test_resnet50_image_embedding.py  #The unittest file of the Operator.
+```
 
 ## Reference
 
-Model paper link.
+- https://pytorch.org/hub/pytorch_vision_resnet/
+- https://arxiv.org/abs/1512.03385
+
+ 
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