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+# Image Embedding with data2vec
 
+*author: David Wang*
 
-# More Resources
 
-        
-        
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+<br />
+
+
+
+## Description
+
+This operator extracts features for image with [data2vec](https://arxiv.org/abs/2202.03555). The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.
+
+<br />
+
+
+## Code Example
+
+Load an image from path './towhee.jpg' to generate an image embedding. 
+
+*Write a pipeline with explicit inputs/outputs name specifications:*
+
+```python
+from towhee import pipe, ops, DataCollection
+
+p = (
+    pipe.input('path')
+        .map('path', 'img', ops.image_decode())
+        .map('img', 'vec', ops.image_embedding.data2vec(model_name='facebook/data2vec-vision-base-ft1k'))
+        .output('img', 'vec')
+)
+
+DataCollection(p('towhee.jpeg')).show()
+```
+<img src="./result2.png" alt="result2" style="height:60px;"/>
+
+
+<br />
+
+
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***data2vec(model_name='facebook/data2vec-vision-base')***
+
+**Parameters:**
+
+
+​  ***model_name***: *str*
+
+The model name in string.
+The default value is "facebook/data2vec-vision-base-ft1k".
+
+Supported model name:
+- facebook/data2vec-vision-base-ft1k
+- facebook/data2vec-vision-large-ft1k
+
+<br />
+
+
+
+## Interface
+
+An image embedding operator takes a [towhee image](link/to/towhee/image/api/doc) 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)* 
+
+​	The decoded image data in towhee.types.Image (numpy.ndarray).
+
+
+
+**Returns:** *numpy.ndarray*
+
+​   The image embedding extracted by model.
+