data2vec
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# Image Embedding with data2vec |
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*author: David Wang* |
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# More Resources |
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<br /> |
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## Description |
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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. |
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<br /> |
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## Code Example |
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Load an image from path './towhee.jpg' to generate an image embedding. |
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*Write a pipeline with explicit inputs/outputs name specifications:* |
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```python |
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from towhee import pipe, ops, DataCollection |
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p = ( |
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pipe.input('path') |
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.map('path', 'img', ops.image_decode()) |
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.map('img', 'vec', ops.image_embedding.data2vec(model_name='facebook/data2vec-vision-base-ft1k')) |
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.output('img', 'vec') |
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) |
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DataCollection(p('towhee.jpeg')).show() |
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``` |
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<img src="./result2.png" alt="result2" style="height:60px;"/> |
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<br /> |
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## Factory Constructor |
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Create the operator via the following factory method |
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***data2vec(model_name='facebook/data2vec-vision-base')*** |
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**Parameters:** |
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***model_name***: *str* |
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The model name in string. |
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The default value is "facebook/data2vec-vision-base-ft1k". |
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Supported model name: |
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- facebook/data2vec-vision-base-ft1k |
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- facebook/data2vec-vision-large-ft1k |
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<br /> |
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## Interface |
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An image embedding operator takes a [towhee image](link/to/towhee/image/api/doc) as input. |
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It uses the pre-trained model specified by model name to generate an image embedding in ndarray. |
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**Parameters:** |
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***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)* |
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The decoded image data in towhee.types.Image (numpy.ndarray). |
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**Returns:** *numpy.ndarray* |
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The image embedding extracted by model. |
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