slip
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# slip |
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# Image-Text Retrieval Embdding with SLIP |
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*author: David Wang* |
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<br /> |
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## Description |
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This operator extracts features for image or text with [SLIP](https://arxiv.org/abs/2112.12750), a multi-task learning framework for combining self-supervised learning and CLIP pre-training. This is an adaptation from [facebookresearch/SLIP](https://github.com/facebookresearch/SLIP). |
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<br /> |
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## Code Example |
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Load an image from path './teddy.jpg' to generate an image embedding. |
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Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding. |
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*Write the pipeline in simplified style*: |
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```python |
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import towhee |
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towhee.glob('./moon.jpeg') \ |
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.image_decode() \ |
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.image_text_embedding.slip(model_name='slip_vit_small', modality='image') \ |
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.show() |
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towhee.dc(['moon in the night.']) \ |
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.image_text_embedding.slip(model_name='slip_vit_small', modality='text') \ |
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.show() |
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``` |
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<img src="./vec1.png" alt="result1" style="height:20px;"/> |
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<img src="./vec2.png" alt="result2" style="height:20px;"/> |
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*Write a same pipeline with explicit inputs/outputs name specifications:* |
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```python |
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import towhee |
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towhee.glob['path']('./moon.jpeg') \ |
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.image_decode['path', 'img']() \ |
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.image_text_embedding.slip['img', 'vec'](model_name='slip_vit_small', modality='image') \ |
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.select['img', 'vec']() \ |
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.show() |
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towhee.dc['text'](['moon in the night.']) \ |
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.image_text_embedding.slip['text','vec'](model_name= 'slip_vit_small', modality='text') \ |
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.select['text', 'vec']() \ |
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.show() |
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``` |
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<img src="./tabular1.png" alt="result1" style="height:60px;"/> |
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<img src="./tabular2.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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***slip(model_name, modality)*** |
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**Parameters:** |
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***model_name:*** *str* |
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The model name of SLIP. Supported model names: |
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- slip_vit_small |
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- slip_vit_base |
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- slip_vit_large |
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***modality:*** *str* |
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Which modality(*image* or *text*) is used to generate the embedding. |
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<br /> |
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## Interface |
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An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray. |
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**Parameters:** |
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***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* or *str* |
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The data (image or text based on specified modality) to generate embedding. |
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**Returns:** *numpy.ndarray* |
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The data embedding extracted by model. |
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After Width: | Height: | Size: 120 KiB |
After Width: | Height: | Size: 16 KiB |
After Width: | Height: | Size: 10 KiB |
After Width: | Height: | Size: 10 KiB |
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