taiyi
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# taiyi |
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# Chinese Image-Text Retrieval Embdding with Taiyi |
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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(in Chinese) with [Taiyi(太乙)](https://arxiv.org/abs/2209.02970) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This method is developed by [IDEA-CCNL](https://github.com/IDEA-CCNL/Fengshenbang-LM/). |
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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('./dog.jpg') \ |
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.image_decode() \ |
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.image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', modality='image') \ |
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.show() |
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towhee.dc(["一只小狗"]) \ |
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.image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', 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']('./dog.jpg') \ |
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.image_decode['path', 'img']() \ |
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.image_text_embedding.taiyi['img', 'vec'](model_name='taiyi-clip-roberta-102m-chinese', modality='image') \ |
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.select['img', 'vec']() \ |
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.show() |
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towhee.dc['text'](["一只小狗"]) \ |
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.image_text_embedding.taiyi['text','vec'](model_name='taiyi-clip-roberta-102m-chinese', 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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***taiyi(model_name, modality)*** |
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**Parameters:** |
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***model_name:*** *str* |
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The model name of Taiyi. Supported model names: |
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- taiyi-clip-roberta-102m-chinese |
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- taiyi-clip-roberta-large-326m-chinese |
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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: 18 KiB |
After Width: | Height: | Size: 112 KiB |
After Width: | Height: | Size: 13 KiB |
After Width: | Height: | Size: 13 KiB |
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