diff --git a/README.md b/README.md index d72f6b6..fdc8b92 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,107 @@ -# taiyi +# Chinese Image-Text Retrieval Embdding with Taiyi + +*author: David Wang* + + +
+ + + +## Description + +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/). + + +
+ + +## Code Example + +Load an image from path './teddy.jpg' to generate an image embedding. + +Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding. + + *Write the pipeline in simplified style*: + +```python +import towhee + +towhee.glob('./dog.jpg') \ + .image_decode() \ + .image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', modality='image') \ + .show() + +towhee.dc(["一只小狗"]) \ + .image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', modality='text') \ + .show() +``` +result1 +result2 + +*Write a same pipeline with explicit inputs/outputs name specifications:* + +```python +import towhee + +towhee.glob['path']('./dog.jpg') \ + .image_decode['path', 'img']() \ + .image_text_embedding.taiyi['img', 'vec'](model_name='taiyi-clip-roberta-102m-chinese', modality='image') \ + .select['img', 'vec']() \ + .show() + +towhee.dc['text'](["一只小狗"]) \ + .image_text_embedding.taiyi['text','vec'](model_name='taiyi-clip-roberta-102m-chinese', modality='text') \ + .select['text', 'vec']() \ + .show() +``` +result1 +result2 + + +
+ + + +## Factory Constructor + +Create the operator via the following factory method + +***taiyi(model_name, modality)*** + +**Parameters:** + +​ ***model_name:*** *str* + +​ The model name of Taiyi. Supported model names: +- taiyi-clip-roberta-102m-chinese +- taiyi-clip-roberta-large-326m-chinese + + +​ ***modality:*** *str* + +​ Which modality(*image* or *text*) is used to generate the embedding. + +
+ + + +## Interface + +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. + + +**Parameters:** + +​ ***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* or *str* + +​ The data (image or text based on specified modality) to generate embedding. + + + +**Returns:** *numpy.ndarray* + +​ The data embedding extracted by model. + + + diff --git a/tabular1.png b/tabular1.png new file mode 100644 index 0000000..30f6db6 Binary files /dev/null and b/tabular1.png differ diff --git a/tabular2.png b/tabular2.png new file mode 100644 index 0000000..d7828fd Binary files /dev/null and b/tabular2.png differ diff --git a/vec1.png b/vec1.png new file mode 100644 index 0000000..9a53cce Binary files /dev/null and b/vec1.png differ diff --git a/vec2.png b/vec2.png new file mode 100644 index 0000000..cb6be07 Binary files /dev/null and b/vec2.png differ