From a3591c62fb6a1420ce588bfbfa35b1e527528a73 Mon Sep 17 00:00:00 2001 From: wxywb Date: Fri, 23 Sep 2022 15:40:57 +0800 Subject: [PATCH] update the doc. Signed-off-by: wxywb --- README.md | 107 ++++++++++++++++++++++++++++++++++++++++++++++++++- tabular1.png | Bin 0 -> 18584 bytes tabular2.png | Bin 0 -> 114151 bytes vec1.png | Bin 0 -> 13394 bytes vec2.png | Bin 0 -> 13255 bytes 5 files changed, 106 insertions(+), 1 deletion(-) create mode 100644 tabular1.png create mode 100644 tabular2.png create mode 100644 vec1.png create mode 100644 vec2.png 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 0000000000000000000000000000000000000000..30f6db6573450f921ee3cbfb812c377f18351ecd GIT binary patch literal 18584 zcmeIY1zS~H7br|gcXvukcXta2g3>Kr(p}OG(%m54-Cfe%Eg;`wauNuzxUe7~AP7>DqVGXKz;S`+uh5Xdcjwz?7Z4D5Qxg#pc_|SQ zVtE@YLlbiY5D>|r*mx+FXel(W>o-!8lEN^eg3`OP$a`Ya&~#tClsd#E!C=UHeKYhV zkT7_g0`k5f+A;AS_omoi=M%)jbkDedYSs5A`8B*TIz7i}- zri}fOu>i6qefG(Ey`#8-x`1|q#R~*p-U}(?{Kkfwhn*c(aPRn;p2$+zjA}L|ruB*I zNn1>rIOZHwuvbJ5tN*qk5)ZVU<0Q9|7bH-+WqXRZ;Oma?OmqhXN!dr6ykJLaF3lbU 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