From aebd7c020745dad18c4b0b60cb807a5c733c721c Mon Sep 17 00:00:00 2001
From: wxywb <xy.wang@zilliz.com>
Date: Thu, 3 Nov 2022 14:42:11 +0800
Subject: [PATCH] add the doc

Signed-off-by: wxywb <xy.wang@zilliz.com>
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-# albef
+# Image-Text Retrieval Embdding with ALBEF
+
+*author: David Wang*
+
+
+<br />
+
+
+
+## Description
+
+This operator extracts features for image or text with [ALBEF](https://arxiv.org/abs/2103.00020) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This research introduced a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. This repo is an adaptation from [salesforce / ALBEF](https://github.com/salesforce/ALBEF)
+
+
+<br />
+
+
+## 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('./teddy.jpg') \
+      .image_decode() \
+      .image_text_embedding.albef(model_name='albef_4m', modality='image') \
+      .show()
+
+towhee.dc(["A teddybear on a skateboard in Times Square."]) \
+      .image_text_embedding.albef(model_name='albef_4m', modality='text') \
+      .show()
+```
+<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
+<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/vec2.png" alt="result2" style="height:20px;"/>
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+import towhee
+
+towhee.glob['path']('./teddy.jpg') \
+      .image_decode['path', 'img']() \
+      .image_text_embedding.albef['img', 'vec'](model_name='albef_4m', modality='image') \
+      .select['img', 'vec']() \
+      .show()
+
+towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
+      .image_text_embedding.albef['text','vec'](model_name='albef_4m', modality='text') \
+      .select['text', 'vec']() \
+      .show()
+```
+<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
+<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>
+
+
+<br />
+
+
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***albef(model_name, modality)***
+
+**Parameters:**
+
+​   ***model_name:*** *str*
+
+​   The model name of ALBEF. Supported model names: 
+- albef_4m      
+- albef_14m
+
+
+​   ***modality:*** *str*
+
+​   Which modality(*image* or *text*) is used to generate the embedding. 
+
+<br />
+
+
+
+## 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.
+
+
+
+