clipcap/README.md

# Image Captioning with ClipCap

*author: David Wang*


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## Description

This operator generates the caption with [ClipCap](https://arxiv.org/abs/2111.09734) which describes the content of the given image. ClipCap uses CLIP encoding as a prefix to the caption, by employing a simple mapping network, and then fine-tunes a language model to generate the image captions. This is an adaptation from [rmokady/CLIP_prefix_caption](https://github.com/rmokady/CLIP_prefix_caption).


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## Code Example

Load an image from path './hulk.jpg' to generate the caption. 

*Write a pipeline with explicit inputs/outputs name specifications:*

```python
from towhee import pipe, ops, DataCollection

p = (
    pipe.input('url')
    .map('url', 'img', ops.image_decode.cv2_rgb())
    .map('img', 'text', ops.image_captioning.clipcap(model_name='clipcap_coco'))
    .output('img', 'text')
)

DataCollection(p('./image.jpg')).show()

```
<img src="./tabular.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***clipcap(model_name)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of ClipCap. Supported model names: 
- clipcap_coco    
- clipcap_conceptual     

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## Interface

An image captioning operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption.


**Parameters:**

​	***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* 

​  The image to generate caption.	

**Returns:** *str*

​   The caption generated by model.