# Fine-grained Image Captioning with CLIP Reward

*author: David Wang*


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## Description

This operator generates the caption with [CLIPReward](https://arxiv.org/abs/2205.13115) which describes the content of the given image. CLIPReward uses CLIP as a reward function and a simple finetuning strategy of the CLIP text encoder to impove grammar that does not require extra text annotation, thus towards to more descriptive and distinctive caption generation. This is an adaptation from [j-min/CLIP-Caption-Reward](https://github.com/j-min/CLIP-Caption-Reward).


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## Code Example

Load an image from path './animals.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.clip_caption_reward(model_name='clipRN50_clips_grammar'))
    .output('img', 'text')
)

DataCollection(p('./animals.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

***clip_caption_reward(model_name)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of CLIPReward. Supported model names: 
- clipRN50_clips_grammar     

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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:**

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

​  The image to generate caption.	



**Returns:** *str*

​   The caption generated by model.