# :label: Tag2Text: Guiding Vision-Language Model via Image Tagging

Official PyTorch Implementation of the <a href="https://arxiv.org/abs/2303.05657">Tag2Text</a>, an efficient and controllable vision-language model with tagging guidance. Code is available now!

Welcome to try out [Tag2Text Web demo🤗](https://huggingface.co/spaces/xinyu1205/Tag2Text)! Both Tagging and Captioning are included.

Tag2Text now is combine with [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything), which can automatically recognize, detect, and segment for an image! Tag2Text showcases powerful image recognition capabilities:
![](./images/tag2text_grounded_sam.jpg)

## :fire: News

- **`2023/05/20`**: Tag2Text is combined with [VideoChat](https://github.com/OpenGVLab/Ask-Anything), Tag2Text provides powerful tagging and captioning capabilities as a fundamental component!
- **`2023/04/20`**: We marry [Tag2Text with with Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) to provide powerful image recognition capabilities!
- **`2023/04/10`**: Code and checkpoint is available Now!
- **`2023/03/14`**: [Tag2Text web demo 🤗](https://huggingface.co/spaces/xinyu1205/Tag2Text) is available on Hugging Face Space!

## :bulb: Highlight

- **Tagging.** Without manual annotations, Tag2Text achieves **superior** image tag recognition ability of [**3,429**](./data/tag_list.txt) commonly human-used categories.
- **Efficient.** Tagging guidance effectively enhances the performance of vision-language models on both **generation-based** and **alignment-based** tasks.
- **Controllable.** Tag2Text permits users to input **desired tags**, providing the flexibility in composing corresponding texts based on the input tags.

<p align="center">
 <table class="tg">
  <tr>
    <td class="tg-c3ow"><img src="images/tag2text_framework.png" align="center" width="800" ></td>
  </tr>
</table>
</p>

## :writing_hand: TODO

- [x] Release demo.
- [x] Release checkpoints.
- [x] Release inference code.
- [ ] Release training codes.
- [ ] Release training datasets.

## :toolbox: Checkpoints

<!-- insert a table -->

<table>
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>backbone</th>
      <th>Data</th>
      <th>Illustration</th>
      <th>Checkpoint</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>Tag2Text-Swin</td>
      <td>Swin-Base</td>
      <td>COCO, VG, SBU, CC-3M, CC-12M</td>
      <td>Demo version with comprehensive captions.</td>
      <td><a href="https://huggingface.co/spaces/xinyu1205/Tag2Text/blob/main/tag2text_swin_14m.pth">Download  link</a></td>
    </tr>
  </tbody>
</table>

## :running: Model Inference

1. Install the dependencies, run:

<pre/>pip install -r requirements.txt</pre>

2. Download Tag2Text pretrained checkpoints.

1. Get the tagging and captioning results:
   <pre/>
   python inference.py  --image images/1641173_2291260800.jpg \
   --pretrained pretrained/tag2text_swin_14m.pth
   </pre>
   Or get the tagging and sepcifed captioning results (optional):
   <pre/>python inference.py  --image images/1641173_2291260800.jpg \
   --pretrained pretrained/tag2text_swin_14m.pth \
   --specified-tags "cloud,sky"</pre>

## :black_nib: Citation

If you find our work to be useful for your research, please consider citing.

```
@article{huang2023tag2text,
  title={Tag2Text: Guiding Vision-Language Model via Image Tagging},
  author={Huang, Xinyu and Zhang, Youcai and Ma, Jinyu and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Li, Yaqian and Guo, Yandong and Zhang, Lei},
  journal={arXiv preprint arXiv:2303.05657},
  year={2023}
}
```

## :hearts: Acknowledgements

This work is done with the help of the amazing code base of [BLIP](https://github.com/salesforce/BLIP), thanks very much!

We also want to thank @Cheng Rui @Shilong Liu @Ren Tianhe for their help in [marrying Tag2Text with Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything).