
# Recognize Anything & Tag2Text

Model card for <a href="https://recognize-anything.github.io/">Recognize Anything: A Strong Image Tagging Model </a> and <a href="https://tag2text.github.io/">Tag2Text: Guiding Vision-Language Model via Image Tagging</a>.

**Recognition and localization are two foundation computer vision tasks.**
- **The Segment Anything Model (SAM)** excels in **localization capabilities**, while it falls short when it comes to **recognition tasks**.
- **The Recognize Anything Model (RAM) and Tag2Text** exhibits **exceptional recognition abilities**, in terms of **both accuracy and scope**.
- 
| ![RAM.jpg](https://github.com/xinyu1205/Tag2Text/raw/main/images/localization_and_recognition.jpg) |
|:--:|
| <b> Pull figure from recognize-anything official repo | Image source: https://recognize-anything.github.io/ </b>|

## TL;DR

Authors from the [paper](https://arxiv.org/abs/2306.03514) write in the abstract:

*We present the Recognize Anything Model~(RAM): a strong foundation model for image tagging. RAM makes a substantial step for large models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. By leveraging large-scale image-text pairs for training instead of manual annotations, RAM introduces a new paradigm for image tagging. We evaluate the tagging capability of RAM on numerous benchmarks and observe an impressive zero-shot performance, which significantly outperforms CLIP and BLIP. Remarkably, RAM even surpasses fully supervised models and exhibits a competitive performance compared with the Google tagging API.*


## BibTex and citation info

```
@article{zhang2023recognize,
  title={Recognize Anything: A Strong Image Tagging Model},
  author={Zhang, Youcai and Huang, Xinyu and Ma, Jinyu and Li, Zhaoyang and Luo, Zhaochuan and Xie, Yanchun and Qin, Yuzhuo and Luo, Tong and Li, Yaqian and Liu, Shilong and others},
  journal={arXiv preprint arXiv:2306.03514},
  year={2023}
}

@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}
}
```

{"language": ["en"], "license": "mit", "tags": ["image tagging, image captioning"], "pipeline_tag": "image-to-text"}
image tagging, image captioning image-to-text en arxiv:2306.03514 license:mit region:us
TAGS
#image tagging, image captioning #image-to-text #en #arxiv-2306.03514 #license-mit #region-us 

Recognize Anything & Tag2Text
=============================


Model card for <a href="URL Anything: A Strong Image Tagging Model  and <a href="URL Guiding Vision-Language Model via Image Tagging.


Recognition and localization are two foundation computer vision tasks.


* The Segment Anything Model (SAM) excels in localization capabilities, while it falls short when it comes to recognition tasks.
* The Recognize Anything Model (RAM) and Tag2Text exhibits exceptional recognition abilities, in terms of both accuracy and scope.
* 



TL;DR
-----


Authors from the paper write in the abstract:


*We present the Recognize Anything Model~(RAM): a strong foundation model for image tagging. RAM makes a substantial step for large models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. By leveraging large-scale image-text pairs for training instead of manual annotations, RAM introduces a new paradigm for image tagging. We evaluate the tagging capability of RAM on numerous benchmarks and observe an impressive zero-shot performance, which significantly outperforms CLIP and BLIP. Remarkably, RAM even surpasses fully supervised models and exhibits a competitive performance compared with the Google tagging API.*


BibTex and citation info
------------------------



TAGS
#image tagging, image captioning #image-to-text #en #arxiv-2306.03514 #license-mit #region-us 
