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---
license: mit
language:
- en
pipeline_tag: image-to-text
tags:
- image tagging, image captioning
---
# 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**.
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| <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}
}
```
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