| | --- |
| | 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**. |
| | - |
| | |  | |
| | |:--:| |
| | | <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} |
| | } |
| | ``` |
| |
|
