Create README.md

README.md

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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**.
+
+
+| ![RAM.jpg](https://github.com/xinyu1205/recognize-anything/blob/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}
+}
+```
+
+
+
+
+