Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+pipeline_tag: zero-shot-image-classification
+tags:
+- image recognition
 ---
+
+# Recognize Anything & Tag2Text
+
+Model card for <a href="https://recognize-anything.github.io/">Recognize Anything Plus Model (RAM++) </a>.
+
+RAM++ is the next generation of RAM, which can recognize any category with high accuracy, including both predefined common categories and diverse open-set categories.
+
+RAM++ outperforms existing SOTA image fundamental reocngition models in terms of common tag categorie, uncommon tag categories, and  human-object interaction phrase.
+
+
+## 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}
+}
+```