--- license: apache-2.0 language: - en pipeline_tag: zero-shot-image-classification tags: - image recognition --- # Recognize Anything & Tag2Text Model card for Recognize Anything Plus Model (RAM++) . 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} } ```