idea-research/ram-grounded-sam

# idea-research/ram-grounded-sam – Replicate Source: [https://replicate.com/idea-research/ram-grounded-sam](https://replicate.com/idea-research/ram-grounded-sam) ## Readme ## [Recognize Anything](https://github.com/xinyu1205/recognize-anything)with[Grounded\-Segment\-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) **Recognize Anything Model \(RAM\)**is an image tagging model, which can recognize any common category with high accuracy\. ## Highlight of RAM RAM is a strong image tagging model, which can recognize any common category with high accuracy\. \-**Strong and general\.**RAM exhibits exceptional image tagging capabilities with powerful zero\-shot generalization; \- RAM showcases impressive zero\-shot performance, significantly outperforming CLIP and BLIP\. \- RAM even surpasses the fully supervised manners \(ML\-Decoder\)\. \- RAM exhibits competitive performance with the Google tagging API\. \-**Reproducible and affordable\.**RAM requires Low reproduction cost with open\-source and annotation\-free dataset; \-**Flexible and versatile\.**RAM offers remarkable flexibility, catering to various application scenarios\.  RAM significantly improves the tagging ability based on the Tag2text framework\. \-**Accuracy\.**RAM utilizes a**data engine**to**generate**additional annotations and**clean**incorrect ones,**higher accuracy**compared to Tag2Text\. \-**Scope\.**RAM upgrades the number of fixed tags from 3,400\+ to**[6,400\+](https://replicate.com/idea-research/data/ram_tag_list.txt)**\(synonymous reduction to 4,500\+ different semantic tags\), covering**more valuable categories**\. ## Citation If you find our work to be useful for your research, please consider citing\. ``` @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{liu2023grounding, title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection}, author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others}, journal={arXiv preprint arXiv:2303.05499}, year={2023} } ``` Model createdover 1 year ago