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license: mit |
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<div align="center"> |
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<h1>SegGPT: Segmenting Everything In Context </h1> |
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[Xinlong Wang](https://www.xloong.wang/)<sup>1*</sup>, [Xiaosong Zhang](https://scholar.google.com/citations?user=98exn6wAAAAJ&hl=en)<sup>1*</sup>, [Yue Cao](http://yue-cao.me/)<sup>1*</sup>, [Wen Wang](https://scholar.google.com/citations?user=1ks0R04AAAAJ&hl)<sup>2</sup>, [Chunhua Shen](https://cshen.github.io/)<sup>2</sup>, [Tiejun Huang](https://scholar.google.com/citations?user=knvEK4AAAAAJ&hl=en)<sup>1,3</sup> |
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<sup>1</sup>[BAAI](https://www.baai.ac.cn/english.html), <sup>2</sup>[ZJU](https://www.zju.edu.cn/english/), <sup>3</sup>[PKU](https://english.pku.edu.cn/) |
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Enjoy the [Demo](https://huggingface.co/spaces/BAAI/SegGPT) and [Code](https://github.com/baaivision/Painter/edit/main/SegGPT) |
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</div> |
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We present SegGPT, a generalist model for segmenting everything in context. With only one single model, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text. |
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SegGPT is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. |
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Our results show strong capabilities in segmenting in-domain and out-of-domain targets, either qualitatively or quantitatively. |
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[[Paper]](https://arxiv.org/abs/2304.03284) |
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[[Code]](https://github.com/baaivision/Painter/edit/main/SegGPT) |
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[[Demo]](https://huggingface.co/spaces/BAAI/SegGPT) |
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## **Model** |
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A pre-trained SegGPT model is available at [🤗 HF link](https://huggingface.co/BAAI/SegGPT/blob/main/seggpt_vit_large.pth). |
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## Citation |
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``` |
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@article{SegGPT, |
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title={SegGPT: Segmenting Everything In Context}, |
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author={Wang, Xinlong and Zhang, Xiaosong and Cao, Yue and Wang, Wen and Shen, Chunhua and Huang, Tiejun}, |
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journal={arXiv preprint arXiv:2304.03284}, |
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year={2023} |
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} |
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``` |
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## Contact |
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**We are hiring** at all levels at BAAI Vision Team, including full-time researchers, engineers and interns. |
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If you are interested in working with us on **foundation model, visual perception and multimodal learning**, please contact [Xinlong Wang](https://www.xloong.wang/) (`wangxinlong@baai.ac.cn`) and [Yue Cao](http://yue-cao.me/) (`caoyue@baai.ac.cn`). |
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