SegGPT model The SegGPT model was proposed in SegGPT: Segmenting Everything In Context by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. Model description SegGPT employs a decoder only (GPT like) Transformer that can generate a segmentation mask given an input image, a prompt image and its corresponding prompt mask. The model achieves remarkable one shot results with 56.1 mIoU on COCO 20 and 85.6 mIoU on FSS 1000. Intended uses & limitations You can use the raw model for one shot image segmentation. How to use Here's how to use the model for one shot semantic segmentation: BibTeX entry and citation info Acknowledgements This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300). 本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116300）支持。
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