The SegGPT model was proposed in SegGPT: Segmenting Everything In Context by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. 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. 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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SegVol

BAAI

The SegVol is a universal and interactive model for volumetric medical image segmentation. SegVol accepts point, box, and text prompts while output volumetric segmentation. By training on 90k unlabeled Computed Tomography (CT) volumes and 6k labeled CTs, this foundation model supports the segmentation of over 200 anatomical categories. We have released 25 open source datasets(M3D-Seg) for training SegVol, and these preprocessed data have been uploaded to ModelScope and HuggingFace. You can use the following script to easily load cases and insert them into Test script and Training script. 我们已经发布了用于训练SegVol的25个开源数据集(M3D-Seg),并将预处理后的数据上传到了ModelScope和HuggingFace。您可以使用下面的script方便地载入，并插入到Test script和Training script中。

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ShareRobot-Bench

BAAI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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