Instructions to use BAAI/SegVol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/SegVol with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/SegVol", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BAAI/SegVol", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add model card metadata
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by nielsr HF Staff - opened
README.md
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Language: [EN / ZH]
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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.
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SegVol是用于体积医学图像分割的通用交互式模型,可以使用点,框和文本作为prompt驱动模型,输出分割结果。
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license: apache-2.0
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library_name: transformers
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pipeline_tag: image-segmentation
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Language: [EN / ZH]
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This repository contains the model of the paper [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://huggingface.co/papers/2311.13385).
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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.
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SegVol是用于体积医学图像分割的通用交互式模型,可以使用点,框和文本作为prompt驱动模型,输出分割结果。
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