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@@ -11,4 +11,9 @@ We offer SAT-Pro, SAT-Nano (both trained on 72 datasets) and another 5 different
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Check our [paper](https://github.com/zhaoziheng/SAT/tree/main) for more details, and [github repo](https://github.com/zhaoziheng/SAT/tree/main?tab=readme-ov-file) for usage instruction.
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⚠️ Each model should be used with paired checkpoint and text encoder checkpoint.
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Check our [paper](https://github.com/zhaoziheng/SAT/tree/main) for more details, and [github repo](https://github.com/zhaoziheng/SAT/tree/main?tab=readme-ov-file) for usage instruction.
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⚠️ Each model should be used with paired checkpoint and text encoder checkpoint.
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In addition, we provide multiple pretrained encoders at ./Pretrain. Enhanced with multi-modal human anatomy knowledge, they significantly boost the segmentation performance and are potentially beneficial for other tasks:
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- A version pretrained only with the textual knowledge (`textual_only.pth`).
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- A version further pretrained with [SAT-DS](https://github.com/zhaoziheng/SAT-DS/tree/main) (`multimodal_sat_ds.pth`). It can be used to reproduce results in our [paper](https://arxiv.org/abs/2312.17183).
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- A version further pretrained with 10% training data from [CVPR 2025: FOUNDATION MODELS FOR TEXT-GUIDED 3D BIOMEDICAL IMAGE SEGMENTATION](https://www.codabench.org/competitions/5651/) (`multimodal_cvpr25.pth`). It's explicitly optimized for the challenge.
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