| task_categories: | |
| - image-segmentation | |
| license: mit | |
| tags: | |
| - parameter-efficient-fine-tuning | |
| - peft | |
| - segment-anything | |
| - sam | |
| - medical-image-segmentation | |
| This repository contains the code for SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation. SALT is a method for adapting large-scale foundation models, particularly the Segment Anything Model (SAM), to domain-specific tasks, such as medical image segmentation, with high parameter efficiency. | |
| Paper: [SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation](https://huggingface.co/papers/2503.16055) | |
| Code: https://github.com/YourUsername/SALT.git | |
| The following datasets, used in the experiments, are available on Hugging Face: | |
| - **ROSE:** (https://huggingface.co/datasets/pythn/ROSE) | |
| - **ARCADE:** (https://huggingface.co/datasets/pythn/ARCADE) | |
| - **DRIVE:** (https://huggingface.co/datasets/pythn/drive) | |
| - **DIAS:** (https://huggingface.co/datasets/pythn/DIAS) | |
| - **Xray-Angio:** (https://huggingface.co/datasets/pythn/DB) |