Add metadata and links to paper and code

README.md

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+---
+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)