Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- medical-imaging
+- image-segmentation
+- semi-supervised-learning
+- sam3
+- angiography
+- mean-teacher
+- pytorch
+---
+
+# SMART: Semi-supervised Medical Adaptive vessel Representation Toolkit
+
+[![Hugging Face Paper](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper-blue)](https://huggingface.co/papers/2603.00881)
+[![arXiv](https://img.shields.io/badge/arXiv-2603.00881-b31b1b.svg)](https://arxiv.org/abs/2603.00881)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-black)](https://github.com/qimingfan10/SMART)
+
+This repository hosts the official pre-trained and fine-tuned model checkpoints for the paper **SMART** (arXiv:2603.00881). 
+
+SMART is a semi-supervised video vessel segmentation framework built on **SAM3 (Segment Anything Model 3)**. It features a Mean Teacher architecture and text prompt support, specifically designed for efficient coronary angiography vessel segmentation.
+
+## 🗂️ Model Weights Overview
+
+We provide all necessary weights to reproduce our experiments, from the original baselines to our final semi-supervised checkpoints:
+
+| Filename | Size | Description |
+| :--- | :--- | :--- |
+| `sam2.1_hiera_large.pt` | 898 MB | Original SAM 2.1 Hiera Large baseline weights. |
+| `sam3_original.pt` | 3.45 GB | Original SAM 3 baseline weights. |
+| `sam3_1p_finetune_checkpoint_100.pt` | 10.1 GB | Supervised fine-tuning checkpoint trained using only 1% labeled data (100 epochs). |
+| `semi_sam3_5labeled_checkpoint_final.pt` | 10.6 GB | **Final SMART checkpoint** trained via semi-supervised learning (Mean Teacher) with 5% labeled data. |
+| `bpe_simple_vocab_16e6.txt.gz` | 1.36 MB | BPE vocabulary file required for the text prompt tokenizer. |
+
+## 🚀 How to Use
+
+You can easily download these weights using the `huggingface_hub` library and integrate them directly into the SMART training/inference pipeline.
+
+### 1. Install Dependencies
+```bash
+pip install huggingface_hub torch torchvision
+
+```
+
+### 2. Download and Load Checkpoints
+
+Here is an example of how to download the final SMART checkpoint and load it into your PyTorch environment:
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# 1. Download the final semi-supervised checkpoint
+ckpt_path = hf_hub_download(
+    repo_id="ly17/TC-SemiSAM-checkpoints", 
+    filename="semi_sam3_5labeled_checkpoint_final.pt"
+)
+print(f"Weights downloaded to: {ckpt_path}")
+
+# 2. Load the state dict
+# state_dict = torch.load(ckpt_path, map_location="cpu")
+# model.load_state_dict(state_dict)
+
+```
+
+### 3. Text Prompt Setup
+
+When running inference with our SMART model, please ensure you use the following default text prompt as specified in our methodology:
+
+```python
+TEXT_PROMPT = "Please segment the blood vessels"
+
+```
+
+*(Note: The model expects a dataset resolution of 512×512, which is resized to a SAM3 input resolution of 1008×1008 with normalization range [-1, 1].)*
+
+## ⚠️ Clinical Disclaimer
+
+These models are released for **research purposes only**. They are not intended for direct clinical decision-making, patient diagnosis, or treatment planning.
+
+
+```