File size: 3,236 Bytes
59b8193 c8843a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
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
[](https://huggingface.co/papers/2603.00881)
[](https://arxiv.org/abs/2603.00881)
[](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.
While we would love to host the XCA angiography sequences on HF Datasets, they contain sensitive medical imaging data. |