README.md

---
license: cc-by-nc-sa-4.0
pipeline_tag: image-segmentation
---

# SAM-MT: Real-Time Interactive Multi-Target Video Segmentation

This repository contains the official checkpoint for **SAM-MT**, presented in the paper [SAM-MT: Real-Time Interactive Multi-Target Video Segmentation](https://huggingface.co/papers/2607.08688).

* **Project Page:** [henghuiding.com/SAM-MT](https://henghuiding.com/SAM-MT/)
* **Repository:** [GitHub - FudanCVL/SAM-MT](https://github.com/FudanCVL/SAM-MT)

**SAM-MT** is an efficient interactive multi-target video segmentation framework that maintains near-single-object efficiency (FPS and VRAM) as target count increases, while maintaining robust video segmentation performance.

<p align="center">
  <img src="https://raw.githubusercontent.com/FudanCVL/SAM-MT/main/assets/teaser.png" width="100%">
</p>

## Highlights
* **Real-time speed**: 36+ FPS with 10 targets on a single NVIDIA RTX A6000 GPU.
* **Individual-global representation**: Models individual targets and global scene within a unified framework.
* **Interactive multi-target video segmentation**: Simple clicks for target specification.

## Quick Start

### Installation
```bash
# clone the repo and enter directory
git clone https://github.com/FudanCVL/SAM-MT.git
cd SAM-MT

# create and activate conda environment
conda create -n sammt python=3.10 -y
conda activate sammt

# install required packages
pip install -r requirements.txt
```

### Inference
By default, place the downloaded checkpoint under the `checkpoints/` directory.

```bash
# Basic inference (coordinates required)
python inference.py

# Interactive Gradio demo
python inference_gradio.py
```

## Citation

If you find SAM-MT useful in your research, please consider citing:

```bibtex
@inproceedings{SAM-MT,
  title={{SAM-MT}: Real-Time Interactive Multi-Target Video Segmentation},
  author={Shen, Ruiqi and Liu, Chang and Ding, Henghui},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}
```