---
title: SAM3
canonical_url: "https://www.modelscope.cn/studios/Warma10032/SAM3"
md_url: "https://www.modelscope.cn/studios/Warma10032/SAM3.md"
repository: Warma10032/SAM3
last_updated: 2026-09-22
sdk_type: gradio
sdk_version: 6.8.0
downloads: 0
stars: 0
---
# SAM3
> SAM3 - Warma10032 在 ModelScope 创建的在线 Demo。图片分割、视频分割。
Warma10032/SAM3 是 ModelScope 魔搭社区上的在线可交互 Demo（创空间），基于 gradio 6.8.0 构建。
- **Repository**: Warma10032/SAM3
- **SDK**: gradio
- **SDK version**: 6.8.0
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-09-22
Source: https://www.modelscope.cn/studios/Warma10032/SAM3
---
# **[SAM3: Segment Anything Model 3](https://huggingface.co/spaces/prithivMLmods/SAM3-Demo)**
SAM3: Segment Anything Model 3 is an experimental, multi-functional computer vision application that provides a unified interface for text-conditioned and click-interactive segmentation. Built around Meta's advanced `facebook/sam3` multi-model architecture, this application seamlessly features separate pipelines optimized for text-to-image segmentation, multi-frame video propagation, and high-speed, point-cumulative image tracking. The suite leverages specialized model variants (`Sam3Model`, `Sam3VideoModel`, and `Sam3TrackerModel`) to support highly precise masks over custom objects, bounding boxes, or temporal frame shifts. Fully GPU-accelerated and wrapped in a web workspace with a stylized Citrus theme, SAM3 serves as an advanced sandbox for researchers and developers deploying production-grade, state-of-the-art pixel intelligence workflows.
| Demo 1 | Demo 2 | Demo 3 |
|---|---|---|
| 
| 
| 
|
https://github.com/user-attachments/assets/76b84265-97a3-4be3-b7db-4b2f104f8b8a
### **Key Features**
* **Text-Conditional Image Segmentation:** Instantly isolates target objects inside static images by matching custom queries (e.g., *"cat"*, *"face"*, *"car wheel"*) against SAM3 instance maps, complete with custom threshold masking.
* **Temporal Video Object Propagation:** Tracks and cuts targeted entities dynamically across successive video frames. By incorporating bfloat16 numerical computation, the video pipeline scales smoothly inside active VRAM constraints.
* **Interactive Point Tracking:** Allows users to interactively click directly on a live input canvas. The model processes each cursor selection as a positive foreground anchor and updates the colored mask overlays instantly.
* **Bespolke Gradio Workspace:** Features an elegant, three-tab layout powered by the Gradio theme engine, integrating clean file-drop inputs, real-time tracking logs, and automated example sets.
* **Supervision and Matplotlib Styling:** Implements structural contour draws and randomized colormap overlays over generated masks to ensure clear human validation on complex textures.
### **Repository Structure**
```text
├── examples/
│ ├── goldencat.webp
│ ├── player.jpg
│ ├── sample_video.mp4
│ ├── sample_video2.mp4
│ └── taxi.jpg
├── app.py
├── LICENSE
├── pre-requirements.txt
├── pyproject.toml
├── README.md
└── requirements.txt
└── uv.lock
```
---
### **Installation and Requirements**
To run SAM3 locally, configure a Python environment with the following dependencies. A compatible CUDA-enabled GPU is strongly recommended to handle real-time segmentation and video processing loops.
**Standard PIP Installation**
1. Update pip:
```bash
pip install pip>=26.1.1
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
#### **Running with `uv` (Recommended)**
`uv` is an extremely fast Python package and project manager written in Rust, ensuring rapid environment setup and complete reproducibility.
**Step 1 — Install `uv`**
* **macOS / Linux:** `curl -LsSf [https://astral.sh/uv/install.sh](https://astral.sh/uv/install.sh) | sh`
* **Windows:** `powershell -c "irm [https://astral.sh/uv/install.ps1](https://astral.sh/uv/install.ps1) | iex"`
**[or]**
* `pip install uv`
**Step 2 — Clone the repository**
```bash
git clone https://github.com/PRITHIVSAKTHIUR/SAM3-Demo.git
cd SAM3-Demo
```
**Step 3 — Initialize the project and install dependencies**
```bash
uv sync
```
**Step 4 — Run the script**
```bash
uv run app.py
```
---
### **Core Requirements List**
The application depends on the following primary packages (defined in `requirements.txt`):
```text
transformers==5.9.0
sentencepiece
opencv-python
imageio[pyav]
torchvision
matplotlib
accelerate
kernels
pillow
gradio==6.6.0
spaces
numpy
torch==2.11.0
peft
```
### **Usage**
Once the application is running, open your browser to the local address provided in your terminal (typically `[http://127.0.0.1:7860/](http://127.0.0.1:7860/)`).
1. **Image Segmentation Tab:** Drop an image, enter a target prompt (e.g., *"player in white"*), and click **Segment Image** to see an annotated map.
2. **Video Segmentation Tab:** Drop an MP4 clip, configure maximum frames/timeout conditions, write a tracking prompt, and click **Segment Video** to run mask propagation.
3. **Image Click Segmentation Tab:** Upload your image, and click any element on the preview canvas to see positive foreground anchors auto-generate segmentation layers instantly.
### **License and Source**
* **License:** [SAM License](https://github.com/ZeroTang/SAM3-Demo/blob/main/LICENSE)
* **GitHub Repository:** [https://github.com/ZeroTang/SAM3-Demo.git](https://github.com/ZeroTang/SAM3-Demo.git)