---
title: sam3
canonical_url: "https://www.modelscope.cn/models/1038lab/sam3"
md_url: "https://www.modelscope.cn/models/1038lab/sam3.md"
repository: 1038lab/sam3
last_updated: 2026-08-26
license: other
parameters: 1.7B
tensor_type:
  - F32
  - F16
  - C64
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 190
stars: 0
tags:
  - sam3
---

# sam3

> sam3 - 1038lab 在 ModelScope 开源的模型。This repository mirrors the official Segment Anything Model 3 (SAM 3) weights released by Meta Superintelligence Labs. SAM 3 is a unified foundation model for prompt-driven segmentation in images and videos. It supports…

1038lab/sam3 是 ModelScope 魔搭社区上的 1.7B 参数机器学习模型，采用 other 许可。

- **Repository**: 1038lab/sam3
- **License**: other
- **Parameters**: 1.7B
- **Tags**: sam3
- **Downloads**: 190
- **Stars**: 0
- **Last updated**: 2026-08-26

Source: https://www.modelscope.cn/models/1038lab/sam3

---

# SAM 3

This repository mirrors the official **Segment Anything Model 3 (SAM 3)** weights released by Meta Superintelligence Labs. SAM 3 is a unified foundation model for prompt-driven segmentation in images and videos. It supports open-vocabulary text prompts and visual prompts (points/boxes/masks). Compared to SAM 2, SAM 3 exhaustively segments each instance of a requested concept and reaches ~75–80% of human-level performance on the SA-CO benchmark (270K unique concepts).

## Highlights
- **Presence token** improves discrimination between closely related prompts.
- **Decoupled detector + tracker** scales better for long video sequences.
- **4M+ automatically annotated concepts** ensure broad coverage of open-world categories.

> Original paper: *SAM 3: Segment Anything with Concepts* (Meta AI, 2024).  
> Resources: [Project Page](https://ai.meta.com/sam3) · [Demo](https://segment-anything.com/)

## Files Included
- `sam3.safetensors` — detector and tracker weights for image + video segmentation.
- Tokenizer/config assets should be copied from the official `facebookresearch/sam3` repository; this mirror only repackages the safetensors weights for self-hosting.

## Quickstart
```bash
pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
pip install git+https://github.com/facebookresearch/sam3.git

python - <<'PY'
from sam3 import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor
model = build_sam3_image_model(
    bpe_path="sam3/assets/bpe_simple_vocab_16e6.txt.gz",
    device="cuda",
    eval_mode=True,
    checkpoint_path="sam3.safetensors",
    load_from_HF=False,
)
processor = Sam3Processor(model, device="cuda")
state = processor.set_image("your_image.jpg")
state = processor.set_text_prompt("white bicycle", state)
print(state["masks"].shape)
PY
```

## Integration Notes
These mirrored weights are used in the **AILab SAM3 ComfyUI node** (RMBG edition) to enable promptable segmentation workflows directly inside ComfyUI. The node loads `sam3.safetensors`, tokenizer assets, and the SAM3 processors locally, so the entire pipeline stays compatible even when offline.

## License & Usage
- This mirror preserves Meta's original weights and is subject to the license on [facebook/sam3](https://huggingface.co/facebook/sam3). You must accept Meta's terms before downloading the official release.
- When hosting this file in your own Hugging Face repository, keep this notice and credit the original authors.
- Cite the SAM 3 paper for any research or product that builds upon these weights.