---
license: apache-2.0
library_name: onnx
pipeline_tag: mask-generation
tags:
  - onnx
  - segment-anything
  - sam2
  - sam-med3d
  - microscopy
  - medical-imaging
  - interactive-segmentation
base_model:
  - facebook/sam2.1-hiera-tiny
  - facebook/sam2.1-hiera-small
  - facebook/sam2.1-hiera-base-plus
  - facebook/sam2.1-hiera-large
---

# Voxide segmentation models (ONNX)

ONNX exports of **SAM 2.1** and **SAM-Med3D (turbo)** used by
[Voxide](https://huggingface.co/voxide), a GPU volume viewer for microscopy, for interactive
segmentation. Each model is an encoder/decoder pair run on the CPU with ONNX Runtime.

Voxide ships the SAM 2.1 Tiny pair. It downloads the others on request from this repository at a
pinned revision and checks every file against the SHA-256 in [`SHA256SUMS`](SHA256SUMS), so a file
that does not match is never loaded.

## Files

| Model | Encoder | Decoder | Use in Voxide |
|---|---|---|---|
| SAM 2.1 Hiera Tiny | `sam2.1_hiera_tiny_encoder.onnx` (110 MB) | `sam2.1_hiera_tiny_decoder.onnx` (17 MB) | 2D slices (default, fastest) |
| SAM 2.1 Hiera Small | `sam2.1_hiera_small_encoder.onnx` (139 MB) | `sam2.1_hiera_small_decoder.onnx` (17 MB) | 2D slices |
| SAM 2.1 Hiera Base+ | `sam2.1_hiera_base_plus_encoder.onnx` (278 MB) | `sam2.1_hiera_base_plus_decoder.onnx` (17 MB) | 2D slices |
| SAM 2.1 Hiera Large | `sam2.1_hiera_large_encoder.onnx` (853 MB) | `sam2.1_hiera_large_decoder.onnx` (17 MB) | 2D slices (best masks, slowest) |
| SAM-Med3D turbo | `sammed3d_turbo_encoder.onnx` (373 MB) | `sammed3d_turbo_decoder.onnx` (31 MB) | 3D volumes |

## Using them without the in-app download

Download the files you need and either use **Models → Import model…** in Voxide or copy them
into Voxide's model folder. The file names must stay as they are: Voxide finds each model by its
`<name>_encoder.onnx` / `<name>_decoder.onnx` pair.

```bash
pip install -U huggingface_hub
hf download voxide/voxide-models --include "sam2.1_hiera_small_*" --local-dir voxide-models
```

## How they were made

Unmodified inference exports of the upstream checkpoints, produced with PyTorch 2.9.0 (ONNX opset
18, IR version 8). No fine-tuning, quantization or other change to the weights.

## License and attribution

Both upstream model families are released under the **Apache License 2.0** (see [`LICENSE`](LICENSE)),
and so are these exports.

- **SAM 2.1** © Meta Platforms, Inc. — <https://github.com/facebookresearch/sam2>
- **SAM-Med3D** © the SAM-Med3D authors — <https://github.com/uni-medical/SAM-Med3D>

If you use these models in published work, please cite the original papers:

```bibtex
@article{ravi2024sam2,
  title   = {SAM 2: Segment Anything in Images and Videos},
  author  = {Ravi, Nikhila and others},
  journal = {arXiv preprint arXiv:2408.00714},
  year    = {2024}
}

@article{wang2023sammed3d,
  title   = {SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images},
  author  = {Wang, Haoyu and others},
  journal = {arXiv preprint arXiv:2310.15161},
  year    = {2023}
}
```