voxide/voxide-models

Voxide segmentation models (ONNX)

ONNX exports of SAM 2.1 and SAM-Med3D (turbo) used by 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, 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.

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), 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:

@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}
}