---
license: cc-by-nc-sa-4.0
tags:
  - sheet-music
  - optical-music-recognition
  - vit_mae
  - feature-extraction
  - mirror
---

# MuSViT — foundation vision model for sheet music (MAESTRO mirror)

Mirror of PRAIG's MuSViT encoders (ECCV 2026), laid out one subdirectory per
size for MAESTRO's download-on-demand loader:

| subdir | upstream | params | hidden |
| --- | --- | --- | --- |
| `musvit/` | [PRAIG/musvit](https://huggingface.co/PRAIG/musvit) | ~0.1 B | 768 |
| `musvit-light/` | [PRAIG/musvit-light](https://huggingface.co/PRAIG/musvit-light) | small | 384 |

Both are standard `vit_mae` checkpoints (`config.json` + `model.safetensors`),
byte-identical to upstream (sha256-verified at mirror time). Pre-trained with
Masked Autoencoders on 9.7 million sheet-music page images from IMSLP; load
through `transformers.ViTModel` for deterministic page embeddings
(1024×1024 input → CLS token).

## License — CC BY-NC-SA 4.0 (non-commercial)

These weights are released by PRAIG under the
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
license and are redistributed here unchanged under the same terms.
**Non-commercial use only.** Attribution: PRAIG (Pattern Recognition and
Artificial Intelligence Group, University of Alicante).

## Citation

```bibtex
@inproceedings{penarrubia2026musvit,
  title     = {MuSViT: A Foundation Vision Model for Sheet Music Representation},
  author    = {Penarrubia, Carlos and Rios-Vila, Antonio and Fuentes-Martinez, Eliseo
               and Martinez-Sevilla, Juan C. and Castellanos, Francisco J. and
               Alfaro-Contreras, Maria and Calvo-Zaragoza, Jorge},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```

The authors acknowledge Edward Guo / IMSLP (Petrucci Music Library) for the
training data. Mirrored for [MAESTRO](https://github.com/AEmotionStudio) by
AEmotionStudio.