The model can be loaded using the PyTorchModelHubMixin from the huggingface_hub package and the code from the mirage_hf.py script that can be downloaded from here.

python

from huggingface_hub import PyTorchModelHubMixin
from mirage_hf import MIRAGEWrapper


class MIRAGEhf(MIRAGEWrapper, PyTorchModelHubMixin):
    def __init__(
        self,
        input_size=512,
        patch_size=32,
        modalities='bscan-slo',
        size='base',
    ):
        super().__init__(
            input_size=input_size,
            patch_size=patch_size,
            modalities=modalities,
            size=size,
        )

# For the MIRAGE model based on ViT-Base
model = MIRAGEhf.from_pretrained("j-morano/MIRAGE-Base")
# For the MIRAGE model based on ViT-Large
model = MIRAGEhf.from_pretrained("j-morano/MIRAGE-Large")

Citation

If you use our code or our model in your research, we would greatly appreciate it if you give a star to the repo and cite our work:

@misc{morano2025mirage,
    title={{MIRAGE}: Multimodal foundation model and benchmark for comprehensive retinal {OCT} image analysis},
    author={José Morano and Botond Fazekas and Emese Sükei and Ronald Fecso and Taha Emre and Markus Gumpinger and Georg Faustmann and Marzieh Oghbaie and Ursula Schmidt-Erfurth and Hrvoje Bogunović},
    year={2025},
    eprint={2506.08900},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2506.08900},
}