MIRAGE Base

--- license: cc-by-nc-nd-4.0 library_name: pytorch pipeline_tag: image-feature-extraction tags: - OCT - SLO - retinal-imaging - classification - segmentation - pytorch_model_hub_mixin - model_hub_mixin --- # MIRAGE-Base This repo contains the the official weights of the MIRAGE-Base model (based on ViT-Base), from the paper ["MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis"](http://www.arxiv.org/abs/2506.08900), by José Morano et al. (2025). Project page: [MIRAGE](https://github.com/j-morano/MIRAGE).  ## MIRAGE models Model | Resolution | Weights --- | --- | --- MIRAGE-Base | 512x512 | [Download](https://huggingface.co/j-morano/MIRAGE-Base) MIRAGE-Large | 512x512 | [Download](https://huggingface.co/j-morano/MIRAGE-Large) ## Usage 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](https://raw.githubusercontent.com/j-morano/MIRAGE/refs/heads/main/hf/mirage_hf.py). ```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](https://github.com/j-morano/MIRAGE) and cite our [work](http://www.arxiv.org/abs/2506.08900): ``` @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}, } ```

Source context: 284 downloads · 1 likes · Pipeline image-feature-extraction · Library pytorch · Repo j-morano/MIRAGE-Base