File size: 1,844 Bytes
66ab751 0b2e772 66ab751 0b2e772 66ab751 0b2e772 66ab751 30e19e0 66ab751 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ---
license: cc-by-nc-4.0
language:
- en
base_model:
- timm/vit_large_patch14_dinov2.lvd142m
pipeline_tag: image-classification
tags:
- Fluorescein Angiography (FA)
- multiclass classification
---
# DINOv2-Large (HyperF_Type)
Classification of hyperfluorescence type on Fluorescein Angiography (FA) images.
Multiclass problem: 0 (leakage), 1 (staining), 2 (no), 3 (pooling), 4 (window defect)
This model is obtained by finetuning the pretrained architecture of HuggingFace ``vit_large_patch14_dinov2.lvd142m``.
Trained on: [AngioReport](https://pubmed.ncbi.nlm.nih.gov/40610046/)
## Usage
Check instruction at the following [repository](https://gitlab.idiap.ch/medai/software/paper/fm-overspecialization)
## Model output structure
`[batch_size, num_classes]` (`num_classes=5`)
## Related publication(s)
```
@article{xu_angioreport_2025,
title = {{AngioReport}: dataset and baseline methods for fundus angiography report generation},
issn = {0007-1161},
url = {https://bjo.bmj.com/content/early/2025/07/02/bjo-2024-327006},
doi = {10.1136/bjo-2024-327006},
journal = {British Journal of Ophthalmology},
author = {Xu, Pusheng and Chotcomwongse, Peranut and Zhang, Weiyi and Chen, Xiaolan and Wu, Xinyuan and Chung, Florence H T and Zhang, Xueli and He, Mingguang and Shi, Danli and Ruamviboonsuk, Paisan},
year = {2025},
}
@misc{he2021maskedautoencodersscalablevision,
title={Masked Autoencoders Are Scalable Vision Learners},
author={Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Dollár and Ross Girshick},
year={2021},
eprint={2111.06377},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2111.06377},
}
```
## Author(s)
* Roberto Pulvirenti, Idiap Research Institute |