Dino Vitb16 Fare4

preprocessor = transforms.Compose([
                transforms.Resize(256, interpolation=3),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
            ])
model = AutoModel.from_pretrained("ch20/dino-vitb16-fare4")

Citation

If you find this model useful, please consider citing our papers:

@inproceedings{croce2024adversarially,
  title={Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics},
  author={Croce, Francesco and Schlarmann, Christian and Singh, Naman Deep and Hein, Matthias},
  year={2025},
  booktitle={{SaTML}}
}

@inproceedings{schlarmann2024robustclip,
    title={Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language 
    Models}, 
    author={Schlarmann, Christian and Singh, Naman Deep and Croce, Francesco and Hein, Matthias},
    year={2024},
    booktitle={{ICML}}
}

--- license: mit library_name: transformers tags: - dino - vision base_model: - facebook/dino-vitb16 --- [[Paper]](https://openreview.net/forum?id=e3scLKNiNg&noteId=e3scLKNiNg) [[GitHub]](https://github.com/fra31/perceptual-metrics) Robust perceptual metric, based on DINO model `facebook/dino-vitb16`. Adversarially fine-tuned with FARE ([Schlarmann et al. (2024)](https://arxiv.org/abs/2402.12336)) on ImageNet with infinity-norm and radius 4/255. ## Usage ```python preprocessor = transforms.Compose([ transforms.Resize(256, interpolation=3), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ]) model = AutoModel.from_pretrained("ch20/dino-vitb16-fare4") ``` ## Citation If you find this model useful, please consider citing our papers: ```bibtex @inproceedings{croce2024adversarially, title={Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics}, author={Croce, Francesco and Schlarmann, Christian and Singh, Naman Deep and Hein, Matthias}, year={2025}, booktitle={{SaTML}} } ``` ```bibtex @inproceedings{schlarmann2024robustclip, title={Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models}, author={Schlarmann, Christian and Singh, Naman Deep and Croce, Francesco and Hein, Matthias}, year={2024}, booktitle={{ICML}} } ```