This is the PVeRA adapter trained on the Caltech101 dataset from the VTAB-1k benchmark. It is based on the ViT-B variant of DINOv2.
If you use this adapter, please cite.
@InProceedings{fillioux2025pvera,
title={{PVeRA}: Probabilistic Vector-Based Random Matrix Adaptation},
author={Fillioux, Leo and Ferrante, Enzo and Cournède, Paul-Henry and Vakalopoulou, Maria and Christodoulidis, Stergios},
booktitle={Proceedings of the Winter Conference on Applications of Computer Vision (WACV)},
year={2026}
}
Model Details
Model Description
This holds the weights for the PVeRA adapter, as well as the linear classification head, using the VTAB-1k (few-shot) version of the Caltech101 dataset. The adapters were introduced in a frozen DINOv2 (ViT-B) model. More information about the training procedure below. The model scored an accuracy of 0.8863 on the predefined test set. Please see the original GitHub repository for instructions on how to download the dataset (here), and for the dataset class implementation (here).
Important note: this does not exactly reproduce the results from the original PVeRA paper (different implementation, average across multiple seed, ...).
The dataset used is the VTAB-1k (few-shot) variant of the Caltech101 dataset.
Training Procedure
Similarly to the original paper, a grid search was performed over 3 adapter learning rates (1e-3, 3e-3, 1e-2), with the final version corresponding to the model with the best validation accuracy. A fixed learning rate is used for the classifier head.
