SPoT: Subpixel Placement of Tokens in Vision Transformers

Martine Hjelkrem-Tan, Marius Aasan, Gabriel Y. Arteaga, Adín Ramírez Rivera

DSB @ IFI @ UiO

SPoT: Subpixel Placement of Tokens

This repo contains code and weights for SPoT: Subpixel Placement of Tokens, accepted for ECLR, ICCVW 2025.

For an introduction to our work, visit the project webpage.

Installation

The package can currently be installed via:

# HTTPS
pip install git+https://github.com/dsb-ifi/SPoT.git

# SSH
pip install git+ssh://git@github.com/dsb-ifi/SPoT.git

Loading models

You can load the Superpixel Transformer model easily via torch.hub:

model = torch.hub.load(
    'dsb-ifi/spot', 
    'spot_vit_base_16_in21k',
    pretrained=True,
    source='github',
)

This will load the model and downloaded the pretrained weights, stored in your local torch.hub directory.

More Examples

We provide a Jupyter notebook as a sandbox for loading, evaluating, and extracting token placements for the models.

Citation

If you find our work useful, please consider citing our paper.

@inproceedings{hjelkremtan2025spot,
  title={{SPoT}: Subpixel Placement of Tokens in Vision Transformers},
  author={Hjelkrem-Tan, Martine and Aasan, Marius and Arteaga, Gabriel Y. and Ram\'irez Rivera, Ad\'in},
  journal={{CVF/ICCV} Efficient Computing under Limited Resources: Visual Computing ({ECLR} {ICCVW})},
  year={2025}
}