canvit-pytorch 0.1.1

CanViT (Canvas Vision Transformer) -- PyTorch

CanViT (Canvas Vision Transformer) -- PyTorch

Reference PyTorch implementation of CanViT (Canvas Vision Transformer).

This is an early release. For details, a preprint version of our manuscript "CanViT: Toward Active Vision Foundation Models" will be available in the coming weeks.

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CanViT is a scalable recurrent architecture for fine-grained vision, and the first Active Vision Foundation Model (AVFM): a foundation model for active vision that is both task-agnostic and policy-agnostic.

CanViT processes scenes through sequences of localized glimpses, integrating observations over time into a persistent scene-wide latent workspace — the canvas — via Canvas Attention, an efficient asymmetric cross-attention mechanism which is based on Scene-Relative Rotary Position Embeddings and eliminates canvas-side QKVO projections.

CanViT-B is pretrained on 1 billion glimpses taken from 13.5 million ImageNet-21k scenes, via passive-to-active dense distillation from a frozen DINOv3 ViT-B teacher, without human annotations.

Quickstart

uv run demos/basic.py

Pretrained checkpoints

We release checkpoints on HuggingFace under the canvit namespace.

The following checkpoints are currently available:

• canvit/canvitb16-add-vpe-pretrain-g128px-s512px-in21k-dv3b16-2026-02-02

Citation

If you use this work, please cite this repository. An updated citation will be available upon preprint release.

@misc{berreby2026canvit,
  title={CanViT: Toward Active Vision Foundation Models},
  author={Berreby, Yoha{\"i}-Eliel and Du, Sabrina and Durand, Audrey and Krishna, Suresh},
  year={2026},
  howpublished={\url{https://github.com/m2b3/CanViT-PyTorch}}
}

License

MIT. See LICENSE.md for details.