CanViT pretraining checkpoint (JAX / Flax NNX)

canvit/canvitb16-add-vpe-pretrain-g128px-s512px-in1k-dv3b16-2026-06-22 in the checkpoint format of canvit-nnx, for JAX / Flax NNX.

CanViT, the Canvas Vision Transformer, is an active-vision foundation model: it sees a scene through a sequence of glimpses and remembers it on a scene-wide canvas.

Paper (NeurIPS 2026) · Code · Project page · All checkpoints

Install

uv add canvit-nnx

Usage

import jax.numpy as jnp
from PIL import Image

from canvit_nnx import CanViTForPretraining, Viewpoint, sample_at_viewpoint
from canvit_nnx.preprocess import preprocess

scene = jnp.asarray(preprocess(512)(Image.open("scene.jpg").convert("RGB")))[None]
model = CanViTForPretraining.from_pretrained("canvit/canvitb16-add-vpe-pretrain-g128px-s512px-in1k-dv3b16-2026-06-22-nnx")
model.eval()
state = model.init_state(batch_size=1, canvas_grid_size=32)
viewpoint = Viewpoint.full_scene(batch_size=1)
glimpse = sample_at_viewpoint(spatial=scene, viewpoint=viewpoint, glimpse_size_px=128)
output = model(glimpse=glimpse, state=state, viewpoint=viewpoint)
state = output.state

Details

License

MIT. CanViT is pretrained by distillation from DINOv3, which is released under the DINOv3 License.

Citation

@inproceedings{berreby2026canvit,
  title={CanViT: Toward Active-Vision Foundation Models},
  author={Berreby, Yoha{\"i}-Eliel and Du, Sabrina and Durand, Audrey and Krishna, B. Suresh},
  booktitle={Advances in Neural Information Processing Systems},
  year={2026},
  eprint={2603.22570},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2603.22570}
}