scrollprize/surface_recto_3dunet

Recto-surface 3D segmentation U-Net (ps256)

Volumetric 3D U-Net that segments the recto writing surface of carbonized Herculaneum papyri directly in micro-CT volumes. It is the recto-surface predictor used in the pipeline behind "Complete virtual unwrapping and reading of a rolled Herculaneum papyrus" (Angelotti et al., Nature, 2026).

Model details

Architecture	nnU-Net-style 3D residual-encoder U-Net with concurrent spatial+channel squeeze-and-excitation (scSE)
Instantiated via	vesuvius NetworkFromConfig (config bundled as config.json)
model_name	ps256_bs2_msr_default
Input	1-channel CT, 256 × 256 × 256 patches, z-score normalised
Output	2 channels (surface vs background), ignore_label = 2
Encoder	features_per_stage [32, 64, 128, 256, 320, 320, 320]; n_blocks_per_stage [1, 3, 4, 6, 6, 6, 6]; 3×3×3 kernels; stride-2 downsampling
Loss	Medial Surface Recall (Skeleton-Recall-style) + Dice/CE
Checkpoint	epoch 3504 · W&B run d5jdo9n1

Training data

Manually annotated recto surfaces (voxelised from surface meshes) of PHerc. 0139, 1667, 0343P, 0500P2, MAN Bp (~116.5k training / ~2.4k validation patches). Full training configuration is in Supplementary Table 1 of the paper.

Files

• checkpoint_inference_ready.pth — full checkpoint; network weights are under key model (808 tensors). The architecture config is embedded under model_config and mirrored in config.json.
• config.json — architecture config for NetworkFromConfig.

How to load

import torch
ck    = torch.load("checkpoint_inference_ready.pth", map_location="cpu", weights_only=False)
state = ck["model"]            # state_dict
cfg   = ck["model_config"]     # == config.json
# Build the network with the vesuvius package (NetworkFromConfig(cfg)), then:
# net.load_state_dict(state)

The vesuvius package and inference code are in https://github.com/ScrollPrize/villa.

Links

• Paper: Angelotti et al., Complete virtual unwrapping and reading of a rolled Herculaneum papyrus. Nature (2026, in press).
• Code: https://github.com/ScrollPrize/villa
• Data: https://scrollprize.org/data_browser · ESRF: https://cultural-heritage.esrf.fr/tomo
• Vesuvius Challenge: https://scrollprize.org

Citation

@article{angelotti2026unwrapping,
  title   = {Complete virtual unwrapping and reading of a rolled Herculaneum papyrus},
  author  = {Angelotti, Giorgio and others},
  journal = {Nature},
  year    = {2026}
}

(DOI to be added on publication.)

License

MIT — released by the Vesuvius Challenge. Note: the underlying tomographic data are distributed under CC BY-NC 4.0 (see the data links above).