PHerc. Paris 4 — volumetric 3D ink segmentation (DINO-guided, self-distilled)
Segments ink directly in 3D in micro-CT of PHerc. Paris 4, where ink is
volumetrically visible. This model provides the independent volumetric validation
of surface-conditioned ink recovery reported in "Complete virtual unwrapping and
reading of a rolled Herculaneum papyrus" (Angelotti et al., arXiv:2606.29085, 2026).
Model details
|
|
| Architecture |
vesuvius NetworkFromConfig nnU-Net-style 3D residual-encoder U-Net (~142 M params), single sigmoid ink head |
| Input |
1-channel CT, 256³ patches, percentile min-max normalisation, full-3D mode (projection half-thickness 3 voxels) |
| Optimisation |
SGD + Nesterov, cosine LR (base 1e-2, warmup 5000), bf16, Dice+BCE with 0.1 label smoothing, weight EMA |
| This checkpoint |
step 78,000 · W&B run 4b07qv8p (ps256_3d_bcedice_dinoguided_paris4_v3_fullsup) |
| Weights |
model (raw) and ema_model (EMA — recommended for inference) |
DINO-guided supervision + self-distillation
Trained in stages: (1) teacher on 8-scroll surface-conditioned ink labels
(PHerc. Paris 4, 0139, 0500P2, 0814, 0841, 1667, MAN5, 9B); (2) a DINO-guided
student using dense ink-likeness from the 3D DINOv2 representation model
scrollprize/dinovol_v2_ps8_with_paris4_352500;
(3) + background masking; (4) self-distillation. DINO guidance compares each
864-D patch token to a reference ink embedding (avg_ref_embedding.npy, the
L2-normalised mean of 256 expert-clicked tokens stored in recorded_embeddings*.npy)
at threshold τ = 0.5. Full configuration is in Supplementary Table 4 of the paper.
Files
ckpt_78k_fullsup.pth — checkpoint with model + ema_model (use ema_model for inference). Training/inference config embedded under config and mirrored in config.json.config.json — training/inference configuration.avg_ref_embedding.npy — (864,) reference ink embedding for DINO guidance.recorded_embeddings.npy, recorded_embeddings_2.npy — (128, 864) each; the expert-clicked DINO tokens (256 total) the reference embedding is averaged from.
How to load
import torch, numpy as np
ck = torch.load("ckpt_78k_fullsup.pth", map_location="cpu", weights_only=False)
state = ck["ema_model"] # recommended
cfg = ck["config"] # == config.json
ref = np.load("avg_ref_embedding.npy") # (864,) reference ink embedding
# Build the network with the vesuvius package (NetworkFromConfig(cfg)), then load_state_dict(state)
The vesuvius package and inference code are in https://github.com/ScrollPrize/villa.
Related models
Links
Citation
@misc{angelotti2026unwrapping,
title = {Complete virtual unwrapping and reading of a rolled Herculaneum papyrus},
author = {Angelotti, Giorgio and others},
year = {2026},
eprint = {2606.29085},
archivePrefix = {arXiv},
primaryClass = {eess.IV},
doi = {10.48550/arXiv.2606.29085}
}
License
MIT — released by the Vesuvius Challenge. The underlying tomographic data are
distributed under CC BY-NC 4.0 (see the data links above).
