dinovol — v2 backbone, patch size 8 (paris4 run, step 352500)
A 3D, DINOv2/DINOv3-style self-supervised representation model trained on
volumetric micro-CT scans of carbonized Herculaneum scrolls. This repository
publishes the EMA teacher backbone from the paris4 training run at
training step 352500.
Training code: dinovol. This is a
representation/feature-extraction model — there is no task-specific head; you
take its dense patch embeddings and use them downstream.
Model details
| Backbone family | DINOv2/EVA ViT, 3D, with 3D RoPE (DINOv3-style) |
model_type | v2 |
| Embedding dim | 864 |
| Depth | 24 blocks |
| Attention heads | 16 |
| MLP | SwiGLU, mlp_ratio 8/3 |
| Register tokens | 4 |
| Patch size | 8 × 8 × 8 |
| Global crop size (train) | 128 × 128 × 128 |
| Input channels | 1 (grayscale CT) |
| Positional encoding | RoPE mixed (base 100, normalize_coords=separate, rescale=2.0, shift=0.05, jitter=1.05); no absolute pos-emb |
| Backbone parameters | 215.9 M |
| Training step | 352500 |
| W&B run | model_v2__shift005_jitter105__r342500__paris4__20260416 |
Pretraining objective: DINO + iBOT + KoLeo, with late dense-feature refinement via Gram anchoring (DINOv3-style), trained with AMP.
Training data
Self-supervised on 11 open-data Herculaneum volumes (scale 0) from
s3://vesuvius-challenge-open-data/:
PHerc0009B, PHerc0500P2, PHerc0814, PHerc1299, PHerc0343P,
PHerc0332, PHerc0139, PHercMAN5, PHerc1451, PHercMANB, PHercParis4.
Files
| File | Size | Use |
|---|---|---|
dinovol_v2_ps8_paris4_step352500_teacher_backbone.pt | ~0.86 GB | Recommended for inference. Slim file: {step, config, teacher backbone weights}. Loads directly with the repo's loader. |
checkpoint_step_352500_paris4.pt | ~5.0 GB | Full training checkpoint (student, EMA teacher, optimizer, scaler, loss buffers, Gram teacher, RNG state). Use to resume training or for full reproducibility. |
config.json | — | The model config block, for quick inspection. |
Both .pt files embed the full training config, so the architecture is rebuilt
automatically — no separate config needed at load time.
Usage
Clone the training repo and use its loader (the config travels inside the weights):
import torch
from huggingface_hub import hf_hub_download
from dinovol_2.eval import embedding_utils as eu
path = hf_hub_download(
"scrollprize/dinovol_v2_ps8_with_paris4_352500",
"dinovol_v2_ps8_paris4_step352500_teacher_backbone.pt",
)
loaded = eu.load_backbone_from_checkpoint(path, device="cuda")
backbone = loaded.backbone.eval()
# dense patch embeddings for a 1-channel volume, dims multiples of patch_size (8)
vol = torch.randn(1, 1, 128, 128, 128, device="cuda")
with torch.no_grad():
out = backbone.forward_features(vol, masks=None, view_kind="global")
patch_tokens = out["x_norm_patchtokens"] # (B, num_patches, 864)
For a normalized, windowed embedding grid over a real OME-Zarr volume, use
eu.compute_patch_embedding_grid(...), which applies the checkpoint's
normalization scheme and tiles the volume. The repo also ships a napari
inspector at dinovol_2/eval/napari_visualizer.py.
License
MIT. See LICENSE.
Caveats
- Trained on single-channel Herculaneum micro-CT; behavior on other modalities is untested.
- Pretraining only — no finetuning / segmentation head is included.