Canonical 2 µm ink-detection model (ResNet-152, 3D)
⚠️ This is a canonical/production ink-detection checkpoint from the Vesuvius Challenge model registry, released here for completeness. The ink detector described in the paper is the ResNet3D-50 family in the
pherc1667-ink-detection-ablationcollection — this ResNet-152 model is a separate, larger variant.
Model details
| Backbone | ResNet-152 3D encoder + 3D decoder ("i3d") |
| Recipe | new_canon_autoresearch_recipe |
| Input | single-channel CT at ~2 µm |
| Framework | PyTorch-Lightning (v2.0.9) checkpoint |
| Checkpoint | epoch 13 · global step 113,246 |
| Weights | under state_dict (backbone.* + decoder) |
| Hyperparameters | in config.json (hyper_parameters: enc, size, pred_shape, with_norm, total_steps) |
Files
r152_3ddec_v2_l5_epoch13.ckpt— PyTorch-Lightning checkpoint (renamed from…i3depoch=13.ckpt; contents unchanged).config.json— the checkpoint'shyper_parameters.
How to load
import torch
ck = torch.load("r152_3ddec_v2_l5_epoch13.ckpt", map_location="cpu", weights_only=False)
state = ck["state_dict"]
hp = ck["hyper_parameters"]
Inference code is in https://github.com/ScrollPrize/villa/tree/main/ink-detection/optimized_inference.
Links
- Vesuvius Challenge: https://scrollprize.org
- Code: https://github.com/ScrollPrize/villa
- Data: https://scrollprize.org/data_browser
- Paper's ink detector (different model):
pherc1667-ink-detection-ablation
License
MIT — released by the Vesuvius Challenge. Underlying tomographic data are distributed under CC BY-NC 4.0.