Vesuvius Nnunet Model

Vesuvius Challenge — nnU-Net Surface Detection Model

A 3D segmentation model for detecting papyrus sheet surfaces in micro-CT scans of the Herculaneum scrolls, trained for the Vesuvius Challenge Surface Detection competition on Kaggle.

Model Description

This model uses nnU-Net v2, the self-configuring deep learning framework for biomedical image segmentation. nnU-Net automatically determines the optimal network architecture, preprocessing, and training strategy from the dataset properties.

The model segments 3D micro-CT volumes into three classes:

• Background (0) — empty space / non-papyrus
• Surface (1) — the outer surface of the papyrus sheet
• Interior (2) — the interior of the papyrus sheet

Architecture

Component	Value
Network	PlainConvUNet (3D)
Stages	6
Features per stage	[32, 64, 128, 256, 320, 320]
Conv op	Conv3d (3×3×3 kernels)
Normalization	InstanceNorm3d
Activation	LeakyReLU (inplace)
Deep supervision	Yes
Patch size	128 × 128 × 128
Batch size	2
Parameters	~31M

Training Details

Detail	Value
Dataset	Dataset011_Vesuvius (786 training volumes)
Epochs	200
Iterations/epoch	250 (train), 50 (val)
Optimizer	SGD (lr=0.01, momentum=0.99, nesterov=True, weight_decay=3e-5)
LR schedule	PolyLR (200 epochs)
Loss	Dice + Cross-Entropy with Deep Supervision
Normalization	CT Normalization (dataset-level statistics)
Foreground oversampling	33%
Mixed precision	Yes (AMP with GradScaler)
torch.compile	Yes
Hardware	NVIDIA A10G (AWS g5.xlarge)
Training time	~11.5 hours
Framework	nnU-Net v2, PyTorch 2.10.0+cu128, CUDNN 9.1

Training Metrics

Metric	Value
Best EMA pseudo Dice	0.4676 (epoch 194)
Best val loss	0.3518 (epoch 187)
Final train loss	0.4127
Final val loss	0.4274

Kaggle Leaderboard

Submission	Post-Processing	Public LB	Private LB
V2 pipeline (best)	1st-place post-proc	0.433	0.449

Note: The Kaggle submission used our V2 pipeline model (custom UNet3D), not this nnU-Net model directly. This nnU-Net model is intended for future ensemble submissions and as a standalone alternative.

Dataset

The Vesuvius Challenge Surface Detection dataset consists of 3D micro-CT scans of carbonized papyrus scrolls from Herculaneum, buried by the eruption of Mount Vesuvius in 79 AD.

• 704 training + 82 validation volumes (786 total)
• Volume shape: 320 × 314 × 314 voxels
• Voxel spacing: 7.91 μm isotropic
• File format: 3D TIFF
• Intensity range: 0–255 (CT values)
• Validation split: Scroll ID 26002

Intensity Statistics (foreground)

Stat	Value
Mean	87.5
Median	81.0
Std	47.7
Min/Max	0 / 255
0.5th/99.5th percentile	0 / 212

How to Use

Prerequisites

pip install nnunetv2 torch

Inference with nnU-Net

from huggingface_hub import snapshot_download

# Download model
model_dir = snapshot_download("bshepp/vesuvius-nnunet-model")

# Set nnU-Net environment variables
import os
os.environ["nnUNet_results"] = model_dir

# Run prediction using nnU-Net CLI
# nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d 011 -c 3d_fullres -tr nnUNetTrainer_200epochs -f all

Loading checkpoint directly

import torch

checkpoint = torch.load("checkpoint_best.pth", map_location="cpu")
# checkpoint contains: network_weights, optimizer_state, epoch, etc.

Files

File	Description	Size
fold_all/checkpoint_best.pth	Best model weights (EMA pseudo Dice)	238 MB
fold_all/checkpoint_final.pth	Final epoch (200) weights	238 MB
plans.json	nnU-Net experiment plan (architecture, preprocessing)	20 KB
dataset.json	Dataset configuration (channels, labels)	<1 KB
dataset_fingerprint.json	Dataset statistics	110 KB
fold_all/training_log_*.txt	Full training log (200 epochs)	80 KB
fold_all/progress.png	Training curves plot	880 KB
fold_all/debug.json	Debug/config snapshot	—

Post-Processing

For best results, apply the 1st-place post-processing pipeline after inference:

1. Binary closing with spherical footprint (radius 2)
2. Height-map patching with bilinear interpolation
3. LUT-based 1-voxel hole plugging (6-connectivity)
4. Global binary_fill_holes

See 1st place writeup for details.

Citation

If you use this model, please cite nnU-Net:

@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

License

MIT

Author

Brian Sheppard (@bshepp)