---
license: cc-by-nc-4.0
tags: [vesuvius-challenge, herculaneum, ct, lasagna, lasagnetta, ome-zarr]
pretty_name: PHercMAN5 Lasagnetta field (a preview of villa's lasagna field)
---

# PHercMAN5 Lasagnetta field

A Lasagnetta preview of villa's lasagna field for the whole PHercMAN5 scan, in villa's own layout, so you can point
lasagna or the spiral fit at it without running a model yourself. The open data has no lasagna field for this scroll.

![cos and surface normals, a slice through the middle of the scroll](preview-xy.png)

## Get it

```bash
hf download SharpMeow/phercman5-lasagnetta --repo-type dataset --local-dir phercman5-lasagnetta
cd phercman5-lasagnetta && sha256sum -c SHA256SUMS
for f in parts/*.tar; do tar -xf "$f"; done
tar -xf PHercMAN5-lasagnetta-meta.tar   # last: the whole-scan PHercMAN5.lasagna.json and preview.json
```

You end up with `PHercMAN5.lasagna.json` and `PHercMAN5_{cos,grad_mag,nx,ny}.ome.zarr`, 4.2 GB in all.

## Load it

```python
import json
import numpy as np
import zarr

meta = json.load(open("PHercMAN5.lasagna.json"))
cos = zarr.open(meta["groups"]["cos"]["zarr"], mode="r")     # cos, 2x down from the CT level read
nx = zarr.open(meta["groups"]["nx"]["zarr"], mode="r")       # normals, 4x down; stored as v * 127 + 128
ny = zarr.open(meta["groups"]["ny"]["zarr"], mode="r")
z, y, x = 228, 490, 537                                      # a voxel on the normals' grid that holds data
a, b = (float(nx[z, y, x]) - 128) / 127, (float(ny[z, y, x]) - 128) / 127
c = np.sqrt(max(0.0, 1 - a * a - b * b))                     # nz is not stored
patch = cos[2 * z, 2 * y:2 * y + 64, 2 * x:2 * x + 64]       # the same place on the cos grid
print("normal", round(a, 3), round(b, 3), round(c, 3), "cos patch mean", float(np.mean(patch)))
```

`load_example.py` is this snippet; it was run on an unpacked part before upload.

## What it is

- Scan: PHercMAN5, `20260311104824-2.399um-0.2m-78keV-masked.zarr`, read at level 2 (2.399 um scan, 9.6 um voxels at that level), Vesuvius Challenge open data
- Model: [Lasagnetta](https://huggingface.co/SharpMeow/lasagnetta) 0.1.1, weights at revision 0f9ad8cd: the
  release recipe (three students averaged, cos blur 0.5, cos offset 0.04)
- Code: [ChaseHendrick/Scrolls](https://github.com/ChaseHendrick/Scrolls) `scripts/lasagnetta` at a011d5e4, tile 320 x 576 x 576
- Channels: cos at OME-Zarr level 3 to 4 (2x down from the level read); grad_mag, nx, ny at level 4 (4x down)
- Run: 8 parts on Hugging Face Jobs (RTX PRO 6000), one x, y, z box each. Every part edge sits on a
  multiple of 512 scan voxels, so every chunk of every level was written by exactly one part, and the parts together
  are the whole field. Values are kept only where the masked scan holds CT; everywhere else is fill 0.
- Previews: xy at z about 17,040 and xz at y about 8,752 (full-resolution voxels), from OME-Zarr level 4.

## How close to villa

Lasagnetta copies villa's lasagna net and runs about 5 times faster. On 30 crops of this scan that no student had
trained on, it matched villa's sheet crossings at peak F1 0.774 (crossings matched within one cos voxel; 25
Oct 2026). The [model card](https://huggingface.co/SharpMeow/lasagnetta) has the method and the pooled numbers. The release's self-check, run on this whole volume before this field was made, pointed to villa's net here (median peak F1 over its spots under 0.82), so use this field as a quick look and run villa's `predict3d` where exact values matter.

![the same field from the side](preview-xz.png)

## Notes

Model output: a preview of another model's output, not a reading of any text.
villa's own field stays the reference where exact values matter.

## Related

The [Lasagnetta guide](https://github.com/ChaseHendrick/Scrolls/blob/main/docs/tools/lasagnetta.md) shows how to run it on your own region, with a self-check that tells you where to
use villa's net instead. villa lasagna fields for ten more scans are on the [Lasagna Fields](https://huggingface.co/spaces/SharpMeow/lasagna-fields) page.

## Credit and terms

The scan is Vesuvius Challenge open data, CC BY-NC 4.0; this derived field is shared under the same terms. Lasagnetta
copies villa's lasagna ([ScrollPrize/villa](https://github.com/ScrollPrize/villa), weights
[scrollprize/lasagna](https://huggingface.co/scrollprize/lasagna), MIT); credit to its authors. Shared by Chase Hendrick.