The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 84, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
PHerc0009B Lasagnetta field
A Lasagnetta preview of villa's lasagna field for the whole PHerc0009B 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.
Get it
hf download SharpMeow/pherc0009b-lasagnetta --repo-type dataset --local-dir pherc0009b-lasagnetta
cd pherc0009b-lasagnetta && sha256sum -c SHA256SUMS
for f in parts/*.tar; do tar -xf "$f"; done
tar -xf PHerc0009B-lasagnetta-meta.tar # last: the whole-scan PHerc0009B.lasagna.json and preview.json
You end up with PHerc0009B.lasagna.json and PHerc0009B_{cos,grad_mag,nx,ny}.ome.zarr, 7.8 GB in all.
Load it
import json
import numpy as np
import zarr
meta = json.load(open("PHerc0009B.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 = 2178, 845, 401 # 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: PHerc0009B,
20250521125136-8.640um-1.2m-116keV-masked.zarr, read at level 0 (8.64 um voxels), Vesuvius Challenge open data - Model: 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
scripts/lasagnettaat a011d5e4, tile 320 x 576 x 576 - Channels: cos at OME-Zarr level 1 to 4 (2x down from the level read); grad_mag, nx, ny at level 2 to 4 (4x down)
- Run: 10 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 4,784 and xz at y about 3,920 (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 0.849 (crossings matched within one cos voxel; 25 Oct 2026). The model card has the method and the pooled numbers. The release's self-check, run on this whole volume, kept Lasagnetta here (median peak F1 over its spots 0.82 or more).
Notes
Model output: a preview of another model's output, not a reading of any text, and nobody has checked it by eye. villa's own field stays the reference where exact values matter.
Related
The Lasagnetta guide 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 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, weights scrollprize/lasagna, MIT); credit to its authors. Shared by Chase Hendrick.
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