Datasets:
Benchmark splits
A benchmark is a JSON file here that names which MitoVerse volumes it uses and their role. The
volumes themselves (../data/<dataset>/<vol>.zarr) are original chunks — never physically cut
into train/val/test. A split file is just an overlay, so the same data backs many benchmarks.
Schema
{
"name": "guay21",
"arrays": {"image": "img", "label": "mito"}, // zarr sub-arrays PyTC reads
"volumes": [
{"id": "guay21_vol0", "zarr": "data/guay21/vol0.zarr", "split": "train"},
{"id": "guay21_vol2", "zarr": "data/guay21/vol2.zarr", "split": "test"}
]
}
split ∈ {train, val, test}. Whole-volume assignment works today.
Regions (coming, via PyTC)
When a benchmark splits within a volume (e.g. MitoEM's train = z 0–400, test = z 500–1000), the
volume stays one store and the ranges go in a regions field — honored once PyTorchConnectomics
supports region specs:
{"id": "wei20_mitoEM-H", "zarr": "data/wei20/mitoEM-H.zarr",
"regions": {"train": [[0,400],[0,4096],[0,4096]], "test": [[500,1000],[0,4096],[0,4096]]}}
Use with PyTorchConnectomics
python <pytc>/lib/mitoverse/scripts/to_pytc.py splits/guay21.json
# emits the cfg.data.{train,val,test}.{image,label} block of *.zarr/img + *.zarr/mito paths
Files
guay21.json— runnable example (3 ingested volumes).mitoem2.0.json— the MitoEM2.0 8-sub-dataset benchmark as an overlay over existing source volumes (muller21, openorganelle, han24, wei20, jiang25, kunduri22). ME2-Pyra = masked crop/regions of wei20.cellmap.json— CellMap challenge over the OpenOrganelle_cropvolumes.mitoem.json— TODO: classic MitoEM (wei20) train/val/test split (region-based, pending PyTC regions).