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Tutorial Configs

Top-level tutorial configs are in this folder and are intended to be runnable with:

python scripts/main.py --config tutorials/<config>.yaml

Active top-level configs

  • tutorials/mito_lucchi++.yaml: Lucchi++ mitochondria segmentation (MONAI UNet).
  • tutorials/mitoEM/H.yaml: MitoEM-Human (EM30-H) instance segmentation (MedNeXt, SDT).
  • tutorials/mitoEM/R.yaml: MitoEM-Rat (EM30-R) instance segmentation (MedNeXt, SDT).
  • tutorials/mitoEM/HR.yaml: Joint EM30-H + EM30-R training (MedNeXt, SDT).
  • tutorials/mito_mitolab.yaml: CEM-MitoLab 2D mitochondria segmentation (MedNeXt).
  • tutorials/mito_betaseg.yaml: BetaSeg mitochondria instance segmentation (MedNeXt, affinity+SDT).
  • tutorials/neuron_snemi.yaml: SNEMI3D neuron segmentation (RSUNet, affinities).
  • tutorials/nuc_nucmm-z.yaml: NucMM zebrafish nuclei segmentation (MONAI UNet, multi-task).
  • tutorials/fiber_linghu26.yaml: Fiber segmentation (MedNeXt, binary+boundary+distance).

Config composition (_base_)

Top-level configs now use inheritance via _base_:

  • connectomics/config/all_profiles.yaml: Canonical registry index loaded by top-level tutorials.
  • connectomics/config/profiles/*.yaml: Section-level registries selected by *.profile.
  • connectomics/config/templates/*.yaml: Explicit list-item templates, currently used for inference.decoding.

_base_ supports:

  • A single file path (_base_: ../connectomics/config/all_profiles.yaml)
  • A list of files (_base_: [a.yaml, b.yaml]) with left-to-right merge order
  • Relative paths resolved from the current config file

Merge semantics:

  • Profile payloads are merged into the destination section first.
  • Explicit keys in the tutorial override profile keys.
  • Explicit lists replace profile lists; they are not additive.
  • Canonical decoding syntax is explicit list templating: - template: decoding_waterz.

Validation

Validate top-level tutorial configs:

python scripts/validate_tutorial_configs.py

This check fails if a config cannot load or if legacy keys reappear (inference.data, data.augmentation.enabled, or inference.test_time_augmentation.act).