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data-and-models

data-and-models

Data modules, datasets, dataclasses, model registry, constructors, feature extraction, and custom data-layout guidance.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: "data-and-models" description: "Data modules, datasets, dataclasses, model registry, constructors, feature extraction, and custom data-layout guidance." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Data and Models

Use this sub-skill for anomalib questions about choosing, configuring, and debugging data or model entry points.

Use this when the user asks about

  • Image datamodules: MVTecAD, Folder, Tabular, PredictDataset
  • Video datamodules: Avenue, ShanghaiTech, UCSDped
  • Depth datamodules: MVTec3D, Folder3D, ADAM3D
  • Dataclasses and batch shapes: ImageItem, VideoItem, DepthItem, and their batch types
  • Model registry and lookup: get_model, list_models, get_datamodule
  • Core model constructors: Padim, Patchcore, EfficientAd, AiVad, Fuvas
  • Backbone / layer selection and feature extraction with TimmFeatureExtractor
  • Custom folder, tabular, video, or depth data layouts
  • Unknown model names, registry lookup errors, or feature-layer mismatches

Do not use this when the user asks about

  • Engine fit / validate / test / predict execution
  • Export, deployment, or inference packaging
  • Pipeline orchestration or benchmark automation
  • CLI install mechanics
  • Studio application content

Fast routing

Primary source surfaces

  • src/anomalib/data/**
  • src/anomalib/models/**
  • docs/source/markdown/guides/how_to/data/**
  • docs/source/markdown/guides/how_to/models/**
  • docs/source/markdown/guides/reference/data/index.md
  • docs/source/markdown/guides/reference/models/index.md
  • examples/api/02_data/**
  • examples/api/03_models/**
  • tests/unit/data/**
  • tests/unit/models/**
  • tests/integration/model/test_models.py

Helpful mental model

  1. Choose the data family first: image, video, depth, custom folder, or tabular.
  2. Confirm the datamodule layout and split mode before choosing a model.
  3. For backbone-based image models, confirm the backbone / layer names before instantiating.
  4. For video models, confirm clip length and target frame semantics.
  5. If the user needs a quick sanity check, use the bundled inspection script instead of guessing.

Discovery and validation script

The bundled script can:

  • list key datamodule and model constructors
  • print the current list_models() registry
  • validate custom folder layouts by calling anomalib's own folder parser
  • validate custom tabular layouts with anomalib-style rules

Example invocations:

python scripts/inspect_data_models.py list
python scripts/inspect_data_models.py check-folder --help
python scripts/inspect_data_models.py check-tabular --help

Notes

  • Prefer the exact public constructors and helpers documented in the bundled references.
  • Keep training, export, and pipeline questions routed to the other sub-skills.
  • Use the bundled references for exact argument defaults, path rules, and dependency caveats.
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