anomalib-training

# Training a Model on a Dataset Training in anomalib always goes through `anomalib.engine.Engine`, which wraps a Lightning `Trainer`. ## Python API — standard benchmark dataset (MVTecAD) ```python from anomalib.data import MVTecAD from anomalib.models import Patchcore from anomalib.engine import Engine datamodule = MVTecAD(root="./datasets/MVTecAD", category="bottle", train_batch_size=32) model = Patchcore() engine = Engine() # any Lightning Trainer kwarg can go here engine.fit(model=model, datamodule=datamodule) results = engine.test(model=model, datamodule=datamodule) ``` `Engine(**kwargs)` forwards unknown kwargs straight to the underlying Lightning `Trainer` (`accelerator`, `devices`, `strategy`, `max_epochs`, `logger`, `callbacks`, `enable_checkpointing`, `val_check_interval`, `barebones`, ...) — there is no separate "Trainer config object" to build. Key `Engine` methods: `fit(model, datamodule=...)`, `train(...)` (fit + test in one call), `test(model=None, datamodule=...)`, `predict(model=None, datamodule=..., dataset=..., data_path=...)`. If `model`/`datamodule` are omitted from `test`/`predict`, the engine reuses the ones passed to `fit`. ## Python API — custom data with the Folder datamodule Use `Folder` whenever your data is laid out as `root/normal_dir/*`, `root/abnormal_dir/*`, and optionally `root/mask_dir/*` (segmentation masks) — no new dataset code needed: ```python from anomalib.data import Folder from anomalib.models import Padim from anomalib.engine import Engine datamodule = Folder( name="custom", # required — used as the datamodule's display name root="./datasets/custom", normal_dir="good", # required abnormal_dir="defect", # optional: enables anomalous test/eval samples mask_dir="mask", # optional: enables pixel-level (segmentation) evaluation train_batch_size=32, eval_batch_size=32, num_workers=8, ) model = Padim() engine = Engine() engine.fit(model=model, datamodule=datamodule) ``` If you only have normal training images and unlabeled/normal-only test images, omit `abnormal_dir` and `mask_dir` — `Folder` will still produce a valid train/test split via `test_split_ratio` (fraction of normal training images held out for testing). Use `normal_test_dir` if you have a separate directory of normal images specifically for the test set. ## CLI ```bash # Standard dataset, defaults anomalib train --model Patchcore --data anomalib.data.MVTecAD # Override a datamodule field anomalib train --model Patchcore --data anomalib.data.MVTecAD --data.category transistor # Override a trainer field (use a gradient-trained model like Stfpm where max_epochs is meaningful) anomalib train --model anomalib.models.Stfpm --data anomalib.data.MVTecAD --trainer.max_epochs 3 # Custom Folder dataset from the CLI anomalib train --model Padim --data anomalib.data.Folder \ --data.name custom --data.root ./datasets/custom \ --data.normal_dir good --data.abnormal_dir defect # From a config file (jsonargparse; combine with any of the above overrides) anomalib train --config path/to/config.yaml ``` CLI wiring lives in `src/anomalib/cli/cli.py` (`AnomalibCLI`); model/data classes are exposed as jsonargparse subclass arguments, so `--model`/`--data` accept either a short name (`Padim`) or a full class path (`anomalib.models.Padim`, `anomalib.data.Folder`). ## Choosing accelerator / devices Pass standard Lightning kwargs to `Engine(...)`: `accelerator="gpu"|"cpu"|"xpu"`, `devices=1`. For Intel XPU specifically, use `SingleXPUStrategy`/`XPUAccelerator` from `anomalib.engine`: ```python from anomalib.engine import Engine, SingleXPUStrategy, XPUAccelerator engine = Engine(strategy=SingleXPUStrategy(), accelerator=XPUAccelerator()) ``` ## Gotchas - Not every model trains via gradient descent — training-free models (e.g. Padim, Patchcore) still go through `engine.fit(...)`; `Engine`/`Trainer` handles the single "epoch" needed to build their memory bank. You don't need special-case code for this. Note that these models override `max_epochs` via their `trainer_arguments` property (e.g. `max_epochs=1`), so any `max_epochs` you pass to `Engine` will be overwritten for such models. - `Folder`'s `mask_dir` is what switches evaluation from image-level (classification) to pixel-level (segmentation) metrics — only pass it if you actually have per-pixel ground-truth masks. - Results (checkpoints, logs, images) are written under `Engine`'s `default_root_dir` (`"results"` by default), nested by model/datamodule/category — check there first when debugging a run. ## Reviewer / self-check - [ ] `Engine(...)` receives Trainer overrides as plain kwargs, not a hand-built `Trainer` object. - [ ] Folder-dataset training specifies `normal_dir` and, if applicable, `abnormal_dir`/`mask_dir` matching the actual on-disk layout. - [ ] CLI invocations use `anomalib.data.<Class>` / `anomalib.models.<Class>` paths that are actually exported (see `anomalib-adding-a-model` / `anomalib-adding-a-datamodule` for how exports work).