Anomalib Tiled Ensemble

Using the Tiled Ensemble Pipeline

The tiled-ensemble pipeline splits each image into overlapping tiles, trains/evaluates a separate model instance per tile position, then merges tile predictions (with optional seam smoothing) back into a full-image anomaly map. Use it for high-resolution images where a single model can't see fine detail at a manageable input size.

Code locations

• src/anomalib/pipelines/tiled_ensemble/train_pipeline.py — TrainTiledEnsemble: composes the job graph (per-tile training, per-tile prediction, merge, seam smoothing, statistics) and picks SerialRunner or ParallelRunner based on the configured accelerator and available CUDA devices.
• src/anomalib/pipelines/tiled_ensemble/test_pipeline.py — EvalTiledEnsemble: runs inference/evaluation for an already-trained ensemble.
• src/anomalib/pipelines/tiled_ensemble/components/ — individual job implementations (model training, prediction, merging, smoothing, metrics).
• src/anomalib/pipelines/tiled_ensemble/components/utils/ensemble_engine.py — TiledEnsembleEngine, an Engine subclass that customizes per-tile checkpoint/workspace naming.

Running it

python tools/tiled_ensemble/train.py --config tools/tiled_ensemble/ens_config.yaml
python tools/tiled_ensemble/eval.py  --config tools/tiled_ensemble/ens_config.yaml \
                                     --root results/Padim/MVTecAD/bottle/v0

train.py runs TrainTiledEnsemble().run() which includes evaluation after training; eval.py runs EvalTiledEnsemble to re-run evaluation against an existing results directory (--root) — use it only when you want to evaluate again without retraining.

Config structure

Start from tools/tiled_ensemble/ens_config.yaml and adjust the fields you need:

seed: 42
accelerator: "cuda" # or "cpu"
default_root_dir: "results"

tiling:
  image_size: [256, 256] # size the full image is resized to before tiling
  tile_size: [128, 128] # size of each tile
  stride: 128 # tile stride; stride < tile_size gives overlapping tiles

normalization_stage: image
thresholding_stage: image

data:
  class_path: anomalib.data.MVTecAD
  init_args:
    root: ./datasets/MVTecAD
    category: bottle
    train_batch_size: 32
    eval_batch_size: 32
    num_workers: 8
    val_split_mode: from_test
    test_split_mode: from_dir

SeamSmoothing:
  apply: False
  sigma: 2
  width: 0.1

TrainModels:
  model:
    class_path: Padim

Key fields:

• tiling.tile_size / tiling.stride — the core tiling geometry; stride < tile_size produces overlap that SeamSmoothing then blends.
• data.class_path — any image anomalib.data.* datamodule that yields ImageBatch (see anomalib-training / anomalib-adding-a-datamodule). Video and depth datamodules are not supported — the tiled collater uses ImageBatch.collate internally.
• TrainModels.model.class_path — the model class trained per tile; must be a standard image model that only requires batch.image as input. Models requiring additional inputs (e.g. CFM which needs point_cloud/depth_map) are not compatible with the tiled collater. Video models are also not compatible.
• SeamSmoothing.apply — when True, applies Gaussian blending at tile boundaries. This is most useful when tiles overlap (stride < tile_size), but can also smooth hard boundaries between non-overlapping tiles. Set False to skip if seam artifacts are not visible.

For a worked reference invocation with a full config, see tests/integration/pipelines/test_tiled_ensemble.py.

Gotchas

• The pipeline trains one model instance per tile position, not one shared model — total training cost scales with the number of tiles, not just image count. Budget accordingly before scaling up tile_size/stride combinations.
• accelerator: cuda with multiple visible GPUs triggers ParallelRunner, which trains multiple tile jobs concurrently across devices — set accelerator: cpu (or restrict visible devices) for deterministic single-process runs while debugging a config.
• Eval (eval.py) needs --root pointing at the exact output directory produced by the matching training run; it does not re-derive this automatically.
• data.init_args must include val_split_mode and test_split_mode — the pipeline reads these directly from the config before datamodule defaults are applied, and will raise KeyError if missing.

Reviewer / self-check

• tiling.tile_size/stride chosen relative to tiling.image_size (stride ≤ tile_size).
• data.class_path and TrainModels.model.class_path both resolve to real, exported classes.
• SeamSmoothing.apply is intentional given whether tiles overlap.
• Training run completed and its results/... path is used correctly as eval.py --root.