Data and image formats

Dataset loaders

data.mvtec_ad(root, "screw")                     # MVTec AD / MVTec layout (converted VisA, etc.)
data.from_folders(train_normal, test_normal, [test_anom_1, test_anom_2])
data.one_vs_rest(root, normal_class="cat")       # semantic AD from a classification dataset

Each returns an AnomalyDataset with train_paths, test_paths, test_labels, test_types and, for MVTec, test_masks (path of the ground-truth mask in ground_truth/<type>/<name>_mask.png, or None for normal images).

High-bit-depth and medical images

pip install "imgad[medical]"     # pydicom + nibabel

Images with more than 8 bits (16-bit PNG/TIFF, DICOM, NIfTI, float arrays) are mapped to the 8 bits the models expect with an intensity rule; 8-bit images are never changed.

Rule	Mapping
auto (default)	8-bit unchanged; DICOM with a window -> that window; otherwise per-image percentiles 0.5–99.5
window:lo,hi	fixed range, the same for every image (e.g. window:-1000,400 for CT in HU, window:0,4095 for a 12-bit detector). Recommended for anomaly detection: per-image rules hide global intensity anomalies
percentile:1,99, minmax	per-image percentiles / min–max
dicom	the WindowCenter/WindowWidth stored in each DICOM file

HFFeatureExtractor("microsoft/rad-dino").set_intensity("window:0,4095")
build_extractor({"type": "hf", "model_name": "microsoft/rad-dino", "intensity": "dicom"})
benchmark(..., intensity="window:-1000,400")       # every extractor / Anomalib model without its own rule
refs = data.volume_slices("scans/", axis="z")       # NIfTI / multi-frame DICOM -> "scan.nii.gz::z=42" refs

CLI: --intensity in fit and benchmark (or intensity: in the YAML). DICOM files (.dcm) are read with their rescale slope/intercept, MONOCHROME1 is inverted, and only pixel data is read: patient metadata is never stored or logged. Scores of volume slices are per slice; aggregate them per volume (e.g. the maximum). The rule is part of the feature-cache key and of saved pipelines. See Medical imaging for medical foundation models, benchmarks and evaluation practices.