Augmentation matrix

The knobs

These are TrainConfig field names, not CLI spellings. The CLI maps its own aliases onto them, so --mosaic sets mosaic_prob.

The last four are the classification pack. Detection families ignore them. mixup is an API-only knob: the CLI --mixup is the alias for the detection mixup_prob.

from libreyolo.data.augment.spec import (    AUG_KNOBS,    aug_support,    ignored_aug_params,    uses_mosaic_gating,) print(sorted(AUG_KNOBS)) table = aug_support("yolo9")print(table["mixup_prob"].status, table["mixup_prob"].note) print(sorted(ignored_aug_params("dfine")))print(uses_mosaic_gating("yolo9"), uses_mosaic_gating("yolonas"))

The three statuses

ignored is the one worth checking before a run, because nothing fails. The CLI warns when an explicitly set training parameter is one the selected family ignores, and the trainer warns when mixup_prob > 0 cannot fire because the family gates MixUp on mosaic and mosaic_prob is zero.

Pipeline archetypes

Every covered family follows one of six pipelines, with a handful of per-family deviations listed below.

In the YOLOX-style pipeline the per-sample preprocessing applies HSV jitter and flips, while the affine warp and MixUp run only inside the mosaic branch. YOLO-NAS instead runs a per-sample affine that is always on, ignores mosaic, and applies MixUp independently, reusing mosaic_scale as the affine scale range.

The DETR-style pipeline is a pass-through transform with no mosaic. Its photometric distortion, zoom-out and IoU-crop are recipe constants rather than configurable knobs, which is why hsv_prob and the geometry knobs never reach it. The classification pipeline uses an ImageFolder transform whose horizontal flip is a fixed 0.5 rather than flip_prob. Semantic scale jitter and HSV come from family class attributes rather than config knobs, and restoration flips are coupled input-and-target operations with a fixed 0.5 probability.

no_aug_epochs is honored everywhere, though what it turns off differs: mosaic and MixUp for YOLOX-style, the affine and MixUp for YOLO-NAS, the strong photometric and crop augmentations plus the learning-rate tail for DETR-style, and the scheduler tail for the rest.

Families by archetype

Twenty-five families are covered. A family outside this list returns an empty ignored set, so no warning is emitted for it.

Deviations

ec and dinov2 are multi-task families, so a knob is marked ignored only when every one of the family's trainable tasks ignores it. That keeps the CLI warning from ever being wrong for one task while right for another.

Dome-DETR inherits D-FINE's transforms unchanged. The one thing it cannot take is multi-scale training, which its config disables rather than the augmentation spec.

Family-specific knobs

Some families carry augmentation knobs on their own TrainConfig subclass rather than on the base. The CLI does not expose these; set them through the Python API.

rot90 applies to detect and OBB on yolo9.

Querying the spec

A Support is a named tuple of status and note, where the note explains why a knob is ignored or gated for that family.

The mosaic gate

For a YOLOX-style family, mixup_prob=0.5 with mosaic_prob=0 disables MixUp entirely, because MixUp applies only to mosaic samples. That combination is easy to reach when turning mosaic off late in training. The trainer logs a warning naming the family, and mixup_gating_warning is the pure function behind it.