AlbuStrongAugment

Bases: BaseCompose

__init__

__init__(augment_space: Dict[str, tuple] = AUGMENT_SPACE, operations: Tuple[int] = (3, 4, 5), probabilites: Tuple[float] = (0.2, 0.3, 0.5), seed: Optional[int] = None, p=1.0) -> None

Strong augment augmentation policy albumentations wrapper.

Augment like there's no tomorrow: Consistently performing neural networks for medical imaging: https://arxiv.org/abs/2206.15274

Parameters

augment_space : Dict[str, tuple], default: AUGMENT_SPACE
    Augmentation space to sample operations from.
operations : Tuple[int], default: [3, 4, 5].
    Number of operations to apply. If None, sample from
    [1, len(augment_space)].
probabilites : Tuple[float], default: [0.2, 0.3, 0.5]
    Probabilities of sampling operations. If None, sample from
    the uniform distribution.
seed : Optional[int], default: None
    Random seed.
p : float, default: 1.0
    Probability of applying the transform.

__call__

__call__(*args, force_apply: bool = False, **data) -> Dict[str, Any]

Apply the StrongAugment transformation pipeline.

__repr__

__repr__() -> str

Return the string representation of the StrongAugment object.