Transformers for applying data augmentation.

class imgaug.augmenters.meta.Sequential)[source]

Data transformer based on the imgaug library.

This class can generate a from an existing one that generates image and instance data. Element of the output dataset will have a set of augmentation transformations applied.


An instance of imgaug.augmenters.Sequential that will be applied to each element of the input dataset.

classmethod from_config(config: sleap.nn.config.optimization.AugmentationConfig)[source]

Create an augmenter from a set of configuration parameters.


config – An AugmentationConfig instance with the desired parameters.


An instance of this class with the specified augmentation configuration.

property input_keys

Return the keys that incoming elements are expected to have.

property output_keys

Return the keys that outgoing elements will have.

transform_dataset(input_ds: →[source]

Create a with elements containing augmented data.


input_ds – A dataset with elements that contain the keys “image” and “instances”. This is typically raw data from a data provider.


A with the same keys as the input, but with images and instance points updated with the applied augmentations.


The “scale” key in examples are not modified when scaling augmentation is applied.