EBSSA#

class tonic.datasets.EBSSA(save_to: str, split: str = 'labelled', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]#

EBSSA

There are six different splits provided in this dataset. The labelled section of the dataset contains 84 recordings and 84 label files. The unlabelled section of the dataset contains 153 recordings in folders marked “Unlabelled”.

@article{afshar2020event,
    title={Event-based object detection and tracking for space situational awareness},
    author={Afshar, Saeed and Nicholson, Andrew Peter and Van Schaik, Andre and Cohen, Gregory},
    journal={IEEE Sensors Journal},
    volume={20},
    number={24},
    pages={15117--15132},
    year={2020},
    publisher={IEEE}
}
Parameters:

  • save_to (string) – Location to save files to on disk.

  • split (string) – Which split to load. One of “labelled”, “unlabelled”, “all”.

  • transform (callable, optional) – A callable of transforms to apply to the data.

  • target_transform (callable, optional) – A callable of transforms to apply to the targets/labels.

  • transforms (callable, optional) – A callable of transforms that is applied to both data and labels at the same time.

__getitem__(index: int) → Tuple[Any, Any][source]#
Returns:

(events, target) where target is dict of bounding box and recording id.

Parameters:

index (int) –

Return type:

Tuple[Any, Any]