DVSGesture#

class tonic.datasets.DVSGesture(save_to: str, train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]#

IBM DVS Gestures

Note

This is (exceptionally) a preprocessed version of the original dataset, where recordings that originally contained multiple labels have already been cut into respective samples. Also temporal precision is reduced to ms.

@inproceedings{amir2017low,
  title={A low power, fully event-based gesture recognition system},
  author={Amir, Arnon and Taba, Brian and Berg, David and Melano, Timothy and McKinstry, Jeffrey and Di Nolfo, Carmelo and Nayak, Tapan and Andreopoulos, Alexander and Garreau, Guillaume and Mendoza, Marcela and others},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7243--7252},
  year={2017}
}
Parameters:
  • save_to (string) – Location to save files to on disk.

  • train (bool) – If True, uses training subset, otherwise testing subset.

  • 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)[source]#
Returns:

a tuple of (events, target) where target is the index of the target class.