NMNIST#

class tonic.prototype.datasets.NMNIST(root: PathLike, train: Optional[bool] = True, first_saccade_only: Optional[bool] = False, keep_compressed: Optional[bool] = False)[source]#

N-MNIST

Events have (xytp) ordering.

@article{orchard2015converting,
  title={Converting static image datasets to spiking neuromorphic datasets using saccades},
  author={Orchard, Garrick and Jayawant, Ajinkya and Cohen, Gregory K and Thakor, Nitish},
  journal={Frontiers in neuroscience},
  volume={9},
  pages={437},
  year={2015},
  publisher={Frontiers}
}
Parameters:
  • root (string) – Location to save files to on disk.

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

  • first_saccade_only (bool) – If True, only work with events of the first of three saccades. Results in about a third of the events overall.

  • keep_compressed (Optional[bool]) –