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