tonic.datasets.threeET_eyetracking

tonic.datasets.threeET_eyetracking#

Module Contents#

Classes#

ThreeET_Eyetracking

3ET DVS eye tracking 3ET
class tonic.datasets.threeET_eyetracking.ThreeET_Eyetracking(save_to: str, split: str = 'train', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]#

Bases: tonic.dataset.Dataset

3ET DVS eye tracking 3ET

@article{chen20233et,
    title={3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network},
    author={Chen, Qinyu and Wang, Zuowen and Liu, Shih-Chii and Gao, Chang},
    journal={arXiv preprint arXiv:2308.11771},
    year={2023}
}
Parameters:

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

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

  • split (string, optional) – The dataset split to use, train or val.

  • 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.

Returns:

A dataset object that can be indexed or iterated over. One sample returns a tuple of (events, targets).

url = 'https://dl.dropboxusercontent.com/s/1hyer8egd8843t9/ThreeET_Eyetracking.zip?dl=0'#
filename = 'ThreeET_Eyetracking.zip'#
file_md5 = 'b6c652b06fdfd85721f39e2dbe12f4e8'#
sensor_size = (240, 180, 2)#
dtype#
ordering#
__getitem__(index: int) → Tuple[Any, Any][source]#
Returns:

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

Parameters:

index (int) –

Return type:

Tuple[Any, Any]

__len__()[source]#
load_filenames(path)[source]#
Contents