tonic.datasets.dvs_lips
#
Module Contents#
Classes#
- class tonic.datasets.dvs_lips.DVSLip(save_to: str, train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]#
Bases:
tonic.dataset.Dataset
@inproceedings{tan2022multi, title={Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading}, author={Tan, Ganchao and Wang, Yang and Han, Han and Cao, Yang and Wu, Feng and Zha, Zheng-Jun}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={20094--20103}, year={2022} } Implementation inspired from original script: https://github.com/tgc1997/event-based-lip-reading/blob/main/utils/dataset.py
- 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.
- base_url = 'https://drive.google.com/file/d/1dBEgtmctTTWJlWnuWxFtk8gfOdVVpkQ0/view'#
- filename = 'DVS-Lip.zip'#
- base_folder = 'DVS-Lip'#
- file_md5 = '2dcb959255122d4cdeb6094ca282494b'#
- sensor_size = (128, 128, 2)#
- dtype#
- ordering#
- classes = ['accused', 'action', 'allow', 'allowed', 'america', 'american', 'another', 'around', 'attacks',...#
- ambiguous_classes = ['action', 'allow', 'allowed', 'america', 'american', 'around', 'being', 'benefit', 'benefits',...#