cerebras.modelzoo.data.vision.classification.config.ImageNet1KProcessorConfig#
- class cerebras.modelzoo.data.vision.classification.config.ImageNet1KProcessorConfig(batch_size: int = <object object at 0x7fc286331b70>, shuffle: bool = True, shuffle_seed: int = 0, num_workers: int = 0, prefetch_factor: int = 10, persistent_workers: bool = True, data_dir: Union[str, List[str]] = <object object at 0x7fc286331b70>, num_classes: int = <object object at 0x7fc286331b70>, mixed_precision: bool = <object object at 0x7fc286331b70>, transforms: List[dict] = <factory>, image_size: int = 224, noaugment: bool = False, drop_last: bool = True, sampler: str = 'random', ra_sampler_num_repeat: int = 3, mixup_alpha: float = 0.1, cutmix_alpha: float = 0.1, use_worker_cache: bool = <object object at 0x7fc286331b70>)[source]#
- use_worker_cache: bool = <object object>#
- batch_size: int = <object object>#
Batch size to be used
- cutmix_alpha: float = 0.1#
- data_dir: Union[str, List[str]] = <object object>#
- drop_last: bool = True#
- image_size: int = 224#
- mixed_precision: bool = <object object>#
- mixup_alpha: float = 0.1#
- noaugment: bool = False#
- num_classes: int = <object object>#
- num_workers: int = 0#
The number of PyTorch processes used in the dataloader
- persistent_workers: bool = True#
Whether or not to keep workers persistent between epochs
- prefetch_factor: int = 10#
The number of batches to prefetch in the dataloader
- ra_sampler_num_repeat: int = 3#
- sampler: str = 'random'#
- shuffle: bool = True#
Whether or not to shuffle the dataset
- shuffle_seed: int = 0#
Seed used for deterministic shuffling
- transforms: List[dict]#