classmethod COCOData.from_dataset(data: Dataset, batch_size: int = 64, shuffle: bool = True, num_workers: int = 0, pin_memory: bool = True, collate_fn: Optional[Callable] = None, num_classes: Optional[int] = None, label_map: Optional[Dict[int, str]] = None, transform_field: Optional[str] = 'transforms') VD[source]#

Create VisionData instance from a Dataset instance.


instance of a Dataset.

batch_size: int, default 64

how many samples per batch to load.

shufflebool, default True:

set to True to have the data reshuffled at every epoch.

num_workers int, default 0:

how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process.

pin_memory bool, default True

If True, the data loader will copy Tensors into CUDA pinned memory before returning them.


merges a list of samples to form a mini-batch of Tensor(s).

num_classesOptional[int], default None

Number of classes in the dataset. If not provided, will be inferred from the dataset.

label_mapOptional[Dict[int, str]], default None

A dictionary mapping class ids to their names.

transform_fieldOptional[str], default: ‘transforms’

Name of transforms field in the dataset which holds transformations of both data and label.