I have a CVAT instance and I want to perform some dataset generation from FiftyOne. My data is very large and stored in MinIO which is referenced in CVAT. How can I make the most of: fiftyone.utils.cvat.import_annotations(sample_collection, project_name=None, project_id=None, task_ids=None, data_path=None, label_types=None, insert_new=True, download_media=False, num_workers=None, occluded_attr=None, group_id_attr=None, backend='cvat', **kwargs)
Imports annotations from the specified CVAT project or task(s) into the given sample collection.

Provide one of project_name, project_id, or task_ids to perform an import.

This method can be configured in any of the following three ways:

Pass the data_path argument to define a mapping between media filenames in CVAT and local filepaths to the same media.
Pass the download_media=True option to download both the annotations and the media files themselves, which are stored in a directory you specify via the data_path argument.
Don’t provide data_path or download_media=True, in which case it is assumed that the CVAT filenames correspond to the base filenames of existing sample filepaths in the provided sample_collection.
Parameters
sample_collection – a fiftyone.core.collections.SampleCollection
project_name (None) – the name of a CVAT project to import
project_id (None) – the ID of a CVAT project to import
task_ids (None) – a CVAT task ID or iterable of CVAT task IDs to import
data_path (None) –
a parameter that defines the correspondence between the filenames in CVAT and the filepaths of sample_collection. Can be any of the following:
a directory on disk where the media files reside. In this case, the filenames must match those in CVAT
a dict mapping CVAT filenames to absolute filepaths to the corresponding media on disk
the path to a JSON manifest on disk containing a mapping between CVAT filenames and absolute filepaths to the media on disk
By default, only annotations whose filename matches an existing filepath in sample_collection will be imported
label_types (None) –
an optional parameter specifying the label types to import. Can be any of the following:
None (default): all label types will be stored in fields of the same name on sample_collection
a list of label types to load. In this case, the labels will be stored in fields of the same names in sample_collection
a dict mapping label types to field names of sample_collection in which to store the labels
"prompt": present an interactive prompt to decide/discard field names in which to store each label type
insert_new (True) – whether to create new samples for any media for which annotations are found in CVAT but which do not exist in sample_collection
download_media (False) – whether to download the images or videos found in CVAT to the directory or filepaths in data_path if not already present
num_workers (None) – a suggested number of threads to use when downloading media
occluded_attr (None) – an optional attribute name in which to store the occlusion information for all spatial labels
group_id_attr (None) – an optional attribute name in which to store the group id for labels
backend ("cvat") – the name of the CVAT backend to use
**kwargs – CVAT authentication credentials to pass to CVATBackendConfig