class documentation

UCF101 is an action recognition data set of realistic action videos, collected from YouTube, having 101 action categories. This data set is an extension of UCF50 data set which has 50 action categories.

With 13,320 videos from 101 action categories, UCF101 gives the largest diversity in terms of actions and with the presence of large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, illumination conditions, etc, it is the most challenging data set to date. As most of the available action recognition data sets are not realistic and are staged by actors, UCF101 aims to encourage further research into action recognition by learning and exploring new realistic action categories.

The videos in 101 action categories are grouped into 25 groups, where each group can consist of 4-7 videos of an action. The videos from the same group may share some common features, such as similar background, similar viewpoint, etc.

Example usage:

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("ucf101", split="test")

session = fo.launch_app(dataset)
Dataset size
6.48 GB
Source
https://www.crcv.ucf.edu/research/data-sets/ucf101
Parameters
foldthe test/train fold to use to arrange the files on disk. The supported values are (1, 2, 3)
Method __init__ Undocumented
Instance Variable fold Undocumented
Property name The name of the dataset.
Property parameters An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.
Property supported_splits A tuple of supported splits for the dataset, or None if the dataset does not have splits.
Property tags A tuple of tags for the dataset.
Method _download_and_prepare Internal implementation of downloading the dataset and preparing it for use in the given directory.

Inherited from ZooDataset (via FiftyOneDataset):

Static Method get_info_path Returns the path to the ZooDatasetInfo for the dataset.
Static Method has_info Determines whether the directory contains ZooDatasetInfo.
Static Method load_info Loads the ZooDatasetInfo from the given dataset directory.
Method download_and_prepare Downloads the dataset and prepares it for use.
Method get_split_dir Returns the directory for the given split of the dataset.
Method has_split Whether the dataset has the given split.
Method has_tag Whether the dataset has the given tag.
Property has_patches Whether the dataset has patches that may need to be applied to already downloaded files.
Property has_splits Whether the dataset has splits.
Property has_tags Whether the dataset has tags.
Property importer_kwargs A dict of default kwargs to pass to this dataset's fiftyone.utils.data.importers.DatasetImporter.
Property is_remote Whether the dataset is remotely-sourced.
Property requires_manual_download Whether this dataset requires some files to be manually downloaded by the user before the dataset can be loaded.
Property supports_partial_downloads Whether the dataset supports downloading partial subsets of its splits.
Method _get_splits_to_download Undocumented
Method _is_dataset_ready Undocumented
Method _is_split_ready Undocumented
Method _patch_if_necessary Internal method called when an already downloaded dataset may need to be patched.
def __init__(self, fold=1): (source)

Undocumented

Undocumented

The name of the dataset.

An optional dict of parameters describing the configuration of the zoo dataset when it was downloaded.

@property
supported_splits = (source)

A tuple of supported splits for the dataset, or None if the dataset does not have splits.

A tuple of tags for the dataset.

def _download_and_prepare(self, dataset_dir, scratch_dir, split): (source)

Internal implementation of downloading the dataset and preparing it for use in the given directory.

Parameters
dataset_dirthe directory in which to construct the dataset. If a split is provided, this is the directory for the split
scratch_dira scratch directory to use to download and prepare any required intermediate files
splitthe split to download, or None if the dataset does not have splits
Returns
tuple of
  • dataset_type: the fiftyone.types.Dataset type of the dataset
  • num_samples: the number of samples in the split. For datasets that support partial downloads, this can be None, which indicates that all content was already downloaded
  • classes: an optional list of class label strings