Table of Contents
Shortcuts

# UCF101

class torchvision.datasets.UCF101(root: str, annotation_path: str, frames_per_clip: int, step_between_clips: int = 1, frame_rate: Optional[int] = None, fold: int = 1, train: bool = True, transform: Optional[Callable] = None, _precomputed_metadata: Optional[Dict[str, Any]] = None, num_workers: int = 1, _video_width: int = 0, _video_height: int = 0, _video_min_dimension: int = 0, _audio_samples: int = 0, output_format: str = 'THWC')

  UCF101 dataset.

  UCF101 is an action recognition video dataset. This dataset consider every video as a collection of video clips of fixed size, specified by `frames_per_clip`, where the step in frames between each clip is given by `step_between_clips`. The dataset itself can be downloaded from the dataset website; annotations that `annotation_path` should be pointing to can be downloaded from here.

  To give an example, for 2 videos with 10 and 15 frames respectively, if `frames_per_clip=5` and `step_between_clips=5`, the dataset size will be (2 + 3) = 5, where the first two elements will come from video 1, and the next three elements from video 2. Note that we drop clips which do not have exactly `frames_per_clip` elements, so not all frames in a video might be present.

  Internally, it uses a VideoClips object to handle clip creation.

  Parameters:

    - root (string) – Root directory of the UCF101 Dataset.

    - annotation_path (str) – path to the folder containing the split files; see docstring above for download instructions of these files

    - frames_per_clip (int) – number of frames in a clip.

    - step_between_clips (int, optional) – number of frames between each clip.

    - fold (int, optional) – which fold to use. Should be between 1 and 3.

    - train (bool, optional) – if `True`, creates a dataset from the train split, otherwise from the `test` split.

    - transform (callable, optional) – A function/transform that takes in a TxHxWxC video and returns a transformed version.

    - output_format (str, optional) – The format of the output video tensors (before transforms). Can be either “THWC” (default) or “TCHW”.

  Returns:

    A 3-tuple with the following entries:

    > - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The T video frames

    > - audio(Tensor[K, L]): the audio frames, where K is the number of channels and L is the number of points

    > - label (int): class of the video clip

  Return type:

    tuple

  Special-members:

  __getitem__(idx: int) → Tuple[Tensor, Tensor, int]
    Parameters:

      index (int) – Index

    Returns:

      Sample and meta data, optionally transformed by the respective transforms.

    Return type:

      (Any)

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.