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# WIDERFace

class torchvision.datasets.WIDERFace(root: str, split: str = 'train', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)

  WIDERFace Dataset.

  Parameters:

    - root (string) –

      Root directory where images and annotations are downloaded to. Expects the following folder structure if download=False:

          <root>
              └── widerface
                  ├── wider_face_split ('wider_face_split.zip' if compressed)
                  ├── WIDER_train ('WIDER_train.zip' if compressed)
                  ├── WIDER_val ('WIDER_val.zip' if compressed)
                  └── WIDER_test ('WIDER_test.zip' if compressed)

    - split (string) – The dataset split to use. One of {`train`, `val`, `test`}. Defaults to `train`.

    - transform (callable, optional) – A function/transform that takes in a PIL image and returns a transformed version. E.g, `transforms.RandomCrop`

    - target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

    - download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

  Special-members:

  __getitem__(index: int) → Tuple[Any, Any]
    Parameters:

      index (int) – Index

    Returns:

      (image, target) where target is a dict of annotations for all faces in the image. target=None for the test split.

    Return type:

      tuple

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