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