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scikit-learn 1.3.2
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# `sklearn.datasets`.fetch_lfw_pairs

sklearn.datasets.fetch_lfw_pairs(*, subset='train', data_home=None, funneled=True, resize=0.5, color=False, slice_=(slice(70, 195, None), slice(78, 172, None)), download_if_missing=True)

  Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).

  Download it if necessary.

  Classes | 2
  Samples total | 13233
  Dimensionality | 5828
  Features | real, between 0 and 255

  In the official README.txt this task is described as the “Restricted” task. As I am not sure as to implement the “Unrestricted” variant correctly, I left it as unsupported for now.

  The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 47.

  Read more in the User Guide.

  Parameters:
    subset{‘train’, ‘test’, ‘10_folds’}, default=’train’

      Select the dataset to load: ‘train’ for the development training set, ‘test’ for the development test set, and ‘10_folds’ for the official evaluation set that is meant to be used with a 10-folds cross validation.

    data_home str or path-like, default=None

      Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.

    funneled bool, default=True

      Download and use the funneled variant of the dataset.

    resize float, default=0.5

      Ratio used to resize the each face picture.

    color bool, default=False

      Keep the 3 RGB channels instead of averaging them to a single gray level channel. If color is True the shape of the data has one more dimension than the shape with color = False.

    slice_tuple of slice, default=(slice(70, 195), slice(78, 172))

      Provide a custom 2D slice (height, width) to extract the ‘interesting’ part of the jpeg files and avoid use statistical correlation from the background.

    download_if_missing bool, default=True

      If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site.

  Returns:
    data `Bunch`

      Dictionary-like object, with the following attributes.

      datandarray of shape (2200, 5828). Shape depends on `subset`.

        Each row corresponds to 2 ravel’d face images of original size 62 x 47 pixels. Changing the `slice_`, `resize` or `subset` parameters will change the shape of the output.

      pairsndarray of shape (2200, 2, 62, 47). Shape depends on `subset`

        Each row has 2 face images corresponding to same or different person from the dataset containing 5749 people. Changing the `slice_`, `resize` or `subset` parameters will change the shape of the output.

      targetnumpy array of shape (2200,). Shape depends on `subset`.

        Labels associated to each pair of images. The two label values being different persons or the same person.

      target_namesnumpy array of shape (2,)

        Explains the target values of the target array. 0 corresponds to “Different person”, 1 corresponds to “same person”.

      DESCRstr

        Description of the Labeled Faces in the Wild (LFW) dataset.