fetch_olivetti_faces#

sklearn.datasets.fetch_olivetti_faces(*, data_home=None, shuffle=False, random_state=0, download_if_missing=True, return_X_y=False, n_retries=3, delay=1.0)[source]#

Load the Olivetti faces data-set from AT&T (classification).

Download it if necessary.

Classes	40
Samples total	400
Dimensionality	4096
Features	real, between 0 and 1

Read more in the User Guide.

Parameters:

data_homestr 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.
shufflebool, default=False: If True the order of the dataset is shuffled to avoid having images of the same person grouped.
random_stateint, RandomState instance or None, default=0: Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls. See Glossary.
download_if_missingbool, 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.
return_X_ybool, default=False: If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

Added in version 0.22.
n_retriesint, default=3: Number of retries when HTTP errors are encountered.

Added in version 1.5.
delayfloat, default=1.0: Number of seconds between retries.

Added in version 1.5.

Returns:

dataBunch

Dictionary-like object, with the following attributes.

data: ndarray, shape (400, 4096): Each row corresponds to a ravelled face image of original size 64 x 64 pixels.
imagesndarray, shape (400, 64, 64): Each row is a face image corresponding to one of the 40 subjects of the dataset.
targetndarray, shape (400,): Labels associated to each face image. Those labels are ranging from 0-39 and correspond to the Subject IDs.
DESCRstr: Description of the modified Olivetti Faces Dataset.

(data, target)tuple if return_X_y=True

Tuple with the data and target objects described above.

Added in version 0.22.

Examples

>>> from sklearn.datasets import fetch_olivetti_faces
>>> olivetti_faces = fetch_olivetti_faces()
>>> olivetti_faces.data.shape
(400, 4096)
>>> olivetti_faces.target.shape
(400,)
>>> olivetti_faces.images.shape
(400, 64, 64)

Gallery examples#

Online learning of a dictionary of parts of faces

Faces dataset decompositions

Pixel importances with a parallel forest of trees

Face completion with a multi-output estimators