sklearn.datasets.fetch_lfw_people
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sklearn.datasets.fetch_lfw_people(data_home=None, funneled=True, resize=0.5, min_faces_per_person=0, color=False, slice_=(slice(70, 195, None), slice(78, 172, None)), download_if_missing=True, return_X_y=False)
[source]
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Load the Labeled Faces in the Wild (LFW) people dataset (classification).
Download it if necessary.
Classes | 5749 |
Samples total | 13233 |
Dimensionality | 5828 |
Features | real, between 0 and 255 |
Read more in the User Guide.
Parameters: |
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data_home : optional, 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 : boolean, optional, default: True -
Download and use the funneled variant of the dataset. -
resize : float, optional, default 0.5 -
Ratio used to resize the each face picture. -
min_faces_per_person : int, optional, default None -
The extracted dataset will only retain pictures of people that have at least min_faces_per_person different pictures. -
color : boolean, optional, 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_ : optional -
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 : optional, True by default -
If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site. -
return_X_y : boolean, default=False. -
If True, returns (dataset.data, dataset.target) instead of a Bunch object. See below for more information about the dataset.data and dataset.target object. |
Returns: |
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dataset : dict-like object with the following attributes: -
dataset.data : numpy array of shape (13233, 2914) -
Each row corresponds to a ravelled face image of original size 62 x 47 pixels. Changing the slice_ or resize parameters will change the shape of the output. -
dataset.images : numpy array of shape (13233, 62, 47) -
Each row is a face image corresponding to one of the 5749 people in the dataset. Changing the slice_ or resize parameters will change the shape of the output. -
dataset.target : numpy array of shape (13233,) -
Labels associated to each face image. Those labels range from 0-5748 and correspond to the person IDs. -
dataset.DESCR : string -
Description of the Labeled Faces in the Wild (LFW) dataset. -
(data, target) : tuple if return_X_y is True -
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Examples using sklearn.datasets.fetch_lfw_people