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)[source]¶
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_homestr, default=None
Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.
- funneledbool, default=True
Download and use the funneled variant of the dataset.
- resizefloat, default=0.5
Ratio used to resize the each face picture.
- colorbool, 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_missingbool, default=True
If False, raise a IOError 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
orsubset
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
orsubset
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.
- DESCRstr
Description of the Labeled Faces in the Wild (LFW) dataset.
- datandarray of shape (2200, 5828). Shape depends on
- data