sklearn.datasets.fetch_lfw_pairs
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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]
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Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).
Download it if necessary.
Classes | 5749 |
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: |
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subset : optional, 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 : 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. -
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. |
Returns: |
- The data is returned as a Bunch object with the following attributes:
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data : numpy array 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. -
pairs : numpy array 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. -
target : numpy 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. -
DESCR : string -
Description of the Labeled Faces in the Wild (LFW) dataset. |