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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] -
Loader for the Labeled Faces in the Wild (LFW) pairs dataset
This dataset is a collection of JPEG pictures of famous people collected on the internet, all details are available on the official website:
http://vis-www.cs.umass.edu/lfw/Each picture is centered on a single face. Each pixel of each channel (color in RGB) is encoded by a float in range 0.0 - 1.0.
The task is called Face Verification: given a pair of two pictures, a binary classifier must predict whether the two images are from the same person.
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 74.
Read more in the User Guide.
Parameters: 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: :
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
orsubset
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
orsubset
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.
sklearn.datasets.fetch_lfw_pairs()
2017-01-15 04:25:41
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