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]

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:

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` 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.

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 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.

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_pairs.html

doc_scikit_learn

2017-01-15 04:25:41

Comments