cblearn.datasets.fetch_vogue_cover_similarity#

cblearn.datasets.fetch_vogue_cover_similarity(data_home=None, download_if_missing=True, shuffle=True, random_state=None, return_triplets=False)[source]#

Load the vogue cover similarity dataset (odd-one-out).

Triplets	1107
Objects (Covers)	60

See Nature and Vogue datasets for a detailed description.

>>> dataset = fetch_vogue_cover_similarity(shuffle=True)  
>>> dataset.image_label[[0, -1]].tolist()  
['Cover_uk_VOgue_MAY10_V_29mar10_bt_268x353.jpg', 'voguecoverapr11_bt_268x353.jpg']
>>> dataset.triplet.shape  
(1107, 3)

Parameters:

data_home (PathLike | None) – 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.
download_if_missing (bool) – optional, default=True
shuffle (bool) – default = True Shuffle the order of triplet constraints.
random_state (RandomState | None) – optional, default = None Initialization for shuffle random generator
return_triplets (bool) – boolean, default=False. If True, returns numpy array instead of a Bunch object.

Returns:

Bunch

Dictionary-like object, with the following attributes.

tripletndarray, shape (n_triplets, 3): Each row corresponding odd-one-out query. The columns represent the odd image and two others.
class_labelndarray, shape (120, ): Names of the scene images.
DESCRstring: Description of the dataset.

tripletsnumpy arrays (n_triplets, 3)

Only present when return_triplets=True.

Return type:

dataset

Raises:

IOError – If the data is not locally available, but download_if_missing=False