Cifar100¶

class paddle.vision.datasets. Cifar100 ( data_file=None, mode='train', transform=None, download=True, backend=None ) [source]

Implementation of Cifar-100 dataset, which has 100 categories.

Parameters

• data_file (str, optional) – path to data file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/cifar

• mode (str, optional) – Either train or test mode. Default ‘train’.

• transform (Callable, optional) – transform to perform on image, None for no transform. Default: None.

• download (bool, optional) – download dataset automatically if data_file is None. Default True.

• backend (str, optional) – Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {‘pil’, ‘cv2’}. If this option is not set, will get backend from paddle.vision.get_image_backend, default backend is ‘pil’. Default: None.

Returns

Dataset. An instance of Cifar100 dataset.

Examples

>>> 
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import Cifar100

>>> cifar100 = Cifar100()
>>> print(len(cifar100))
50000

>>> for i in range(5):  # only show first 5 images
...     img, label = cifar100[i]
...     # do something with img and label
...     print(type(img), img.size, label)
...     # <class 'PIL.Image.Image'> (32, 32) 19


>>> transform = T.Compose(
...     [
...         T.Resize(64),
...         T.ToTensor(),
...         T.Normalize(
...             mean=[0.5, 0.5, 0.5],
...             std=[0.5, 0.5, 0.5],
...             to_rgb=True,
...         ),
...     ]
... )

>>> cifar100_test = Cifar100(
...     mode="test",
...     transform=transform,  # apply transform to every image
...     backend="cv2",  # use OpenCV as image transform backend
... )
>>> print(len(cifar100_test))
10000

>>> for img, label in itertools.islice(iter(cifar100_test), 5):  # only show first 5 images
...     # do something with img and label
...     print(type(img), img.shape, label)
...     # <class 'paddle.Tensor'> [3, 64, 64] 49