FashionMNIST

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

Implementation of Fashion-MNIST dataset.

Parameters

  • image_path (str, optional) – Path to image file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/fashion-mnist.

  • label_path (str, optional) – Path to label file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/fashion-mnist.

  • 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) – Whether to download dataset automatically if image_path label_path is not set. 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 FashionMNIST dataset.

Examples

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


>>> fashion_mnist = FashionMNIST()
>>> print(len(fashion_mnist))
60000

>>> for i in range(5):  # only show first 5 images
...     img, label = fashion_mnist[i]
...     # do something with img and label
...     print(type(img), img.size, label)
...     # <class 'PIL.Image.Image'> (28, 28) [9]


>>> transform = T.Compose(
...     [
...         T.ToTensor(),
...         T.Normalize(
...             mean=[127.5],
...             std=[127.5],
...         ),
...     ]
... )

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

>>> for img, label in itertools.islice(iter(fashion_mnist_test), 5):  # only show first 5 images
...     # do something with img and label
...     print(type(img), img.shape, label)
...     # <class 'paddle.Tensor'> [1, 28, 28] [9]