VOC2012¶

class paddle.vision.datasets. VOC2012 ( data_file=None, mode='train', transform=None, download=True, backend=None ) [source]

Implementation of VOC2012 dataset.

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/voc2012.

• 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 VOC2012 dataset.

Examples

>>> 
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import VOC2012


>>> voc2012 = VOC2012()
>>> print(len(voc2012))
2913

>>> for i in range(5):  # only show first 5 images
...     img, label = voc2012[i]
...     # do something with img and label
...     print(type(img), img.size)
...     # <class 'PIL.JpegImagePlugin.JpegImageFile'> (500, 281)
...     print(type(label), label.size)
...     # <class 'PIL.PngImagePlugin.PngImageFile'> (500, 281)


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

>>> voc2012_test = VOC2012(
...     mode="test",
...     transform=transform,  # apply transform to every image
...     backend="cv2",  # use OpenCV as image transform backend
... )
>>> print(len(voc2012_test))
1464

>>> for img, label in itertools.islice(iter(voc2012_test), 5):  # only show first 5 images
...     # do something with img and label
...     print(type(img), img.shape)
...     # <class 'paddle.Tensor'> [3, 281, 500]
...     print(type(label), label.shape)
...     # <class 'numpy.ndarray'> (281, 500)