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from fuel.datasets import H5PYDataset |
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from fuel.transformers.defaults import uint8_pixels_to_floatX |
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from fuel.utils import find_in_data_path |
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class CIFAR100(H5PYDataset): |
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"""The CIFAR100 dataset of natural images. |
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This dataset is a labeled subset of the ``80 million tiny images`` |
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dataset [TINY]. It consists of 60,000 32 x 32 colour images labelled |
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into 100 fine-grained classes and 20 super-classes. There are |
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600 images per fine-grained class. There are 50,000 training |
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images and 10,000 test images [CIFAR100]. |
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The dataset contains three sources: |
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- features: the images themselves, |
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- coarse_labels: the superclasses 1-20, |
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- fine_labels: the fine-grained classes 1-100. |
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.. [TINY] Antonio Torralba, Rob Fergus and William T. Freeman, |
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*80 million tiny images: a large dataset for non-parametric |
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object and scene recognition*, Pattern Analysis and Machine |
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Intelligence, IEEE Transactions on 30.11 (2008): 1958-1970. |
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.. [CIFAR100] Alex Krizhevsky, *Learning Multiple Layers of Features |
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from Tiny Images*, technical report, 2009. |
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Parameters |
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---------- |
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which_sets : tuple of str |
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Which split to load. Valid values are 'train' and 'test', |
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corresponding to the training set (50,000 examples) and the test |
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set (10,000 examples). Note that CIFAR100 does not have a |
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validation set; usually you will create your own |
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training/validation split using the `subset` argument. |
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""" |
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filename = 'cifar100.hdf5' |
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default_transformers = uint8_pixels_to_floatX(('features',)) |
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def __init__(self, which_sets, **kwargs): |
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kwargs.setdefault('load_in_memory', True) |
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super(CIFAR100, self).__init__( |
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file_or_path=find_in_data_path(self.filename), |
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which_sets=which_sets, **kwargs) |
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mila-udem /
fuel
