CIFAR 100¶
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class
olympus.datasets.cifar100.CIFAR100(data_path)[source]¶ Bases:
olympus.datasets.dataset.AllDatasetSee
CIFAR10The full specification can be found at here.
Superclass Classes aquatic mammals beaver, dolphin, otter, seal, whale fish aquarium fish, flatfish, ray, shark, trout flowers orchids, poppies, roses, sunflowers, tulips food containers bottles, bowls, cans, cups, plates fruit and vegetables apples, mushrooms, oranges, pears, sweet peppers household electrical devices clock, computer keyboard, lamp, telephone, television household furniture bed, chair, couch, table, wardrobe insects bee, beetle, butterfly, caterpillar, cockroach large carnivores bear, leopard, lion, tiger, wolf large man-made outdoor things bridge, castle, house, road, skyscraper large natural outdoor scenes cloud, forest, mountain, plain, sea large omnivores and herbivores camel, cattle, chimpanzee, elephant, kangaroo medium-sized mammals fox, porcupine, possum, raccoon, skunk non-insect invertebrates crab, lobster, snail, spider, worm people baby, boy, girl, man, woman reptiles crocodile, dinosaur, lizard, snake, turtle small mammals hamster, mouse, rabbit, shrew, squirrel trees maple, oak, palm, pine, willow vehicles 1 bicycle, bus, motorcycle, pickup truck, train vehicles 2 lawn-mower, rocket, streetcar, tank, tractor References
[1] Alex Krizhevsky, “Learning Multiple Layers of Features from Tiny Images”, 2009. Attributes: - classes: List[int]
Return the mapping between samples index and their class
- input_shape: (3, 32, 32)
Size of a sample stored in this dataset
- target_shape: (100,)
There are 100 classes see above for a full description
- train_size: 40000
Size of the train dataset
- valid_size: 10000
Size of the validation dataset
- test_size: 10000
Size of the test dataset
Methods
categories()Dataset tags so we can filter what we want depending on the task transforms()register_datapipe_as_function register_function