{"cifar100": {"description": "The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images\nper class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.\nThere are two labels per image - fine label (actual class) and coarse label (superclass).\n", "citation": "@TECHREPORT{Krizhevsky09learningmultiple,\n    author = {Alex Krizhevsky},\n    title = {Learning multiple layers of features from tiny images},\n    institution = {},\n    year = {2009}\n}\n", "homepage": "https://www.cs.toronto.edu/~kriz/cifar.html", "license": "", "features": {"img": {"id": null, "_type": "Image"}, "fine_label": {"num_classes": 100, "names": ["apple", "aquarium_fish", "baby", "bear", "beaver", "bed", "bee", "beetle", "bicycle", "bottle", "bowl", "boy", "bridge", "bus", "butterfly", "camel", "can", "castle", "caterpillar", "cattle", "chair", "chimpanzee", "clock", "cloud", "cockroach", "couch", "cra", "crocodile", "cup", "dinosaur", "dolphin", "elephant", "flatfish", "forest", "fox", "girl", "hamster", "house", "kangaroo", "keyboard", "lamp", "lawn_mower", "leopard", "lion", "lizard", "lobster", "man", "maple_tree", "motorcycle", "mountain", "mouse", "mushroom", "oak_tree", "orange", "orchid", "otter", "palm_tree", "pear", "pickup_truck", "pine_tree", "plain", "plate", "poppy", "porcupine", "possum", "rabbit", "raccoon", "ray", "road", "rocket", "rose", "sea", "seal", "shark", "shrew", "skunk", "skyscraper", "snail", "snake", "spider", "squirrel", "streetcar", "sunflower", "sweet_pepper", "table", "tank", "telephone", "television", "tiger", "tractor", "train", "trout", "tulip", "turtle", "wardrobe", "whale", "willow_tree", "wolf", "woman", "worm"], "names_file": null, "id": null, "_type": "ClassLabel"}, "coarse_label": {"num_classes": 20, "names": ["aquatic_mammals", "fish", "flowers", "food_containers", "fruit_and_vegetables", "household_electrical_devices", "household_furniture", "insects", "large_carnivores", "large_man-made_outdoor_things", "large_natural_outdoor_scenes", "large_omnivores_and_herbivores", "medium_mammals", "non-insect_invertebrates", "people", "reptiles", "small_mammals", "trees", "vehicles_1", "vehicles_2"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "image-classification", "image_column": "img", "label_column": "fine_label", "labels": ["apple", "aquarium_fish", "baby", "bear", "beaver", "bed", "bee", "beetle", "bicycle", "bottle", "bowl", "boy", "bridge", "bus", "butterfly", "camel", "can", "castle", "caterpillar", "cattle", "chair", "chimpanzee", "clock", "cloud", "cockroach", "couch", "cra", "crocodile", "cup", "dinosaur", "dolphin", "elephant", "flatfish", "forest", "fox", "girl", "hamster", "house", "kangaroo", "keyboard", "lamp", "lawn_mower", "leopard", "lion", "lizard", "lobster", "man", "maple_tree", "motorcycle", "mountain", "mouse", "mushroom", "oak_tree", "orange", "orchid", "otter", "palm_tree", "pear", "pickup_truck", "pine_tree", "plain", "plate", "poppy", "porcupine", "possum", "rabbit", "raccoon", "ray", "road", "rocket", "rose", "sea", "seal", "shark", "shrew", "skunk", "skyscraper", "snail", "snake", "spider", "squirrel", "streetcar", "sunflower", "sweet_pepper", "table", "tank", "telephone", "television", "tiger", "tractor", "train", "trout", "tulip", "turtle", "wardrobe", "whale", "willow_tree", "wolf", "woman", "worm"]}], "builder_name": "cifar100", "config_name": "cifar100", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 112751396, "num_examples": 50000, "dataset_name": "cifar100"}, "test": {"name": "test", "num_bytes": 22605519, "num_examples": 10000, "dataset_name": "cifar100"}}, "download_checksums": {"https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz": {"num_bytes": 169001437, "checksum": "85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7"}}, "download_size": 169001437, "post_processing_size": null, "dataset_size": 135356915, "size_in_bytes": 304358352}}