def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = (
'apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio',
'beef_tartare', 'beet_salad', 'beignets', 'bibimbap', 'bread_pudding',
'breakfast_burrito', 'bruschetta', 'caesar_salad', 'cannoli',
'caprese_salad', 'carrot_cake', 'ceviche', 'cheesecake',
'cheese_plate', 'chicken_curry', 'chicken_quesadilla', 'chicken_wings',
'chocolate_cake', 'chocolate_mousse', 'churros', 'clam_chowder',
'club_sandwich', 'crab_cakes', 'creme_brulee', 'croque_madame',
'cup_cakes', 'deviled_eggs', 'donuts', 'dumplings', 'edamame',
'eggs_benedict', 'escargots', 'falafel', 'filet_mignon',
'fish_and_chips', 'foie_gras', 'french_fries', 'french_onion_soup',
'french_toast', 'fried_calamari', 'fried_rice', 'frozen_yogurt',
'garlic_bread', 'gnocchi', 'greek_salad', 'grilled_cheese_sandwich',
'grilled_salmon', 'guacamole', 'gyoza', 'hamburger',
'hot_and_sour_soup', 'hot_dog', 'huevos_rancheros', 'hummus',
'ice_cream', 'lasagna', 'lobster_bisque', 'lobster_roll_sandwich',
'macaroni_and_cheese', 'macarons', 'miso_soup', 'mussels', 'nachos',
'omelette', 'onion_rings', 'oysters', 'pad_thai', 'paella', 'pancakes',
'panna_cotta', 'peking_duck', 'pho', 'pizza', 'pork_chop', 'poutine',
'prime_rib', 'pulled_pork_sandwich', 'ramen', 'ravioli',
'red_velvet_cake', 'risotto', 'samosa', 'sashimi', 'scallops',
'seaweed_salad', 'shrimp_and_grits', 'spaghetti_bolognese',
'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake',
'sushi', 'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles')
image_dirs, label_dirs = sf.read_subset_cls(subset_dir,
shuffle=shuffle,
sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = (
'beaver', 'dolphin', 'otter', 'seal', 'whale',
'aquarium_fish', 'flatfish', 'ray', 'shark', 'trout',
'orchid', 'poppy', 'rose', 'sunflower', 'tulip',
'bottle', 'bowl', 'can', 'cup', 'plate',
'apple', 'mushroom', 'orange', 'pear', 'sweet_pepper',
'clock', 'keyboard', 'lamp', 'telephone', 'television',
'bed', 'chair', 'couch', 'table', 'wardrobe',
'bee', 'beetle', 'butterfly', 'caterpillar', 'cockroach',
'bear', 'leopard', 'lion', 'tiger', 'wolf',
'bridge', 'castle', 'house', 'road', 'skyscraper',
'cloud', 'forest', 'mountain', 'plain', 'sea',
'camel', 'cattle', 'chimpanzee', 'elephant', 'kangaroo',
'fox', 'porcupine', 'possum', 'raccoon', 'skunk',
'crab', 'lobster', 'snail', 'spider', 'worm',
'baby', 'boy', 'girl', 'man', 'woman',
'crocodile', 'dinosaur', 'lizard', 'snake', 'turtle',
'hamster', 'mouse', 'rabbit', 'shrew', 'squirrel',
'maple_tree', 'oak_tree', 'palm_tree', 'pine_tree', 'willow_tree',
'bicycle', 'bus', 'motorcycle', 'pickup_truck', 'train',
'lawn_mower', 'rocket', 'streetcar', 'tank', 'tractor'
)
image_dirs, label_dirs = sf.read_subset_cls(subset_dir, shuffle=shuffle, sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = ()
# Shuffling is disabled in order to minimize random file access
image_dirs, label_dirs = sf.read_subset_cls(subset_dir, shuffle=False, sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = ('fake', 'real')
image_dirs, label_dirs = sf.read_subset_cls(subset_dir,
shuffle=shuffle,
sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = []
for _, item in class_dict.items():
class_names.append(item.split(', ')[0])
class_names = tuple(class_names)
# Shuffling is disabled in order to minimize random file access
image_dirs, label_dirs = sf.read_subset_cls(subset_dir, shuffle=False, sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = []
for _, item in class_dict.items():
class_names.append(item.split(', ')[0])
class_names = tuple(class_names)
image_dirs, label_dirs = sf.read_subset_cls(subset_dir,
shuffle=False,
sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = ('Abyssinian', 'Bengal', 'Birman', 'Bombay',
'British_Shorthair', 'Egyptian_Mau', 'Maine_Coon',
'Persian', 'Ragdoll', 'Russian_Blue', 'Siamese', 'Sphynx',
'american_bulldog', 'american_pit_bull_terrier',
'basset_hound', 'beagle', 'boxer', 'chihuahua',
'english_cocker_spaniel', 'english_setter',
'german_shorthaired', 'great_pyrenees', 'havanese',
'japanese_chin', 'keeshond', 'leonberger',
'miniature_pinscher', 'newfoundland', 'pomeranian', 'pug',
'saint_bernard', 'samoyed', 'scottish_terrier', 'shiba_inu',
'staffordshire_bull_terrier', 'wheaten_terrier',
'yorkshire_terrier')
image_dirs, label_dirs = sf.read_subset_cls(subset_dir,
shuffle=shuffle,
sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = (
'Chihuahua', 'Japanese_spaniel', 'Maltese_dog', 'Pekinese', 'Shih-Tzu',
'Blenheim_spaniel', 'papillon', 'toy_terrier', 'Rhodesian_ridgeback',
'Afghan_hound', 'basset', 'beagle', 'bloodhound', 'bluetick',
'black-and-tan_coonhound', 'Walker_hound', 'English_foxhound',
'redbone', 'borzoi', 'Irish_wolfhound', 'Italian_greyhound', 'whippet',
'Ibizan_hound', 'Norwegian_elkhound', 'otterhound', 'Saluki',
'Scottish_deerhound', 'Weimaraner', 'Staffordshire_bullterrier',
'American_Staffordshire_terrier', 'Bedlington_terrier',
'Border_terrier', 'Kerry_blue_terrier', 'Irish_terrier',
'Norfolk_terrier', 'Norwich_terrier', 'Yorkshire_terrier',
'wire-haired_fox_terrier', 'Lakeland_terrier', 'Sealyham_terrier',
'Airedale', 'cairn', 'Australian_terrier', 'Dandie_Dinmont',
'Boston_bull', 'miniature_schnauzer', 'giant_schnauzer',
'standard_schnauzer', 'Scotch_terrier', 'Tibetan_terrier',
'silky_terrier', 'soft-coated_wheaten_terrier',
'West_Highland_white_terrier', 'Lhasa', 'flat-coated_retriever',
'curly-coated_retriever', 'golden_retriever', 'Labrador_retriever',
'Chesapeake_Bay_retriever', 'German_short-haired_pointer', 'vizsla',
'English_setter', 'Irish_setter', 'Gordon_setter', 'Brittany_spaniel',
'clumber', 'English_springer', 'Welsh_springer_spaniel',
'cocker_spaniel', 'Sussex_spaniel', 'Irish_water_spaniel', 'kuvasz',
'schipperke', 'groenendael', 'malinois', 'briard', 'kelpie',
'komondor', 'Old_English_sheepdog', 'Shetland_sheepdog', 'collie',
'Border_collie', 'Bouvier_des_Flandres', 'Rottweiler',
'German_shepherd', 'Doberman', 'miniature_pinscher',
'Greater_Swiss_Mountain_dog', 'Bernese_mountain_dog', 'Appenzeller',
'EntleBucher', 'boxer', 'bull_mastiff', 'Tibetan_mastiff',
'French_bulldog', 'Great_Dane', 'Saint_Bernard', 'Eskimo_dog',
'malamute', 'Siberian_husky', 'affenpinscher', 'basenji', 'pug',
'Leonberg', 'Newfoundland', 'Great_Pyrenees', 'Samoyed', 'Pomeranian',
'chow', 'keeshond', 'Brabancon_griffon', 'Pembroke', 'Cardigan',
'toy_poodle', 'miniature_poodle', 'standard_poodle',
'Mexican_hairless', 'dingo', 'dhole', 'African_hunting_dog')
image_dirs, label_dirs = sf.read_subset_cls(subset_dir,
shuffle=shuffle,
sample_size=sample_size)
return image_dirs, label_dirs, class_names
def read_subset(subset_dir, shuffle=False, sample_size=None):
class_names = (
'Black_footed_Albatross', 'Laysan_Albatross', 'Sooty_Albatross',
'Groove_billed_Ani', 'Crested_Auklet', 'Least_Auklet',
'Parakeet_Auklet', 'Rhinoceros_Auklet', 'Brewer_Blackbird',
'Red_winged_Blackbird', 'Rusty_Blackbird', 'Yellow_headed_Blackbird',
'Bobolink', 'Indigo_Bunting', 'Lazuli_Bunting', 'Painted_Bunting',
'Cardinal', 'Spotted_Catbird', 'Gray_Catbird', 'Yellow_breasted_Chat',
'Eastern_Towhee', 'Chuck_will_Widow', 'Brandt_Cormorant',
'Red_faced_Cormorant', 'Pelagic_Cormorant', 'Bronzed_Cowbird',
'Shiny_Cowbird', 'Brown_Creeper', 'American_Crow', 'Fish_Crow',
'Black_billed_Cuckoo', 'Mangrove_Cuckoo', 'Yellow_billed_Cuckoo',
'Gray_crowned_Rosy_Finch', 'Purple_Finch', 'Northern_Flicker',
'Acadian_Flycatcher', 'Great_Crested_Flycatcher', 'Least_Flycatcher',
'Olive_sided_Flycatcher', 'Scissor_tailed_Flycatcher',
'Vermilion_Flycatcher', 'Yellow_bellied_Flycatcher', 'Frigatebird',
'Northern_Fulmar', 'Gadwall', 'American_Goldfinch',
'European_Goldfinch', 'Boat_tailed_Grackle', 'Eared_Grebe',
'Horned_Grebe', 'Pied_billed_Grebe', 'Western_Grebe', 'Blue_Grosbeak',
'Evening_Grosbeak', 'Pine_Grosbeak', 'Rose_breasted_Grosbeak',
'Pigeon_Guillemot', 'California_Gull', 'Glaucous_winged_Gull',
'Heermann_Gull', 'Herring_Gull', 'Ivory_Gull', 'Ring_billed_Gull',
'Slaty_backed_Gull', 'Western_Gull', 'Anna_Hummingbird',
'Ruby_throated_Hummingbird', 'Rufous_Hummingbird', 'Green_Violetear',
'Long_tailed_Jaeger', 'Pomarine_Jaeger', 'Blue_Jay', 'Florida_Jay',
'Green_Jay', 'Dark_eyed_Junco', 'Tropical_Kingbird', 'Gray_Kingbird',
'Belted_Kingfisher', 'Green_Kingfisher', 'Pied_Kingfisher',
'Ringed_Kingfisher', 'White_breasted_Kingfisher',
'Red_legged_Kittiwake', 'Horned_Lark', 'Pacific_Loon', 'Mallard',
'Western_Meadowlark', 'Hooded_Merganser', 'Red_breasted_Merganser',
'Mockingbird', 'Nighthawk', 'Clark_Nutcracker',
'White_breasted_Nuthatch', 'Baltimore_Oriole', 'Hooded_Oriole',
'Orchard_Oriole', 'Scott_Oriole', 'Ovenbird', 'Brown_Pelican',
'White_Pelican', 'Western_Wood_Pewee', 'Sayornis', 'American_Pipit',
'Whip_poor_Will', 'Horned_Puffin', 'Common_Raven',
'White_necked_Raven', 'American_Redstart', 'Geococcyx',
'Loggerhead_Shrike', 'Great_Grey_Shrike', 'Baird_Sparrow',
'Black_throated_Sparrow', 'Brewer_Sparrow', 'Chipping_Sparrow',
'Clay_colored_Sparrow', 'House_Sparrow', 'Field_Sparrow',
'Fox_Sparrow', 'Grasshopper_Sparrow', 'Harris_Sparrow',
'Henslow_Sparrow', 'Le_Conte_Sparrow', 'Lincoln_Sparrow',
'Nelson_Sharp_tailed_Sparrow', 'Savannah_Sparrow', 'Seaside_Sparrow',
'Song_Sparrow', 'Tree_Sparrow', 'Vesper_Sparrow',
'White_crowned_Sparrow', 'White_throated_Sparrow',
'Cape_Glossy_Starling', 'Bank_Swallow', 'Barn_Swallow',
'Cliff_Swallow', 'Tree_Swallow', 'Scarlet_Tanager', 'Summer_Tanager',
'Artic_Tern', 'Black_Tern', 'Caspian_Tern', 'Common_Tern',
'Elegant_Tern', 'Forsters_Tern', 'Least_Tern', 'Green_tailed_Towhee',
'Brown_Thrasher', 'Sage_Thrasher', 'Black_capped_Vireo',
'Blue_headed_Vireo', 'Philadelphia_Vireo', 'Red_eyed_Vireo',
'Warbling_Vireo', 'White_eyed_Vireo', 'Yellow_throated_Vireo',
'Bay_breasted_Warbler', 'Black_and_white_Warbler',
'Black_throated_Blue_Warbler', 'Blue_winged_Warbler', 'Canada_Warbler',
'Cape_May_Warbler', 'Cerulean_Warbler', 'Chestnut_sided_Warbler',
'Golden_winged_Warbler', 'Hooded_Warbler', 'Kentucky_Warbler',
'Magnolia_Warbler', 'Mourning_Warbler', 'Myrtle_Warbler',
'Nashville_Warbler', 'Orange_crowned_Warbler', 'Palm_Warbler',
'Pine_Warbler', 'Prairie_Warbler', 'Prothonotary_Warbler',
'Swainson_Warbler', 'Tennessee_Warbler', 'Wilson_Warbler',
'Worm_eating_Warbler', 'Yellow_Warbler', 'Northern_Waterthrush',
'Louisiana_Waterthrush', 'Bohemian_Waxwing', 'Cedar_Waxwing',
'American_Three_toed_Woodpecker', 'Pileated_Woodpecker',
'Red_bellied_Woodpecker', 'Red_cockaded_Woodpecker',
'Red_headed_Woodpecker', 'Downy_Woodpecker', 'Bewick_Wren',
'Cactus_Wren', 'Carolina_Wren', 'House_Wren', 'Marsh_Wren',
'Rock_Wren', 'Winter_Wren', 'Common_Yellowthroat')
image_dirs, label_dirs = sf.read_subset_cls(subset_dir,
shuffle=shuffle,
sample_size=sample_size)
return image_dirs, label_dirs, class_names