Update parquet files

imagenet_refs.json

@@ -1,1002 +0,0 @@
-{
-    "n01440764": "tench",
-    "n01443537": "goldfish",
-    "n01484850": "great_white_shark",
-    "n01491361": "tiger_shark",
-    "n01494475": "hammerhead",
-    "n01496331": "electric_ray",
-    "n01498041": "stingray",
-    "n01514668": "cock",
-    "n01514859": "hen",
-    "n01518878": "ostrich",
-    "n01530575": "brambling",
-    "n01531178": "goldfinch",
-    "n01532829": "house_finch",
-    "n01534433": "junco",
-    "n01537544": "indigo_bunting",
-    "n01558993": "robin",
-    "n01560419": "bulbul",
-    "n01580077": "jay",
-    "n01582220": "magpie",
-    "n01592084": "chickadee",
-    "n01601694": "water_ouzel",
-    "n01608432": "kite",
-    "n01614925": "bald_eagle",
-    "n01616318": "vulture",
-    "n01622779": "great_grey_owl",
-    "n01629819": "European_fire_salamander",
-    "n01630670": "common_newt",
-    "n01631663": "eft",
-    "n01632458": "spotted_salamander",
-    "n01632777": "axolotl",
-    "n01641577": "bullfrog",
-    "n01644373": "tree_frog",
-    "n01644900": "tailed_frog",
-    "n01664065": "loggerhead",
-    "n01665541": "leatherback_turtle",
-    "n01667114": "mud_turtle",
-    "n01667778": "terrapin",
-    "n01669191": "box_turtle",
-    "n01675722": "banded_gecko",
-    "n01677366": "common_iguana",
-    "n01682714": "American_chameleon",
-    "n01685808": "whiptail",
-    "n01687978": "agama",
-    "n01688243": "frilled_lizard",
-    "n01689811": "alligator_lizard",
-    "n01692333": "Gila_monster",
-    "n01693334": "green_lizard",
-    "n01694178": "African_chameleon",
-    "n01695060": "Komodo_dragon",
-    "n01697457": "African_crocodile",
-    "n01698640": "American_alligator",
-    "n01704323": "triceratops",
-    "n01728572": "thunder_snake",
-    "n01728920": "ringneck_snake",
-    "n01729322": "hognose_snake",
-    "n01729977": "green_snake",
-    "n01734418": "king_snake",
-    "n01735189": "garter_snake",
-    "n01737021": "water_snake",
-    "n01739381": "vine_snake",
-    "n01740131": "night_snake",
-    "n01742172": "boa_constrictor",
-    "n01744401": "rock_python",
-    "n01748264": "Indian_cobra",
-    "n01749939": "green_mamba",
-    "n01751748": "sea_snake",
-    "n01753488": "horned_viper",
-    "n01755581": "diamondback",
-    "n01756291": "sidewinder",
-    "n01768244": "trilobite",
-    "n01770081": "harvestman",
-    "n01770393": "scorpion",
-    "n01773157": "black_and_gold_garden_spider",
-    "n01773549": "barn_spider",
-    "n01773797": "garden_spider",
-    "n01774384": "black_widow",
-    "n01774750": "tarantula",
-    "n01775062": "wolf_spider",
-    "n01776313": "tick",
-    "n01784675": "centipede",
-    "n01795545": "black_grouse",
-    "n01796340": "ptarmigan",
-    "n01797886": "ruffed_grouse",
-    "n01798484": "prairie_chicken",
-    "n01806143": "peacock",
-    "n01806567": "quail",
-    "n01807496": "partridge",
-    "n01817953": "African_grey",
-    "n01818515": "macaw",
-    "n01819313": "sulphur-crested_cockatoo",
-    "n01820546": "lorikeet",
-    "n01824575": "coucal",
-    "n01828970": "bee_eater",
-    "n01829413": "hornbill",
-    "n01833805": "hummingbird",
-    "n01843065": "jacamar",
-    "n01843383": "toucan",
-    "n01847000": "drake",
-    "n01855032": "red-breasted_merganser",
-    "n01855672": "goose",
-    "n01860187": "black_swan",
-    "n01871265": "tusker",
-    "n01872401": "echidna",
-    "n01873310": "platypus",
-    "n01877812": "wallaby",
-    "n01882714": "koala",
-    "n01883070": "wombat",
-    "n01910747": "jellyfish",
-    "n01914609": "sea_anemone",
-    "n01917289": "brain_coral",
-    "n01924916": "flatworm",
-    "n01930112": "nematode",
-    "n01943899": "conch",
-    "n01944390": "snail",
-    "n01945685": "slug",
-    "n01950731": "sea_slug",
-    "n01955084": "chiton",
-    "n01968897": "chambered_nautilus",
-    "n01978287": "Dungeness_crab",
-    "n01978455": "rock_crab",
-    "n01980166": "fiddler_crab",
-    "n01981276": "king_crab",
-    "n01983481": "American_lobster",
-    "n01984695": "spiny_lobster",
-    "n01985128": "crayfish",
-    "n01986214": "hermit_crab",
-    "n01990800": "isopod",
-    "n02002556": "white_stork",
-    "n02002724": "black_stork",
-    "n02006656": "spoonbill",
-    "n02007558": "flamingo",
-    "n02009229": "little_blue_heron",
-    "n02009912": "American_egret",
-    "n02011460": "bittern",
-    "n02012849": "crane",
-    "n02013706": "limpkin",
-    "n02017213": "European_gallinule",
-    "n02018207": "American_coot",
-    "n02018795": "bustard",
-    "n02025239": "ruddy_turnstone",
-    "n02027492": "red-backed_sandpiper",
-    "n02028035": "redshank",
-    "n02033041": "dowitcher",
-    "n02037110": "oystercatcher",
-    "n02051845": "pelican",
-    "n02056570": "king_penguin",
-    "n02058221": "albatross",
-    "n02066245": "grey_whale",
-    "n02071294": "killer_whale",
-    "n02074367": "dugong",
-    "n02077923": "sea_lion",
-    "n02085620": "Chihuahua",
-    "n02085782": "Japanese_spaniel",
-    "n02085936": "Maltese_dog",
-    "n02086079": "Pekinese",
-    "n02086240": "Shih-Tzu",
-    "n02086646": "Blenheim_spaniel",
-    "n02086910": "papillon",
-    "n02087046": "toy_terrier",
-    "n02087394": "Rhodesian_ridgeback",
-    "n02088094": "Afghan_hound",
-    "n02088238": "basset",
-    "n02088364": "beagle",
-    "n02088466": "bloodhound",
-    "n02088632": "bluetick",
-    "n02089078": "black-and-tan_coonhound",
-    "n02089867": "Walker_hound",
-    "n02089973": "English_foxhound",
-    "n02090379": "redbone",
-    "n02090622": "borzoi",
-    "n02090721": "Irish_wolfhound",
-    "n02091032": "Italian_greyhound",
-    "n02091134": "whippet",
-    "n02091244": "Ibizan_hound",
-    "n02091467": "Norwegian_elkhound",
-    "n02091635": "otterhound",
-    "n02091831": "Saluki",
-    "n02092002": "Scottish_deerhound",
-    "n02092339": "Weimaraner",
-    "n02093256": "Staffordshire_bullterrier",
-    "n02093428": "American_Staffordshire_terrier",
-    "n02093647": "Bedlington_terrier",
-    "n02093754": "Border_terrier",
-    "n02093859": "Kerry_blue_terrier",
-    "n02093991": "Irish_terrier",
-    "n02094114": "Norfolk_terrier",
-    "n02094258": "Norwich_terrier",
-    "n02094433": "Yorkshire_terrier",
-    "n02095314": "wire-haired_fox_terrier",
-    "n02095570": "Lakeland_terrier",
-    "n02095889": "Sealyham_terrier",
-    "n02096051": "Airedale",
-    "n02096177": "cairn",
-    "n02096294": "Australian_terrier",
-    "n02096437": "Dandie_Dinmont",
-    "n02096585": "Boston_bull",
-    "n02097047": "miniature_schnauzer",
-    "n02097130": "giant_schnauzer",
-    "n02097209": "standard_schnauzer",
-    "n02097298": "Scotch_terrier",
-    "n02097474": "Tibetan_terrier",
-    "n02097658": "silky_terrier",
-    "n02098105": "soft-coated_wheaten_terrier",
-    "n02098286": "West_Highland_white_terrier",
-    "n02098413": "Lhasa",
-    "n02099267": "flat-coated_retriever",
-    "n02099429": "curly-coated_retriever",
-    "n02099601": "golden_retriever",
-    "n02099712": "Labrador_retriever",
-    "n02099849": "Chesapeake_Bay_retriever",
-    "n02100236": "German_short-haired_pointer",
-    "n02100583": "vizsla",
-    "n02100735": "English_setter",
-    "n02100877": "Irish_setter",
-    "n02101006": "Gordon_setter",
-    "n02101388": "Brittany_spaniel",
-    "n02101556": "clumber",
-    "n02102040": "English_springer",
-    "n02102177": "Welsh_springer_spaniel",
-    "n02102318": "cocker_spaniel",
-    "n02102480": "Sussex_spaniel",
-    "n02102973": "Irish_water_spaniel",
-    "n02104029": "kuvasz",
-    "n02104365": "schipperke",
-    "n02105056": "groenendael",
-    "n02105162": "malinois",
-    "n02105251": "briard",
-    "n02105412": "kelpie",
-    "n02105505": "komondor",
-    "n02105641": "Old_English_sheepdog",
-    "n02105855": "Shetland_sheepdog",
-    "n02106030": "collie",
-    "n02106166": "Border_collie",
-    "n02106382": "Bouvier_des_Flandres",
-    "n02106550": "Rottweiler",
-    "n02106662": "German_shepherd",
-    "n02107142": "Doberman",
-    "n02107312": "miniature_pinscher",
-    "n02107574": "Greater_Swiss_Mountain_dog",
-    "n02107683": "Bernese_mountain_dog",
-    "n02107908": "Appenzeller",
-    "n02108000": "EntleBucher",
-    "n02108089": "boxer",
-    "n02108422": "bull_mastiff",
-    "n02108551": "Tibetan_mastiff",
-    "n02108915": "French_bulldog",
-    "n02109047": "Great_Dane",
-    "n02109525": "Saint_Bernard",
-    "n02109961": "Eskimo_dog",
-    "n02110063": "malamute",
-    "n02110185": "Siberian_husky",
-    "n02110341": "dalmatian",
-    "n02110627": "affenpinscher",
-    "n02110806": "basenji",
-    "n02110958": "pug",
-    "n02111129": "Leonberg",
-    "n02111277": "Newfoundland",
-    "n02111500": "Great_Pyrenees",
-    "n02111889": "Samoyed",
-    "n02112018": "Pomeranian",
-    "n02112137": "chow",
-    "n02112350": "keeshond",
-    "n02112706": "Brabancon_griffon",
-    "n02113023": "Pembroke",
-    "n02113186": "Cardigan",
-    "n02113624": "toy_poodle",
-    "n02113712": "miniature_poodle",
-    "n02113799": "standard_poodle",
-    "n02113978": "Mexican_hairless",
-    "n02114367": "timber_wolf",
-    "n02114548": "white_wolf",
-    "n02114712": "red_wolf",
-    "n02114855": "coyote",
-    "n02115641": "dingo",
-    "n02115913": "dhole",
-    "n02116738": "African_hunting_dog",
-    "n02117135": "hyena",
-    "n02119022": "red_fox",
-    "n02119789": "kit_fox",
-    "n02120079": "Arctic_fox",
-    "n02120505": "grey_fox",
-    "n02123045": "tabby",
-    "n02123159": "tiger_cat",
-    "n02123394": "Persian_cat",
-    "n02123597": "Siamese_cat",
-    "n02124075": "Egyptian_cat",
-    "n02125311": "cougar",
-    "n02127052": "lynx",
-    "n02128385": "leopard",
-    "n02128757": "snow_leopard",
-    "n02128925": "jaguar",
-    "n02129165": "lion",
-    "n02129604": "tiger",
-    "n02130308": "cheetah",
-    "n02132136": "brown_bear",
-    "n02133161": "American_black_bear",
-    "n02134084": "ice_bear",
-    "n02134418": "sloth_bear",
-    "n02137549": "mongoose",
-    "n02138441": "meerkat",
-    "n02165105": "tiger_beetle",
-    "n02165456": "ladybug",
-    "n02167151": "ground_beetle",
-    "n02168699": "long-horned_beetle",
-    "n02169497": "leaf_beetle",
-    "n02172182": "dung_beetle",
-    "n02174001": "rhinoceros_beetle",
-    "n02177972": "weevil",
-    "n02190166": "fly",
-    "n02206856": "bee",
-    "n02219486": "ant",
-    "n02226429": "grasshopper",
-    "n02229544": "cricket",
-    "n02231487": "walking_stick",
-    "n02233338": "cockroach",
-    "n02236044": "mantis",
-    "n02256656": "cicada",
-    "n02259212": "leafhopper",
-    "n02264363": "lacewing",
-    "n02268443": "dragonfly",
-    "n02268853": "damselfly",
-    "n02276258": "admiral",
-    "n02277742": "ringlet",
-    "n02279972": "monarch",
-    "n02280649": "cabbage_butterfly",
-    "n02281406": "sulphur_butterfly",
-    "n02281787": "lycaenid",
-    "n02317335": "starfish",
-    "n02319095": "sea_urchin",
-    "n02321529": "sea_cucumber",
-    "n02325366": "wood_rabbit",
-    "n02326432": "hare",
-    "n02328150": "Angora",
-    "n02342885": "hamster",
-    "n02346627": "porcupine",
-    "n02356798": "fox_squirrel",
-    "n02361337": "marmot",
-    "n02363005": "beaver",
-    "n02364673": "guinea_pig",
-    "n02389026": "sorrel",
-    "n02391049": "zebra",
-    "n02395406": "hog",
-    "n02396427": "wild_boar",
-    "n02397096": "warthog",
-    "n02398521": "hippopotamus",
-    "n02403003": "ox",
-    "n02408429": "water_buffalo",
-    "n02410509": "bison",
-    "n02412080": "ram",
-    "n02415577": "bighorn",
-    "n02417914": "ibex",
-    "n02422106": "hartebeest",
-    "n02422699": "impala",
-    "n02423022": "gazelle",
-    "n02437312": "Arabian_camel",
-    "n02437616": "llama",
-    "n02441942": "weasel",
-    "n02442845": "mink",
-    "n02443114": "polecat",
-    "n02443484": "black-footed_ferret",
-    "n02444819": "otter",
-    "n02445715": "skunk",
-    "n02447366": "badger",
-    "n02454379": "armadillo",
-    "n02457408": "three-toed_sloth",
-    "n02480495": "orangutan",
-    "n02480855": "gorilla",
-    "n02481823": "chimpanzee",
-    "n02483362": "gibbon",
-    "n02483708": "siamang",
-    "n02484975": "guenon",
-    "n02486261": "patas",
-    "n02486410": "baboon",
-    "n02487347": "macaque",
-    "n02488291": "langur",
-    "n02488702": "colobus",
-    "n02489166": "proboscis_monkey",
-    "n02490219": "marmoset",
-    "n02492035": "capuchin",
-    "n02492660": "howler_monkey",
-    "n02493509": "titi",
-    "n02493793": "spider_monkey",
-    "n02494079": "squirrel_monkey",
-    "n02497673": "Madagascar_cat",
-    "n02500267": "indri",
-    "n02504013": "Indian_elephant",
-    "n02504458": "African_elephant",
-    "n02509815": "lesser_panda",
-    "n02510455": "giant_panda",
-    "n02514041": "barracouta",
-    "n02526121": "eel",
-    "n02536864": "coho",
-    "n02606052": "rock_beauty",
-    "n02607072": "anemone_fish",
-    "n02640242": "sturgeon",
-    "n02641379": "gar",
-    "n02643566": "lionfish",
-    "n02655020": "puffer",
-    "n02666196": "abacus",
-    "n02667093": "abaya",
-    "n02669723": "academic_gown",
-    "n02672831": "accordion",
-    "n02676566": "acoustic_guitar",
-    "n02687172": "aircraft_carrier",
-    "n02690373": "airliner",
-    "n02692877": "airship",
-    "n02699494": "altar",
-    "n02701002": "ambulance",
-    "n02704792": "amphibian",
-    "n02708093": "analog_clock",
-    "n02727426": "apiary",
-    "n02730930": "apron",
-    "n02747177": "ashcan",
-    "n02749479": "assault_rifle",
-    "n02769748": "backpack",
-    "n02776631": "bakery",
-    "n02777292": "balance_beam",
-    "n02782093": "balloon",
-    "n02783161": "ballpoint",
-    "n02786058": "Band_Aid",
-    "n02787622": "banjo",
-    "n02788148": "bannister",
-    "n02790996": "barbell",
-    "n02791124": "barber_chair",
-    "n02791270": "barbershop",
-    "n02793495": "barn",
-    "n02794156": "barometer",
-    "n02795169": "barrel",
-    "n02797295": "barrow",
-    "n02799071": "baseball",
-    "n02802426": "basketball",
-    "n02804414": "bassinet",
-    "n02804610": "bassoon",
-    "n02807133": "bathing_cap",
-    "n02808304": "bath_towel",
-    "n02808440": "bathtub",
-    "n02814533": "beach_wagon",
-    "n02814860": "beacon",
-    "n02815834": "beaker",
-    "n02817516": "bearskin",
-    "n02823428": "beer_bottle",
-    "n02823750": "beer_glass",
-    "n02825657": "bell_cote",
-    "n02834397": "bib",
-    "n02835271": "bicycle-built-for-two",
-    "n02837789": "bikini",
-    "n02840245": "binder",
-    "n02841315": "binoculars",
-    "n02843684": "birdhouse",
-    "n02859443": "boathouse",
-    "n02860847": "bobsled",
-    "n02865351": "bolo_tie",
-    "n02869837": "bonnet",
-    "n02870880": "bookcase",
-    "n02871525": "bookshop",
-    "n02877765": "bottlecap",
-    "n02879718": "bow",
-    "n02883205": "bow_tie",
-    "n02892201": "brass",
-    "n02892767": "brassiere",
-    "n02894605": "breakwater",
-    "n02895154": "breastplate",
-    "n02906734": "broom",
-    "n02909870": "bucket",
-    "n02910353": "buckle",
-    "n02916936": "bulletproof_vest",
-    "n02917067": "bullet_train",
-    "n02927161": "butcher_shop",
-    "n02930766": "cab",
-    "n02939185": "caldron",
-    "n02948072": "candle",
-    "n02950826": "cannon",
-    "n02951358": "canoe",
-    "n02951585": "can_opener",
-    "n02963159": "cardigan",
-    "n02965783": "car_mirror",
-    "n02966193": "carousel",
-    "n02966687": "carpenter's_kit",
-    "n02971356": "carton",
-    "n02974003": "car_wheel",
-    "n02977058": "cash_machine",
-    "n02978881": "cassette",
-    "n02979186": "cassette_player",
-    "n02980441": "castle",
-    "n02981792": "catamaran",
-    "n02988304": "CD_player",
-    "n02992211": "cello",
-    "n02992529": "cellular_telephone",
-    "n02999410": "chain",
-    "n03000134": "chainlink_fence",
-    "n03000247": "chain_mail",
-    "n03000684": "chain_saw",
-    "n03014705": "chest",
-    "n03016953": "chiffonier",
-    "n03017168": "chime",
-    "n03018349": "china_cabinet",
-    "n03026506": "Christmas_stocking",
-    "n03028079": "church",
-    "n03032252": "cinema",
-    "n03041632": "cleaver",
-    "n03042490": "cliff_dwelling",
-    "n03045698": "cloak",
-    "n03047690": "clog",
-    "n03062245": "cocktail_shaker",
-    "n03063599": "coffee_mug",
-    "n03063689": "coffeepot",
-    "n03065424": "coil",
-    "n03075370": "combination_lock",
-    "n03085013": "computer_keyboard",
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imagenette-160px/imagenette-train.parquet

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+oid sha256:df9027d02d6d6248e3c8053566cfdb1365db4f2eaff15181325230884ad6ab69
+size 74689471

imagenette-160px/imagenette-validation.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba73baad05c7ae37119f9e16de02e305024969dc259b42a7e98308c84d082c2a
+size 31007275

imagenette.py

@@ -1,182 +0,0 @@
-import os
-import json
-from pathlib import Path
-
-import datasets
-from datasets.tasks import ImageClassification
-
-# Adapted from https://huggingface.co/datasets/nateraw/imagenette/blob/main/imagenette.py
-
-_CITATION = """
-@misc{imagenette,
-  author    = "Jeremy Howard",
-  title     = "imagenette",
-  url       = "https://github.com/fastai/imagenette/"
-}
-"""
-
-_DESCRIPTION = """\
-# ImageNette
-
-Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
-
-'Imagenette' is pronounced just like 'Imagenet', except with a corny inauthentic French accent.
-If you've seen Peter Sellars in The Pink Panther, then think something like that.
-It's important to ham up the accent as much as possible, otherwise people might not be sure whether you're refering to "Imagenette" or "Imagenet".
-(Note to native French speakers: to avoid confusion, be sure to use a corny inauthentic American accent when saying "Imagenet".
-Think something like the philosophy restaurant skit from Monty Python's The Meaning of Life.)
-
-This version of the dataset allows researchers/practitioners to quickly try out
-ideas and share with others. The dataset comes in three variants:
-  * Full size
-  * 320 px
-  * 160 px
-
-The '320 px' and '160 px' versions have their shortest side resized to that size, with their aspect ratio maintained.
-
-
-Too easy for you? In that case, you might want to try Imagewoof.
-
-# Imagewoof
-Imagewoof is a subset of 10 classes from Imagenet that aren't so easy to classify, since they're all dog breeds.
-The breeds are: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, Old English sheepdog.
-(No we will not enter in to any discussion in to whether a dingo is in fact a dog.
-Any suggestions to the contrary are un-Australian. Thank you for your cooperation.)
-
-Full size download;
-320 px download;
-160 px download.
-"""
-
-_URL_PREFIX = "https://s3.amazonaws.com/fast-ai-imageclas/"
-_URL_IMAGENET_REFS = 'https://huggingface.co/datasets/jerpint/imagenette/raw/main/imagenet_refs.json'
-
-_LABELS = {
-    "imagenette": [
-        "cassette_player",
-        "chain_saw",
-        "church",
-        "English_springer",
-        "French_horn",
-        "garbage_truck",
-        "gas_pump",
-        "golf_ball",
-        "parachute",
-        "tench",
-    ],
-    "imagewoof": [
-        "Australian_terrier",
-        "beagle",
-        "Border_terrier",
-        "dingo",
-        "English_foxhound",
-        "golden_retriever",
-        "Old_English_sheepdog",
-        "Rhodesian_ridgeback",
-        "Samoyed",
-        "Shih-Tzu",
-    ],
-}
-
-
-_NAME_TO_DIR = {
-    "imagenette-full-res": "imagenette2",
-    "imagenette-320px": "imagenette2-320",
-    "imagenette-160px": "imagenette2-160",
-    "imagewoof-full-res": "imagewoof2",
-    "imagewoof-320px": "imagewoof2-320",
-    "imagewoof-160px": "imagewoof2-160",
-}
-
-
-class ImagenetteConfig(datasets.BuilderConfig):
-    """BuilderConfig for Imagenette."""
-
-    def __init__(self, name, **kwargs):
-        super(ImagenetteConfig, self).__init__(
-            name=name, description="{} version.".format(name), **kwargs
-        )
-
-        self.dataset = name.split("-")[0]
-        self.labels = _LABELS[self.dataset]
-        self.name = name
-
-
-
-def _make_builder_configs():
-    return [ImagenetteConfig(name) for name in _NAME_TO_DIR]
-
-
-class Imagenette(datasets.GeneratorBasedBuilder):
-    """A smaller subset of 10 easily classified classes from Imagenet."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = _make_builder_configs()
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # builder=self,
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "labels": datasets.ClassLabel(names=self.config.labels),
-                }
-            ),
-            supervised_keys=("path", "labels"),
-            homepage="https://github.com/fastai/imagenette",
-            citation=_CITATION,
-            task_templates=[
-                ImageClassification(
-                    image_column="path",
-                    label_column="labels",
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        print(self.__dict__.keys())
-        print(self.config)
-        name = self.config.name
-        dirname = _NAME_TO_DIR[name]
-
-                
-        # Download the ref:label map for imagenet
-        refs_path = dl_manager.download(_URL_IMAGENET_REFS)
-        with open(refs_path) as f:
-            self.ref_to_label = json.load(f)
-            
-           
-        url = _URL_PREFIX + "{}.tgz".format(dirname)
-        path = dl_manager.download_and_extract(url)
-        train_path = os.path.join(path, dirname, "train")
-        val_path = os.path.join(path, dirname, "val")
-        assert os.path.exists(train_path)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "datapath": train_path,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "datapath": val_path,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, datapath):
-        """Yields examples."""
-
-        for path in Path(datapath).glob("**/*.JPEG"):
-            record = {
-                # In Imagenette, the parent folder of the file is
-                # the imagenet reference to the label name.
-                "image": str(path),
-                "labels": self.ref_to_label[path.parent.name],
-            }
-            yield path.name, record