image to bytes from main process to worker processes for massive multiprocessing speedup

ForNet.py

@@ -8,7 +8,6 @@ import queue
 import re
 import urllib
 import zipfile
-from collections import OrderedDict
 from math import floor
 from typing import Optional
 
@@ -17,1019 +16,16 @@ import numpy as np
 from datasets import config
 from datasets.arrow_dataset import Dataset
 from datasets.arrow_reader import ArrowReader
+from datasets.features.image import image_to_bytes
 from datasets.fingerprint import Hasher
-from PIL import ImageFilter
+from PIL import Image, ImageFilter
 from torchvision import transforms as T
 from tqdm import tqdm
 
-logger = datasets.logging.get_logger(__name__)
+from classes import IMAGENET2012_CLASSES
 
+logger = datasets.logging.get_logger(__name__)
 
-# taken from https://huggingface.co/datasets/ILSVRC/imagenet-1k/blob/main/classes.py
-IMAGENET2012_CLASSES = OrderedDict(
-    {
-        "n01440764": "tench, Tinca tinca",
-        "n01443537": "goldfish, Carassius auratus",
-        "n01484850": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
-        "n01491361": "tiger shark, Galeocerdo cuvieri",
-        "n01494475": "hammerhead, hammerhead shark",
-        "n01496331": "electric ray, crampfish, numbfish, torpedo",
-        "n01498041": "stingray",
-        "n01514668": "cock",
-        "n01514859": "hen",
-        "n01518878": "ostrich, Struthio camelus",
-        "n01530575": "brambling, Fringilla montifringilla",
-        "n01531178": "goldfinch, Carduelis carduelis",
-        "n01532829": "house finch, linnet, Carpodacus mexicanus",
-        "n01534433": "junco, snowbird",
-        "n01537544": "indigo bunting, indigo finch, indigo bird, Passerina cyanea",
-        "n01558993": "robin, American robin, Turdus migratorius",
-        "n01560419": "bulbul",
-        "n01580077": "jay",
-        "n01582220": "magpie",
-        "n01592084": "chickadee",
-        "n01601694": "water ouzel, dipper",
-        "n01608432": "kite",
-        "n01614925": "bald eagle, American eagle, Haliaeetus leucocephalus",
-        "n01616318": "vulture",
-        "n01622779": "great grey owl, great gray owl, Strix nebulosa",
-        "n01629819": "European fire salamander, Salamandra salamandra",
-        "n01630670": "common newt, Triturus vulgaris",
-        "n01631663": "eft",
-        "n01632458": "spotted salamander, Ambystoma maculatum",
-        "n01632777": "axolotl, mud puppy, Ambystoma mexicanum",
-        "n01641577": "bullfrog, Rana catesbeiana",
-        "n01644373": "tree frog, tree-frog",
-        "n01644900": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
-        "n01664065": "loggerhead, loggerhead turtle, Caretta caretta",
-        "n01665541": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
-        "n01667114": "mud turtle",
-        "n01667778": "terrapin",
-        "n01669191": "box turtle, box tortoise",
-        "n01675722": "banded gecko",
-        "n01677366": "common iguana, iguana, Iguana iguana",
-        "n01682714": "American chameleon, anole, Anolis carolinensis",
-        "n01685808": "whiptail, whiptail lizard",
-        "n01687978": "agama",
-        "n01688243": "frilled lizard, Chlamydosaurus kingi",
-        "n01689811": "alligator lizard",
-        "n01692333": "Gila monster, Heloderma suspectum",
-        "n01693334": "green lizard, Lacerta viridis",
-        "n01694178": "African chameleon, Chamaeleo chamaeleon",
-        "n01695060": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
-        "n01697457": "African crocodile, Nile crocodile, Crocodylus niloticus",
-        "n01698640": "American alligator, Alligator mississipiensis",
-        "n01704323": "triceratops",
-        "n01728572": "thunder snake, worm snake, Carphophis amoenus",
-        "n01728920": "ringneck snake, ring-necked snake, ring snake",
-        "n01729322": "hognose snake, puff adder, sand viper",
-        "n01729977": "green snake, grass snake",
-        "n01734418": "king snake, kingsnake",
-        "n01735189": "garter snake, grass snake",
-        "n01737021": "water snake",
-        "n01739381": "vine snake",
-        "n01740131": "night snake, Hypsiglena torquata",
-        "n01742172": "boa constrictor, Constrictor constrictor",
-        "n01744401": "rock python, rock snake, Python sebae",
-        "n01748264": "Indian cobra, Naja naja",
-        "n01749939": "green mamba",
-        "n01751748": "sea snake",
-        "n01753488": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
-        "n01755581": "diamondback, diamondback rattlesnake, Crotalus adamanteus",
-        "n01756291": "sidewinder, horned rattlesnake, Crotalus cerastes",
-        "n01768244": "trilobite",
-        "n01770081": "harvestman, daddy longlegs, Phalangium opilio",
-        "n01770393": "scorpion",
-        "n01773157": "black and gold garden spider, Argiope aurantia",
-        "n01773549": "barn spider, Araneus cavaticus",
-        "n01773797": "garden spider, Aranea diademata",
-        "n01774384": "black widow, Latrodectus mactans",
-        "n01774750": "tarantula",
-        "n01775062": "wolf spider, hunting spider",
-        "n01776313": "tick",
-        "n01784675": "centipede",
-        "n01795545": "black grouse",
-        "n01796340": "ptarmigan",
-        "n01797886": "ruffed grouse, partridge, Bonasa umbellus",
-        "n01798484": "prairie chicken, prairie grouse, prairie fowl",
-        "n01806143": "peacock",
-        "n01806567": "quail",
-        "n01807496": "partridge",
-        "n01817953": "African grey, African gray, Psittacus erithacus",
-        "n01818515": "macaw",
-        "n01819313": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
-        "n01820546": "lorikeet",
-        "n01824575": "coucal",
-        "n01828970": "bee eater",
-        "n01829413": "hornbill",
-        "n01833805": "hummingbird",
-        "n01843065": "jacamar",
-        "n01843383": "toucan",
-        "n01847000": "drake",
-        "n01855032": "red-breasted merganser, Mergus serrator",
-        "n01855672": "goose",
-        "n01860187": "black swan, Cygnus atratus",
-        "n01871265": "tusker",
-        "n01872401": "echidna, spiny anteater, anteater",
-        "n01873310": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
-        "n01877812": "wallaby, brush kangaroo",
-        "n01882714": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
-        "n01883070": "wombat",
-        "n01910747": "jellyfish",
-        "n01914609": "sea anemone, anemone",
-        "n01917289": "brain coral",
-        "n01924916": "flatworm, platyhelminth",
-        "n01930112": "nematode, nematode worm, roundworm",
-        "n01943899": "conch",
-        "n01944390": "snail",
-        "n01945685": "slug",
-        "n01950731": "sea slug, nudibranch",
-        "n01955084": "chiton, coat-of-mail shell, sea cradle, polyplacophore",
-        "n01968897": "chambered nautilus, pearly nautilus, nautilus",
-        "n01978287": "Dungeness crab, Cancer magister",
-        "n01978455": "rock crab, Cancer irroratus",
-        "n01980166": "fiddler crab",
-        "n01981276": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
-        "n01983481": "American lobster, Northern lobster, Maine lobster, Homarus americanus",
-        "n01984695": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
-        "n01985128": "crayfish, crawfish, crawdad, crawdaddy",
-        "n01986214": "hermit crab",
-        "n01990800": "isopod",
-        "n02002556": "white stork, Ciconia ciconia",
-        "n02002724": "black stork, Ciconia nigra",
-        "n02006656": "spoonbill",
-        "n02007558": "flamingo",
-        "n02009229": "little blue heron, Egretta caerulea",
-        "n02009912": "American egret, great white heron, Egretta albus",
-        "n02011460": "bittern",
-        "n02012849": "crane",
-        "n02013706": "limpkin, Aramus pictus",
-        "n02017213": "European gallinule, Porphyrio porphyrio",
-        "n02018207": "American coot, marsh hen, mud hen, water hen, Fulica americana",
-        "n02018795": "bustard",
-        "n02025239": "ruddy turnstone, Arenaria interpres",
-        "n02027492": "red-backed sandpiper, dunlin, Erolia alpina",
-        "n02028035": "redshank, Tringa totanus",
-        "n02033041": "dowitcher",
-        "n02037110": "oystercatcher, oyster catcher",
-        "n02051845": "pelican",
-        "n02056570": "king penguin, Aptenodytes patagonica",
-        "n02058221": "albatross, mollymawk",
-        "n02066245": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
-        "n02071294": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
-        "n02074367": "dugong, Dugong dugon",
-        "n02077923": "sea lion",
-        "n02085620": "Chihuahua",
-        "n02085782": "Japanese spaniel",
-        "n02085936": "Maltese dog, Maltese terrier, Maltese",
-        "n02086079": "Pekinese, Pekingese, Peke",
-        "n02086240": "Shih-Tzu",
-        "n02086646": "Blenheim spaniel",
-        "n02086910": "papillon",
-        "n02087046": "toy terrier",
-        "n02087394": "Rhodesian ridgeback",
-        "n02088094": "Afghan hound, Afghan",
-        "n02088238": "basset, basset hound",
-        "n02088364": "beagle",
-        "n02088466": "bloodhound, sleuthhound",
-        "n02088632": "bluetick",
-        "n02089078": "black-and-tan coonhound",
-        "n02089867": "Walker hound, Walker foxhound",
-        "n02089973": "English foxhound",
-        "n02090379": "redbone",
-        "n02090622": "borzoi, Russian wolfhound",
-        "n02090721": "Irish wolfhound",
-        "n02091032": "Italian greyhound",
-        "n02091134": "whippet",
-        "n02091244": "Ibizan hound, Ibizan Podenco",
-        "n02091467": "Norwegian elkhound, elkhound",
-        "n02091635": "otterhound, otter hound",
-        "n02091831": "Saluki, gazelle hound",
-        "n02092002": "Scottish deerhound, deerhound",
-        "n02092339": "Weimaraner",
-        "n02093256": "Staffordshire bullterrier, Staffordshire bull terrier",
-        "n02093428": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull 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, Sealyham",
-        "n02096051": "Airedale, Airedale terrier",
-        "n02096177": "cairn, cairn terrier",
-        "n02096294": "Australian terrier",
-        "n02096437": "Dandie Dinmont, Dandie Dinmont terrier",
-        "n02096585": "Boston bull, Boston terrier",
-        "n02097047": "miniature schnauzer",
-        "n02097130": "giant schnauzer",
-        "n02097209": "standard schnauzer",
-        "n02097298": "Scotch terrier, Scottish terrier, Scottie",
-        "n02097474": "Tibetan terrier, chrysanthemum dog",
-        "n02097658": "silky terrier, Sydney silky",
-        "n02098105": "soft-coated wheaten terrier",
-        "n02098286": "West Highland white terrier",
-        "n02098413": "Lhasa, Lhasa apso",
-        "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, Hungarian pointer",
-        "n02100735": "English setter",
-        "n02100877": "Irish setter, red setter",
-        "n02101006": "Gordon setter",
-        "n02101388": "Brittany spaniel",
-        "n02101556": "clumber, clumber spaniel",
-        "n02102040": "English springer, English springer spaniel",
-        "n02102177": "Welsh springer spaniel",
-        "n02102318": "cocker spaniel, English cocker spaniel, cocker",
-        "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, bobtail",
-        "n02105855": "Shetland sheepdog, Shetland sheep dog, Shetland",
-        "n02106030": "collie",
-        "n02106166": "Border collie",
-        "n02106382": "Bouvier des Flandres, Bouviers des Flandres",
-        "n02106550": "Rottweiler",
-        "n02106662": "German shepherd, German shepherd dog, German police dog, alsatian",
-        "n02107142": "Doberman, Doberman pinscher",
-        "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, St Bernard",
-        "n02109961": "Eskimo dog, husky",
-        "n02110063": "malamute, malemute, Alaskan malamute",
-        "n02110185": "Siberian husky",
-        "n02110341": "dalmatian, coach dog, carriage dog",
-        "n02110627": "affenpinscher, monkey pinscher, monkey dog",
-        "n02110806": "basenji",
-        "n02110958": "pug, pug-dog",
-        "n02111129": "Leonberg",
-        "n02111277": "Newfoundland, Newfoundland dog",
-        "n02111500": "Great Pyrenees",
-        "n02111889": "Samoyed, Samoyede",
-        "n02112018": "Pomeranian",
-        "n02112137": "chow, chow chow",
-        "n02112350": "keeshond",
-        "n02112706": "Brabancon griffon",
-        "n02113023": "Pembroke, Pembroke Welsh corgi",
-        "n02113186": "Cardigan, Cardigan Welsh corgi",
-        "n02113624": "toy poodle",
-        "n02113712": "miniature poodle",
-        "n02113799": "standard poodle",
-        "n02113978": "Mexican hairless",
-        "n02114367": "timber wolf, grey wolf, gray wolf, Canis lupus",
-        "n02114548": "white wolf, Arctic wolf, Canis lupus tundrarum",
-        "n02114712": "red wolf, maned wolf, Canis rufus, Canis niger",
-        "n02114855": "coyote, prairie wolf, brush wolf, Canis latrans",
-        "n02115641": "dingo, warrigal, warragal, Canis dingo",
-        "n02115913": "dhole, Cuon alpinus",
-        "n02116738": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
-        "n02117135": "hyena, hyaena",
-        "n02119022": "red fox, Vulpes vulpes",
-        "n02119789": "kit fox, Vulpes macrotis",
-        "n02120079": "Arctic fox, white fox, Alopex lagopus",
-        "n02120505": "grey fox, gray fox, Urocyon cinereoargenteus",
-        "n02123045": "tabby, tabby cat",
-        "n02123159": "tiger cat",
-        "n02123394": "Persian cat",
-        "n02123597": "Siamese cat, Siamese",
-        "n02124075": "Egyptian cat",
-        "n02125311": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
-        "n02127052": "lynx, catamount",
-        "n02128385": "leopard, Panthera pardus",
-        "n02128757": "snow leopard, ounce, Panthera uncia",
-        "n02128925": "jaguar, panther, Panthera onca, Felis onca",
-        "n02129165": "lion, king of beasts, Panthera leo",
-        "n02129604": "tiger, Panthera tigris",
-        "n02130308": "cheetah, chetah, Acinonyx jubatus",
-        "n02132136": "brown bear, bruin, Ursus arctos",
-        "n02133161": "American black bear, black bear, Ursus americanus, Euarctos americanus",
-        "n02134084": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
-        "n02134418": "sloth bear, Melursus ursinus, Ursus ursinus",
-        "n02137549": "mongoose",
-        "n02138441": "meerkat, mierkat",
-        "n02165105": "tiger beetle",
-        "n02165456": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
-        "n02167151": "ground beetle, carabid beetle",
-        "n02168699": "long-horned beetle, longicorn, longicorn beetle",
-        "n02169497": "leaf beetle, chrysomelid",
-        "n02172182": "dung beetle",
-        "n02174001": "rhinoceros beetle",
-        "n02177972": "weevil",
-        "n02190166": "fly",
-        "n02206856": "bee",
-        "n02219486": "ant, emmet, pismire",
-        "n02226429": "grasshopper, hopper",
-        "n02229544": "cricket",
-        "n02231487": "walking stick, walkingstick, stick insect",
-        "n02233338": "cockroach, roach",
-        "n02236044": "mantis, mantid",
-        "n02256656": "cicada, cicala",
-        "n02259212": "leafhopper",
-        "n02264363": "lacewing, lacewing fly",
-        "n02268443": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
-        "n02268853": "damselfly",
-        "n02276258": "admiral",
-        "n02277742": "ringlet, ringlet butterfly",
-        "n02279972": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
-        "n02280649": "cabbage butterfly",
-        "n02281406": "sulphur butterfly, sulfur butterfly",
-        "n02281787": "lycaenid, lycaenid butterfly",
-        "n02317335": "starfish, sea star",
-        "n02319095": "sea urchin",
-        "n02321529": "sea cucumber, holothurian",
-        "n02325366": "wood rabbit, cottontail, cottontail rabbit",
-        "n02326432": "hare",
-        "n02328150": "Angora, Angora rabbit",
-        "n02342885": "hamster",
-        "n02346627": "porcupine, hedgehog",
-        "n02356798": "fox squirrel, eastern fox squirrel, Sciurus niger",
-        "n02361337": "marmot",
-        "n02363005": "beaver",
-        "n02364673": "guinea pig, Cavia cobaya",
-        "n02389026": "sorrel",
-        "n02391049": "zebra",
-        "n02395406": "hog, pig, grunter, squealer, Sus scrofa",
-        "n02396427": "wild boar, boar, Sus scrofa",
-        "n02397096": "warthog",
-        "n02398521": "hippopotamus, hippo, river horse, Hippopotamus amphibius",
-        "n02403003": "ox",
-        "n02408429": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
-        "n02410509": "bison",
-        "n02412080": "ram, tup",
-        "n02415577": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
-        "n02417914": "ibex, Capra ibex",
-        "n02422106": "hartebeest",
-        "n02422699": "impala, Aepyceros melampus",
-        "n02423022": "gazelle",
-        "n02437312": "Arabian camel, dromedary, Camelus dromedarius",
-        "n02437616": "llama",
-        "n02441942": "weasel",
-        "n02442845": "mink",
-        "n02443114": "polecat, fitch, foulmart, foumart, Mustela putorius",
-        "n02443484": "black-footed ferret, ferret, Mustela nigripes",
-        "n02444819": "otter",
-        "n02445715": "skunk, polecat, wood pussy",
-        "n02447366": "badger",
-        "n02454379": "armadillo",
-        "n02457408": "three-toed sloth, ai, Bradypus tridactylus",
-        "n02480495": "orangutan, orang, orangutang, Pongo pygmaeus",
-        "n02480855": "gorilla, Gorilla gorilla",
-        "n02481823": "chimpanzee, chimp, Pan troglodytes",
-        "n02483362": "gibbon, Hylobates lar",
-        "n02483708": "siamang, Hylobates syndactylus, Symphalangus syndactylus",
-        "n02484975": "guenon, guenon monkey",
-        "n02486261": "patas, hussar monkey, Erythrocebus patas",
-        "n02486410": "baboon",
-        "n02487347": "macaque",
-        "n02488291": "langur",
-        "n02488702": "colobus, colobus monkey",
-        "n02489166": "proboscis monkey, Nasalis larvatus",
-        "n02490219": "marmoset",
-        "n02492035": "capuchin, ringtail, Cebus capucinus",
-        "n02492660": "howler monkey, howler",
-        "n02493509": "titi, titi monkey",
-        "n02493793": "spider monkey, Ateles geoffroyi",
-        "n02494079": "squirrel monkey, Saimiri sciureus",
-        "n02497673": "Madagascar cat, ring-tailed lemur, Lemur catta",
-        "n02500267": "indri, indris, Indri indri, Indri brevicaudatus",
-        "n02504013": "Indian elephant, Elephas maximus",
-        "n02504458": "African elephant, Loxodonta africana",
-        "n02509815": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
-        "n02510455": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
-        "n02514041": "barracouta, snoek",
-        "n02526121": "eel",
-        "n02536864": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
-        "n02606052": "rock beauty, Holocanthus tricolor",
-        "n02607072": "anemone fish",
-        "n02640242": "sturgeon",
-        "n02641379": "gar, garfish, garpike, billfish, Lepisosteus osseus",
-        "n02643566": "lionfish",
-        "n02655020": "puffer, pufferfish, blowfish, globefish",
-        "n02666196": "abacus",
-        "n02667093": "abaya",
-        "n02669723": "academic gown, academic robe, judge's robe",
-        "n02672831": "accordion, piano accordion, squeeze box",
-        "n02676566": "acoustic guitar",
-        "n02687172": "aircraft carrier, carrier, flattop, attack aircraft carrier",
-        "n02690373": "airliner",
-        "n02692877": "airship, dirigible",
-        "n02699494": "altar",
-        "n02701002": "ambulance",
-        "n02704792": "amphibian, amphibious vehicle",
-        "n02708093": "analog clock",
-        "n02727426": "apiary, bee house",
-        "n02730930": "apron",
-        "n02747177": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
-        "n02749479": "assault rifle, assault gun",
-        "n02769748": "backpack, back pack, knapsack, packsack, rucksack, haversack",
-        "n02776631": "bakery, bakeshop, bakehouse",
-        "n02777292": "balance beam, beam",
-        "n02782093": "balloon",
-        "n02783161": "ballpoint, ballpoint pen, ballpen, Biro",
-        "n02786058": "Band Aid",
-        "n02787622": "banjo",
-        "n02788148": "bannister, banister, balustrade, balusters, handrail",
-        "n02790996": "barbell",
-        "n02791124": "barber chair",
-        "n02791270": "barbershop",
-        "n02793495": "barn",
-        "n02794156": "barometer",
-        "n02795169": "barrel, cask",
-        "n02797295": "barrow, garden cart, lawn cart, wheelbarrow",
-        "n02799071": "baseball",
-        "n02802426": "basketball",
-        "n02804414": "bassinet",
-        "n02804610": "bassoon",
-        "n02807133": "bathing cap, swimming cap",
-        "n02808304": "bath towel",
-        "n02808440": "bathtub, bathing tub, bath, tub",
-        "n02814533": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
-        "n02814860": "beacon, lighthouse, beacon light, pharos",
-        "n02815834": "beaker",
-        "n02817516": "bearskin, busby, shako",
-        "n02823428": "beer bottle",
-        "n02823750": "beer glass",
-        "n02825657": "bell cote, bell cot",
-        "n02834397": "bib",
-        "n02835271": "bicycle-built-for-two, tandem bicycle, tandem",
-        "n02837789": "bikini, two-piece",
-        "n02840245": "binder, ring-binder",
-        "n02841315": "binoculars, field glasses, opera glasses",
-        "n02843684": "birdhouse",
-        "n02859443": "boathouse",
-        "n02860847": "bobsled, bobsleigh, bob",
-        "n02865351": "bolo tie, bolo, bola tie, bola",
-        "n02869837": "bonnet, poke bonnet",
-        "n02870880": "bookcase",
-        "n02871525": "bookshop, bookstore, bookstall",
-        "n02877765": "bottlecap",
-        "n02879718": "bow",
-        "n02883205": "bow tie, bow-tie, bowtie",
-        "n02892201": "brass, memorial tablet, plaque",
-        "n02892767": "brassiere, bra, bandeau",
-        "n02894605": "breakwater, groin, groyne, mole, bulwark, seawall, jetty",
-        "n02895154": "breastplate, aegis, egis",
-        "n02906734": "broom",
-        "n02909870": "bucket, pail",
-        "n02910353": "buckle",
-        "n02916936": "bulletproof vest",
-        "n02917067": "bullet train, bullet",
-        "n02927161": "butcher shop, meat market",
-        "n02930766": "cab, hack, taxi, taxicab",
-        "n02939185": "caldron, cauldron",
-        "n02948072": "candle, taper, wax light",
-        "n02950826": "cannon",
-        "n02951358": "canoe",
-        "n02951585": "can opener, tin opener",
-        "n02963159": "cardigan",
-        "n02965783": "car mirror",
-        "n02966193": "carousel, carrousel, merry-go-round, roundabout, whirligig",
-        "n02966687": "carpenter's kit, tool kit",
-        "n02971356": "carton",
-        "n02974003": "car wheel",
-        "n02977058": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
-        "n02978881": "cassette",
-        "n02979186": "cassette player",
-        "n02980441": "castle",
-        "n02981792": "catamaran",
-        "n02988304": "CD player",
-        "n02992211": "cello, violoncello",
-        "n02992529": "cellular telephone, cellular phone, cellphone, cell, mobile phone",
-        "n02999410": "chain",
-        "n03000134": "chainlink fence",
-        "n03000247": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
-        "n03000684": "chain saw, chainsaw",
-        "n03014705": "chest",
-        "n03016953": "chiffonier, commode",
-        "n03017168": "chime, bell, gong",
-        "n03018349": "china cabinet, china closet",
-        "n03026506": "Christmas stocking",
-        "n03028079": "church, church building",
-        "n03032252": "cinema, movie theater, movie theatre, movie house, picture palace",
-        "n03041632": "cleaver, meat cleaver, chopper",
-        "n03042490": "cliff dwelling",
-        "n03045698": "cloak",
-        "n03047690": "clog, geta, patten, sabot",
-        "n03062245": "cocktail shaker",
-        "n03063599": "coffee mug",
-        "n03063689": "coffeepot",
-        "n03065424": "coil, spiral, volute, whorl, helix",
-        "n03075370": "combination lock",
-        "n03085013": "computer keyboard, keypad",
-        "n03089624": "confectionery, confectionary, candy store",
-        "n03095699": "container ship, containership, container vessel",
-        "n03100240": "convertible",
-        "n03109150": "corkscrew, bottle screw",
-        "n03110669": "cornet, horn, trumpet, trump",
-        "n03124043": "cowboy boot",
-        "n03124170": "cowboy hat, ten-gallon hat",
-        "n03125729": "cradle",
-        "n03126707": "crane2",
-        "n03127747": "crash helmet",
-        "n03127925": "crate",
-        "n03131574": "crib, cot",
-        "n03133878": "Crock Pot",
-        "n03134739": "croquet ball",
-        "n03141823": "crutch",
-        "n03146219": "cuirass",
-        "n03160309": "dam, dike, dyke",
-        "n03179701": "desk",
-        "n03180011": "desktop computer",
-        "n03187595": "dial telephone, dial phone",
-        "n03188531": "diaper, nappy, napkin",
-        "n03196217": "digital clock",
-        "n03197337": "digital watch",
-        "n03201208": "dining table, board",
-        "n03207743": "dishrag, dishcloth",
-        "n03207941": "dishwasher, dish washer, dishwashing machine",
-        "n03208938": "disk brake, disc brake",
-        "n03216828": "dock, dockage, docking facility",
-        "n03218198": "dogsled, dog sled, dog sleigh",
-        "n03220513": "dome",
-        "n03223299": "doormat, welcome mat",
-        "n03240683": "drilling platform, offshore rig",
-        "n03249569": "drum, membranophone, tympan",
-        "n03250847": "drumstick",
-        "n03255030": "dumbbell",
-        "n03259280": "Dutch oven",
-        "n03271574": "electric fan, blower",
-        "n03272010": "electric guitar",
-        "n03272562": "electric locomotive",
-        "n03290653": "entertainment center",
-        "n03291819": "envelope",
-        "n03297495": "espresso maker",
-        "n03314780": "face powder",
-        "n03325584": "feather boa, boa",
-        "n03337140": "file, file cabinet, filing cabinet",
-        "n03344393": "fireboat",
-        "n03345487": "fire engine, fire truck",
-        "n03347037": "fire screen, fireguard",
-        "n03355925": "flagpole, flagstaff",
-        "n03372029": "flute, transverse flute",
-        "n03376595": "folding chair",
-        "n03379051": "football helmet",
-        "n03384352": "forklift",
-        "n03388043": "fountain",
-        "n03388183": "fountain pen",
-        "n03388549": "four-poster",
-        "n03393912": "freight car",
-        "n03394916": "French horn, horn",
-        "n03400231": "frying pan, frypan, skillet",
-        "n03404251": "fur coat",
-        "n03417042": "garbage truck, dustcart",
-        "n03424325": "gasmask, respirator, gas helmet",
-        "n03425413": "gas pump, gasoline pump, petrol pump, island dispenser",
-        "n03443371": "goblet",
-        "n03444034": "go-kart",
-        "n03445777": "golf ball",
-        "n03445924": "golfcart, golf cart",
-        "n03447447": "gondola",
-        "n03447721": "gong, tam-tam",
-        "n03450230": "gown",
-        "n03452741": "grand piano, grand",
-        "n03457902": "greenhouse, nursery, glasshouse",
-        "n03459775": "grille, radiator grille",
-        "n03461385": "grocery store, grocery, food market, market",
-        "n03467068": "guillotine",
-        "n03476684": "hair slide",
-        "n03476991": "hair spray",
-        "n03478589": "half track",
-        "n03481172": "hammer",
-        "n03482405": "hamper",
-        "n03483316": "hand blower, blow dryer, blow drier, hair dryer, hair drier",
-        "n03485407": "hand-held computer, hand-held microcomputer",
-        "n03485794": "handkerchief, hankie, hanky, hankey",
-        "n03492542": "hard disc, hard disk, fixed disk",
-        "n03494278": "harmonica, mouth organ, harp, mouth harp",
-        "n03495258": "harp",
-        "n03496892": "harvester, reaper",
-        "n03498962": "hatchet",
-        "n03527444": "holster",
-        "n03529860": "home theater, home theatre",
-        "n03530642": "honeycomb",
-        "n03532672": "hook, claw",
-        "n03534580": "hoopskirt, crinoline",
-        "n03535780": "horizontal bar, high bar",
-        "n03538406": "horse cart, horse-cart",
-        "n03544143": "hourglass",
-        "n03584254": "iPod",
-        "n03584829": "iron, smoothing iron",
-        "n03590841": "jack-o'-lantern",
-        "n03594734": "jean, blue jean, denim",
-        "n03594945": "jeep, landrover",
-        "n03595614": "jersey, T-shirt, tee shirt",
-        "n03598930": "jigsaw puzzle",
-        "n03599486": "jinrikisha, ricksha, rickshaw",
-        "n03602883": "joystick",
-        "n03617480": "kimono",
-        "n03623198": "knee pad",
-        "n03627232": "knot",
-        "n03630383": "lab coat, laboratory coat",
-        "n03633091": "ladle",
-        "n03637318": "lampshade, lamp shade",
-        "n03642806": "laptop, laptop computer",
-        "n03649909": "lawn mower, mower",
-        "n03657121": "lens cap, lens cover",
-        "n03658185": "letter opener, paper knife, paperknife",
-        "n03661043": "library",
-        "n03662601": "lifeboat",
-        "n03666591": "lighter, light, igniter, ignitor",
-        "n03670208": "limousine, limo",
-        "n03673027": "liner, ocean liner",
-        "n03676483": "lipstick, lip rouge",
-        "n03680355": "Loafer",
-        "n03690938": "lotion",
-        "n03691459": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
-        "n03692522": "loupe, jeweler's loupe",
-        "n03697007": "lumbermill, sawmill",
-        "n03706229": "magnetic compass",
-        "n03709823": "mailbag, postbag",
-        "n03710193": "mailbox, letter box",
-        "n03710637": "maillot",
-        "n03710721": "maillot, tank suit",
-        "n03717622": "manhole cover",
-        "n03720891": "maraca",
-        "n03721384": "marimba, xylophone",
-        "n03724870": "mask",
-        "n03729826": "matchstick",
-        "n03733131": "maypole",
-        "n03733281": "maze, labyrinth",
-        "n03733805": "measuring cup",
-        "n03742115": "medicine chest, medicine cabinet",
-        "n03743016": "megalith, megalithic structure",
-        "n03759954": "microphone, mike",
-        "n03761084": "microwave, microwave oven",
-        "n03763968": "military uniform",
-        "n03764736": "milk can",
-        "n03769881": "minibus",
-        "n03770439": "miniskirt, mini",
-        "n03770679": "minivan",
-        "n03773504": "missile",
-        "n03775071": "mitten",
-        "n03775546": "mixing bowl",
-        "n03776460": "mobile home, manufactured home",
-        "n03777568": "Model T",
-        "n03777754": "modem",
-        "n03781244": "monastery",
-        "n03782006": "monitor",
-        "n03785016": "moped",
-        "n03786901": "mortar",
-        "n03787032": "mortarboard",
-        "n03788195": "mosque",
-        "n03788365": "mosquito net",
-        "n03791053": "motor scooter, scooter",
-        "n03792782": "mountain bike, all-terrain bike, off-roader",
-        "n03792972": "mountain tent",
-        "n03793489": "mouse, computer mouse",
-        "n03794056": "mousetrap",
-        "n03796401": "moving van",
-        "n03803284": "muzzle",
-        "n03804744": "nail",
-        "n03814639": "neck brace",
-        "n03814906": "necklace",
-        "n03825788": "nipple",
-        "n03832673": "notebook, notebook computer",
-        "n03837869": "obelisk",
-        "n03838899": "oboe, hautboy, hautbois",
-        "n03840681": "ocarina, sweet potato",
-        "n03841143": "odometer, hodometer, mileometer, milometer",
-        "n03843555": "oil filter",
-        "n03854065": "organ, pipe organ",
-        "n03857828": "oscilloscope, scope, cathode-ray oscilloscope, CRO",
-        "n03866082": "overskirt",
-        "n03868242": "oxcart",
-        "n03868863": "oxygen mask",
-        "n03871628": "packet",
-        "n03873416": "paddle, boat paddle",
-        "n03874293": "paddlewheel, paddle wheel",
-        "n03874599": "padlock",
-        "n03876231": "paintbrush",
-        "n03877472": "pajama, pyjama, pj's, jammies",
-        "n03877845": "palace",
-        "n03884397": "panpipe, pandean pipe, syrinx",
-        "n03887697": "paper towel",
-        "n03888257": "parachute, chute",
-        "n03888605": "parallel bars, bars",
-        "n03891251": "park bench",
-        "n03891332": "parking meter",
-        "n03895866": "passenger car, coach, carriage",
-        "n03899768": "patio, terrace",
-        "n03902125": "pay-phone, pay-station",
-        "n03903868": "pedestal, plinth, footstall",
-        "n03908618": "pencil box, pencil case",
-        "n03908714": "pencil sharpener",
-        "n03916031": "perfume, essence",
-        "n03920288": "Petri dish",
-        "n03924679": "photocopier",
-        "n03929660": "pick, plectrum, plectron",
-        "n03929855": "pickelhaube",
-        "n03930313": "picket fence, paling",
-        "n03930630": "pickup, pickup truck",
-        "n03933933": "pier",
-        "n03935335": "piggy bank, penny bank",
-        "n03937543": "pill bottle",
-        "n03938244": "pillow",
-        "n03942813": "ping-pong ball",
-        "n03944341": "pinwheel",
-        "n03947888": "pirate, pirate ship",
-        "n03950228": "pitcher, ewer",
-        "n03954731": "plane, carpenter's plane, woodworking plane",
-        "n03956157": "planetarium",
-        "n03958227": "plastic bag",
-        "n03961711": "plate rack",
-        "n03967562": "plow, plough",
-        "n03970156": "plunger, plumber's helper",
-        "n03976467": "Polaroid camera, Polaroid Land camera",
-        "n03976657": "pole",
-        "n03977966": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
-        "n03980874": "poncho",
-        "n03982430": "pool table, billiard table, snooker table",
-        "n03983396": "pop bottle, soda bottle",
-        "n03991062": "pot, flowerpot",
-        "n03992509": "potter's wheel",
-        "n03995372": "power drill",
-        "n03998194": "prayer rug, prayer mat",
-        "n04004767": "printer",
-        "n04005630": "prison, prison house",
-        "n04008634": "projectile, missile",
-        "n04009552": "projector",
-        "n04019541": "puck, hockey puck",
-        "n04023962": "punching bag, punch bag, punching ball, punchball",
-        "n04026417": "purse",
-        "n04033901": "quill, quill pen",
-        "n04033995": "quilt, comforter, comfort, puff",
-        "n04037443": "racer, race car, racing car",
-        "n04039381": "racket, racquet",
-        "n04040759": "radiator",
-        "n04041544": "radio, wireless",
-        "n04044716": "radio telescope, radio reflector",
-        "n04049303": "rain barrel",
-        "n04065272": "recreational vehicle, RV, R.V.",
-        "n04067472": "reel",
-        "n04069434": "reflex camera",
-        "n04070727": "refrigerator, icebox",
-        "n04074963": "remote control, remote",
-        "n04081281": "restaurant, eating house, eating place, eatery",
-        "n04086273": "revolver, six-gun, six-shooter",
-        "n04090263": "rifle",
-        "n04099969": "rocking chair, rocker",
-        "n04111531": "rotisserie",
-        "n04116512": "rubber eraser, rubber, pencil eraser",
-        "n04118538": "rugby ball",
-        "n04118776": "rule, ruler",
-        "n04120489": "running shoe",
-        "n04125021": "safe",
-        "n04127249": "safety pin",
-        "n04131690": "saltshaker, salt shaker",
-        "n04133789": "sandal",
-        "n04136333": "sarong",
-        "n04141076": "sax, saxophone",
-        "n04141327": "scabbard",
-        "n04141975": "scale, weighing machine",
-        "n04146614": "school bus",
-        "n04147183": "schooner",
-        "n04149813": "scoreboard",
-        "n04152593": "screen, CRT screen",
-        "n04153751": "screw",
-        "n04154565": "screwdriver",
-        "n04162706": "seat belt, seatbelt",
-        "n04179913": "sewing machine",
-        "n04192698": "shield, buckler",
-        "n04200800": "shoe shop, shoe-shop, shoe store",
-        "n04201297": "shoji",
-        "n04204238": "shopping basket",
-        "n04204347": "shopping cart",
-        "n04208210": "shovel",
-        "n04209133": "shower cap",
-        "n04209239": "shower curtain",
-        "n04228054": "ski",
-        "n04229816": "ski mask",
-        "n04235860": "sleeping bag",
-        "n04238763": "slide rule, slipstick",
-        "n04239074": "sliding door",
-        "n04243546": "slot, one-armed bandit",
-        "n04251144": "snorkel",
-        "n04252077": "snowmobile",
-        "n04252225": "snowplow, snowplough",
-        "n04254120": "soap dispenser",
-        "n04254680": "soccer ball",
-        "n04254777": "sock",
-        "n04258138": "solar dish, solar collector, solar furnace",
-        "n04259630": "sombrero",
-        "n04263257": "soup bowl",
-        "n04264628": "space bar",
-        "n04265275": "space heater",
-        "n04266014": "space shuttle",
-        "n04270147": "spatula",
-        "n04273569": "speedboat",
-        "n04275548": "spider web, spider's web",
-        "n04277352": "spindle",
-        "n04285008": "sports car, sport car",
-        "n04286575": "spotlight, spot",
-        "n04296562": "stage",
-        "n04310018": "steam locomotive",
-        "n04311004": "steel arch bridge",
-        "n04311174": "steel drum",
-        "n04317175": "stethoscope",
-        "n04325704": "stole",
-        "n04326547": "stone wall",
-        "n04328186": "stopwatch, stop watch",
-        "n04330267": "stove",
-        "n04332243": "strainer",
-        "n04335435": "streetcar, tram, tramcar, trolley, trolley car",
-        "n04336792": "stretcher",
-        "n04344873": "studio couch, day bed",
-        "n04346328": "stupa, tope",
-        "n04347754": "submarine, pigboat, sub, U-boat",
-        "n04350905": "suit, suit of clothes",
-        "n04355338": "sundial",
-        "n04355933": "sunglass",
-        "n04356056": "sunglasses, dark glasses, shades",
-        "n04357314": "sunscreen, sunblock, sun blocker",
-        "n04366367": "suspension bridge",
-        "n04367480": "swab, swob, mop",
-        "n04370456": "sweatshirt",
-        "n04371430": "swimming trunks, bathing trunks",
-        "n04371774": "swing",
-        "n04372370": "switch, electric switch, electrical switch",
-        "n04376876": "syringe",
-        "n04380533": "table lamp",
-        "n04389033": "tank, army tank, armored combat vehicle, armoured combat vehicle",
-        "n04392985": "tape player",
-        "n04398044": "teapot",
-        "n04399382": "teddy, teddy bear",
-        "n04404412": "television, television system",
-        "n04409515": "tennis ball",
-        "n04417672": "thatch, thatched roof",
-        "n04418357": "theater curtain, theatre curtain",
-        "n04423845": "thimble",
-        "n04428191": "thresher, thrasher, threshing machine",
-        "n04429376": "throne",
-        "n04435653": "tile roof",
-        "n04442312": "toaster",
-        "n04443257": "tobacco shop, tobacconist shop, tobacconist",
-        "n04447861": "toilet seat",
-        "n04456115": "torch",
-        "n04458633": "totem pole",
-        "n04461696": "tow truck, tow car, wrecker",
-        "n04462240": "toyshop",
-        "n04465501": "tractor",
-        "n04467665": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
-        "n04476259": "tray",
-        "n04479046": "trench coat",
-        "n04482393": "tricycle, trike, velocipede",
-        "n04483307": "trimaran",
-        "n04485082": "tripod",
-        "n04486054": "triumphal arch",
-        "n04487081": "trolleybus, trolley coach, trackless trolley",
-        "n04487394": "trombone",
-        "n04493381": "tub, vat",
-        "n04501370": "turnstile",
-        "n04505470": "typewriter keyboard",
-        "n04507155": "umbrella",
-        "n04509417": "unicycle, monocycle",
-        "n04515003": "upright, upright piano",
-        "n04517823": "vacuum, vacuum cleaner",
-        "n04522168": "vase",
-        "n04523525": "vault",
-        "n04525038": "velvet",
-        "n04525305": "vending machine",
-        "n04532106": "vestment",
-        "n04532670": "viaduct",
-        "n04536866": "violin, fiddle",
-        "n04540053": "volleyball",
-        "n04542943": "waffle iron",
-        "n04548280": "wall clock",
-        "n04548362": "wallet, billfold, notecase, pocketbook",
-        "n04550184": "wardrobe, closet, press",
-        "n04552348": "warplane, military plane",
-        "n04553703": "washbasin, handbasin, washbowl, lavabo, wash-hand basin",
-        "n04554684": "washer, automatic washer, washing machine",
-        "n04557648": "water bottle",
-        "n04560804": "water jug",
-        "n04562935": "water tower",
-        "n04579145": "whiskey jug",
-        "n04579432": "whistle",
-        "n04584207": "wig",
-        "n04589890": "window screen",
-        "n04590129": "window shade",
-        "n04591157": "Windsor tie",
-        "n04591713": "wine bottle",
-        "n04592741": "wing",
-        "n04596742": "wok",
-        "n04597913": "wooden spoon",
-        "n04599235": "wool, woolen, woollen",
-        "n04604644": "worm fence, snake fence, snake-rail fence, Virginia fence",
-        "n04606251": "wreck",
-        "n04612504": "yawl",
-        "n04613696": "yurt",
-        "n06359193": "web site, website, internet site, site",
-        "n06596364": "comic book",
-        "n06785654": "crossword puzzle, crossword",
-        "n06794110": "street sign",
-        "n06874185": "traffic light, traffic signal, stoplight",
-        "n07248320": "book jacket, dust cover, dust jacket, dust wrapper",
-        "n07565083": "menu",
-        "n07579787": "plate",
-        "n07583066": "guacamole",
-        "n07584110": "consomme",
-        "n07590611": "hot pot, hotpot",
-        "n07613480": "trifle",
-        "n07614500": "ice cream, icecream",
-        "n07615774": "ice lolly, lolly, lollipop, popsicle",
-        "n07684084": "French loaf",
-        "n07693725": "bagel, beigel",
-        "n07695742": "pretzel",
-        "n07697313": "cheeseburger",
-        "n07697537": "hotdog, hot dog, red hot",
-        "n07711569": "mashed potato",
-        "n07714571": "head cabbage",
-        "n07714990": "broccoli",
-        "n07715103": "cauliflower",
-        "n07716358": "zucchini, courgette",
-        "n07716906": "spaghetti squash",
-        "n07717410": "acorn squash",
-        "n07717556": "butternut squash",
-        "n07718472": "cucumber, cuke",
-        "n07718747": "artichoke, globe artichoke",
-        "n07720875": "bell pepper",
-        "n07730033": "cardoon",
-        "n07734744": "mushroom",
-        "n07742313": "Granny Smith",
-        "n07745940": "strawberry",
-        "n07747607": "orange",
-        "n07749582": "lemon",
-        "n07753113": "fig",
-        "n07753275": "pineapple, ananas",
-        "n07753592": "banana",
-        "n07754684": "jackfruit, jak, jack",
-        "n07760859": "custard apple",
-        "n07768694": "pomegranate",
-        "n07802026": "hay",
-        "n07831146": "carbonara",
-        "n07836838": "chocolate sauce, chocolate syrup",
-        "n07860988": "dough",
-        "n07871810": "meat loaf, meatloaf",
-        "n07873807": "pizza, pizza pie",
-        "n07875152": "potpie",
-        "n07880968": "burrito",
-        "n07892512": "red wine",
-        "n07920052": "espresso",
-        "n07930864": "cup",
-        "n07932039": "eggnog",
-        "n09193705": "alp",
-        "n09229709": "bubble",
-        "n09246464": "cliff, drop, drop-off",
-        "n09256479": "coral reef",
-        "n09288635": "geyser",
-        "n09332890": "lakeside, lakeshore",
-        "n09399592": "promontory, headland, head, foreland",
-        "n09421951": "sandbar, sand bar",
-        "n09428293": "seashore, coast, seacoast, sea-coast",
-        "n09468604": "valley, vale",
-        "n09472597": "volcano",
-        "n09835506": "ballplayer, baseball player",
-        "n10148035": "groom, bridegroom",
-        "n10565667": "scuba diver",
-        "n11879895": "rapeseed",
-        "n11939491": "daisy",
-        "n12057211": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
-        "n12144580": "corn",
-        "n12267677": "acorn",
-        "n12620546": "hip, rose hip, rosehip",
-        "n12768682": "buckeye, horse chestnut, conker",
-        "n12985857": "coral fungus",
-        "n12998815": "agaric",
-        "n13037406": "gyromitra",
-        "n13040303": "stinkhorn, carrion fungus",
-        "n13044778": "earthstar",
-        "n13052670": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
-        "n13054560": "bolete",
-        "n13133613": "ear, spike, capitulum",
-        "n15075141": "toilet tissue, toilet paper, bathroom tissue",
-    }
-)
 
 _CITATION = """\
 @misc{nauen2025foraug,
@@ -1073,8 +69,6 @@ class RecombineDataset(Dataset):
         mask_smoothing_sigma,
         rel_jut_out,
         orig_img_prob,
-        fg_in_nonant=None,
-        size_fact=1.0,
         **kwargs,
     ):
         """Create the ForNet recombination dataset.
@@ -1100,8 +94,12 @@ class RecombineDataset(Dataset):
             "same",
             "orig",
         ], f"Invalid background_combination {background_combination}"
-        assert fg_size_mode in ["range", "min", "max", "mean"], f"Invalid fg_size_mode {fg_size_mode}"
-        assert fg_in_nonant is None or -1 <= fg_in_nonant < 9, f"fg_in_nonant={fg_in_nonant} not in [0, 8] or None"
+        assert fg_size_mode in [
+            "range",
+            "min",
+            "max",
+            "mean",
+        ], f"Invalid fg_size_mode {fg_size_mode}"
         self.background_combination = background_combination
         self.fg_scale_jitter = fg_scale_jitter
         self.pruning_ratio = pruning_ratio
@@ -1113,8 +111,6 @@ class RecombineDataset(Dataset):
         self.epochs = 0
         self._epoch = 0
         self.cls_to_idx = {}
-        self.fg_in_nonant = fg_in_nonant
-        self.size_fact = size_fact
 
         bg_rat_indices = super()._getitem(0)["bg_rat_idx_file"]
         self.train = "train" in bg_rat_indices.split("/")[-1]
@@ -1182,9 +178,6 @@ class RecombineDataset(Dataset):
         bg_img = bg_item["bg"].convert("RGB")
         bg_size = bg_img.size
         bg_area = bg_size[0] * bg_size[1]
-        if self.fg_in_nonant is not None:
-            bg_area = bg_area / 9
-
         orig_fg_ratio = fg_item["fg/bg_area"]
         bg_fg_ratio = bg_item["fg/bg_area"]
 
@@ -1203,10 +196,10 @@ class RecombineDataset(Dataset):
 
         fg_scale = (
             np.random.uniform(
-                goal_fg_ratio_lower * (1 - self.fg_scale_jitter), goal_fg_ratio_upper * (1 + self.fg_scale_jitter)
+                goal_fg_ratio_lower * (1 - self.fg_scale_jitter),
+                goal_fg_ratio_upper * (1 + self.fg_scale_jitter),
             )
             / fg_size_factor
-            * self.size_fact
         )
 
         goal_shape_y = round(np.sqrt(bg_area * fg_scale * fg_img.size[1] / fg_img.size[0]))
@@ -1216,7 +209,10 @@ class RecombineDataset(Dataset):
 
         if fg_img.size[0] > bg_size[0] or fg_img.size[1] > bg_size[1]:
             # random crop to fit
-            goal_w, goal_h = (min(fg_img.size[0], bg_size[0]), min(fg_img.size[1], bg_size[1]))
+            goal_w, goal_h = (
+                min(fg_img.size[0], bg_size[0]),
+                min(fg_img.size[1], bg_size[1]),
+            )
             fg_img = T.RandomCrop((goal_h, goal_w))(fg_img) if self.train else T.CenterCrop((goal_h, goal_w))(fg_img)
 
         # paste fg on bg
@@ -1237,12 +233,6 @@ class RecombineDataset(Dataset):
         y_min = -self.rel_jut_out * fg_img.size[1]
         y_max = bg_size[1] - fg_img.size[1] * (1 - self.rel_jut_out)
 
-        if self.fg_in_nonant is not None and self.fg_in_nonant >= 0:
-            x_min = (self.fg_in_nonant % 3) * bg_size[0] / 3
-            x_max = ((self.fg_in_nonant % 3) + 1) * bg_size[0] / 3 - fg_img.size[0]
-            y_min = (self.fg_in_nonant // 3) * bg_size[1] / 3
-            y_max = ((self.fg_in_nonant // 3) + 1) * bg_size[1] / 3 - fg_img.size[1]
-
         if x_min > x_max:
             x_min = x_max = (x_min + x_max) / 2
         if y_min > y_max:
@@ -1277,8 +267,6 @@ _CONFIG_HASH_IGNORE_KWARGS = [
     "mask_smoothing_sigma",
     "rel_jut_out",
     "orig_img_prob",
-    "fg_in_nonant",
-    "size_fact",
 ]
 
 
@@ -1295,8 +283,6 @@ class ForNetConfig(datasets.BuilderConfig):
         mask_smoothing_sigma,
         rel_jut_out,
         orig_img_prob,
-        fg_in_nonant=None,
-        size_fact=1.0,
         **kwargs,
     ):
         """BuilderConfig for ForNet.
@@ -1313,8 +299,6 @@ class ForNetConfig(datasets.BuilderConfig):
         self.mask_smoothing_sigma = mask_smoothing_sigma
         self.rel_jut_out = rel_jut_out
         self.orig_img_prob = orig_img_prob
-        self.fg_in_nonant = fg_in_nonant
-        self.size_fact = size_fact
 
     def __str__(self):
         return f"ForNetConfig(name={self.name}, version={self.version}, data_dir={self.data_dir}, data_files={self.data_files}, description={self.description}, background_combination={self.background_combination}, fg_scale_jitter={self.fg_scale_jitter}, pruning_ratio={self.pruning_ratio}, fg_size_mode={self.fg_size_mode}, fg_bates_n={self.fg_bates_n}, mask_smoothing_sigma={self.mask_smoothing_sigma}, rel_jut_out={self.rel_jut_out}, orig_img_prob={self.orig_img_prob})"
@@ -1434,9 +418,6 @@ class ForNet(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
-        # test if we have access to ILSVRC/imagenet-1k
-        _ = datasets.load_dataset("ILSVRC/imagenet-1k", split="train", trust_remote_code=True)
-
         urls_to_download = _CONST_URLS + _PATCH_URLS
         dl_paths = dl_manager.download(urls_to_download)
 
@@ -1484,7 +465,7 @@ class ForNet(datasets.GeneratorBasedBuilder):
     def _generate_examples(
         self, patch_files, split, hf_indices, cls_to_idx_loc, fg_bg_ratios
     ):  # TODO: Parallelize this with multiple tar extractor processes and also multiple recombiner processes. Iterate through imagenet in main thread only, I guess...
-        logger.info(f"Generating examples from {len(patch_files)} patch files")
+        print(f"Generating examples from {len(patch_files)} patch files")
         logger.info("Opening files")
         class_to_zipfile = {}
         for f in patch_files:
@@ -1496,7 +477,7 @@ class ForNet(datasets.GeneratorBasedBuilder):
                         name_start = "/".join(name.split("/")[:-2])
         if len(name_start) > 0:
             name_start += "/"
-        logger.info(f"Loading extra information: {hf_indices}, {fg_bg_ratios}")
+        print(f"Loading extra information: {hf_indices}, {fg_bg_ratios}")
         with open(hf_indices, "r") as f:
             path_to_in_idx = json.load(f)
         idx_to_path = {v: k for k, v in path_to_in_idx.items()}
@@ -1506,7 +487,7 @@ class ForNet(datasets.GeneratorBasedBuilder):
         fg_bg_ratios = {"/".join(k.split("/")[-2:]).split(".")[0]: v for k, v in fg_bg_ratios.items()}
         # print("fg_bg_ratios", list(fg_bg_ratios.items())[:5])
 
-        logger.info("Starting extraction with ImageNet")
+        print("Starting extraction with ImageNet")
         foraug_idx = 0
 
         manager = multiprocessing.Manager()
@@ -1517,8 +498,12 @@ class ForNet(datasets.GeneratorBasedBuilder):
         ret_queue = manager.Queue(maxsize=4 * num_workers)
         comm_dict = manager.dict()
         comm_dict["running"] = True
+        running = True
         comm_dict["n_errors"] = 0
 
+        if num_workers > 8:
+            num_workers -= 2  # leave some cores for the main process and imagenet iterator
+
         in_proc = multiprocessing.Process(target=_in_iterator, args=(in_queue, split))
         in_proc.start()
         zip_procs = [
@@ -1543,7 +528,7 @@ class ForNet(datasets.GeneratorBasedBuilder):
 
         last_errors = 0
         cls_to_idx = {}
-        while comm_dict["running"]:
+        while running:
             if not ret_queue.empty():
                 data = ret_queue.get()
                 in_cls = data["path"].split("/")[0]
@@ -1554,20 +539,24 @@ class ForNet(datasets.GeneratorBasedBuilder):
                     data["bg_rat_idx_file"] = cls_to_idx_loc
                 yield foraug_idx, data
                 foraug_idx += 1
-
-            if comm_dict["n_errors"] > last_errors:
-                last_errors = comm_dict["n_errors"]
-                tqdm.write(
-                    f"@step {foraug_idx}: errors {last_errors}; error rate {last_errors / foraug_idx:.2%} (expected {6_610 / 1_274_227:.2%})"
-                )
-
-            if ret_queue.empty() and in_queue.empty() and not in_proc.is_alive():
-                comm_dict["running"] = False
-                tqdm.write("Finished imagenet iteration; waiting for zip loaders to finish")
+            else:
+                if in_queue.empty() and not in_proc.is_alive():
+                    comm_dict["running"] = False
+                    running = False
+                    tqdm.write("Finished imagenet iteration; waiting for zip loaders to finish")
+
+            if foraug_idx % 10_000 == 0:
+                errors = comm_dict["n_errors"]
+                if errors > last_errors:
+                    last_errors = errors
+                    tqdm.write(
+                        f"@step {foraug_idx}: errors {errors}; error rate {errors / foraug_idx:.2%} (expected {6_610 / 1_274_227:.2%})"
+                    )
 
         in_proc.join()
         for proc in zip_procs:
             proc.join()
+        exit()
 
         # tqdm.write("Finished all processes")
         while not ret_queue.empty():
@@ -1578,7 +567,7 @@ class ForNet(datasets.GeneratorBasedBuilder):
             cls_to_idx[in_cls].append(foraug_idx)
             yield foraug_idx, data
             foraug_idx += 1
-        # tqdm.write("Done")
+        tqdm.write("Done")
         with open(cls_to_idx_loc, "w") as f:
             json.dump(cls_to_idx, f)
 
@@ -1632,8 +621,6 @@ class ForNet(datasets.GeneratorBasedBuilder):
             mask_smoothing_sigma=self.config.mask_smoothing_sigma,
             rel_jut_out=self.config.rel_jut_out,
             orig_img_prob=self.config.orig_img_prob,
-            fg_in_nonant=self.config.fg_in_nonant,
-            size_fact=self.config.size_fact,
             **dataset_kwargs,
         )
 
@@ -1649,13 +636,21 @@ class ForNet(datasets.GeneratorBasedBuilder):
 def _in_iterator(in_queue, split):
     if split == "val":
         split = "validation"
-    imagenet = datasets.load_dataset("ILSVRC/imagenet-1k", split=split, trust_remote_code=True)
+    imagenet = datasets.load_dataset("ILSVRC/imagenet-1k", split=split)
     for idx, ex in enumerate(imagenet):
         in_queue.put((idx, ex["image"]))
 
 
 def _zip_loader(
-    in_queue, ret_queue, comm_dict, patch_files, class_to_zipfile, name_start, file_ending, idx_to_path, fg_bg_ratios
+    in_queue,
+    ret_queue,
+    comm_dict,
+    patch_files,
+    class_to_zipfile,
+    name_start,
+    file_ending,
+    idx_to_path,
+    fg_bg_ratios,
 ):
     while comm_dict["running"]:
         if not in_queue.empty():
@@ -1670,7 +665,10 @@ def _zip_loader(
                 with zipfile.ZipFile(class_to_zipfile[in_class], "r") as zf, (
                     zf.open(f"{name_start}{patch_name}.{file_ending}", "r")
                     if file_ending == "pkl"
-                    else gzip.GzipFile(fileobj=zf.open(f"{name_start}{patch_name}.{file_ending}", "r"), mode="r")
+                    else gzip.GzipFile(
+                        fileobj=zf.open(f"{name_start}{patch_name}.{file_ending}", "r"),
+                        mode="r",
+                    )
                 ) as pklf:
                     patch_data = pickle.load(pklf)
             except KeyError:
@@ -1680,19 +678,28 @@ def _zip_loader(
             in_img = in_img.convert("RGB")
 
             if "bg_diff" in patch_data:
-                if in_img.size != (patch_data["bg_diff"].shape[1], patch_data["bg_diff"].shape[0]):
+                if in_img.size != (
+                    patch_data["bg_diff"].shape[1],
+                    patch_data["bg_diff"].shape[0],
+                ):
                     in_img = in_img.resize((patch_data["bg_diff"].shape[1], patch_data["bg_diff"].shape[0]))
             else:
                 max_size = max(in_img.size)
                 if max_size > 512:
-                    goal_size = (round(in_img.size[0] * 512 / max_size), round(in_img.size[1] * 512 / max_size))
+                    goal_size = (
+                        round(in_img.size[0] * 512 / max_size),
+                        round(in_img.size[1] * 512 / max_size),
+                    )
                     in_img = in_img.resize(goal_size)
 
-            in_img = np.array(in_img)
+            in_arr = np.array(in_img)
 
             if "bg_diff" in patch_data:
                 bg_diff = patch_data["bg_diff"]
-                bg_img = in_img.astype(np.int64) + bg_diff
+                bg_img = in_arr.astype(np.int64) + bg_diff
+                bg_img = bg_img.clip(0, 255).astype(np.uint8)
+                bg_img = Image.fromarray(bg_img)
+                bg_img = image_to_bytes(bg_img)
             else:
                 bg_img = None
 
@@ -1700,10 +707,18 @@ def _zip_loader(
                 x_offs, y_offs = patch_data["fg_off"]
                 fg_mask = patch_data["fg_mask"]
 
-                fg_crop = in_img[y_offs : y_offs + fg_mask.shape[0], x_offs : x_offs + fg_mask.shape[1]]
+                fg_crop = in_arr[
+                    y_offs : y_offs + fg_mask.shape[0],
+                    x_offs : x_offs + fg_mask.shape[1],
+                ]
                 fg_img = np.concatenate([fg_crop, fg_mask * 255], axis=-1).clip(0, 255).astype(np.uint8)
+                fg_img = Image.fromarray(fg_img)
+                fg_img = image_to_bytes(fg_img)
             else:
                 fg_img = None
+
+            in_img = image_to_bytes(in_img)
+
             ret_queue.put(
                 {
                     "path": patch_name,