task_categories:image-classification task_ids:multi-class-image-classification annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:en license:other arxiv:1409.0575 arxiv:1912.07726 arxiv:1811.12231 arxiv:2109.13228 region:us
ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images
@article{imagenet15russakovsky,
    Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
    Title = { {ImageNet Large Scale Visual Recognition Challenge} },
    Year = {2015},
    journal   = {International Journal of Computer Vision (IJCV)},
    doi = {10.1007/s11263-015-0816-y},
    volume={115},
    number={3},
    pages={211-252}
}
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
license_details: imagenet-agreement
multilinguality:
- monolingual
paperswithcode_id: imagenet
pretty_name: ImageNet
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
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  Terms of Access:

  [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet
  database (the "Database") at Princeton University and Stanford University. In exchange
  for such permission, Researcher hereby agrees to the following terms and conditions:

  1. Researcher shall use the Database only for non-commercial research and educational
  purposes.

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  any and all claims arising from Researcher''s use of the Database, including but
  not limited to Researcher''s use of any copies of copyrighted images that he or
  she may create from the Database.

  4. Researcher may provide research associates and colleagues with access to the
  Database provided that they first agree to be bound by these terms and conditions.

  5. Princeton University, Stanford University and Hugging Face reserve the right
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  shall also be bound by these terms and conditions, and Researcher hereby represents
  that he or she is fully authorized to enter into this agreement on behalf of such
  employer.

  7. The law of the State of New Jersey shall apply to all disputes under this agreement.'
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          0: tench, Tinca tinca
          1: goldfish, Carassius auratus
          2: great white shark, white shark, man-eater, man-eating shark, Carcharodon
            carcharias
          3: tiger shark, Galeocerdo cuvieri
          4: hammerhead, hammerhead shark
          5: electric ray, crampfish, numbfish, torpedo
          6: stingray
          7: cock
          8: hen
          9: ostrich, Struthio camelus
          10: brambling, Fringilla montifringilla
          11: goldfinch, Carduelis carduelis
          12: house finch, linnet, Carpodacus mexicanus
          13: junco, snowbird
          14: indigo bunting, indigo finch, indigo bird, Passerina cyanea
          15: robin, American robin, Turdus migratorius
          16: bulbul
          17: jay
          18: magpie
          19: chickadee
          20: water ouzel, dipper
          21: kite
          22: bald eagle, American eagle, Haliaeetus leucocephalus
          23: vulture
          24: great grey owl, great gray owl, Strix nebulosa
          25: European fire salamander, Salamandra salamandra
          26: common newt, Triturus vulgaris
          27: eft
          28: spotted salamander, Ambystoma maculatum
          29: axolotl, mud puppy, Ambystoma mexicanum
          30: bullfrog, Rana catesbeiana
          31: tree frog, tree-frog
          32: tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
          33: loggerhead, loggerhead turtle, Caretta caretta
          34: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
          35: mud turtle
          36: terrapin
          37: box turtle, box tortoise
          38: banded gecko
          39: common iguana, iguana, Iguana iguana
          40: American chameleon, anole, Anolis carolinensis
          41: whiptail, whiptail lizard
          42: agama
          43: frilled lizard, Chlamydosaurus kingi
          44: alligator lizard
          45: Gila monster, Heloderma suspectum
          46: green lizard, Lacerta viridis
          47: African chameleon, Chamaeleo chamaeleon
          48: Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
          49: African crocodile, Nile crocodile, Crocodylus niloticus
          50: American alligator, Alligator mississipiensis
          51: triceratops
          52: thunder snake, worm snake, Carphophis amoenus
          53: ringneck snake, ring-necked snake, ring snake
          54: hognose snake, puff adder, sand viper
          55: green snake, grass snake
          56: king snake, kingsnake
          57: garter snake, grass snake
          58: water snake
          59: vine snake
          60: night snake, Hypsiglena torquata
          61: boa constrictor, Constrictor constrictor
          62: rock python, rock snake, Python sebae
          63: Indian cobra, Naja naja
          64: green mamba
          65: sea snake
          66: horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
          67: diamondback, diamondback rattlesnake, Crotalus adamanteus
          68: sidewinder, horned rattlesnake, Crotalus cerastes
          69: trilobite
          70: harvestman, daddy longlegs, Phalangium opilio
          71: scorpion
          72: black and gold garden spider, Argiope aurantia
          73: barn spider, Araneus cavaticus
          74: garden spider, Aranea diademata
          75: black widow, Latrodectus mactans
          76: tarantula
          77: wolf spider, hunting spider
          78: tick
          79: centipede
          80: black grouse
          81: ptarmigan
          82: ruffed grouse, partridge, Bonasa umbellus
          83: prairie chicken, prairie grouse, prairie fowl
          84: peacock
          85: quail
          86: partridge
          87: African grey, African gray, Psittacus erithacus
          88: macaw
          89: sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
          90: lorikeet
          91: coucal
          92: bee eater
          93: hornbill
          94: hummingbird
          95: jacamar
          96: toucan
          97: drake
          98: red-breasted merganser, Mergus serrator
          99: goose
          100: black swan, Cygnus atratus
          101: tusker
          102: echidna, spiny anteater, anteater
          103: platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
            anatinus
          104: wallaby, brush kangaroo
          105: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
          106: wombat
          107: jellyfish
          108: sea anemone, anemone
          109: brain coral
          110: flatworm, platyhelminth
          111: nematode, nematode worm, roundworm
          112: conch
          113: snail
          114: slug
          115: sea slug, nudibranch
          116: chiton, coat-of-mail shell, sea cradle, polyplacophore
          117: chambered nautilus, pearly nautilus, nautilus
          118: Dungeness crab, Cancer magister
          119: rock crab, Cancer irroratus
          120: fiddler crab
          121: king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
            camtschatica
          122: American lobster, Northern lobster, Maine lobster, Homarus americanus
          123: spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
          124: crayfish, crawfish, crawdad, crawdaddy
          125: hermit crab
          126: isopod
          127: white stork, Ciconia ciconia
          128: black stork, Ciconia nigra
          129: spoonbill
          130: flamingo
          131: little blue heron, Egretta caerulea
          132: American egret, great white heron, Egretta albus
          133: bittern
          134: crane
          135: limpkin, Aramus pictus
          136: European gallinule, Porphyrio porphyrio
          137: American coot, marsh hen, mud hen, water hen, Fulica americana
          138: bustard
          139: ruddy turnstone, Arenaria interpres
          140: red-backed sandpiper, dunlin, Erolia alpina
          141: redshank, Tringa totanus
          142: dowitcher
          143: oystercatcher, oyster catcher
          144: pelican
          145: king penguin, Aptenodytes patagonica
          146: albatross, mollymawk
          147: grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
            robustus
          148: killer whale, killer, orca, grampus, sea wolf, Orcinus orca
          149: dugong, Dugong dugon
          150: sea lion
          151: Chihuahua
          152: Japanese spaniel
          153: Maltese dog, Maltese terrier, Maltese
          154: Pekinese, Pekingese, Peke
          155: Shih-Tzu
          156: Blenheim spaniel
          157: papillon
          158: toy terrier
          159: Rhodesian ridgeback
          160: Afghan hound, Afghan
          161: basset, basset hound
          162: beagle
          163: bloodhound, sleuthhound
          164: bluetick
          165: black-and-tan coonhound
          166: Walker hound, Walker foxhound
          167: English foxhound
          168: redbone
          169: borzoi, Russian wolfhound
          170: Irish wolfhound
          171: Italian greyhound
          172: whippet
          173: Ibizan hound, Ibizan Podenco
          174: Norwegian elkhound, elkhound
          175: otterhound, otter hound
          176: Saluki, gazelle hound
          177: Scottish deerhound, deerhound
          178: Weimaraner
          179: Staffordshire bullterrier, Staffordshire bull terrier
          180: American Staffordshire terrier, Staffordshire terrier, American pit
            bull terrier, pit bull terrier
          181: Bedlington terrier
          182: Border terrier
          183: Kerry blue terrier
          184: Irish terrier
          185: Norfolk terrier
          186: Norwich terrier
          187: Yorkshire terrier
          188: wire-haired fox terrier
          189: Lakeland terrier
          190: Sealyham terrier, Sealyham
          191: Airedale, Airedale terrier
          192: cairn, cairn terrier
          193: Australian terrier
          194: Dandie Dinmont, Dandie Dinmont terrier
          195: Boston bull, Boston terrier
          196: miniature schnauzer
          197: giant schnauzer
          198: standard schnauzer
          199: Scotch terrier, Scottish terrier, Scottie
          200: Tibetan terrier, chrysanthemum dog
          201: silky terrier, Sydney silky
          202: soft-coated wheaten terrier
          203: West Highland white terrier
          204: Lhasa, Lhasa apso
          205: flat-coated retriever
          206: curly-coated retriever
          207: golden retriever
          208: Labrador retriever
          209: Chesapeake Bay retriever
          210: German short-haired pointer
          211: vizsla, Hungarian pointer
          212: English setter
          213: Irish setter, red setter
          214: Gordon setter
          215: Brittany spaniel
          216: clumber, clumber spaniel
          217: English springer, English springer spaniel
          218: Welsh springer spaniel
          219: cocker spaniel, English cocker spaniel, cocker
          220: Sussex spaniel
          221: Irish water spaniel
          222: kuvasz
          223: schipperke
          224: groenendael
          225: malinois
          226: briard
          227: kelpie
          228: komondor
          229: Old English sheepdog, bobtail
          230: Shetland sheepdog, Shetland sheep dog, Shetland
          231: collie
          232: Border collie
          233: Bouvier des Flandres, Bouviers des Flandres
          234: Rottweiler
          235: German shepherd, German shepherd dog, German police dog, alsatian
          236: Doberman, Doberman pinscher
          237: miniature pinscher
          238: Greater Swiss Mountain dog
          239: Bernese mountain dog
          240: Appenzeller
          241: EntleBucher
          242: boxer
          243: bull mastiff
          244: Tibetan mastiff
          245: French bulldog
          246: Great Dane
          247: Saint Bernard, St Bernard
          248: Eskimo dog, husky
          249: malamute, malemute, Alaskan malamute
          250: Siberian husky
          251: dalmatian, coach dog, carriage dog
          252: affenpinscher, monkey pinscher, monkey dog
          253: basenji
          254: pug, pug-dog
          255: Leonberg
          256: Newfoundland, Newfoundland dog
          257: Great Pyrenees
          258: Samoyed, Samoyede
          259: Pomeranian
          260: chow, chow chow
          261: keeshond
          262: Brabancon griffon
          263: Pembroke, Pembroke Welsh corgi
          264: Cardigan, Cardigan Welsh corgi
          265: toy poodle
          266: miniature poodle
          267: standard poodle
          268: Mexican hairless
          269: timber wolf, grey wolf, gray wolf, Canis lupus
          270: white wolf, Arctic wolf, Canis lupus tundrarum
          271: red wolf, maned wolf, Canis rufus, Canis niger
          272: coyote, prairie wolf, brush wolf, Canis latrans
          273: dingo, warrigal, warragal, Canis dingo
          274: dhole, Cuon alpinus
          275: African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
          276: hyena, hyaena
          277: red fox, Vulpes vulpes
          278: kit fox, Vulpes macrotis
          279: Arctic fox, white fox, Alopex lagopus
          280: grey fox, gray fox, Urocyon cinereoargenteus
          281: tabby, tabby cat
          282: tiger cat
          283: Persian cat
          284: Siamese cat, Siamese
          285: Egyptian cat
          286: cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
          287: lynx, catamount
          288: leopard, Panthera pardus
          289: snow leopard, ounce, Panthera uncia
          290: jaguar, panther, Panthera onca, Felis onca
          291: lion, king of beasts, Panthera leo
          292: tiger, Panthera tigris
          293: cheetah, chetah, Acinonyx jubatus
          294: brown bear, bruin, Ursus arctos
          295: American black bear, black bear, Ursus americanus, Euarctos americanus
          296: ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
          297: sloth bear, Melursus ursinus, Ursus ursinus
          298: mongoose
          299: meerkat, mierkat
          300: tiger beetle
          301: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
          302: ground beetle, carabid beetle
          303: long-horned beetle, longicorn, longicorn beetle
          304: leaf beetle, chrysomelid
          305: dung beetle
          306: rhinoceros beetle
          307: weevil
          308: fly
          309: bee
          310: ant, emmet, pismire
          311: grasshopper, hopper
          312: cricket
          313: walking stick, walkingstick, stick insect
          314: cockroach, roach
          315: mantis, mantid
          316: cicada, cicala
          317: leafhopper
          318: lacewing, lacewing fly
          319: dragonfly, darning needle, devil's darning needle, sewing needle, snake
            feeder, snake doctor, mosquito hawk, skeeter hawk
          320: damselfly
          321: admiral
          322: ringlet, ringlet butterfly
          323: monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
          324: cabbage butterfly
          325: sulphur butterfly, sulfur butterfly
          326: lycaenid, lycaenid butterfly
          327: starfish, sea star
          328: sea urchin
          329: sea cucumber, holothurian
          330: wood rabbit, cottontail, cottontail rabbit
          331: hare
          332: Angora, Angora rabbit
          333: hamster
          334: porcupine, hedgehog
          335: fox squirrel, eastern fox squirrel, Sciurus niger
          336: marmot
          337: beaver
          338: guinea pig, Cavia cobaya
          339: sorrel
          340: zebra
          341: hog, pig, grunter, squealer, Sus scrofa
          342: wild boar, boar, Sus scrofa
          343: warthog
          344: hippopotamus, hippo, river horse, Hippopotamus amphibius
          345: ox
          346: water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
          347: bison
          348: ram, tup
          349: bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
            sheep, Ovis canadensis
          350: ibex, Capra ibex
          351: hartebeest
          352: impala, Aepyceros melampus
          353: gazelle
          354: Arabian camel, dromedary, Camelus dromedarius
          355: llama
          356: weasel
          357: mink
          358: polecat, fitch, foulmart, foumart, Mustela putorius
          359: black-footed ferret, ferret, Mustela nigripes
          360: otter
          361: skunk, polecat, wood pussy
          362: badger
          363: armadillo
          364: three-toed sloth, ai, Bradypus tridactylus
          365: orangutan, orang, orangutang, Pongo pygmaeus
          366: gorilla, Gorilla gorilla
          367: chimpanzee, chimp, Pan troglodytes
          368: gibbon, Hylobates lar
          369: siamang, Hylobates syndactylus, Symphalangus syndactylus
          370: guenon, guenon monkey
          371: patas, hussar monkey, Erythrocebus patas
          372: baboon
          373: macaque
          374: langur
          375: colobus, colobus monkey
          376: proboscis monkey, Nasalis larvatus
          377: marmoset
          378: capuchin, ringtail, Cebus capucinus
          379: howler monkey, howler
          380: titi, titi monkey
          381: spider monkey, Ateles geoffroyi
          382: squirrel monkey, Saimiri sciureus
          383: Madagascar cat, ring-tailed lemur, Lemur catta
          384: indri, indris, Indri indri, Indri brevicaudatus
          385: Indian elephant, Elephas maximus
          386: African elephant, Loxodonta africana
          387: lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
          388: giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
          389: barracouta, snoek
          390: eel
          391: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
          392: rock beauty, Holocanthus tricolor
          393: anemone fish
          394: sturgeon
          395: gar, garfish, garpike, billfish, Lepisosteus osseus
          396: lionfish
          397: puffer, pufferfish, blowfish, globefish
          398: abacus
          399: abaya
          400: academic gown, academic robe, judge's robe
          401: accordion, piano accordion, squeeze box
          402: acoustic guitar
          403: aircraft carrier, carrier, flattop, attack aircraft carrier
          404: airliner
          405: airship, dirigible
          406: altar
          407: ambulance
          408: amphibian, amphibious vehicle
          409: analog clock
          410: apiary, bee house
          411: apron
          412: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
            dustbin, trash barrel, trash bin
          413: assault rifle, assault gun
          414: backpack, back pack, knapsack, packsack, rucksack, haversack
          415: bakery, bakeshop, bakehouse
          416: balance beam, beam
          417: balloon
          418: ballpoint, ballpoint pen, ballpen, Biro
          419: Band Aid
          420: banjo
          421: bannister, banister, balustrade, balusters, handrail
          422: barbell
          423: barber chair
          424: barbershop
          425: barn
          426: barometer
          427: barrel, cask
          428: barrow, garden cart, lawn cart, wheelbarrow
          429: baseball
          430: basketball
          431: bassinet
          432: bassoon
          433: bathing cap, swimming cap
          434: bath towel
          435: bathtub, bathing tub, bath, tub
          436: beach wagon, station wagon, wagon, estate car, beach waggon, station
            waggon, waggon
          437: beacon, lighthouse, beacon light, pharos
          438: beaker
          439: bearskin, busby, shako
          440: beer bottle
          441: beer glass
          442: bell cote, bell cot
          443: bib
          444: bicycle-built-for-two, tandem bicycle, tandem
          445: bikini, two-piece
          446: binder, ring-binder
          447: binoculars, field glasses, opera glasses
          448: birdhouse
          449: boathouse
          450: bobsled, bobsleigh, bob
          451: bolo tie, bolo, bola tie, bola
          452: bonnet, poke bonnet
          453: bookcase
          454: bookshop, bookstore, bookstall
          455: bottlecap
          456: bow
          457: bow tie, bow-tie, bowtie
          458: brass, memorial tablet, plaque
          459: brassiere, bra, bandeau
          460: breakwater, groin, groyne, mole, bulwark, seawall, jetty
          461: breastplate, aegis, egis
          462: broom
          463: bucket, pail
          464: buckle
          465: bulletproof vest
          466: bullet train, bullet
          467: butcher shop, meat market
          468: cab, hack, taxi, taxicab
          469: caldron, cauldron
          470: candle, taper, wax light
          471: cannon
          472: canoe
          473: can opener, tin opener
          474: cardigan
          475: car mirror
          476: carousel, carrousel, merry-go-round, roundabout, whirligig
          477: carpenter's kit, tool kit
          478: carton
          479: car wheel
          480: cash machine, cash dispenser, automated teller machine, automatic teller
            machine, automated teller, automatic teller, ATM
          481: cassette
          482: cassette player
          483: castle
          484: catamaran
          485: CD player
          486: cello, violoncello
          487: cellular telephone, cellular phone, cellphone, cell, mobile phone
          488: chain
          489: chainlink fence
          490: chain mail, ring mail, mail, chain armor, chain armour, ring armor,
            ring armour
          491: chain saw, chainsaw
          492: chest
          493: chiffonier, commode
          494: chime, bell, gong
          495: china cabinet, china closet
          496: Christmas stocking
          497: church, church building
          498: cinema, movie theater, movie theatre, movie house, picture palace
          499: cleaver, meat cleaver, chopper
          500: cliff dwelling
          501: cloak
          502: clog, geta, patten, sabot
          503: cocktail shaker
          504: coffee mug
          505: coffeepot
          506: coil, spiral, volute, whorl, helix
          507: combination lock
          508: computer keyboard, keypad
          509: confectionery, confectionary, candy store
          510: container ship, containership, container vessel
          511: convertible
          512: corkscrew, bottle screw
          513: cornet, horn, trumpet, trump
          514: cowboy boot
          515: cowboy hat, ten-gallon hat
          516: cradle
          517: crane2
          518: crash helmet
          519: crate
          520: crib, cot
          521: Crock Pot
          522: croquet ball
          523: crutch
          524: cuirass
          525: dam, dike, dyke
          526: desk
          527: desktop computer
          528: dial telephone, dial phone
          529: diaper, nappy, napkin
          530: digital clock
          531: digital watch
          532: dining table, board
          533: dishrag, dishcloth
          534: dishwasher, dish washer, dishwashing machine
          535: disk brake, disc brake
          536: dock, dockage, docking facility
          537: dogsled, dog sled, dog sleigh
          538: dome
          539: doormat, welcome mat
          540: drilling platform, offshore rig
          541: drum, membranophone, tympan
          542: drumstick
          543: dumbbell
          544: Dutch oven
          545: electric fan, blower
          546: electric guitar
          547: electric locomotive
          548: entertainment center
          549: envelope
          550: espresso maker
          551: face powder
          552: feather boa, boa
          553: file, file cabinet, filing cabinet
          554: fireboat
          555: fire engine, fire truck
          556: fire screen, fireguard
          557: flagpole, flagstaff
          558: flute, transverse flute
          559: folding chair
          560: football helmet
          561: forklift
          562: fountain
          563: fountain pen
          564: four-poster
          565: freight car
          566: French horn, horn
          567: frying pan, frypan, skillet
          568: fur coat
          569: garbage truck, dustcart
          570: gasmask, respirator, gas helmet
          571: gas pump, gasoline pump, petrol pump, island dispenser
          572: goblet
          573: go-kart
          574: golf ball
          575: golfcart, golf cart
          576: gondola
          577: gong, tam-tam
          578: gown
          579: grand piano, grand
          580: greenhouse, nursery, glasshouse
          581: grille, radiator grille
          582: grocery store, grocery, food market, market
          583: guillotine
          584: hair slide
          585: hair spray
          586: half track
          587: hammer
          588: hamper
          589: hand blower, blow dryer, blow drier, hair dryer, hair drier
          590: hand-held computer, hand-held microcomputer
          591: handkerchief, hankie, hanky, hankey
          592: hard disc, hard disk, fixed disk
          593: harmonica, mouth organ, harp, mouth harp
          594: harp
          595: harvester, reaper
          596: hatchet
          597: holster
          598: home theater, home theatre
          599: honeycomb
          600: hook, claw
          601: hoopskirt, crinoline
          602: horizontal bar, high bar
          603: horse cart, horse-cart
          604: hourglass
          605: iPod
          606: iron, smoothing iron
          607: jack-o'-lantern
          608: jean, blue jean, denim
          609: jeep, landrover
          610: jersey, T-shirt, tee shirt
          611: jigsaw puzzle
          612: jinrikisha, ricksha, rickshaw
          613: joystick
          614: kimono
          615: knee pad
          616: knot
          617: lab coat, laboratory coat
          618: ladle
          619: lampshade, lamp shade
          620: laptop, laptop computer
          621: lawn mower, mower
          622: lens cap, lens cover
          623: letter opener, paper knife, paperknife
          624: library
          625: lifeboat
          626: lighter, light, igniter, ignitor
          627: limousine, limo
          628: liner, ocean liner
          629: lipstick, lip rouge
          630: Loafer
          631: lotion
          632: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
          633: loupe, jeweler's loupe
          634: lumbermill, sawmill
          635: magnetic compass
          636: mailbag, postbag
          637: mailbox, letter box
          638: maillot
          639: maillot, tank suit
          640: manhole cover
          641: maraca
          642: marimba, xylophone
          643: mask
          644: matchstick
          645: maypole
          646: maze, labyrinth
          647: measuring cup
          648: medicine chest, medicine cabinet
          649: megalith, megalithic structure
          650: microphone, mike
          651: microwave, microwave oven
          652: military uniform
          653: milk can
          654: minibus
          655: miniskirt, mini
          656: minivan
          657: missile
          658: mitten
          659: mixing bowl
          660: mobile home, manufactured home
          661: Model T
          662: modem
          663: monastery
          664: monitor
          665: moped
          666: mortar
          667: mortarboard
          668: mosque
          669: mosquito net
          670: motor scooter, scooter
          671: mountain bike, all-terrain bike, off-roader
          672: mountain tent
          673: mouse, computer mouse
          674: mousetrap
          675: moving van
          676: muzzle
          677: nail
          678: neck brace
          679: necklace
          680: nipple
          681: notebook, notebook computer
          682: obelisk
          683: oboe, hautboy, hautbois
          684: ocarina, sweet potato
          685: odometer, hodometer, mileometer, milometer
          686: oil filter
          687: organ, pipe organ
          688: oscilloscope, scope, cathode-ray oscilloscope, CRO
          689: overskirt
          690: oxcart
          691: oxygen mask
          692: packet
          693: paddle, boat paddle
          694: paddlewheel, paddle wheel
          695: padlock
          696: paintbrush
          697: pajama, pyjama, pj's, jammies
          698: palace
          699: panpipe, pandean pipe, syrinx
          700: paper towel
          701: parachute, chute
          702: parallel bars, bars
          703: park bench
          704: parking meter
          705: passenger car, coach, carriage
          706: patio, terrace
          707: pay-phone, pay-station
          708: pedestal, plinth, footstall
          709: pencil box, pencil case
          710: pencil sharpener
          711: perfume, essence
          712: Petri dish
          713: photocopier
          714: pick, plectrum, plectron
          715: pickelhaube
          716: picket fence, paling
          717: pickup, pickup truck
          718: pier
          719: piggy bank, penny bank
          720: pill bottle
          721: pillow
          722: ping-pong ball
          723: pinwheel
          724: pirate, pirate ship
          725: pitcher, ewer
          726: plane, carpenter's plane, woodworking plane
          727: planetarium
          728: plastic bag
          729: plate rack
          730: plow, plough
          731: plunger, plumber's helper
          732: Polaroid camera, Polaroid Land camera
          733: pole
          734: police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
          735: poncho
          736: pool table, billiard table, snooker table
          737: pop bottle, soda bottle
          738: pot, flowerpot
          739: potter's wheel
          740: power drill
          741: prayer rug, prayer mat
          742: printer
          743: prison, prison house
          744: projectile, missile
          745: projector
          746: puck, hockey puck
          747: punching bag, punch bag, punching ball, punchball
          748: purse
          749: quill, quill pen
          750: quilt, comforter, comfort, puff
          751: racer, race car, racing car
          752: racket, racquet
          753: radiator
          754: radio, wireless
          755: radio telescope, radio reflector
          756: rain barrel
          757: recreational vehicle, RV, R.V.
          758: reel
          759: reflex camera
          760: refrigerator, icebox
          761: remote control, remote
          762: restaurant, eating house, eating place, eatery
          763: revolver, six-gun, six-shooter
          764: rifle
          765: rocking chair, rocker
          766: rotisserie
          767: rubber eraser, rubber, pencil eraser
          768: rugby ball
          769: rule, ruler
          770: running shoe
          771: safe
          772: safety pin
          773: saltshaker, salt shaker
          774: sandal
          775: sarong
          776: sax, saxophone
          777: scabbard
          778: scale, weighing machine
          779: school bus
          780: schooner
          781: scoreboard
          782: screen, CRT screen
          783: screw
          784: screwdriver
          785: seat belt, seatbelt
          786: sewing machine
          787: shield, buckler
          788: shoe shop, shoe-shop, shoe store
          789: shoji
          790: shopping basket
          791: shopping cart
          792: shovel
          793: shower cap
          794: shower curtain
          795: ski
          796: ski mask
          797: sleeping bag
          798: slide rule, slipstick
          799: sliding door
          800: slot, one-armed bandit
          801: snorkel
          802: snowmobile
          803: snowplow, snowplough
          804: soap dispenser
          805: soccer ball
          806: sock
          807: solar dish, solar collector, solar furnace
          808: sombrero
          809: soup bowl
          810: space bar
          811: space heater
          812: space shuttle
          813: spatula
          814: speedboat
          815: spider web, spider's web
          816: spindle
          817: sports car, sport car
          818: spotlight, spot
          819: stage
          820: steam locomotive
          821: steel arch bridge
          822: steel drum
          823: stethoscope
          824: stole
          825: stone wall
          826: stopwatch, stop watch
          827: stove
          828: strainer
          829: streetcar, tram, tramcar, trolley, trolley car
          830: stretcher
          831: studio couch, day bed
          832: stupa, tope
          833: submarine, pigboat, sub, U-boat
          834: suit, suit of clothes
          835: sundial
          836: sunglass
          837: sunglasses, dark glasses, shades
          838: sunscreen, sunblock, sun blocker
          839: suspension bridge
          840: swab, swob, mop
          841: sweatshirt
          842: swimming trunks, bathing trunks
          843: swing
          844: switch, electric switch, electrical switch
          845: syringe
          846: table lamp
          847: tank, army tank, armored combat vehicle, armoured combat vehicle
          848: tape player
          849: teapot
          850: teddy, teddy bear
          851: television, television system
          852: tennis ball
          853: thatch, thatched roof
          854: theater curtain, theatre curtain
          855: thimble
          856: thresher, thrasher, threshing machine
          857: throne
          858: tile roof
          859: toaster
          860: tobacco shop, tobacconist shop, tobacconist
          861: toilet seat
          862: torch
          863: totem pole
          864: tow truck, tow car, wrecker
          865: toyshop
          866: tractor
          867: trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
            semi
          868: tray
          869: trench coat
          870: tricycle, trike, velocipede
          871: trimaran
          872: tripod
          873: triumphal arch
          874: trolleybus, trolley coach, trackless trolley
          875: trombone
          876: tub, vat
          877: turnstile
          878: typewriter keyboard
          879: umbrella
          880: unicycle, monocycle
          881: upright, upright piano
          882: vacuum, vacuum cleaner
          883: vase
          884: vault
          885: velvet
          886: vending machine
          887: vestment
          888: viaduct
          889: violin, fiddle
          890: volleyball
          891: waffle iron
          892: wall clock
          893: wallet, billfold, notecase, pocketbook
          894: wardrobe, closet, press
          895: warplane, military plane
          896: washbasin, handbasin, washbowl, lavabo, wash-hand basin
          897: washer, automatic washer, washing machine
          898: water bottle
          899: water jug
          900: water tower
          901: whiskey jug
          902: whistle
          903: wig
          904: window screen
          905: window shade
          906: Windsor tie
          907: wine bottle
          908: wing
          909: wok
          910: wooden spoon
          911: wool, woolen, woollen
          912: worm fence, snake fence, snake-rail fence, Virginia fence
          913: wreck
          914: yawl
          915: yurt
          916: web site, website, internet site, site
          917: comic book
          918: crossword puzzle, crossword
          919: street sign
          920: traffic light, traffic signal, stoplight
          921: book jacket, dust cover, dust jacket, dust wrapper
          922: menu
          923: plate
          924: guacamole
          925: consomme
          926: hot pot, hotpot
          927: trifle
          928: ice cream, icecream
          929: ice lolly, lolly, lollipop, popsicle
          930: French loaf
          931: bagel, beigel
          932: pretzel
          933: cheeseburger
          934: hotdog, hot dog, red hot
          935: mashed potato
          936: head cabbage
          937: broccoli
          938: cauliflower
          939: zucchini, courgette
          940: spaghetti squash
          941: acorn squash
          942: butternut squash
          943: cucumber, cuke
          944: artichoke, globe artichoke
          945: bell pepper
          946: cardoon
          947: mushroom
          948: Granny Smith
          949: strawberry
          950: orange
          951: lemon
          952: fig
          953: pineapple, ananas
          954: banana
          955: jackfruit, jak, jack
          956: custard apple
          957: pomegranate
          958: hay
          959: carbonara
          960: chocolate sauce, chocolate syrup
          961: dough
          962: meat loaf, meatloaf
          963: pizza, pizza pie
          964: potpie
          965: burrito
          966: red wine
          967: espresso
          968: cup
          969: eggnog
          970: alp
          971: bubble
          972: cliff, drop, drop-off
          973: coral reef
          974: geyser
          975: lakeside, lakeshore
          976: promontory, headland, head, foreland
          977: sandbar, sand bar
          978: seashore, coast, seacoast, sea-coast
          979: valley, vale
          980: volcano
          981: ballplayer, baseball player
          982: groom, bridegroom
          983: scuba diver
          984: rapeseed
          985: daisy
          986: yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
            Cypripedium parviflorum
          987: corn
          988: acorn
          989: hip, rose hip, rosehip
          990: buckeye, horse chestnut, conker
          991: coral fungus
          992: agaric
          993: gyromitra
          994: stinkhorn, carrion fungus
          995: earthstar
          996: hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
          997: bolete
          998: ear, spike, capitulum
          999: toilet tissue, toilet paper, bathroom tissue
  splits:
  - name: test
    num_bytes: 13613661561
    num_examples: 100000
  - name: train
    num_bytes: 146956944242
    num_examples: 1281167
  - name: validation
    num_bytes: 6709003386
    num_examples: 50000
  download_size: 166009941208
  dataset_size: 167279609189
---

# Dataset Card for ImageNet

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://image-net.org/index.php
- **Repository:**
- **Paper:** https://arxiv.org/abs/1409.0575
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171
- **Point of Contact:** mailto: imagenet.help.desk@gmail.com  

### Dataset Summary

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.

💡 This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used **subset** of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the [patch](https://drive.google.com/file/d/16RYnHpVOW0XKCsn3G3S9GTHUyoV2-4WX/view) which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](https://ieeexplore.ieee.org/abstract/document/5206848), please check the download section of the [main website](https://image-net.org/download-images.php).

### Supported Tasks and Leaderboards

- `image-classification`: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171).

To evaluate the `imagenet-classification` accuracy on the test split, one must first create an account at https://image-net.org. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at https://image-net.org/challenges/LSVRC/eval_server.php The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:

```
670 778 794 387 650
217 691 564 909 364
737 369 430 531 124
755 930 755 512 152
```

The export format is described in full in "readme.txt" within the 2013 development kit available here: https://image-net.org/data/ILSVRC/2013/ILSVRC2013_devkit.tgz. Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See `imagenet2012_labels.txt`.

### Languages

The class labels in the dataset are in English.

## Dataset Structure

### Data Instances

An example looks like below: 

```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
  'label': 23
}
```

### Data Fields

The data instances have the following fields:

- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: an `int` classification label. -1 for `test` set as the labels are missing.

The labels are indexed based on a sorted list of synset ids such as `n07565083` which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file [LOC_synset_mapping.txt](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt) available on Kaggle challenge page. You can also use `dataset_instance.features["labels"].int2str` function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.

<details>
  <summary>
  Click here to see the full list of ImageNet class labels mapping:
  </summary>

  |id|Class|
  |--|-----|
  |0 | tench, Tinca tinca|
  |1 | goldfish, Carassius auratus|
  |2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias|
  |3 | tiger shark, Galeocerdo cuvieri|
  |4 | hammerhead, hammerhead shark|
  |5 | electric ray, crampfish, numbfish, torpedo|
  |6 | stingray|
  |7 | cock|
  |8 | hen|
  |9 | ostrich, Struthio camelus|
  |10 | brambling, Fringilla montifringilla|
  |11 | goldfinch, Carduelis carduelis|
  |12 | house finch, linnet, Carpodacus mexicanus|
  |13 | junco, snowbird|
  |14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea|
  |15 | robin, American robin, Turdus migratorius|
  |16 | bulbul|
  |17 | jay|
  |18 | magpie|
  |19 | chickadee|
  |20 | water ouzel, dipper|
  |21 | kite|
  |22 | bald eagle, American eagle, Haliaeetus leucocephalus|
  |23 | vulture|
  |24 | great grey owl, great gray owl, Strix nebulosa|
  |25 | European fire salamander, Salamandra salamandra|
  |26 | common newt, Triturus vulgaris|
  |27 | eft|
  |28 | spotted salamander, Ambystoma maculatum|
  |29 | axolotl, mud puppy, Ambystoma mexicanum|
  |30 | bullfrog, Rana catesbeiana|
  |31 | tree frog, tree-frog|
  |32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui|
  |33 | loggerhead, loggerhead turtle, Caretta caretta|
  |34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea|
  |35 | mud turtle|
  |36 | terrapin|
  |37 | box turtle, box tortoise|
  |38 | banded gecko|
  |39 | common iguana, iguana, Iguana iguana|
  |40 | American chameleon, anole, Anolis carolinensis|
  |41 | whiptail, whiptail lizard|
  |42 | agama|
  |43 | frilled lizard, Chlamydosaurus kingi|
  |44 | alligator lizard|
  |45 | Gila monster, Heloderma suspectum|
  |46 | green lizard, Lacerta viridis|
  |47 | African chameleon, Chamaeleo chamaeleon|
  |48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis|
  |49 | African crocodile, Nile crocodile, Crocodylus niloticus|
  |50 | American alligator, Alligator mississipiensis|
  |51 | triceratops|
  |52 | thunder snake, worm snake, Carphophis amoenus|
  |53 | ringneck snake, ring-necked snake, ring snake|
  |54 | hognose snake, puff adder, sand viper|
  |55 | green snake, grass snake|
  |56 | king snake, kingsnake|
  |57 | garter snake, grass snake|
  |58 | water snake|
  |59 | vine snake|
  |60 | night snake, Hypsiglena torquata|
  |61 | boa constrictor, Constrictor constrictor|
  |62 | rock python, rock snake, Python sebae|
  |63 | Indian cobra, Naja naja|
  |64 | green mamba|
  |65 | sea snake|
  |66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus|
  |67 | diamondback, diamondback rattlesnake, Crotalus adamanteus|
  |68 | sidewinder, horned rattlesnake, Crotalus cerastes|
  |69 | trilobite|
  |70 | harvestman, daddy longlegs, Phalangium opilio|
  |71 | scorpion|
  |72 | black and gold garden spider, Argiope aurantia|
  |73 | barn spider, Araneus cavaticus|
  |74 | garden spider, Aranea diademata|
  |75 | black widow, Latrodectus mactans|
  |76 | tarantula|
  |77 | wolf spider, hunting spider|
  |78 | tick|
  |79 | centipede|
  |80 | black grouse|
  |81 | ptarmigan|
  |82 | ruffed grouse, partridge, Bonasa umbellus|
  |83 | prairie chicken, prairie grouse, prairie fowl|
  |84 | peacock|
  |85 | quail|
  |86 | partridge|
  |87 | African grey, African gray, Psittacus erithacus|
  |88 | macaw|
  |89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita|
  |90 | lorikeet|
  |91 | coucal|
  |92 | bee eater|
  |93 | hornbill|
  |94 | hummingbird|
  |95 | jacamar|
  |96 | toucan|
  |97 | drake|
  |98 | red-breasted merganser, Mergus serrator|
  |99 | goose|
  |100 | black swan, Cygnus atratus|
  |101 | tusker|
  |102 | echidna, spiny anteater, anteater|
  |103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus|
  |104 | wallaby, brush kangaroo|
  |105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus|
  |106 | wombat|
  |107 | jellyfish|
  |108 | sea anemone, anemone|
  |109 | brain coral|
  |110 | flatworm, platyhelminth|
  |111 | nematode, nematode worm, roundworm|
  |112 | conch|
  |113 | snail|
  |114 | slug|
  |115 | sea slug, nudibranch|
  |116 | chiton, coat-of-mail shell, sea cradle, polyplacophore|
  |117 | chambered nautilus, pearly nautilus, nautilus|
  |118 | Dungeness crab, Cancer magister|
  |119 | rock crab, Cancer irroratus|
  |120 | fiddler crab|
  |121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica|
  |122 | American lobster, Northern lobster, Maine lobster, Homarus americanus|
  |123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish|
  |124 | crayfish, crawfish, crawdad, crawdaddy|
  |125 | hermit crab|
  |126 | isopod|
  |127 | white stork, Ciconia ciconia|
  |128 | black stork, Ciconia nigra|
  |129 | spoonbill|
  |130 | flamingo|
  |131 | little blue heron, Egretta caerulea|
  |132 | American egret, great white heron, Egretta albus|
  |133 | bittern|
  |134 | crane|
  |135 | limpkin, Aramus pictus|
  |136 | European gallinule, Porphyrio porphyrio|
  |137 | American coot, marsh hen, mud hen, water hen, Fulica americana|
  |138 | bustard|
  |139 | ruddy turnstone, Arenaria interpres|
  |140 | red-backed sandpiper, dunlin, Erolia alpina|
  |141 | redshank, Tringa totanus|
  |142 | dowitcher|
  |143 | oystercatcher, oyster catcher|
  |144 | pelican|
  |145 | king penguin, Aptenodytes patagonica|
  |146 | albatross, mollymawk|
  |147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus|
  |148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca|
  |149 | dugong, Dugong dugon|
  |150 | sea lion|
  |151 | Chihuahua|
  |152 | Japanese spaniel|
  |153 | Maltese dog, Maltese terrier, Maltese|
  |154 | Pekinese, Pekingese, Peke|
  |155 | Shih-Tzu|
  |156 | Blenheim spaniel|
  |157 | papillon|
  |158 | toy terrier|
  |159 | Rhodesian ridgeback|
  |160 | Afghan hound, Afghan|
  |161 | basset, basset hound|
  |162 | beagle|
  |163 | bloodhound, sleuthhound|
  |164 | bluetick|
  |165 | black-and-tan coonhound|
  |166 | Walker hound, Walker foxhound|
  |167 | English foxhound|
  |168 | redbone|
  |169 | borzoi, Russian wolfhound|
  |170 | Irish wolfhound|
  |171 | Italian greyhound|
  |172 | whippet|
  |173 | Ibizan hound, Ibizan Podenco|
  |174 | Norwegian elkhound, elkhound|
  |175 | otterhound, otter hound|
  |176 | Saluki, gazelle hound|
  |177 | Scottish deerhound, deerhound|
  |178 | Weimaraner|
  |179 | Staffordshire bullterrier, Staffordshire bull terrier|
  |180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier|
  |181 | Bedlington terrier|
  |182 | Border terrier|
  |183 | Kerry blue terrier|
  |184 | Irish terrier|
  |185 | Norfolk terrier|
  |186 | Norwich terrier|
  |187 | Yorkshire terrier|
  |188 | wire-haired fox terrier|
  |189 | Lakeland terrier|
  |190 | Sealyham terrier, Sealyham|
  |191 | Airedale, Airedale terrier|
  |192 | cairn, cairn terrier|
  |193 | Australian terrier|
  |194 | Dandie Dinmont, Dandie Dinmont terrier|
  |195 | Boston bull, Boston terrier|
  |196 | miniature schnauzer|
  |197 | giant schnauzer|
  |198 | standard schnauzer|
  |199 | Scotch terrier, Scottish terrier, Scottie|
  |200 | Tibetan terrier, chrysanthemum dog|
  |201 | silky terrier, Sydney silky|
  |202 | soft-coated wheaten terrier|
  |203 | West Highland white terrier|
  |204 | Lhasa, Lhasa apso|
  |205 | flat-coated retriever|
  |206 | curly-coated retriever|
  |207 | golden retriever|
  |208 | Labrador retriever|
  |209 | Chesapeake Bay retriever|
  |210 | German short-haired pointer|
  |211 | vizsla, Hungarian pointer|
  |212 | English setter|
  |213 | Irish setter, red setter|
  |214 | Gordon setter|
  |215 | Brittany spaniel|
  |216 | clumber, clumber spaniel|
  |217 | English springer, English springer spaniel|
  |218 | Welsh springer spaniel|
  |219 | cocker spaniel, English cocker spaniel, cocker|
  |220 | Sussex spaniel|
  |221 | Irish water spaniel|
  |222 | kuvasz|
  |223 | schipperke|
  |224 | groenendael|
  |225 | malinois|
  |226 | briard|
  |227 | kelpie|
  |228 | komondor|
  |229 | Old English sheepdog, bobtail|
  |230 | Shetland sheepdog, Shetland sheep dog, Shetland|
  |231 | collie|
  |232 | Border collie|
  |233 | Bouvier des Flandres, Bouviers des Flandres|
  |234 | Rottweiler|
  |235 | German shepherd, German shepherd dog, German police dog, alsatian|
  |236 | Doberman, Doberman pinscher|
  |237 | miniature pinscher|
  |238 | Greater Swiss Mountain dog|
  |239 | Bernese mountain dog|
  |240 | Appenzeller|
  |241 | EntleBucher|
  |242 | boxer|
  |243 | bull mastiff|
  |244 | Tibetan mastiff|
  |245 | French bulldog|
  |246 | Great Dane|
  |247 | Saint Bernard, St Bernard|
  |248 | Eskimo dog, husky|
  |249 | malamute, malemute, Alaskan malamute|
  |250 | Siberian husky|
  |251 | dalmatian, coach dog, carriage dog|
  |252 | affenpinscher, monkey pinscher, monkey dog|
  |253 | basenji|
  |254 | pug, pug-dog|
  |255 | Leonberg|
  |256 | Newfoundland, Newfoundland dog|
  |257 | Great Pyrenees|
  |258 | Samoyed, Samoyede|
  |259 | Pomeranian|
  |260 | chow, chow chow|
  |261 | keeshond|
  |262 | Brabancon griffon|
  |263 | Pembroke, Pembroke Welsh corgi|
  |264 | Cardigan, Cardigan Welsh corgi|
  |265 | toy poodle|
  |266 | miniature poodle|
  |267 | standard poodle|
  |268 | Mexican hairless|
  |269 | timber wolf, grey wolf, gray wolf, Canis lupus|
  |270 | white wolf, Arctic wolf, Canis lupus tundrarum|
  |271 | red wolf, maned wolf, Canis rufus, Canis niger|
  |272 | coyote, prairie wolf, brush wolf, Canis latrans|
  |273 | dingo, warrigal, warragal, Canis dingo|
  |274 | dhole, Cuon alpinus|
  |275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus|
  |276 | hyena, hyaena|
  |277 | red fox, Vulpes vulpes|
  |278 | kit fox, Vulpes macrotis|
  |279 | Arctic fox, white fox, Alopex lagopus|
  |280 | grey fox, gray fox, Urocyon cinereoargenteus|
  |281 | tabby, tabby cat|
  |282 | tiger cat|
  |283 | Persian cat|
  |284 | Siamese cat, Siamese|
  |285 | Egyptian cat|
  |286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor|
  |287 | lynx, catamount|
  |288 | leopard, Panthera pardus|
  |289 | snow leopard, ounce, Panthera uncia|
  |290 | jaguar, panther, Panthera onca, Felis onca|
  |291 | lion, king of beasts, Panthera leo|
  |292 | tiger, Panthera tigris|
  |293 | cheetah, chetah, Acinonyx jubatus|
  |294 | brown bear, bruin, Ursus arctos|
  |295 | American black bear, black bear, Ursus americanus, Euarctos americanus|
  |296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus|
  |297 | sloth bear, Melursus ursinus, Ursus ursinus|
  |298 | mongoose|
  |299 | meerkat, mierkat|
  |300 | tiger beetle|
  |301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle|
  |302 | ground beetle, carabid beetle|
  |303 | long-horned beetle, longicorn, longicorn beetle|
  |304 | leaf beetle, chrysomelid|
  |305 | dung beetle|
  |306 | rhinoceros beetle|
  |307 | weevil|
  |308 | fly|
  |309 | bee|
  |310 | ant, emmet, pismire|
  |311 | grasshopper, hopper|
  |312 | cricket|
  |313 | walking stick, walkingstick, stick insect|
  |314 | cockroach, roach|
  |315 | mantis, mantid|
  |316 | cicada, cicala|
  |317 | leafhopper|
  |318 | lacewing, lacewing fly|
  |319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk|
  |320 | damselfly|
  |321 | admiral|
  |322 | ringlet, ringlet butterfly|
  |323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus|
  |324 | cabbage butterfly|
  |325 | sulphur butterfly, sulfur butterfly|
  |326 | lycaenid, lycaenid butterfly|
  |327 | starfish, sea star|
  |328 | sea urchin|
  |329 | sea cucumber, holothurian|
  |330 | wood rabbit, cottontail, cottontail rabbit|
  |331 | hare|
  |332 | Angora, Angora rabbit|
  |333 | hamster|
  |334 | porcupine, hedgehog|
  |335 | fox squirrel, eastern fox squirrel, Sciurus niger|
  |336 | marmot|
  |337 | beaver|
  |338 | guinea pig, Cavia cobaya|
  |339 | sorrel|
  |340 | zebra|
  |341 | hog, pig, grunter, squealer, Sus scrofa|
  |342 | wild boar, boar, Sus scrofa|
  |343 | warthog|
  |344 | hippopotamus, hippo, river horse, Hippopotamus amphibius|
  |345 | ox|
  |346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis|
  |347 | bison|
  |348 | ram, tup|
  |349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis|
  |350 | ibex, Capra ibex|
  |351 | hartebeest|
  |352 | impala, Aepyceros melampus|
  |353 | gazelle|
  |354 | Arabian camel, dromedary, Camelus dromedarius|
  |355 | llama|
  |356 | weasel|
  |357 | mink|
  |358 | polecat, fitch, foulmart, foumart, Mustela putorius|
  |359 | black-footed ferret, ferret, Mustela nigripes|
  |360 | otter|
  |361 | skunk, polecat, wood pussy|
  |362 | badger|
  |363 | armadillo|
  |364 | three-toed sloth, ai, Bradypus tridactylus|
  |365 | orangutan, orang, orangutang, Pongo pygmaeus|
  |366 | gorilla, Gorilla gorilla|
  |367 | chimpanzee, chimp, Pan troglodytes|
  |368 | gibbon, Hylobates lar|
  |369 | siamang, Hylobates syndactylus, Symphalangus syndactylus|
  |370 | guenon, guenon monkey|
  |371 | patas, hussar monkey, Erythrocebus patas|
  |372 | baboon|
  |373 | macaque|
  |374 | langur|
  |375 | colobus, colobus monkey|
  |376 | proboscis monkey, Nasalis larvatus|
  |377 | marmoset|
  |378 | capuchin, ringtail, Cebus capucinus|
  |379 | howler monkey, howler|
  |380 | titi, titi monkey|
  |381 | spider monkey, Ateles geoffroyi|
  |382 | squirrel monkey, Saimiri sciureus|
  |383 | Madagascar cat, ring-tailed lemur, Lemur catta|
  |384 | indri, indris, Indri indri, Indri brevicaudatus|
  |385 | Indian elephant, Elephas maximus|
  |386 | African elephant, Loxodonta africana|
  |387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens|
  |388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca|
  |389 | barracouta, snoek|
  |390 | eel|
  |391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch|
  |392 | rock beauty, Holocanthus tricolor|
  |393 | anemone fish|
  |394 | sturgeon|
  |395 | gar, garfish, garpike, billfish, Lepisosteus osseus|
  |396 | lionfish|
  |397 | puffer, pufferfish, blowfish, globefish|
  |398 | abacus|
  |399 | abaya|
  |400 | academic gown, academic robe, judge's robe|
  |401 | accordion, piano accordion, squeeze box|
  |402 | acoustic guitar|
  |403 | aircraft carrier, carrier, flattop, attack aircraft carrier|
  |404 | airliner|
  |405 | airship, dirigible|
  |406 | altar|
  |407 | ambulance|
  |408 | amphibian, amphibious vehicle|
  |409 | analog clock|
  |410 | apiary, bee house|
  |411 | apron|
  |412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin|
  |413 | assault rifle, assault gun|
  |414 | backpack, back pack, knapsack, packsack, rucksack, haversack|
  |415 | bakery, bakeshop, bakehouse|
  |416 | balance beam, beam|
  |417 | balloon|
  |418 | ballpoint, ballpoint pen, ballpen, Biro|
  |419 | Band Aid|
  |420 | banjo|
  |421 | bannister, banister, balustrade, balusters, handrail|
  |422 | barbell|
  |423 | barber chair|
  |424 | barbershop|
  |425 | barn|
  |426 | barometer|
  |427 | barrel, cask|
  |428 | barrow, garden cart, lawn cart, wheelbarrow|
  |429 | baseball|
  |430 | basketball|
  |431 | bassinet|
  |432 | bassoon|
  |433 | bathing cap, swimming cap|
  |434 | bath towel|
  |435 | bathtub, bathing tub, bath, tub|
  |436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon|
  |437 | beacon, lighthouse, beacon light, pharos|
  |438 | beaker|
  |439 | bearskin, busby, shako|
  |440 | beer bottle|
  |441 | beer glass|
  |442 | bell cote, bell cot|
  |443 | bib|
  |444 | bicycle-built-for-two, tandem bicycle, tandem|
  |445 | bikini, two-piece|
  |446 | binder, ring-binder|
  |447 | binoculars, field glasses, opera glasses|
  |448 | birdhouse|
  |449 | boathouse|
  |450 | bobsled, bobsleigh, bob|
  |451 | bolo tie, bolo, bola tie, bola|
  |452 | bonnet, poke bonnet|
  |453 | bookcase|
  |454 | bookshop, bookstore, bookstall|
  |455 | bottlecap|
  |456 | bow|
  |457 | bow tie, bow-tie, bowtie|
  |458 | brass, memorial tablet, plaque|
  |459 | brassiere, bra, bandeau|
  |460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty|
  |461 | breastplate, aegis, egis|
  |462 | broom|
  |463 | bucket, pail|
  |464 | buckle|
  |465 | bulletproof vest|
  |466 | bullet train, bullet|
  |467 | butcher shop, meat market|
  |468 | cab, hack, taxi, taxicab|
  |469 | caldron, cauldron|
  |470 | candle, taper, wax light|
  |471 | cannon|
  |472 | canoe|
  |473 | can opener, tin opener|
  |474 | cardigan|
  |475 | car mirror|
  |476 | carousel, carrousel, merry-go-round, roundabout, whirligig|
  |477 | carpenter's kit, tool kit|
  |478 | carton|
  |479 | car wheel|
  |480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM|
  |481 | cassette|
  |482 | cassette player|
  |483 | castle|
  |484 | catamaran|
  |485 | CD player|
  |486 | cello, violoncello|
  |487 | cellular telephone, cellular phone, cellphone, cell, mobile phone|
  |488 | chain|
  |489 | chainlink fence|
  |490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour|
  |491 | chain saw, chainsaw|
  |492 | chest|
  |493 | chiffonier, commode|
  |494 | chime, bell, gong|
  |495 | china cabinet, china closet|
  |496 | Christmas stocking|
  |497 | church, church building|
  |498 | cinema, movie theater, movie theatre, movie house, picture palace|
  |499 | cleaver, meat cleaver, chopper|
  |500 | cliff dwelling|
  |501 | cloak|
  |502 | clog, geta, patten, sabot|
  |503 | cocktail shaker|
  |504 | coffee mug|
  |505 | coffeepot|
  |506 | coil, spiral, volute, whorl, helix|
  |507 | combination lock|
  |508 | computer keyboard, keypad|
  |509 | confectionery, confectionary, candy store|
  |510 | container ship, containership, container vessel|
  |511 | convertible|
  |512 | corkscrew, bottle screw|
  |513 | cornet, horn, trumpet, trump|
  |514 | cowboy boot|
  |515 | cowboy hat, ten-gallon hat|
  |516 | cradle|
  |517 | crane_1|
  |518 | crash helmet|
  |519 | crate|
  |520 | crib, cot|
  |521 | Crock Pot|
  |522 | croquet ball|
  |523 | crutch|
  |524 | cuirass|
  |525 | dam, dike, dyke|
  |526 | desk|
  |527 | desktop computer|
  |528 | dial telephone, dial phone|
  |529 | diaper, nappy, napkin|
  |530 | digital clock|
  |531 | digital watch|
  |532 | dining table, board|
  |533 | dishrag, dishcloth|
  |534 | dishwasher, dish washer, dishwashing machine|
  |535 | disk brake, disc brake|
  |536 | dock, dockage, docking facility|
  |537 | dogsled, dog sled, dog sleigh|
  |538 | dome|
  |539 | doormat, welcome mat|
  |540 | drilling platform, offshore rig|
  |541 | drum, membranophone, tympan|
  |542 | drumstick|
  |543 | dumbbell|
  |544 | Dutch oven|
  |545 | electric fan, blower|
  |546 | electric guitar|
  |547 | electric locomotive|
  |548 | entertainment center|
  |549 | envelope|
  |550 | espresso maker|
  |551 | face powder|
  |552 | feather boa, boa|
  |553 | file, file cabinet, filing cabinet|
  |554 | fireboat|
  |555 | fire engine, fire truck|
  |556 | fire screen, fireguard|
  |557 | flagpole, flagstaff|
  |558 | flute, transverse flute|
  |559 | folding chair|
  |560 | football helmet|
  |561 | forklift|
  |562 | fountain|
  |563 | fountain pen|
  |564 | four-poster|
  |565 | freight car|
  |566 | French horn, horn|
  |567 | frying pan, frypan, skillet|
  |568 | fur coat|
  |569 | garbage truck, dustcart|
  |570 | gasmask, respirator, gas helmet|
  |571 | gas pump, gasoline pump, petrol pump, island dispenser|
  |572 | goblet|
  |573 | go-kart|
  |574 | golf ball|
  |575 | golfcart, golf cart|
  |576 | gondola|
  |577 | gong, tam-tam|
  |578 | gown|
  |579 | grand piano, grand|
  |580 | greenhouse, nursery, glasshouse|
  |581 | grille, radiator grille|
  |582 | grocery store, grocery, food market, market|
  |583 | guillotine|
  |584 | hair slide|
  |585 | hair spray|
  |586 | half track|
  |587 | hammer|
  |588 | hamper|
  |589 | hand blower, blow dryer, blow drier, hair dryer, hair drier|
  |590 | hand-held computer, hand-held microcomputer|
  |591 | handkerchief, hankie, hanky, hankey|
  |592 | hard disc, hard disk, fixed disk|
  |593 | harmonica, mouth organ, harp, mouth harp|
  |594 | harp|
  |595 | harvester, reaper|
  |596 | hatchet|
  |597 | holster|
  |598 | home theater, home theatre|
  |599 | honeycomb|
  |600 | hook, claw|
  |601 | hoopskirt, crinoline|
  |602 | horizontal bar, high bar|
  |603 | horse cart, horse-cart|
  |604 | hourglass|
  |605 | iPod|
  |606 | iron, smoothing iron|
  |607 | jack-o'-lantern|
  |608 | jean, blue jean, denim|
  |609 | jeep, landrover|
  |610 | jersey, T-shirt, tee shirt|
  |611 | jigsaw puzzle|
  |612 | jinrikisha, ricksha, rickshaw|
  |613 | joystick|
  |614 | kimono|
  |615 | knee pad|
  |616 | knot|
  |617 | lab coat, laboratory coat|
  |618 | ladle|
  |619 | lampshade, lamp shade|
  |620 | laptop, laptop computer|
  |621 | lawn mower, mower|
  |622 | lens cap, lens cover|
  |623 | letter opener, paper knife, paperknife|
  |624 | library|
  |625 | lifeboat|
  |626 | lighter, light, igniter, ignitor|
  |627 | limousine, limo|
  |628 | liner, ocean liner|
  |629 | lipstick, lip rouge|
  |630 | Loafer|
  |631 | lotion|
  |632 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system|
  |633 | loupe, jeweler's loupe|
  |634 | lumbermill, sawmill|
  |635 | magnetic compass|
  |636 | mailbag, postbag|
  |637 | mailbox, letter box|
  |638 | maillot|
  |639 | maillot, tank suit|
  |640 | manhole cover|
  |641 | maraca|
  |642 | marimba, xylophone|
  |643 | mask|
  |644 | matchstick|
  |645 | maypole|
  |646 | maze, labyrinth|
  |647 | measuring cup|
  |648 | medicine chest, medicine cabinet|
  |649 | megalith, megalithic structure|
  |650 | microphone, mike|
  |651 | microwave, microwave oven|
  |652 | military uniform|
  |653 | milk can|
  |654 | minibus|
  |655 | miniskirt, mini|
  |656 | minivan|
  |657 | missile|
  |658 | mitten|
  |659 | mixing bowl|
  |660 | mobile home, manufactured home|
  |661 | Model T|
  |662 | modem|
  |663 | monastery|
  |664 | monitor|
  |665 | moped|
  |666 | mortar|
  |667 | mortarboard|
  |668 | mosque|
  |669 | mosquito net|
  |670 | motor scooter, scooter|
  |671 | mountain bike, all-terrain bike, off-roader|
  |672 | mountain tent|
  |673 | mouse, computer mouse|
  |674 | mousetrap|
  |675 | moving van|
  |676 | muzzle|
  |677 | nail|
  |678 | neck brace|
  |679 | necklace|
  |680 | nipple|
  |681 | notebook, notebook computer|
  |682 | obelisk|
  |683 | oboe, hautboy, hautbois|
  |684 | ocarina, sweet potato|
  |685 | odometer, hodometer, mileometer, milometer|
  |686 | oil filter|
  |687 | organ, pipe organ|
  |688 | oscilloscope, scope, cathode-ray oscilloscope, CRO|
  |689 | overskirt|
  |690 | oxcart|
  |691 | oxygen mask|
  |692 | packet|
  |693 | paddle, boat paddle|
  |694 | paddlewheel, paddle wheel|
  |695 | padlock|
  |696 | paintbrush|
  |697 | pajama, pyjama, pj's, jammies|
  |698 | palace|
  |699 | panpipe, pandean pipe, syrinx|
  |700 | paper towel|
  |701 | parachute, chute|
  |702 | parallel bars, bars|
  |703 | park bench|
  |704 | parking meter|
  |705 | passenger car, coach, carriage|
  |706 | patio, terrace|
  |707 | pay-phone, pay-station|
  |708 | pedestal, plinth, footstall|
  |709 | pencil box, pencil case|
  |710 | pencil sharpener|
  |711 | perfume, essence|
  |712 | Petri dish|
  |713 | photocopier|
  |714 | pick, plectrum, plectron|
  |715 | pickelhaube|
  |716 | picket fence, paling|
  |717 | pickup, pickup truck|
  |718 | pier|
  |719 | piggy bank, penny bank|
  |720 | pill bottle|
  |721 | pillow|
  |722 | ping-pong ball|
  |723 | pinwheel|
  |724 | pirate, pirate ship|
  |725 | pitcher, ewer|
  |726 | plane, carpenter's plane, woodworking plane|
  |727 | planetarium|
  |728 | plastic bag|
  |729 | plate rack|
  |730 | plow, plough|
  |731 | plunger, plumber's helper|
  |732 | Polaroid camera, Polaroid Land camera|
  |733 | pole|
  |734 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria|
  |735 | poncho|
  |736 | pool table, billiard table, snooker table|
  |737 | pop bottle, soda bottle|
  |738 | pot, flowerpot|
  |739 | potter's wheel|
  |740 | power drill|
  |741 | prayer rug, prayer mat|
  |742 | printer|
  |743 | prison, prison house|
  |744 | projectile, missile|
  |745 | projector|
  |746 | puck, hockey puck|
  |747 | punching bag, punch bag, punching ball, punchball|
  |748 | purse|
  |749 | quill, quill pen|
  |750 | quilt, comforter, comfort, puff|
  |751 | racer, race car, racing car|
  |752 | racket, racquet|
  |753 | radiator|
  |754 | radio, wireless|
  |755 | radio telescope, radio reflector|
  |756 | rain barrel|
  |757 | recreational vehicle, RV, R.V.|
  |758 | reel|
  |759 | reflex camera|
  |760 | refrigerator, icebox|
  |761 | remote control, remote|
  |762 | restaurant, eating house, eating place, eatery|
  |763 | revolver, six-gun, six-shooter|
  |764 | rifle|
  |765 | rocking chair, rocker|
  |766 | rotisserie|
  |767 | rubber eraser, rubber, pencil eraser|
  |768 | rugby ball|
  |769 | rule, ruler|
  |770 | running shoe|
  |771 | safe|
  |772 | safety pin|
  |773 | saltshaker, salt shaker|
  |774 | sandal|
  |775 | sarong|
  |776 | sax, saxophone|
  |777 | scabbard|
  |778 | scale, weighing machine|
  |779 | school bus|
  |780 | schooner|
  |781 | scoreboard|
  |782 | screen, CRT screen|
  |783 | screw|
  |784 | screwdriver|
  |785 | seat belt, seatbelt|
  |786 | sewing machine|
  |787 | shield, buckler|
  |788 | shoe shop, shoe-shop, shoe store|
  |789 | shoji|
  |790 | shopping basket|
  |791 | shopping cart|
  |792 | shovel|
  |793 | shower cap|
  |794 | shower curtain|
  |795 | ski|
  |796 | ski mask|
  |797 | sleeping bag|
  |798 | slide rule, slipstick|
  |799 | sliding door|
  |800 | slot, one-armed bandit|
  |801 | snorkel|
  |802 | snowmobile|
  |803 | snowplow, snowplough|
  |804 | soap dispenser|
  |805 | soccer ball|
  |806 | sock|
  |807 | solar dish, solar collector, solar furnace|
  |808 | sombrero|
  |809 | soup bowl|
  |810 | space bar|
  |811 | space heater|
  |812 | space shuttle|
  |813 | spatula|
  |814 | speedboat|
  |815 | spider web, spider's web|
  |816 | spindle|
  |817 | sports car, sport car|
  |818 | spotlight, spot|
  |819 | stage|
  |820 | steam locomotive|
  |821 | steel arch bridge|
  |822 | steel drum|
  |823 | stethoscope|
  |824 | stole|
  |825 | stone wall|
  |826 | stopwatch, stop watch|
  |827 | stove|
  |828 | strainer|
  |829 | streetcar, tram, tramcar, trolley, trolley car|
  |830 | stretcher|
  |831 | studio couch, day bed|
  |832 | stupa, tope|
  |833 | submarine, pigboat, sub, U-boat|
  |834 | suit, suit of clothes|
  |835 | sundial|
  |836 | sunglass|
  |837 | sunglasses, dark glasses, shades|
  |838 | sunscreen, sunblock, sun blocker|
  |839 | suspension bridge|
  |840 | swab, swob, mop|
  |841 | sweatshirt|
  |842 | swimming trunks, bathing trunks|
  |843 | swing|
  |844 | switch, electric switch, electrical switch|
  |845 | syringe|
  |846 | table lamp|
  |847 | tank, army tank, armored combat vehicle, armoured combat vehicle|
  |848 | tape player|
  |849 | teapot|
  |850 | teddy, teddy bear|
  |851 | television, television system|
  |852 | tennis ball|
  |853 | thatch, thatched roof|
  |854 | theater curtain, theatre curtain|
  |855 | thimble|
  |856 | thresher, thrasher, threshing machine|
  |857 | throne|
  |858 | tile roof|
  |859 | toaster|
  |860 | tobacco shop, tobacconist shop, tobacconist|
  |861 | toilet seat|
  |862 | torch|
  |863 | totem pole|
  |864 | tow truck, tow car, wrecker|
  |865 | toyshop|
  |866 | tractor|
  |867 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi|
  |868 | tray|
  |869 | trench coat|
  |870 | tricycle, trike, velocipede|
  |871 | trimaran|
  |872 | tripod|
  |873 | triumphal arch|
  |874 | trolleybus, trolley coach, trackless trolley|
  |875 | trombone|
  |876 | tub, vat|
  |877 | turnstile|
  |878 | typewriter keyboard|
  |879 | umbrella|
  |880 | unicycle, monocycle|
  |881 | upright, upright piano|
  |882 | vacuum, vacuum cleaner|
  |883 | vase|
  |884 | vault|
  |885 | velvet|
  |886 | vending machine|
  |887 | vestment|
  |888 | viaduct|
  |889 | violin, fiddle|
  |890 | volleyball|
  |891 | waffle iron|
  |892 | wall clock|
  |893 | wallet, billfold, notecase, pocketbook|
  |894 | wardrobe, closet, press|
  |895 | warplane, military plane|
  |896 | washbasin, handbasin, washbowl, lavabo, wash-hand basin|
  |897 | washer, automatic washer, washing machine|
  |898 | water bottle|
  |899 | water jug|
  |900 | water tower|
  |901 | whiskey jug|
  |902 | whistle|
  |903 | wig|
  |904 | window screen|
  |905 | window shade|
  |906 | Windsor tie|
  |907 | wine bottle|
  |908 | wing|
  |909 | wok|
  |910 | wooden spoon|
  |911 | wool, woolen, woollen|
  |912 | worm fence, snake fence, snake-rail fence, Virginia fence|
  |913 | wreck|
  |914 | yawl|
  |915 | yurt|
  |916 | web site, website, internet site, site|
  |917 | comic book|
  |918 | crossword puzzle, crossword|
  |919 | street sign|
  |920 | traffic light, traffic signal, stoplight|
  |921 | book jacket, dust cover, dust jacket, dust wrapper|
  |922 | menu|
  |923 | plate|
  |924 | guacamole|
  |925 | consomme|
  |926 | hot pot, hotpot|
  |927 | trifle|
  |928 | ice cream, icecream|
  |929 | ice lolly, lolly, lollipop, popsicle|
  |930 | French loaf|
  |931 | bagel, beigel|
  |932 | pretzel|
  |933 | cheeseburger|
  |934 | hotdog, hot dog, red hot|
  |935 | mashed potato|
  |936 | head cabbage|
  |937 | broccoli|
  |938 | cauliflower|
  |939 | zucchini, courgette|
  |940 | spaghetti squash|
  |941 | acorn squash|
  |942 | butternut squash|
  |943 | cucumber, cuke|
  |944 | artichoke, globe artichoke|
  |945 | bell pepper|
  |946 | cardoon|
  |947 | mushroom|
  |948 | Granny Smith|
  |949 | strawberry|
  |950 | orange|
  |951 | lemon|
  |952 | fig|
  |953 | pineapple, ananas|
  |954 | banana|
  |955 | jackfruit, jak, jack|
  |956 | custard apple|
  |957 | pomegranate|
  |958 | hay|
  |959 | carbonara|
  |960 | chocolate sauce, chocolate syrup|
  |961 | dough|
  |962 | meat loaf, meatloaf|
  |963 | pizza, pizza pie|
  |964 | potpie|
  |965 | burrito|
  |966 | red wine|
  |967 | espresso|
  |968 | cup|
  |969 | eggnog|
  |970 | alp|
  |971 | bubble|
  |972 | cliff, drop, drop-off|
  |973 | coral reef|
  |974 | geyser|
  |975 | lakeside, lakeshore|
  |976 | promontory, headland, head, foreland|
  |977 | sandbar, sand bar|
  |978 | seashore, coast, seacoast, sea-coast|
  |979 | valley, vale|
  |980 | volcano|
  |981 | ballplayer, baseball player|
  |982 | groom, bridegroom|
  |983 | scuba diver|
  |984 | rapeseed|
  |985 | daisy|
  |986 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum|
  |987 | corn|
  |988 | acorn|
  |989 | hip, rose hip, rosehip|
  |990 | buckeye, horse chestnut, conker|
  |991 | coral fungus|
  |992 | agaric|
  |993 | gyromitra|
  |994 | stinkhorn, carrion fungus|
  |995 | earthstar|
  |996 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa|
  |997 | bolete|
  |998 | ear, spike, capitulum|
  |999 | toilet tissue, toilet paper, bathroom tissue|
</details>

### Data Splits

|             |train  |validation| test  |
|-------------|------:|---------:|------:|
|# of examples|1281167|50000     |100000 |

## Dataset Creation

### Curation Rationale

The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.

### Source Data

#### Initial Data Collection and Normalization

Initial data for ImageNet image classification task consists of photographs collected from [Flickr](https://www.flickr.com) and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs [1](https://ieeexplore.ieee.org/abstract/document/5206848). The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.

#### Who are the source language producers?

WordNet synsets further quality controlled by human annotators. The images are from Flickr.

### Annotations

#### Annotation process

The annotation process of collecting ImageNet for image classification task is a three step process.

1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
1. Collecting the candidate image for these object categories using a search engine.
1. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.

See the section 3.1 in [1](https://arxiv.org/abs/1409.0575) for more details on data collection procedure and [2](https://ieeexplore.ieee.org/abstract/document/5206848) for general information on ImageNet.

#### Who are the annotators?

Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See [1](https://arxiv.org/abs/1409.0575) for more details.

### Personal and Sensitive Information

The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](https://image-net.org/face-obfuscation/) on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been [an attempt](https://arxiv.org/abs/1912.07726) at filtering and balancing the people subtree in the larger ImageNet.

## Considerations for Using the Data

### Social Impact of Dataset

The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in [1](https://arxiv.org/abs/1409.0575) for a discussion on social impact of the dataset.

### Discussion of Biases

1. A [study](https://image-net.org/update-sep-17-2019.php) of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
1. A [study](https://arxiv.org/abs/1811.12231) has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
1. Another [study](https://arxiv.org/abs/2109.13228) more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
1. ImageNet data with face obfuscation is also provided at [this link](https://image-net.org/face-obfuscation/)
1. A study on genealogy of ImageNet is can be found at [this link](https://journals.sagepub.com/doi/full/10.1177/20539517211035955) about the "norms, values, and assumptions" in ImageNet.
1. See [this study](https://arxiv.org/abs/1912.07726) on filtering and balancing the distribution of people subtree in the larger complete ImageNet.

### Other Known Limitations

1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](https://arxiv.org/abs/2109.13228) [[2]](https://arxiv.org/abs/1409.0575) [[3]](https://ieeexplore.ieee.org/abstract/document/5206848).

## Additional Information

### Dataset Curators

Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):

- Olga Russakovsky
- Jia Deng
- Hao Su
- Jonathan Krause
- Sanjeev Satheesh
- Wei Dong
- Richard Socher
- Li-Jia Li
- Kai Li
- Sean Ma
- Zhiheng Huang
- Andrej Karpathy
- Aditya Khosla
- Michael Bernstein
- Alexander C Berg
- Li Fei-Fei

### Licensing Information

In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:

1. Researcher shall use the Database only for non-commercial research and educational purposes.
1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
1. The law of the State of New Jersey shall apply to all disputes under this agreement.

### Citation Information

```bibtex
@article{imagenet15russakovsky,
    Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
    Title = { {ImageNet Large Scale Visual Recognition Challenge} },
    Year = {2015},
    journal   = {International Journal of Computer Vision (IJCV)},
    doi = {10.1007/s11263-015-0816-y},
    volume={115},
    number={3},
    pages={211-252}
}
```

### Contributions

Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.