diff --git a/torchvision/prototype/datasets/_builtin/__init__.py b/torchvision/prototype/datasets/_builtin/__init__.py
index 395c1f95764..9a47ad4c99f 100644
--- a/torchvision/prototype/datasets/_builtin/__init__.py
+++ b/torchvision/prototype/datasets/_builtin/__init__.py
@@ -2,6 +2,7 @@
 from .celeba import CelebA
 from .cifar import Cifar10, Cifar100
 from .coco import Coco
+from .imagenet import ImageNet
 from .mnist import MNIST, FashionMNIST, KMNIST, EMNIST, QMNIST
 from .sbd import SBD
 from .voc import VOC
diff --git a/torchvision/prototype/datasets/_builtin/imagenet.categories b/torchvision/prototype/datasets/_builtin/imagenet.categories
new file mode 100644
index 00000000000..18e24b85311
--- /dev/null
+++ b/torchvision/prototype/datasets/_builtin/imagenet.categories
@@ -0,0 +1,1000 @@
+tench,n01440764
+goldfish,n01443537
+great white shark,n01484850
+tiger shark,n01491361
+hammerhead,n01494475
+electric ray,n01496331
+stingray,n01498041
+cock,n01514668
+hen,n01514859
+ostrich,n01518878
+brambling,n01530575
+goldfinch,n01531178
+house finch,n01532829
+junco,n01534433
+indigo bunting,n01537544
+robin,n01558993
+bulbul,n01560419
+jay,n01580077
+magpie,n01582220
+chickadee,n01592084
+water ouzel,n01601694
+kite,n01608432
+bald eagle,n01614925
+vulture,n01616318
+great grey owl,n01622779
+European fire salamander,n01629819
+common newt,n01630670
+eft,n01631663
+spotted salamander,n01632458
+axolotl,n01632777
+bullfrog,n01641577
+tree frog,n01644373
+tailed frog,n01644900
+loggerhead,n01664065
+leatherback turtle,n01665541
+mud turtle,n01667114
+terrapin,n01667778
+box turtle,n01669191
+banded gecko,n01675722
+common iguana,n01677366
+American chameleon,n01682714
+whiptail,n01685808
+agama,n01687978
+frilled lizard,n01688243
+alligator lizard,n01689811
+Gila monster,n01692333
+green lizard,n01693334
+African chameleon,n01694178
+Komodo dragon,n01695060
+African crocodile,n01697457
+American alligator,n01698640
+triceratops,n01704323
+thunder snake,n01728572
+ringneck snake,n01728920
+hognose snake,n01729322
+green snake,n01729977
+king snake,n01734418
+garter snake,n01735189
+water snake,n01737021
+vine snake,n01739381
+night snake,n01740131
+boa constrictor,n01742172
+rock python,n01744401
+Indian cobra,n01748264
+green mamba,n01749939
+sea snake,n01751748
+horned viper,n01753488
+diamondback,n01755581
+sidewinder,n01756291
+trilobite,n01768244
+harvestman,n01770081
+scorpion,n01770393
+black and gold garden spider,n01773157
+barn spider,n01773549
+garden spider,n01773797
+black widow,n01774384
+tarantula,n01774750
+wolf spider,n01775062
+tick,n01776313
+centipede,n01784675
+black grouse,n01795545
+ptarmigan,n01796340
+ruffed grouse,n01797886
+prairie chicken,n01798484
+peacock,n01806143
+quail,n01806567
+partridge,n01807496
+African grey,n01817953
+macaw,n01818515
+sulphur-crested cockatoo,n01819313
+lorikeet,n01820546
+coucal,n01824575
+bee eater,n01828970
+hornbill,n01829413
+hummingbird,n01833805
+jacamar,n01843065
+toucan,n01843383
+drake,n01847000
+red-breasted merganser,n01855032
+goose,n01855672
+black swan,n01860187
+tusker,n01871265
+echidna,n01872401
+platypus,n01873310
+wallaby,n01877812
+koala,n01882714
+wombat,n01883070
+jellyfish,n01910747
+sea anemone,n01914609
+brain coral,n01917289
+flatworm,n01924916
+nematode,n01930112
+conch,n01943899
+snail,n01944390
+slug,n01945685
+sea slug,n01950731
+chiton,n01955084
+chambered nautilus,n01968897
+Dungeness crab,n01978287
+rock crab,n01978455
+fiddler crab,n01980166
+king crab,n01981276
+American lobster,n01983481
+spiny lobster,n01984695
+crayfish,n01985128
+hermit crab,n01986214
+isopod,n01990800
+white stork,n02002556
+black stork,n02002724
+spoonbill,n02006656
+flamingo,n02007558
+little blue heron,n02009229
+American egret,n02009912
+bittern,n02011460
+crane,n02012849
+limpkin,n02013706
+European gallinule,n02017213
+American coot,n02018207
+bustard,n02018795
+ruddy turnstone,n02025239
+red-backed sandpiper,n02027492
+redshank,n02028035
+dowitcher,n02033041
+oystercatcher,n02037110
+pelican,n02051845
+king penguin,n02056570
+albatross,n02058221
+grey whale,n02066245
+killer whale,n02071294
+dugong,n02074367
+sea lion,n02077923
+Chihuahua,n02085620
+Japanese spaniel,n02085782
+Maltese dog,n02085936
+Pekinese,n02086079
+Shih-Tzu,n02086240
+Blenheim spaniel,n02086646
+papillon,n02086910
+toy terrier,n02087046
+Rhodesian ridgeback,n02087394
+Afghan hound,n02088094
+basset,n02088238
+beagle,n02088364
+bloodhound,n02088466
+bluetick,n02088632
+black-and-tan coonhound,n02089078
+Walker hound,n02089867
+English foxhound,n02089973
+redbone,n02090379
+borzoi,n02090622
+Irish wolfhound,n02090721
+Italian greyhound,n02091032
+whippet,n02091134
+Ibizan hound,n02091244
+Norwegian elkhound,n02091467
+otterhound,n02091635
+Saluki,n02091831
+Scottish deerhound,n02092002
+Weimaraner,n02092339
+Staffordshire bullterrier,n02093256
+American Staffordshire terrier,n02093428
+Bedlington terrier,n02093647
+Border terrier,n02093754
+Kerry blue terrier,n02093859
+Irish terrier,n02093991
+Norfolk terrier,n02094114
+Norwich terrier,n02094258
+Yorkshire terrier,n02094433
+wire-haired fox terrier,n02095314
+Lakeland terrier,n02095570
+Sealyham terrier,n02095889
+Airedale,n02096051
+cairn,n02096177
+Australian terrier,n02096294
+Dandie Dinmont,n02096437
+Boston bull,n02096585
+miniature schnauzer,n02097047
+giant schnauzer,n02097130
+standard schnauzer,n02097209
+Scotch terrier,n02097298
+Tibetan terrier,n02097474
+silky terrier,n02097658
+soft-coated wheaten terrier,n02098105
+West Highland white terrier,n02098286
+Lhasa,n02098413
+flat-coated retriever,n02099267
+curly-coated retriever,n02099429
+golden retriever,n02099601
+Labrador retriever,n02099712
+Chesapeake Bay retriever,n02099849
+German short-haired pointer,n02100236
+vizsla,n02100583
+English setter,n02100735
+Irish setter,n02100877
+Gordon setter,n02101006
+Brittany spaniel,n02101388
+clumber,n02101556
+English springer,n02102040
+Welsh springer spaniel,n02102177
+cocker spaniel,n02102318
+Sussex spaniel,n02102480
+Irish water spaniel,n02102973
+kuvasz,n02104029
+schipperke,n02104365
+groenendael,n02105056
+malinois,n02105162
+briard,n02105251
+kelpie,n02105412
+komondor,n02105505
+Old English sheepdog,n02105641
+Shetland sheepdog,n02105855
+collie,n02106030
+Border collie,n02106166
+Bouvier des Flandres,n02106382
+Rottweiler,n02106550
+German shepherd,n02106662
+Doberman,n02107142
+miniature pinscher,n02107312
+Greater Swiss Mountain dog,n02107574
+Bernese mountain dog,n02107683
+Appenzeller,n02107908
+EntleBucher,n02108000
+boxer,n02108089
+bull mastiff,n02108422
+Tibetan mastiff,n02108551
+French bulldog,n02108915
+Great Dane,n02109047
+Saint Bernard,n02109525
+Eskimo dog,n02109961
+malamute,n02110063
+Siberian husky,n02110185
+dalmatian,n02110341
+affenpinscher,n02110627
+basenji,n02110806
+pug,n02110958
+Leonberg,n02111129
+Newfoundland,n02111277
+Great Pyrenees,n02111500
+Samoyed,n02111889
+Pomeranian,n02112018
+chow,n02112137
+keeshond,n02112350
+Brabancon griffon,n02112706
+Pembroke,n02113023
+Cardigan,n02113186
+toy poodle,n02113624
+miniature poodle,n02113712
+standard poodle,n02113799
+Mexican hairless,n02113978
+timber wolf,n02114367
+white wolf,n02114548
+red wolf,n02114712
+coyote,n02114855
+dingo,n02115641
+dhole,n02115913
+African hunting dog,n02116738
+hyena,n02117135
+red fox,n02119022
+kit fox,n02119789
+Arctic fox,n02120079
+grey fox,n02120505
+tabby,n02123045
+tiger cat,n02123159
+Persian cat,n02123394
+Siamese cat,n02123597
+Egyptian cat,n02124075
+cougar,n02125311
+lynx,n02127052
+leopard,n02128385
+snow leopard,n02128757
+jaguar,n02128925
+lion,n02129165
+tiger,n02129604
+cheetah,n02130308
+brown bear,n02132136
+American black bear,n02133161
+ice bear,n02134084
+sloth bear,n02134418
+mongoose,n02137549
+meerkat,n02138441
+tiger beetle,n02165105
+ladybug,n02165456
+ground beetle,n02167151
+long-horned beetle,n02168699
+leaf beetle,n02169497
+dung beetle,n02172182
+rhinoceros beetle,n02174001
+weevil,n02177972
+fly,n02190166
+bee,n02206856
+ant,n02219486
+grasshopper,n02226429
+cricket,n02229544
+walking stick,n02231487
+cockroach,n02233338
+mantis,n02236044
+cicada,n02256656
+leafhopper,n02259212
+lacewing,n02264363
+dragonfly,n02268443
+damselfly,n02268853
+admiral,n02276258
+ringlet,n02277742
+monarch,n02279972
+cabbage butterfly,n02280649
+sulphur butterfly,n02281406
+lycaenid,n02281787
+starfish,n02317335
+sea urchin,n02319095
+sea cucumber,n02321529
+wood rabbit,n02325366
+hare,n02326432
+Angora,n02328150
+hamster,n02342885
+porcupine,n02346627
+fox squirrel,n02356798
+marmot,n02361337
+beaver,n02363005
+guinea pig,n02364673
+sorrel,n02389026
+zebra,n02391049
+hog,n02395406
+wild boar,n02396427
+warthog,n02397096
+hippopotamus,n02398521
+ox,n02403003
+water buffalo,n02408429
+bison,n02410509
+ram,n02412080
+bighorn,n02415577
+ibex,n02417914
+hartebeest,n02422106
+impala,n02422699
+gazelle,n02423022
+Arabian camel,n02437312
+llama,n02437616
+weasel,n02441942
+mink,n02442845
+polecat,n02443114
+black-footed ferret,n02443484
+otter,n02444819
+skunk,n02445715
+badger,n02447366
+armadillo,n02454379
+three-toed sloth,n02457408
+orangutan,n02480495
+gorilla,n02480855
+chimpanzee,n02481823
+gibbon,n02483362
+siamang,n02483708
+guenon,n02484975
+patas,n02486261
+baboon,n02486410
+macaque,n02487347
+langur,n02488291
+colobus,n02488702
+proboscis monkey,n02489166
+marmoset,n02490219
+capuchin,n02492035
+howler monkey,n02492660
+titi,n02493509
+spider monkey,n02493793
+squirrel monkey,n02494079
+Madagascar cat,n02497673
+indri,n02500267
+Indian elephant,n02504013
+African elephant,n02504458
+lesser panda,n02509815
+giant panda,n02510455
+barracouta,n02514041
+eel,n02526121
+coho,n02536864
+rock beauty,n02606052
+anemone fish,n02607072
+sturgeon,n02640242
+gar,n02641379
+lionfish,n02643566
+puffer,n02655020
+abacus,n02666196
+abaya,n02667093
+academic gown,n02669723
+accordion,n02672831
+acoustic guitar,n02676566
+aircraft carrier,n02687172
+airliner,n02690373
+airship,n02692877
+altar,n02699494
+ambulance,n02701002
+amphibian,n02704792
+analog clock,n02708093
+apiary,n02727426
+apron,n02730930
+ashcan,n02747177
+assault rifle,n02749479
+backpack,n02769748
+bakery,n02776631
+balance beam,n02777292
+balloon,n02782093
+ballpoint,n02783161
+Band Aid,n02786058
+banjo,n02787622
+bannister,n02788148
+barbell,n02790996
+barber chair,n02791124
+barbershop,n02791270
+barn,n02793495
+barometer,n02794156
+barrel,n02795169
+barrow,n02797295
+baseball,n02799071
+basketball,n02802426
+bassinet,n02804414
+bassoon,n02804610
+bathing cap,n02807133
+bath towel,n02808304
+bathtub,n02808440
+beach wagon,n02814533
+beacon,n02814860
+beaker,n02815834
+bearskin,n02817516
+beer bottle,n02823428
+beer glass,n02823750
+bell cote,n02825657
+bib,n02834397
+bicycle-built-for-two,n02835271
+bikini,n02837789
+binder,n02840245
+binoculars,n02841315
+birdhouse,n02843684
+boathouse,n02859443
+bobsled,n02860847
+bolo tie,n02865351
+bonnet,n02869837
+bookcase,n02870880
+bookshop,n02871525
+bottlecap,n02877765
+bow,n02879718
+bow tie,n02883205
+brass,n02892201
+brassiere,n02892767
+breakwater,n02894605
+breastplate,n02895154
+broom,n02906734
+bucket,n02909870
+buckle,n02910353
+bulletproof vest,n02916936
+bullet train,n02917067
+butcher shop,n02927161
+cab,n02930766
+caldron,n02939185
+candle,n02948072
+cannon,n02950826
+canoe,n02951358
+can opener,n02951585
+cardigan,n02963159
+car mirror,n02965783
+carousel,n02966193
+carpenter's kit,n02966687
+carton,n02971356
+car wheel,n02974003
+cash machine,n02977058
+cassette,n02978881
+cassette player,n02979186
+castle,n02980441
+catamaran,n02981792
+CD player,n02988304
+cello,n02992211
+cellular telephone,n02992529
+chain,n02999410
+chainlink fence,n03000134
+chain mail,n03000247
+chain saw,n03000684
+chest,n03014705
+chiffonier,n03016953
+chime,n03017168
+china cabinet,n03018349
+Christmas stocking,n03026506
+church,n03028079
+cinema,n03032252
+cleaver,n03041632
+cliff dwelling,n03042490
+cloak,n03045698
+clog,n03047690
+cocktail shaker,n03062245
+coffee mug,n03063599
+coffeepot,n03063689
+coil,n03065424
+combination lock,n03075370
+computer keyboard,n03085013
+confectionery,n03089624
+container ship,n03095699
+convertible,n03100240
+corkscrew,n03109150
+cornet,n03110669
+cowboy boot,n03124043
+cowboy hat,n03124170
+cradle,n03125729
+construction crane,n03126707
+crash helmet,n03127747
+crate,n03127925
+crib,n03131574
+Crock Pot,n03133878
+croquet ball,n03134739
+crutch,n03141823
+cuirass,n03146219
+dam,n03160309
+desk,n03179701
+desktop computer,n03180011
+dial telephone,n03187595
+diaper,n03188531
+digital clock,n03196217
+digital watch,n03197337
+dining table,n03201208
+dishrag,n03207743
+dishwasher,n03207941
+disk brake,n03208938
+dock,n03216828
+dogsled,n03218198
+dome,n03220513
+doormat,n03223299
+drilling platform,n03240683
+drum,n03249569
+drumstick,n03250847
+dumbbell,n03255030
+Dutch oven,n03259280
+electric fan,n03271574
+electric guitar,n03272010
+electric locomotive,n03272562
+entertainment center,n03290653
+envelope,n03291819
+espresso maker,n03297495
+face powder,n03314780
+feather boa,n03325584
+file,n03337140
+fireboat,n03344393
+fire engine,n03345487
+fire screen,n03347037
+flagpole,n03355925
+flute,n03372029
+folding chair,n03376595
+football helmet,n03379051
+forklift,n03384352
+fountain,n03388043
+fountain pen,n03388183
+four-poster,n03388549
+freight car,n03393912
+French horn,n03394916
+frying pan,n03400231
+fur coat,n03404251
+garbage truck,n03417042
+gasmask,n03424325
+gas pump,n03425413
+goblet,n03443371
+go-kart,n03444034
+golf ball,n03445777
+golfcart,n03445924
+gondola,n03447447
+gong,n03447721
+gown,n03450230
+grand piano,n03452741
+greenhouse,n03457902
+grille,n03459775
+grocery store,n03461385
+guillotine,n03467068
+hair slide,n03476684
+hair spray,n03476991
+half track,n03478589
+hammer,n03481172
+hamper,n03482405
+hand blower,n03483316
+hand-held computer,n03485407
+handkerchief,n03485794
+hard disc,n03492542
+harmonica,n03494278
+harp,n03495258
+harvester,n03496892
+hatchet,n03498962
+holster,n03527444
+home theater,n03529860
+honeycomb,n03530642
+hook,n03532672
+hoopskirt,n03534580
+horizontal bar,n03535780
+horse cart,n03538406
+hourglass,n03544143
+iPod,n03584254
+iron,n03584829
+jack-o'-lantern,n03590841
+jean,n03594734
+jeep,n03594945
+jersey,n03595614
+jigsaw puzzle,n03598930
+jinrikisha,n03599486
+joystick,n03602883
+kimono,n03617480
+knee pad,n03623198
+knot,n03627232
+lab coat,n03630383
+ladle,n03633091
+lampshade,n03637318
+laptop,n03642806
+lawn mower,n03649909
+lens cap,n03657121
+letter opener,n03658185
+library,n03661043
+lifeboat,n03662601
+lighter,n03666591
+limousine,n03670208
+liner,n03673027
+lipstick,n03676483
+Loafer,n03680355
+lotion,n03690938
+loudspeaker,n03691459
+loupe,n03692522
+lumbermill,n03697007
+magnetic compass,n03706229
+mailbag,n03709823
+mailbox,n03710193
+maillot,n03710637
+tank suit,n03710721
+manhole cover,n03717622
+maraca,n03720891
+marimba,n03721384
+mask,n03724870
+matchstick,n03729826
+maypole,n03733131
+maze,n03733281
+measuring cup,n03733805
+medicine chest,n03742115
+megalith,n03743016
+microphone,n03759954
+microwave,n03761084
+military uniform,n03763968
+milk can,n03764736
+minibus,n03769881
+miniskirt,n03770439
+minivan,n03770679
+missile,n03773504
+mitten,n03775071
+mixing bowl,n03775546
+mobile home,n03776460
+Model T,n03777568
+modem,n03777754
+monastery,n03781244
+monitor,n03782006
+moped,n03785016
+mortar,n03786901
+mortarboard,n03787032
+mosque,n03788195
+mosquito net,n03788365
+motor scooter,n03791053
+mountain bike,n03792782
+mountain tent,n03792972
+mouse,n03793489
+mousetrap,n03794056
+moving van,n03796401
+muzzle,n03803284
+nail,n03804744
+neck brace,n03814639
+necklace,n03814906
+nipple,n03825788
+notebook,n03832673
+obelisk,n03837869
+oboe,n03838899
+ocarina,n03840681
+odometer,n03841143
+oil filter,n03843555
+organ,n03854065
+oscilloscope,n03857828
+overskirt,n03866082
+oxcart,n03868242
+oxygen mask,n03868863
+packet,n03871628
+paddle,n03873416
+paddlewheel,n03874293
+padlock,n03874599
+paintbrush,n03876231
+pajama,n03877472
+palace,n03877845
+panpipe,n03884397
+paper towel,n03887697
+parachute,n03888257
+parallel bars,n03888605
+park bench,n03891251
+parking meter,n03891332
+passenger car,n03895866
+patio,n03899768
+pay-phone,n03902125
+pedestal,n03903868
+pencil box,n03908618
+pencil sharpener,n03908714
+perfume,n03916031
+Petri dish,n03920288
+photocopier,n03924679
+pick,n03929660
+pickelhaube,n03929855
+picket fence,n03930313
+pickup,n03930630
+pier,n03933933
+piggy bank,n03935335
+pill bottle,n03937543
+pillow,n03938244
+ping-pong ball,n03942813
+pinwheel,n03944341
+pirate,n03947888
+pitcher,n03950228
+plane,n03954731
+planetarium,n03956157
+plastic bag,n03958227
+plate rack,n03961711
+plow,n03967562
+plunger,n03970156
+Polaroid camera,n03976467
+pole,n03976657
+police van,n03977966
+poncho,n03980874
+pool table,n03982430
+pop bottle,n03983396
+pot,n03991062
+potter's wheel,n03992509
+power drill,n03995372
+prayer rug,n03998194
+printer,n04004767
+prison,n04005630
+projectile,n04008634
+projector,n04009552
+puck,n04019541
+punching bag,n04023962
+purse,n04026417
+quill,n04033901
+quilt,n04033995
+racer,n04037443
+racket,n04039381
+radiator,n04040759
+radio,n04041544
+radio telescope,n04044716
+rain barrel,n04049303
+recreational vehicle,n04065272
+reel,n04067472
+reflex camera,n04069434
+refrigerator,n04070727
+remote control,n04074963
+restaurant,n04081281
+revolver,n04086273
+rifle,n04090263
+rocking chair,n04099969
+rotisserie,n04111531
+rubber eraser,n04116512
+rugby ball,n04118538
+rule,n04118776
+running shoe,n04120489
+safe,n04125021
+safety pin,n04127249
+saltshaker,n04131690
+sandal,n04133789
+sarong,n04136333
+sax,n04141076
+scabbard,n04141327
+scale,n04141975
+school bus,n04146614
+schooner,n04147183
+scoreboard,n04149813
+screen,n04152593
+screw,n04153751
+screwdriver,n04154565
+seat belt,n04162706
+sewing machine,n04179913
+shield,n04192698
+shoe shop,n04200800
+shoji,n04201297
+shopping basket,n04204238
+shopping cart,n04204347
+shovel,n04208210
+shower cap,n04209133
+shower curtain,n04209239
+ski,n04228054
+ski mask,n04229816
+sleeping bag,n04235860
+slide rule,n04238763
+sliding door,n04239074
+slot,n04243546
+snorkel,n04251144
+snowmobile,n04252077
+snowplow,n04252225
+soap dispenser,n04254120
+soccer ball,n04254680
+sock,n04254777
+solar dish,n04258138
+sombrero,n04259630
+soup bowl,n04263257
+space bar,n04264628
+space heater,n04265275
+space shuttle,n04266014
+spatula,n04270147
+speedboat,n04273569
+spider web,n04275548
+spindle,n04277352
+sports car,n04285008
+spotlight,n04286575
+stage,n04296562
+steam locomotive,n04310018
+steel arch bridge,n04311004
+steel drum,n04311174
+stethoscope,n04317175
+stole,n04325704
+stone wall,n04326547
+stopwatch,n04328186
+stove,n04330267
+strainer,n04332243
+streetcar,n04335435
+stretcher,n04336792
+studio couch,n04344873
+stupa,n04346328
+submarine,n04347754
+suit,n04350905
+sundial,n04355338
+sunglass,n04355933
+sunglasses,n04356056
+sunscreen,n04357314
+suspension bridge,n04366367
+swab,n04367480
+sweatshirt,n04370456
+swimming trunks,n04371430
+swing,n04371774
+switch,n04372370
+syringe,n04376876
+table lamp,n04380533
+tank,n04389033
+tape player,n04392985
+teapot,n04398044
+teddy,n04399382
+television,n04404412
+tennis ball,n04409515
+thatch,n04417672
+theater curtain,n04418357
+thimble,n04423845
+thresher,n04428191
+throne,n04429376
+tile roof,n04435653
+toaster,n04442312
+tobacco shop,n04443257
+toilet seat,n04447861
+torch,n04456115
+totem pole,n04458633
+tow truck,n04461696
+toyshop,n04462240
+tractor,n04465501
+trailer truck,n04467665
+tray,n04476259
+trench coat,n04479046
+tricycle,n04482393
+trimaran,n04483307
+tripod,n04485082
+triumphal arch,n04486054
+trolleybus,n04487081
+trombone,n04487394
+tub,n04493381
+turnstile,n04501370
+typewriter keyboard,n04505470
+umbrella,n04507155
+unicycle,n04509417
+upright,n04515003
+vacuum,n04517823
+vase,n04522168
+vault,n04523525
+velvet,n04525038
+vending machine,n04525305
+vestment,n04532106
+viaduct,n04532670
+violin,n04536866
+volleyball,n04540053
+waffle iron,n04542943
+wall clock,n04548280
+wallet,n04548362
+wardrobe,n04550184
+warplane,n04552348
+washbasin,n04553703
+washer,n04554684
+water bottle,n04557648
+water jug,n04560804
+water tower,n04562935
+whiskey jug,n04579145
+whistle,n04579432
+wig,n04584207
+window screen,n04589890
+window shade,n04590129
+Windsor tie,n04591157
+wine bottle,n04591713
+wing,n04592741
+wok,n04596742
+wooden spoon,n04597913
+wool,n04599235
+worm fence,n04604644
+wreck,n04606251
+yawl,n04612504
+yurt,n04613696
+web site,n06359193
+comic book,n06596364
+crossword puzzle,n06785654
+street sign,n06794110
+traffic light,n06874185
+book jacket,n07248320
+menu,n07565083
+plate,n07579787
+guacamole,n07583066
+consomme,n07584110
+hot pot,n07590611
+trifle,n07613480
+ice cream,n07614500
+ice lolly,n07615774
+French loaf,n07684084
+bagel,n07693725
+pretzel,n07695742
+cheeseburger,n07697313
+hotdog,n07697537
+mashed potato,n07711569
+head cabbage,n07714571
+broccoli,n07714990
+cauliflower,n07715103
+zucchini,n07716358
+spaghetti squash,n07716906
+acorn squash,n07717410
+butternut squash,n07717556
+cucumber,n07718472
+artichoke,n07718747
+bell pepper,n07720875
+cardoon,n07730033
+mushroom,n07734744
+Granny Smith,n07742313
+strawberry,n07745940
+orange,n07747607
+lemon,n07749582
+fig,n07753113
+pineapple,n07753275
+banana,n07753592
+jackfruit,n07754684
+custard apple,n07760859
+pomegranate,n07768694
+hay,n07802026
+carbonara,n07831146
+chocolate sauce,n07836838
+dough,n07860988
+meat loaf,n07871810
+pizza,n07873807
+potpie,n07875152
+burrito,n07880968
+red wine,n07892512
+espresso,n07920052
+cup,n07930864
+eggnog,n07932039
+alp,n09193705
+bubble,n09229709
+cliff,n09246464
+coral reef,n09256479
+geyser,n09288635
+lakeside,n09332890
+promontory,n09399592
+sandbar,n09421951
+seashore,n09428293
+valley,n09468604
+volcano,n09472597
+ballplayer,n09835506
+groom,n10148035
+scuba diver,n10565667
+rapeseed,n11879895
+daisy,n11939491
+yellow lady's slipper,n12057211
+corn,n12144580
+acorn,n12267677
+hip,n12620546
+buckeye,n12768682
+coral fungus,n12985857
+agaric,n12998815
+gyromitra,n13037406
+stinkhorn,n13040303
+earthstar,n13044778
+hen-of-the-woods,n13052670
+bolete,n13054560
+ear,n13133613
+toilet tissue,n15075141
diff --git a/torchvision/prototype/datasets/_builtin/imagenet.py b/torchvision/prototype/datasets/_builtin/imagenet.py
new file mode 100644
index 00000000000..d568fa2fcfc
--- /dev/null
+++ b/torchvision/prototype/datasets/_builtin/imagenet.py
@@ -0,0 +1,171 @@
+import io
+import pathlib
+import re
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+from torchdata.datapipes.iter import IterDataPipe, LineReader, KeyZipper, Mapper, TarArchiveReader, Filter, Shuffler
+from torchvision.prototype.datasets.utils import (
+    Dataset,
+    DatasetConfig,
+    DatasetInfo,
+    HttpResource,
+    OnlineResource,
+    DatasetType,
+)
+from torchvision.prototype.datasets.utils._internal import (
+    INFINITE_BUFFER_SIZE,
+    BUILTIN_DIR,
+    path_comparator,
+    Enumerator,
+    getitem,
+    read_mat,
+    FrozenMapping,
+)
+
+
+class ImageNet(Dataset):
+    @property
+    def info(self) -> DatasetInfo:
+        name = "imagenet"
+        categories, wnids = zip(*DatasetInfo.read_categories_file(BUILTIN_DIR / f"{name}.categories"))
+
+        return DatasetInfo(
+            name,
+            type=DatasetType.IMAGE,
+            categories=categories,
+            homepage="https://www.image-net.org/",
+            valid_options=dict(split=("train", "val")),
+            extra=dict(
+                wnid_to_category=FrozenMapping(zip(wnids, categories)),
+                category_to_wnid=FrozenMapping(zip(categories, wnids)),
+            ),
+        )
+
+    @property
+    def category_to_wnid(self) -> Dict[str, str]:
+        return self.info.extra.category_to_wnid
+
+    @property
+    def wnid_to_category(self) -> Dict[str, str]:
+        return self.info.extra.wnid_to_category
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        if config.split == "train":
+            images = HttpResource(
+                "ILSVRC2012_img_train.tar",
+                sha256="b08200a27a8e34218a0e58fde36b0fe8f73bc377f4acea2d91602057c3ca45bb",
+            )
+        else:  # config.split == "val"
+            images = HttpResource(
+                "ILSVRC2012_img_val.tar",
+                sha256="c7e06a6c0baccf06d8dbeb6577d71efff84673a5dbdd50633ab44f8ea0456ae0",
+            )
+
+        devkit = HttpResource(
+            "ILSVRC2012_devkit_t12.tar.gz",
+            sha256="b59243268c0d266621fd587d2018f69e906fb22875aca0e295b48cafaa927953",
+        )
+
+        return [images, devkit]
+
+    _TRAIN_IMAGE_NAME_PATTERN = re.compile(r"(?P<wnid>n\d{8})_\d+[.]JPEG")
+
+    def _collate_train_data(self, data: Tuple[str, io.IOBase]) -> Tuple[Tuple[int, str, str], Tuple[str, io.IOBase]]:
+        path = pathlib.Path(data[0])
+        wnid = self._TRAIN_IMAGE_NAME_PATTERN.match(path.name).group("wnid")  # type: ignore[union-attr]
+        category = self.wnid_to_category[wnid]
+        label = self.categories.index(category)
+        return (label, category, wnid), data
+
+    _VAL_IMAGE_NAME_PATTERN = re.compile(r"ILSVRC2012_val_(?P<id>\d{8})[.]JPEG")
+
+    def _val_image_key(self, data: Tuple[str, Any]) -> int:
+        path = pathlib.Path(data[0])
+        return int(self._VAL_IMAGE_NAME_PATTERN.match(path.name).group("id"))  # type: ignore[union-attr]
+
+    def _collate_val_data(
+        self, data: Tuple[Tuple[int, int], Tuple[str, io.IOBase]]
+    ) -> Tuple[Tuple[int, str, str], Tuple[str, io.IOBase]]:
+        label_data, image_data = data
+        _, label = label_data
+        category = self.categories[label]
+        wnid = self.category_to_wnid[category]
+        return (label, category, wnid), image_data
+
+    def _collate_and_decode_sample(
+        self,
+        data: Tuple[Tuple[int, str, str], Tuple[str, io.IOBase]],
+        *,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> Dict[str, Any]:
+        ann_data, image_data = data
+        label, category, wnid = ann_data
+        path, buffer = image_data
+        return dict(
+            path=path,
+            image=decoder(buffer) if decoder else buffer,
+            label=torch.tensor(label),
+            category=category,
+            wnid=wnid,
+        )
+
+    def _make_datapipe(
+        self,
+        resource_dps: List[IterDataPipe],
+        *,
+        config: DatasetConfig,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> IterDataPipe[Dict[str, Any]]:
+        images_dp, devkit_dp = resource_dps
+
+        images_dp = TarArchiveReader(images_dp)
+
+        if config.split == "train":
+            # the train archive is a tar of tars
+            dp = TarArchiveReader(images_dp)
+            # dp = Shuffler(dp, buffer_size=INFINITE_BUFFER_SIZE)
+            dp = Mapper(dp, self._collate_train_data)
+        else:
+            devkit_dp = TarArchiveReader(devkit_dp)
+            devkit_dp = Filter(devkit_dp, path_comparator("name", "ILSVRC2012_validation_ground_truth.txt"))
+            devkit_dp = LineReader(devkit_dp, return_path=False)
+            devkit_dp = Mapper(devkit_dp, int)
+            devkit_dp = Enumerator(devkit_dp, 1)
+            devkit_dp = Shuffler(devkit_dp, buffer_size=INFINITE_BUFFER_SIZE)
+
+            dp = KeyZipper(
+                devkit_dp,
+                images_dp,
+                key_fn=getitem(0),
+                ref_key_fn=self._val_image_key,
+                buffer_size=INFINITE_BUFFER_SIZE,
+            )
+            dp = Mapper(dp, self._collate_val_data)
+
+        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+
+    # Although the WordNet IDs (wnids) are unique, the corresponding categories are not. For example, both n02012849
+    # and n03126707 are labeled 'crane' while the first means the bird and the latter means the construction equipment
+    _WNID_MAP = {
+        "n03126707": "construction crane",
+        "n03710721": "tank suit",
+    }
+
+    def _generate_categories(self, root: pathlib.Path) -> List[Tuple[str, str]]:
+        resources = self.resources(self.default_config)
+        devkit_dp = resources[1].to_datapipe(root / self.name)
+        devkit_dp = TarArchiveReader(devkit_dp)
+        devkit_dp = Filter(devkit_dp, path_comparator("name", "meta.mat"))
+
+        meta = next(iter(devkit_dp))[1]
+        synsets = read_mat(meta, squeeze_me=True)["synsets"]
+        categories_and_wnids = [
+            (self._WNID_MAP.get(wnid, category.split(",", 1)[0]), wnid)
+            for _, wnid, category, _, num_children, *_ in synsets
+            # if num_children > 0, we are looking at a superclass that has no direct instance
+            if num_children == 0
+        ]
+        categories_and_wnids.sort(key=lambda category_and_wnid: category_and_wnid[1])
+
+        return categories_and_wnids
diff --git a/torchvision/prototype/datasets/generate_category_files.py b/torchvision/prototype/datasets/generate_category_files.py
index a5623cd6db2..cb7f734a43d 100644
--- a/torchvision/prototype/datasets/generate_category_files.py
+++ b/torchvision/prototype/datasets/generate_category_files.py
@@ -1,13 +1,8 @@
 import argparse
+import csv
 import sys
-import unittest.mock
-import warnings
-
-with warnings.catch_warnings():
-    warnings.filterwarnings("ignore", message=r"The categories file .+? does not exist.", category=UserWarning)
-
-    from torchvision.prototype import datasets
 
+from torchvision.prototype import datasets
 from torchvision.prototype.datasets._api import find
 from torchvision.prototype.datasets.utils._internal import BUILTIN_DIR
 
@@ -16,21 +11,18 @@ def main(*names, force=False):
     root = datasets.home()
 
     for name in names:
-        file = BUILTIN_DIR / f"{name}.categories"
-        if file.exists() and not force:
+        path = BUILTIN_DIR / f"{name}.categories"
+        if path.exists() and not force:
             continue
 
         dataset = find(name)
         try:
-            with unittest.mock.patch(
-                "torchvision.prototype.datasets.utils._dataset.DatasetInfo._read_categories_file", return_value=[]
-            ):
-                categories = dataset._generate_categories(root)
+            categories = dataset._generate_categories(root)
         except NotImplementedError:
             continue
 
-        with open(file, "w") as fh:
-            fh.write("\n".join(categories) + "\n")
+        with open(path, "w", newline="") as file:
+            csv.writer(file).writerows(categories)
 
 
 def parse_args(argv=None):
diff --git a/torchvision/prototype/datasets/utils/_dataset.py b/torchvision/prototype/datasets/utils/_dataset.py
index b0c55674c43..dfd235db89f 100644
--- a/torchvision/prototype/datasets/utils/_dataset.py
+++ b/torchvision/prototype/datasets/utils/_dataset.py
@@ -1,11 +1,8 @@
 import abc
+import csv
 import enum
 import io
-import os
 import pathlib
-import textwrap
-import warnings
-from collections.abc import Mapping
 from typing import (
     Any,
     Callable,
@@ -14,8 +11,6 @@
     Optional,
     Sequence,
     Union,
-    NoReturn,
-    Iterable,
     Tuple,
 )
 
@@ -26,76 +21,17 @@
     sequence_to_str,
 )
 
+from ._internal import FrozenBunch, make_repr
 from ._resource import OnlineResource
 
 
-def make_repr(name: str, items: Iterable[Tuple[str, Any]]):
-    def to_str(sep: str) -> str:
-        return sep.join([f"{key}={value}" for key, value in items])
-
-    prefix = f"{name}("
-    postfix = ")"
-    body = to_str(", ")
-
-    line_length = int(os.environ.get("COLUMNS", 80))
-    body_too_long = (len(prefix) + len(body) + len(postfix)) > line_length
-    multiline_body = len(str(body).splitlines()) > 1
-    if not (body_too_long or multiline_body):
-        return prefix + body + postfix
-
-    body = textwrap.indent(to_str(",\n"), " " * 2)
-    return f"{prefix}\n{body}\n{postfix}"
-
-
 class DatasetType(enum.Enum):
     RAW = enum.auto()
     IMAGE = enum.auto()
 
 
-class DatasetConfig(Mapping):
-    def __init__(self, *args, **kwargs):
-        data = dict(*args, **kwargs)
-        self.__dict__["__data__"] = data
-        self.__dict__["__final_hash__"] = hash(tuple(data.items()))
-
-    def __getitem__(self, name: str) -> Any:
-        return self.__dict__["__data__"][name]
-
-    def __iter__(self):
-        return iter(self.__dict__["__data__"].keys())
-
-    def __len__(self):
-        return len(self.__dict__["__data__"])
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError as error:
-            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") from error
-
-    def __setitem__(self, key: Any, value: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __setattr__(self, key: Any, value: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __delitem__(self, key: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __delattr__(self, item: Any) -> NoReturn:
-        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
-
-    def __hash__(self) -> int:
-        return self.__dict__["__final_hash__"]
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, DatasetConfig):
-            return NotImplemented
-
-        return hash(self) == hash(other)
-
-    def __repr__(self) -> str:
-        return make_repr(type(self).__name__, self.items())
+class DatasetConfig(FrozenBunch):
+    pass
 
 
 class DatasetInfo:
@@ -109,6 +45,7 @@ def __init__(
         homepage: Optional[str] = None,
         license: Optional[str] = None,
         valid_options: Optional[Dict[str, Sequence]] = None,
+        extra: Optional[Dict[str, Any]] = None,
     ) -> None:
         self.name = name.lower()
         self.type = DatasetType[type.upper()] if isinstance(type, str) else type
@@ -118,7 +55,8 @@ def __init__(
         elif isinstance(categories, int):
             categories = [str(label) for label in range(categories)]
         elif isinstance(categories, (str, pathlib.Path)):
-            categories = self._read_categories_file(pathlib.Path(categories).expanduser().resolve())
+            path = pathlib.Path(categories).expanduser().resolve()
+            categories, *_ = zip(*self.read_categories_file(path))
         self.categories = tuple(categories)
 
         self.citation = citation
@@ -137,16 +75,12 @@ def __init__(
             )
         self._valid_options: Dict[str, Sequence] = valid_options
 
-    @staticmethod
-    def _read_categories_file(path: pathlib.Path) -> List[str]:
-        if not path.exists() or not path.is_file():
-            warnings.warn(
-                f"The categories file {path} does not exist. Continuing without loaded categories.", UserWarning
-            )
-            return []
+        self.extra = FrozenBunch(extra or dict())
 
-        with open(path, "r") as file:
-            return [line.strip() for line in file]
+    @staticmethod
+    def read_categories_file(path: pathlib.Path) -> List[List[str]]:
+        with open(path, "r", newline="") as file:
+            return [row for row in csv.reader(file)]
 
     @property
     def default_config(self) -> DatasetConfig:
@@ -231,5 +165,5 @@ def to_datapipe(
         resource_dps = [resource.to_datapipe(root) for resource in self.resources(config)]
         return self._make_datapipe(resource_dps, config=config, decoder=decoder)
 
-    def _generate_categories(self, root: pathlib.Path) -> Sequence[str]:
+    def _generate_categories(self, root: pathlib.Path) -> Sequence[Union[str, Sequence[str]]]:
         raise NotImplementedError
diff --git a/torchvision/prototype/datasets/utils/_internal.py b/torchvision/prototype/datasets/utils/_internal.py
index 473851209a5..c9ae756e0d6 100644
--- a/torchvision/prototype/datasets/utils/_internal.py
+++ b/torchvision/prototype/datasets/utils/_internal.py
@@ -1,12 +1,29 @@
 import collections.abc
+import csv
 import difflib
 import enum
 import gzip
 import io
 import lzma
+import os
 import os.path
 import pathlib
-from typing import Collection, Sequence, Callable, Union, Any, Tuple, TypeVar, Iterator, Dict, Optional
+import textwrap
+from collections.abc import Mapping
+from typing import (
+    Collection,
+    Sequence,
+    Callable,
+    Union,
+    Any,
+    Tuple,
+    TypeVar,
+    Iterator,
+    Dict,
+    Optional,
+    NoReturn,
+    Iterable,
+)
 
 import numpy as np
 import PIL.Image
@@ -18,6 +35,10 @@
     "BUILTIN_DIR",
     "sequence_to_str",
     "add_suggestion",
+    "make_repr",
+    "FrozenMapping",
+    "FrozenBunch",
+    "create_categories_file",
     "read_mat",
     "image_buffer_from_array",
     "SequenceIterator",
@@ -62,6 +83,82 @@ def add_suggestion(
     return f"{msg.strip()} {hint}"
 
 
+def make_repr(name: str, items: Iterable[Tuple[str, Any]]):
+    def to_str(sep: str) -> str:
+        return sep.join([f"{key}={value}" for key, value in items])
+
+    prefix = f"{name}("
+    postfix = ")"
+    body = to_str(", ")
+
+    line_length = int(os.environ.get("COLUMNS", 80))
+    body_too_long = (len(prefix) + len(body) + len(postfix)) > line_length
+    multiline_body = len(str(body).splitlines()) > 1
+    if not (body_too_long or multiline_body):
+        return prefix + body + postfix
+
+    body = textwrap.indent(to_str(",\n"), " " * 2)
+    return f"{prefix}\n{body}\n{postfix}"
+
+
+class FrozenMapping(Mapping):
+    def __init__(self, *args, **kwargs):
+        data = dict(*args, **kwargs)
+        self.__dict__["__data__"] = data
+        self.__dict__["__final_hash__"] = hash(tuple(data.items()))
+
+    def __getitem__(self, name: str) -> Any:
+        return self.__dict__["__data__"][name]
+
+    def __iter__(self):
+        return iter(self.__dict__["__data__"].keys())
+
+    def __len__(self):
+        return len(self.__dict__["__data__"])
+
+    def __setitem__(self, key: Any, value: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __delitem__(self, key: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __hash__(self) -> int:
+        return self.__dict__["__final_hash__"]
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, FrozenMapping):
+            return NotImplemented
+
+        return hash(self) == hash(other)
+
+    def __repr__(self):
+        return repr(self.__dict__["__data__"])
+
+
+class FrozenBunch(FrozenMapping):
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self[name]
+        except KeyError as error:
+            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") from error
+
+    def __setattr__(self, key: Any, value: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __delattr__(self, item: Any) -> NoReturn:
+        raise RuntimeError(f"'{type(self).__name__}' object is immutable")
+
+    def __repr__(self) -> str:
+        return make_repr(type(self).__name__, self.items())
+
+
+def create_categories_file(
+    root: Union[str, pathlib.Path], name: str, categories: Sequence[Union[str, Sequence[str]]], **fmtparams: Any
+) -> None:
+    with open(pathlib.Path(root) / f"{name}.categories", "w", newline="") as file:
+        csv.writer(file, **fmtparams).writerows(categories)
+
+
 def read_mat(buffer: io.IOBase, **kwargs: Any) -> Any:
     try:
         import scipy.io as sio