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Dataset Card for ImageNet

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 which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [2], please check the download section of the main website.

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.

To evaluate the imagenet-classification accuracy on the test split, one must first create an account at This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at 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: 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.


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 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.

Click here to see the full list of ImageNet class labels mapping:
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

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 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. 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.


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.
  2. Collecting the candidate image for these object categories using a search engine.
  3. 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 for more details on data collection procedure and 2 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 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] 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 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 for a discussion on social impact of the dataset.

Discussion of Biases

  1. A study 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.
  2. A study 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.
  3. Another study 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.
  4. ImageNet data with face obfuscation is also provided at this link
  5. A study on genealogy of ImageNet is can be found at this link about the "norms, values, and assumptions" in ImageNet.
  6. See this study 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] [2] [3].

Additional Information

Dataset Curators

Authors of [1] and [2]:

  • 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.
  2. 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.
  3. 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.
  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 and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
  6. 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.
  7. The law of the State of New Jersey shall apply to all disputes under this agreement.

Citation Information

    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},


Thanks to @apsdehal for adding this dataset.

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