benjamin-paine/imagenet-1k

Repack Information

This repository contains a complete repack of ILSVRC/imagenet-1k in Parquet format, with no arbitrary code execution. Images were not resampled.

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 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 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.

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.
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

@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 for adding this dataset.