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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 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 152The 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. LanguagesThe class labels in the dataset are in English. Dataset Structure Data InstancesAn example looks like below: { 'image': , 'label': 23 } Data FieldsThe 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 RationaleThe 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 NormalizationInitial 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 processThe annotation process of collecting ImageNet for image classification task is a three step process. Defining the 1000 object categories for the image classification task. These categories have evolved over the years. Collecting the candidate image for these object categories using a search engine. 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 InformationThe 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 DatasetThe 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 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. 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. 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. ImageNet data with face obfuscation is also provided at this link A study on genealogy of ImageNet is can be found at this link about the "norms, values, and assumptions" in ImageNet. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet. Other Known Limitations 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 CuratorsAuthors 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 InformationIn 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: Researcher shall use the Database only for non-commercial research and educational purposes. 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. 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. 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. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 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. 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} } ContributionsThanks to @apsdehal for adding this dataset. |
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