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  2. classes.py +1022 -0
  3. imagenet-1k.py +113 -0
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+ extra_gated_prompt: 'By clicking on “Access repository” below, you also agree to ImageNet
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+ [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet
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+ database (the "Database") at Princeton University and Stanford University. In exchange
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+ 1. Researcher shall use the Database only for non-commercial research and educational
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+ non-infringement or fitness for a particular purpose.
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+ 3. Researcher accepts full responsibility for his or her use of the Database and
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+ shall defend and indemnify the ImageNet team, Princeton University, Stanford University
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+ and Hugging Face, including their employees, Trustees, officers and agents, against
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+ any and all claims arising from Researcher''s use of the Database, including but
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+ not limited to Researcher''s use of any copies of copyrighted images that he or
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+ she may create from the Database.
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+ 4. Researcher may provide research associates and colleagues with access to the
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+ Database provided that they first agree to be bound by these terms and conditions.
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+ 5. Princeton University, Stanford University and Hugging Face reserve the right
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+ to terminate Researcher''s access to the Database at any time.
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+ 6. If Researcher is employed by a for-profit, commercial entity, Researcher''s employer
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+ shall also be bound by these terms and conditions, and Researcher hereby represents
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+ that he or she is fully authorized to enter into this agreement on behalf of such
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+ 0: tench, Tinca tinca
65
+ 1: goldfish, Carassius auratus
66
+ 2: great white shark, white shark, man-eater, man-eating shark, Carcharodon
67
+ carcharias
68
+ 3: tiger shark, Galeocerdo cuvieri
69
+ 4: hammerhead, hammerhead shark
70
+ 5: electric ray, crampfish, numbfish, torpedo
71
+ 6: stingray
72
+ 7: cock
73
+ 8: hen
74
+ 9: ostrich, Struthio camelus
75
+ 10: brambling, Fringilla montifringilla
76
+ 11: goldfinch, Carduelis carduelis
77
+ 12: house finch, linnet, Carpodacus mexicanus
78
+ 13: junco, snowbird
79
+ 14: indigo bunting, indigo finch, indigo bird, Passerina cyanea
80
+ 15: robin, American robin, Turdus migratorius
81
+ 16: bulbul
82
+ 17: jay
83
+ 18: magpie
84
+ 19: chickadee
85
+ 20: water ouzel, dipper
86
+ 21: kite
87
+ 22: bald eagle, American eagle, Haliaeetus leucocephalus
88
+ 23: vulture
89
+ 24: great grey owl, great gray owl, Strix nebulosa
90
+ 25: European fire salamander, Salamandra salamandra
91
+ 26: common newt, Triturus vulgaris
92
+ 27: eft
93
+ 28: spotted salamander, Ambystoma maculatum
94
+ 29: axolotl, mud puppy, Ambystoma mexicanum
95
+ 30: bullfrog, Rana catesbeiana
96
+ 31: tree frog, tree-frog
97
+ 32: tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
98
+ 33: loggerhead, loggerhead turtle, Caretta caretta
99
+ 34: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
100
+ 35: mud turtle
101
+ 36: terrapin
102
+ 37: box turtle, box tortoise
103
+ 38: banded gecko
104
+ 39: common iguana, iguana, Iguana iguana
105
+ 40: American chameleon, anole, Anolis carolinensis
106
+ 41: whiptail, whiptail lizard
107
+ 42: agama
108
+ 43: frilled lizard, Chlamydosaurus kingi
109
+ 44: alligator lizard
110
+ 45: Gila monster, Heloderma suspectum
111
+ 46: green lizard, Lacerta viridis
112
+ 47: African chameleon, Chamaeleo chamaeleon
113
+ 48: Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
114
+ 49: African crocodile, Nile crocodile, Crocodylus niloticus
115
+ 50: American alligator, Alligator mississipiensis
116
+ 51: triceratops
117
+ 52: thunder snake, worm snake, Carphophis amoenus
118
+ 53: ringneck snake, ring-necked snake, ring snake
119
+ 54: hognose snake, puff adder, sand viper
120
+ 55: green snake, grass snake
121
+ 56: king snake, kingsnake
122
+ 57: garter snake, grass snake
123
+ 58: water snake
124
+ 59: vine snake
125
+ 60: night snake, Hypsiglena torquata
126
+ 61: boa constrictor, Constrictor constrictor
127
+ 62: rock python, rock snake, Python sebae
128
+ 63: Indian cobra, Naja naja
129
+ 64: green mamba
130
+ 65: sea snake
131
+ 66: horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
132
+ 67: diamondback, diamondback rattlesnake, Crotalus adamanteus
133
+ 68: sidewinder, horned rattlesnake, Crotalus cerastes
134
+ 69: trilobite
135
+ 70: harvestman, daddy longlegs, Phalangium opilio
136
+ 71: scorpion
137
+ 72: black and gold garden spider, Argiope aurantia
138
+ 73: barn spider, Araneus cavaticus
139
+ 74: garden spider, Aranea diademata
140
+ 75: black widow, Latrodectus mactans
141
+ 76: tarantula
142
+ 77: wolf spider, hunting spider
143
+ 78: tick
144
+ 79: centipede
145
+ 80: black grouse
146
+ 81: ptarmigan
147
+ 82: ruffed grouse, partridge, Bonasa umbellus
148
+ 83: prairie chicken, prairie grouse, prairie fowl
149
+ 84: peacock
150
+ 85: quail
151
+ 86: partridge
152
+ 87: African grey, African gray, Psittacus erithacus
153
+ 88: macaw
154
+ 89: sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
155
+ 90: lorikeet
156
+ 91: coucal
157
+ 92: bee eater
158
+ 93: hornbill
159
+ 94: hummingbird
160
+ 95: jacamar
161
+ 96: toucan
162
+ 97: drake
163
+ 98: red-breasted merganser, Mergus serrator
164
+ 99: goose
165
+ 100: black swan, Cygnus atratus
166
+ 101: tusker
167
+ 102: echidna, spiny anteater, anteater
168
+ 103: platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
169
+ anatinus
170
+ 104: wallaby, brush kangaroo
171
+ 105: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
172
+ 106: wombat
173
+ 107: jellyfish
174
+ 108: sea anemone, anemone
175
+ 109: brain coral
176
+ 110: flatworm, platyhelminth
177
+ 111: nematode, nematode worm, roundworm
178
+ 112: conch
179
+ 113: snail
180
+ 114: slug
181
+ 115: sea slug, nudibranch
182
+ 116: chiton, coat-of-mail shell, sea cradle, polyplacophore
183
+ 117: chambered nautilus, pearly nautilus, nautilus
184
+ 118: Dungeness crab, Cancer magister
185
+ 119: rock crab, Cancer irroratus
186
+ 120: fiddler crab
187
+ 121: king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
188
+ camtschatica
189
+ 122: American lobster, Northern lobster, Maine lobster, Homarus americanus
190
+ 123: spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
191
+ 124: crayfish, crawfish, crawdad, crawdaddy
192
+ 125: hermit crab
193
+ 126: isopod
194
+ 127: white stork, Ciconia ciconia
195
+ 128: black stork, Ciconia nigra
196
+ 129: spoonbill
197
+ 130: flamingo
198
+ 131: little blue heron, Egretta caerulea
199
+ 132: American egret, great white heron, Egretta albus
200
+ 133: bittern
201
+ 134: crane
202
+ 135: limpkin, Aramus pictus
203
+ 136: European gallinule, Porphyrio porphyrio
204
+ 137: American coot, marsh hen, mud hen, water hen, Fulica americana
205
+ 138: bustard
206
+ 139: ruddy turnstone, Arenaria interpres
207
+ 140: red-backed sandpiper, dunlin, Erolia alpina
208
+ 141: redshank, Tringa totanus
209
+ 142: dowitcher
210
+ 143: oystercatcher, oyster catcher
211
+ 144: pelican
212
+ 145: king penguin, Aptenodytes patagonica
213
+ 146: albatross, mollymawk
214
+ 147: grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
215
+ robustus
216
+ 148: killer whale, killer, orca, grampus, sea wolf, Orcinus orca
217
+ 149: dugong, Dugong dugon
218
+ 150: sea lion
219
+ 151: Chihuahua
220
+ 152: Japanese spaniel
221
+ 153: Maltese dog, Maltese terrier, Maltese
222
+ 154: Pekinese, Pekingese, Peke
223
+ 155: Shih-Tzu
224
+ 156: Blenheim spaniel
225
+ 157: papillon
226
+ 158: toy terrier
227
+ 159: Rhodesian ridgeback
228
+ 160: Afghan hound, Afghan
229
+ 161: basset, basset hound
230
+ 162: beagle
231
+ 163: bloodhound, sleuthhound
232
+ 164: bluetick
233
+ 165: black-and-tan coonhound
234
+ 166: Walker hound, Walker foxhound
235
+ 167: English foxhound
236
+ 168: redbone
237
+ 169: borzoi, Russian wolfhound
238
+ 170: Irish wolfhound
239
+ 171: Italian greyhound
240
+ 172: whippet
241
+ 173: Ibizan hound, Ibizan Podenco
242
+ 174: Norwegian elkhound, elkhound
243
+ 175: otterhound, otter hound
244
+ 176: Saluki, gazelle hound
245
+ 177: Scottish deerhound, deerhound
246
+ 178: Weimaraner
247
+ 179: Staffordshire bullterrier, Staffordshire bull terrier
248
+ 180: American Staffordshire terrier, Staffordshire terrier, American pit
249
+ bull terrier, pit bull terrier
250
+ 181: Bedlington terrier
251
+ 182: Border terrier
252
+ 183: Kerry blue terrier
253
+ 184: Irish terrier
254
+ 185: Norfolk terrier
255
+ 186: Norwich terrier
256
+ 187: Yorkshire terrier
257
+ 188: wire-haired fox terrier
258
+ 189: Lakeland terrier
259
+ 190: Sealyham terrier, Sealyham
260
+ 191: Airedale, Airedale terrier
261
+ 192: cairn, cairn terrier
262
+ 193: Australian terrier
263
+ 194: Dandie Dinmont, Dandie Dinmont terrier
264
+ 195: Boston bull, Boston terrier
265
+ 196: miniature schnauzer
266
+ 197: giant schnauzer
267
+ 198: standard schnauzer
268
+ 199: Scotch terrier, Scottish terrier, Scottie
269
+ 200: Tibetan terrier, chrysanthemum dog
270
+ 201: silky terrier, Sydney silky
271
+ 202: soft-coated wheaten terrier
272
+ 203: West Highland white terrier
273
+ 204: Lhasa, Lhasa apso
274
+ 205: flat-coated retriever
275
+ 206: curly-coated retriever
276
+ 207: golden retriever
277
+ 208: Labrador retriever
278
+ 209: Chesapeake Bay retriever
279
+ 210: German short-haired pointer
280
+ 211: vizsla, Hungarian pointer
281
+ 212: English setter
282
+ 213: Irish setter, red setter
283
+ 214: Gordon setter
284
+ 215: Brittany spaniel
285
+ 216: clumber, clumber spaniel
286
+ 217: English springer, English springer spaniel
287
+ 218: Welsh springer spaniel
288
+ 219: cocker spaniel, English cocker spaniel, cocker
289
+ 220: Sussex spaniel
290
+ 221: Irish water spaniel
291
+ 222: kuvasz
292
+ 223: schipperke
293
+ 224: groenendael
294
+ 225: malinois
295
+ 226: briard
296
+ 227: kelpie
297
+ 228: komondor
298
+ 229: Old English sheepdog, bobtail
299
+ 230: Shetland sheepdog, Shetland sheep dog, Shetland
300
+ 231: collie
301
+ 232: Border collie
302
+ 233: Bouvier des Flandres, Bouviers des Flandres
303
+ 234: Rottweiler
304
+ 235: German shepherd, German shepherd dog, German police dog, alsatian
305
+ 236: Doberman, Doberman pinscher
306
+ 237: miniature pinscher
307
+ 238: Greater Swiss Mountain dog
308
+ 239: Bernese mountain dog
309
+ 240: Appenzeller
310
+ 241: EntleBucher
311
+ 242: boxer
312
+ 243: bull mastiff
313
+ 244: Tibetan mastiff
314
+ 245: French bulldog
315
+ 246: Great Dane
316
+ 247: Saint Bernard, St Bernard
317
+ 248: Eskimo dog, husky
318
+ 249: malamute, malemute, Alaskan malamute
319
+ 250: Siberian husky
320
+ 251: dalmatian, coach dog, carriage dog
321
+ 252: affenpinscher, monkey pinscher, monkey dog
322
+ 253: basenji
323
+ 254: pug, pug-dog
324
+ 255: Leonberg
325
+ 256: Newfoundland, Newfoundland dog
326
+ 257: Great Pyrenees
327
+ 258: Samoyed, Samoyede
328
+ 259: Pomeranian
329
+ 260: chow, chow chow
330
+ 261: keeshond
331
+ 262: Brabancon griffon
332
+ 263: Pembroke, Pembroke Welsh corgi
333
+ 264: Cardigan, Cardigan Welsh corgi
334
+ 265: toy poodle
335
+ 266: miniature poodle
336
+ 267: standard poodle
337
+ 268: Mexican hairless
338
+ 269: timber wolf, grey wolf, gray wolf, Canis lupus
339
+ 270: white wolf, Arctic wolf, Canis lupus tundrarum
340
+ 271: red wolf, maned wolf, Canis rufus, Canis niger
341
+ 272: coyote, prairie wolf, brush wolf, Canis latrans
342
+ 273: dingo, warrigal, warragal, Canis dingo
343
+ 274: dhole, Cuon alpinus
344
+ 275: African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
345
+ 276: hyena, hyaena
346
+ 277: red fox, Vulpes vulpes
347
+ 278: kit fox, Vulpes macrotis
348
+ 279: Arctic fox, white fox, Alopex lagopus
349
+ 280: grey fox, gray fox, Urocyon cinereoargenteus
350
+ 281: tabby, tabby cat
351
+ 282: tiger cat
352
+ 283: Persian cat
353
+ 284: Siamese cat, Siamese
354
+ 285: Egyptian cat
355
+ 286: cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
356
+ 287: lynx, catamount
357
+ 288: leopard, Panthera pardus
358
+ 289: snow leopard, ounce, Panthera uncia
359
+ 290: jaguar, panther, Panthera onca, Felis onca
360
+ 291: lion, king of beasts, Panthera leo
361
+ 292: tiger, Panthera tigris
362
+ 293: cheetah, chetah, Acinonyx jubatus
363
+ 294: brown bear, bruin, Ursus arctos
364
+ 295: American black bear, black bear, Ursus americanus, Euarctos americanus
365
+ 296: ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
366
+ 297: sloth bear, Melursus ursinus, Ursus ursinus
367
+ 298: mongoose
368
+ 299: meerkat, mierkat
369
+ 300: tiger beetle
370
+ 301: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
371
+ 302: ground beetle, carabid beetle
372
+ 303: long-horned beetle, longicorn, longicorn beetle
373
+ 304: leaf beetle, chrysomelid
374
+ 305: dung beetle
375
+ 306: rhinoceros beetle
376
+ 307: weevil
377
+ 308: fly
378
+ 309: bee
379
+ 310: ant, emmet, pismire
380
+ 311: grasshopper, hopper
381
+ 312: cricket
382
+ 313: walking stick, walkingstick, stick insect
383
+ 314: cockroach, roach
384
+ 315: mantis, mantid
385
+ 316: cicada, cicala
386
+ 317: leafhopper
387
+ 318: lacewing, lacewing fly
388
+ 319: dragonfly, darning needle, devil's darning needle, sewing needle, snake
389
+ feeder, snake doctor, mosquito hawk, skeeter hawk
390
+ 320: damselfly
391
+ 321: admiral
392
+ 322: ringlet, ringlet butterfly
393
+ 323: monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
394
+ 324: cabbage butterfly
395
+ 325: sulphur butterfly, sulfur butterfly
396
+ 326: lycaenid, lycaenid butterfly
397
+ 327: starfish, sea star
398
+ 328: sea urchin
399
+ 329: sea cucumber, holothurian
400
+ 330: wood rabbit, cottontail, cottontail rabbit
401
+ 331: hare
402
+ 332: Angora, Angora rabbit
403
+ 333: hamster
404
+ 334: porcupine, hedgehog
405
+ 335: fox squirrel, eastern fox squirrel, Sciurus niger
406
+ 336: marmot
407
+ 337: beaver
408
+ 338: guinea pig, Cavia cobaya
409
+ 339: sorrel
410
+ 340: zebra
411
+ 341: hog, pig, grunter, squealer, Sus scrofa
412
+ 342: wild boar, boar, Sus scrofa
413
+ 343: warthog
414
+ 344: hippopotamus, hippo, river horse, Hippopotamus amphibius
415
+ 345: ox
416
+ 346: water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
417
+ 347: bison
418
+ 348: ram, tup
419
+ 349: bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
420
+ sheep, Ovis canadensis
421
+ 350: ibex, Capra ibex
422
+ 351: hartebeest
423
+ 352: impala, Aepyceros melampus
424
+ 353: gazelle
425
+ 354: Arabian camel, dromedary, Camelus dromedarius
426
+ 355: llama
427
+ 356: weasel
428
+ 357: mink
429
+ 358: polecat, fitch, foulmart, foumart, Mustela putorius
430
+ 359: black-footed ferret, ferret, Mustela nigripes
431
+ 360: otter
432
+ 361: skunk, polecat, wood pussy
433
+ 362: badger
434
+ 363: armadillo
435
+ 364: three-toed sloth, ai, Bradypus tridactylus
436
+ 365: orangutan, orang, orangutang, Pongo pygmaeus
437
+ 366: gorilla, Gorilla gorilla
438
+ 367: chimpanzee, chimp, Pan troglodytes
439
+ 368: gibbon, Hylobates lar
440
+ 369: siamang, Hylobates syndactylus, Symphalangus syndactylus
441
+ 370: guenon, guenon monkey
442
+ 371: patas, hussar monkey, Erythrocebus patas
443
+ 372: baboon
444
+ 373: macaque
445
+ 374: langur
446
+ 375: colobus, colobus monkey
447
+ 376: proboscis monkey, Nasalis larvatus
448
+ 377: marmoset
449
+ 378: capuchin, ringtail, Cebus capucinus
450
+ 379: howler monkey, howler
451
+ 380: titi, titi monkey
452
+ 381: spider monkey, Ateles geoffroyi
453
+ 382: squirrel monkey, Saimiri sciureus
454
+ 383: Madagascar cat, ring-tailed lemur, Lemur catta
455
+ 384: indri, indris, Indri indri, Indri brevicaudatus
456
+ 385: Indian elephant, Elephas maximus
457
+ 386: African elephant, Loxodonta africana
458
+ 387: lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
459
+ 388: giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
460
+ 389: barracouta, snoek
461
+ 390: eel
462
+ 391: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
463
+ 392: rock beauty, Holocanthus tricolor
464
+ 393: anemone fish
465
+ 394: sturgeon
466
+ 395: gar, garfish, garpike, billfish, Lepisosteus osseus
467
+ 396: lionfish
468
+ 397: puffer, pufferfish, blowfish, globefish
469
+ 398: abacus
470
+ 399: abaya
471
+ 400: academic gown, academic robe, judge's robe
472
+ 401: accordion, piano accordion, squeeze box
473
+ 402: acoustic guitar
474
+ 403: aircraft carrier, carrier, flattop, attack aircraft carrier
475
+ 404: airliner
476
+ 405: airship, dirigible
477
+ 406: altar
478
+ 407: ambulance
479
+ 408: amphibian, amphibious vehicle
480
+ 409: analog clock
481
+ 410: apiary, bee house
482
+ 411: apron
483
+ 412: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
484
+ dustbin, trash barrel, trash bin
485
+ 413: assault rifle, assault gun
486
+ 414: backpack, back pack, knapsack, packsack, rucksack, haversack
487
+ 415: bakery, bakeshop, bakehouse
488
+ 416: balance beam, beam
489
+ 417: balloon
490
+ 418: ballpoint, ballpoint pen, ballpen, Biro
491
+ 419: Band Aid
492
+ 420: banjo
493
+ 421: bannister, banister, balustrade, balusters, handrail
494
+ 422: barbell
495
+ 423: barber chair
496
+ 424: barbershop
497
+ 425: barn
498
+ 426: barometer
499
+ 427: barrel, cask
500
+ 428: barrow, garden cart, lawn cart, wheelbarrow
501
+ 429: baseball
502
+ 430: basketball
503
+ 431: bassinet
504
+ 432: bassoon
505
+ 433: bathing cap, swimming cap
506
+ 434: bath towel
507
+ 435: bathtub, bathing tub, bath, tub
508
+ 436: beach wagon, station wagon, wagon, estate car, beach waggon, station
509
+ waggon, waggon
510
+ 437: beacon, lighthouse, beacon light, pharos
511
+ 438: beaker
512
+ 439: bearskin, busby, shako
513
+ 440: beer bottle
514
+ 441: beer glass
515
+ 442: bell cote, bell cot
516
+ 443: bib
517
+ 444: bicycle-built-for-two, tandem bicycle, tandem
518
+ 445: bikini, two-piece
519
+ 446: binder, ring-binder
520
+ 447: binoculars, field glasses, opera glasses
521
+ 448: birdhouse
522
+ 449: boathouse
523
+ 450: bobsled, bobsleigh, bob
524
+ 451: bolo tie, bolo, bola tie, bola
525
+ 452: bonnet, poke bonnet
526
+ 453: bookcase
527
+ 454: bookshop, bookstore, bookstall
528
+ 455: bottlecap
529
+ 456: bow
530
+ 457: bow tie, bow-tie, bowtie
531
+ 458: brass, memorial tablet, plaque
532
+ 459: brassiere, bra, bandeau
533
+ 460: breakwater, groin, groyne, mole, bulwark, seawall, jetty
534
+ 461: breastplate, aegis, egis
535
+ 462: broom
536
+ 463: bucket, pail
537
+ 464: buckle
538
+ 465: bulletproof vest
539
+ 466: bullet train, bullet
540
+ 467: butcher shop, meat market
541
+ 468: cab, hack, taxi, taxicab
542
+ 469: caldron, cauldron
543
+ 470: candle, taper, wax light
544
+ 471: cannon
545
+ 472: canoe
546
+ 473: can opener, tin opener
547
+ 474: cardigan
548
+ 475: car mirror
549
+ 476: carousel, carrousel, merry-go-round, roundabout, whirligig
550
+ 477: carpenter's kit, tool kit
551
+ 478: carton
552
+ 479: car wheel
553
+ 480: cash machine, cash dispenser, automated teller machine, automatic teller
554
+ machine, automated teller, automatic teller, ATM
555
+ 481: cassette
556
+ 482: cassette player
557
+ 483: castle
558
+ 484: catamaran
559
+ 485: CD player
560
+ 486: cello, violoncello
561
+ 487: cellular telephone, cellular phone, cellphone, cell, mobile phone
562
+ 488: chain
563
+ 489: chainlink fence
564
+ 490: chain mail, ring mail, mail, chain armor, chain armour, ring armor,
565
+ ring armour
566
+ 491: chain saw, chainsaw
567
+ 492: chest
568
+ 493: chiffonier, commode
569
+ 494: chime, bell, gong
570
+ 495: china cabinet, china closet
571
+ 496: Christmas stocking
572
+ 497: church, church building
573
+ 498: cinema, movie theater, movie theatre, movie house, picture palace
574
+ 499: cleaver, meat cleaver, chopper
575
+ 500: cliff dwelling
576
+ 501: cloak
577
+ 502: clog, geta, patten, sabot
578
+ 503: cocktail shaker
579
+ 504: coffee mug
580
+ 505: coffeepot
581
+ 506: coil, spiral, volute, whorl, helix
582
+ 507: combination lock
583
+ 508: computer keyboard, keypad
584
+ 509: confectionery, confectionary, candy store
585
+ 510: container ship, containership, container vessel
586
+ 511: convertible
587
+ 512: corkscrew, bottle screw
588
+ 513: cornet, horn, trumpet, trump
589
+ 514: cowboy boot
590
+ 515: cowboy hat, ten-gallon hat
591
+ 516: cradle
592
+ 517: crane2
593
+ 518: crash helmet
594
+ 519: crate
595
+ 520: crib, cot
596
+ 521: Crock Pot
597
+ 522: croquet ball
598
+ 523: crutch
599
+ 524: cuirass
600
+ 525: dam, dike, dyke
601
+ 526: desk
602
+ 527: desktop computer
603
+ 528: dial telephone, dial phone
604
+ 529: diaper, nappy, napkin
605
+ 530: digital clock
606
+ 531: digital watch
607
+ 532: dining table, board
608
+ 533: dishrag, dishcloth
609
+ 534: dishwasher, dish washer, dishwashing machine
610
+ 535: disk brake, disc brake
611
+ 536: dock, dockage, docking facility
612
+ 537: dogsled, dog sled, dog sleigh
613
+ 538: dome
614
+ 539: doormat, welcome mat
615
+ 540: drilling platform, offshore rig
616
+ 541: drum, membranophone, tympan
617
+ 542: drumstick
618
+ 543: dumbbell
619
+ 544: Dutch oven
620
+ 545: electric fan, blower
621
+ 546: electric guitar
622
+ 547: electric locomotive
623
+ 548: entertainment center
624
+ 549: envelope
625
+ 550: espresso maker
626
+ 551: face powder
627
+ 552: feather boa, boa
628
+ 553: file, file cabinet, filing cabinet
629
+ 554: fireboat
630
+ 555: fire engine, fire truck
631
+ 556: fire screen, fireguard
632
+ 557: flagpole, flagstaff
633
+ 558: flute, transverse flute
634
+ 559: folding chair
635
+ 560: football helmet
636
+ 561: forklift
637
+ 562: fountain
638
+ 563: fountain pen
639
+ 564: four-poster
640
+ 565: freight car
641
+ 566: French horn, horn
642
+ 567: frying pan, frypan, skillet
643
+ 568: fur coat
644
+ 569: garbage truck, dustcart
645
+ 570: gasmask, respirator, gas helmet
646
+ 571: gas pump, gasoline pump, petrol pump, island dispenser
647
+ 572: goblet
648
+ 573: go-kart
649
+ 574: golf ball
650
+ 575: golfcart, golf cart
651
+ 576: gondola
652
+ 577: gong, tam-tam
653
+ 578: gown
654
+ 579: grand piano, grand
655
+ 580: greenhouse, nursery, glasshouse
656
+ 581: grille, radiator grille
657
+ 582: grocery store, grocery, food market, market
658
+ 583: guillotine
659
+ 584: hair slide
660
+ 585: hair spray
661
+ 586: half track
662
+ 587: hammer
663
+ 588: hamper
664
+ 589: hand blower, blow dryer, blow drier, hair dryer, hair drier
665
+ 590: hand-held computer, hand-held microcomputer
666
+ 591: handkerchief, hankie, hanky, hankey
667
+ 592: hard disc, hard disk, fixed disk
668
+ 593: harmonica, mouth organ, harp, mouth harp
669
+ 594: harp
670
+ 595: harvester, reaper
671
+ 596: hatchet
672
+ 597: holster
673
+ 598: home theater, home theatre
674
+ 599: honeycomb
675
+ 600: hook, claw
676
+ 601: hoopskirt, crinoline
677
+ 602: horizontal bar, high bar
678
+ 603: horse cart, horse-cart
679
+ 604: hourglass
680
+ 605: iPod
681
+ 606: iron, smoothing iron
682
+ 607: jack-o'-lantern
683
+ 608: jean, blue jean, denim
684
+ 609: jeep, landrover
685
+ 610: jersey, T-shirt, tee shirt
686
+ 611: jigsaw puzzle
687
+ 612: jinrikisha, ricksha, rickshaw
688
+ 613: joystick
689
+ 614: kimono
690
+ 615: knee pad
691
+ 616: knot
692
+ 617: lab coat, laboratory coat
693
+ 618: ladle
694
+ 619: lampshade, lamp shade
695
+ 620: laptop, laptop computer
696
+ 621: lawn mower, mower
697
+ 622: lens cap, lens cover
698
+ 623: letter opener, paper knife, paperknife
699
+ 624: library
700
+ 625: lifeboat
701
+ 626: lighter, light, igniter, ignitor
702
+ 627: limousine, limo
703
+ 628: liner, ocean liner
704
+ 629: lipstick, lip rouge
705
+ 630: Loafer
706
+ 631: lotion
707
+ 632: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
708
+ 633: loupe, jeweler's loupe
709
+ 634: lumbermill, sawmill
710
+ 635: magnetic compass
711
+ 636: mailbag, postbag
712
+ 637: mailbox, letter box
713
+ 638: maillot
714
+ 639: maillot, tank suit
715
+ 640: manhole cover
716
+ 641: maraca
717
+ 642: marimba, xylophone
718
+ 643: mask
719
+ 644: matchstick
720
+ 645: maypole
721
+ 646: maze, labyrinth
722
+ 647: measuring cup
723
+ 648: medicine chest, medicine cabinet
724
+ 649: megalith, megalithic structure
725
+ 650: microphone, mike
726
+ 651: microwave, microwave oven
727
+ 652: military uniform
728
+ 653: milk can
729
+ 654: minibus
730
+ 655: miniskirt, mini
731
+ 656: minivan
732
+ 657: missile
733
+ 658: mitten
734
+ 659: mixing bowl
735
+ 660: mobile home, manufactured home
736
+ 661: Model T
737
+ 662: modem
738
+ 663: monastery
739
+ 664: monitor
740
+ 665: moped
741
+ 666: mortar
742
+ 667: mortarboard
743
+ 668: mosque
744
+ 669: mosquito net
745
+ 670: motor scooter, scooter
746
+ 671: mountain bike, all-terrain bike, off-roader
747
+ 672: mountain tent
748
+ 673: mouse, computer mouse
749
+ 674: mousetrap
750
+ 675: moving van
751
+ 676: muzzle
752
+ 677: nail
753
+ 678: neck brace
754
+ 679: necklace
755
+ 680: nipple
756
+ 681: notebook, notebook computer
757
+ 682: obelisk
758
+ 683: oboe, hautboy, hautbois
759
+ 684: ocarina, sweet potato
760
+ 685: odometer, hodometer, mileometer, milometer
761
+ 686: oil filter
762
+ 687: organ, pipe organ
763
+ 688: oscilloscope, scope, cathode-ray oscilloscope, CRO
764
+ 689: overskirt
765
+ 690: oxcart
766
+ 691: oxygen mask
767
+ 692: packet
768
+ 693: paddle, boat paddle
769
+ 694: paddlewheel, paddle wheel
770
+ 695: padlock
771
+ 696: paintbrush
772
+ 697: pajama, pyjama, pj's, jammies
773
+ 698: palace
774
+ 699: panpipe, pandean pipe, syrinx
775
+ 700: paper towel
776
+ 701: parachute, chute
777
+ 702: parallel bars, bars
778
+ 703: park bench
779
+ 704: parking meter
780
+ 705: passenger car, coach, carriage
781
+ 706: patio, terrace
782
+ 707: pay-phone, pay-station
783
+ 708: pedestal, plinth, footstall
784
+ 709: pencil box, pencil case
785
+ 710: pencil sharpener
786
+ 711: perfume, essence
787
+ 712: Petri dish
788
+ 713: photocopier
789
+ 714: pick, plectrum, plectron
790
+ 715: pickelhaube
791
+ 716: picket fence, paling
792
+ 717: pickup, pickup truck
793
+ 718: pier
794
+ 719: piggy bank, penny bank
795
+ 720: pill bottle
796
+ 721: pillow
797
+ 722: ping-pong ball
798
+ 723: pinwheel
799
+ 724: pirate, pirate ship
800
+ 725: pitcher, ewer
801
+ 726: plane, carpenter's plane, woodworking plane
802
+ 727: planetarium
803
+ 728: plastic bag
804
+ 729: plate rack
805
+ 730: plow, plough
806
+ 731: plunger, plumber's helper
807
+ 732: Polaroid camera, Polaroid Land camera
808
+ 733: pole
809
+ 734: police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
810
+ 735: poncho
811
+ 736: pool table, billiard table, snooker table
812
+ 737: pop bottle, soda bottle
813
+ 738: pot, flowerpot
814
+ 739: potter's wheel
815
+ 740: power drill
816
+ 741: prayer rug, prayer mat
817
+ 742: printer
818
+ 743: prison, prison house
819
+ 744: projectile, missile
820
+ 745: projector
821
+ 746: puck, hockey puck
822
+ 747: punching bag, punch bag, punching ball, punchball
823
+ 748: purse
824
+ 749: quill, quill pen
825
+ 750: quilt, comforter, comfort, puff
826
+ 751: racer, race car, racing car
827
+ 752: racket, racquet
828
+ 753: radiator
829
+ 754: radio, wireless
830
+ 755: radio telescope, radio reflector
831
+ 756: rain barrel
832
+ 757: recreational vehicle, RV, R.V.
833
+ 758: reel
834
+ 759: reflex camera
835
+ 760: refrigerator, icebox
836
+ 761: remote control, remote
837
+ 762: restaurant, eating house, eating place, eatery
838
+ 763: revolver, six-gun, six-shooter
839
+ 764: rifle
840
+ 765: rocking chair, rocker
841
+ 766: rotisserie
842
+ 767: rubber eraser, rubber, pencil eraser
843
+ 768: rugby ball
844
+ 769: rule, ruler
845
+ 770: running shoe
846
+ 771: safe
847
+ 772: safety pin
848
+ 773: saltshaker, salt shaker
849
+ 774: sandal
850
+ 775: sarong
851
+ 776: sax, saxophone
852
+ 777: scabbard
853
+ 778: scale, weighing machine
854
+ 779: school bus
855
+ 780: schooner
856
+ 781: scoreboard
857
+ 782: screen, CRT screen
858
+ 783: screw
859
+ 784: screwdriver
860
+ 785: seat belt, seatbelt
861
+ 786: sewing machine
862
+ 787: shield, buckler
863
+ 788: shoe shop, shoe-shop, shoe store
864
+ 789: shoji
865
+ 790: shopping basket
866
+ 791: shopping cart
867
+ 792: shovel
868
+ 793: shower cap
869
+ 794: shower curtain
870
+ 795: ski
871
+ 796: ski mask
872
+ 797: sleeping bag
873
+ 798: slide rule, slipstick
874
+ 799: sliding door
875
+ 800: slot, one-armed bandit
876
+ 801: snorkel
877
+ 802: snowmobile
878
+ 803: snowplow, snowplough
879
+ 804: soap dispenser
880
+ 805: soccer ball
881
+ 806: sock
882
+ 807: solar dish, solar collector, solar furnace
883
+ 808: sombrero
884
+ 809: soup bowl
885
+ 810: space bar
886
+ 811: space heater
887
+ 812: space shuttle
888
+ 813: spatula
889
+ 814: speedboat
890
+ 815: spider web, spider's web
891
+ 816: spindle
892
+ 817: sports car, sport car
893
+ 818: spotlight, spot
894
+ 819: stage
895
+ 820: steam locomotive
896
+ 821: steel arch bridge
897
+ 822: steel drum
898
+ 823: stethoscope
899
+ 824: stole
900
+ 825: stone wall
901
+ 826: stopwatch, stop watch
902
+ 827: stove
903
+ 828: strainer
904
+ 829: streetcar, tram, tramcar, trolley, trolley car
905
+ 830: stretcher
906
+ 831: studio couch, day bed
907
+ 832: stupa, tope
908
+ 833: submarine, pigboat, sub, U-boat
909
+ 834: suit, suit of clothes
910
+ 835: sundial
911
+ 836: sunglass
912
+ 837: sunglasses, dark glasses, shades
913
+ 838: sunscreen, sunblock, sun blocker
914
+ 839: suspension bridge
915
+ 840: swab, swob, mop
916
+ 841: sweatshirt
917
+ 842: swimming trunks, bathing trunks
918
+ 843: swing
919
+ 844: switch, electric switch, electrical switch
920
+ 845: syringe
921
+ 846: table lamp
922
+ 847: tank, army tank, armored combat vehicle, armoured combat vehicle
923
+ 848: tape player
924
+ 849: teapot
925
+ 850: teddy, teddy bear
926
+ 851: television, television system
927
+ 852: tennis ball
928
+ 853: thatch, thatched roof
929
+ 854: theater curtain, theatre curtain
930
+ 855: thimble
931
+ 856: thresher, thrasher, threshing machine
932
+ 857: throne
933
+ 858: tile roof
934
+ 859: toaster
935
+ 860: tobacco shop, tobacconist shop, tobacconist
936
+ 861: toilet seat
937
+ 862: torch
938
+ 863: totem pole
939
+ 864: tow truck, tow car, wrecker
940
+ 865: toyshop
941
+ 866: tractor
942
+ 867: trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
943
+ semi
944
+ 868: tray
945
+ 869: trench coat
946
+ 870: tricycle, trike, velocipede
947
+ 871: trimaran
948
+ 872: tripod
949
+ 873: triumphal arch
950
+ 874: trolleybus, trolley coach, trackless trolley
951
+ 875: trombone
952
+ 876: tub, vat
953
+ 877: turnstile
954
+ 878: typewriter keyboard
955
+ 879: umbrella
956
+ 880: unicycle, monocycle
957
+ 881: upright, upright piano
958
+ 882: vacuum, vacuum cleaner
959
+ 883: vase
960
+ 884: vault
961
+ 885: velvet
962
+ 886: vending machine
963
+ 887: vestment
964
+ 888: viaduct
965
+ 889: violin, fiddle
966
+ 890: volleyball
967
+ 891: waffle iron
968
+ 892: wall clock
969
+ 893: wallet, billfold, notecase, pocketbook
970
+ 894: wardrobe, closet, press
971
+ 895: warplane, military plane
972
+ 896: washbasin, handbasin, washbowl, lavabo, wash-hand basin
973
+ 897: washer, automatic washer, washing machine
974
+ 898: water bottle
975
+ 899: water jug
976
+ 900: water tower
977
+ 901: whiskey jug
978
+ 902: whistle
979
+ 903: wig
980
+ 904: window screen
981
+ 905: window shade
982
+ 906: Windsor tie
983
+ 907: wine bottle
984
+ 908: wing
985
+ 909: wok
986
+ 910: wooden spoon
987
+ 911: wool, woolen, woollen
988
+ 912: worm fence, snake fence, snake-rail fence, Virginia fence
989
+ 913: wreck
990
+ 914: yawl
991
+ 915: yurt
992
+ 916: web site, website, internet site, site
993
+ 917: comic book
994
+ 918: crossword puzzle, crossword
995
+ 919: street sign
996
+ 920: traffic light, traffic signal, stoplight
997
+ 921: book jacket, dust cover, dust jacket, dust wrapper
998
+ 922: menu
999
+ 923: plate
1000
+ 924: guacamole
1001
+ 925: consomme
1002
+ 926: hot pot, hotpot
1003
+ 927: trifle
1004
+ 928: ice cream, icecream
1005
+ 929: ice lolly, lolly, lollipop, popsicle
1006
+ 930: French loaf
1007
+ 931: bagel, beigel
1008
+ 932: pretzel
1009
+ 933: cheeseburger
1010
+ 934: hotdog, hot dog, red hot
1011
+ 935: mashed potato
1012
+ 936: head cabbage
1013
+ 937: broccoli
1014
+ 938: cauliflower
1015
+ 939: zucchini, courgette
1016
+ 940: spaghetti squash
1017
+ 941: acorn squash
1018
+ 942: butternut squash
1019
+ 943: cucumber, cuke
1020
+ 944: artichoke, globe artichoke
1021
+ 945: bell pepper
1022
+ 946: cardoon
1023
+ 947: mushroom
1024
+ 948: Granny Smith
1025
+ 949: strawberry
1026
+ 950: orange
1027
+ 951: lemon
1028
+ 952: fig
1029
+ 953: pineapple, ananas
1030
+ 954: banana
1031
+ 955: jackfruit, jak, jack
1032
+ 956: custard apple
1033
+ 957: pomegranate
1034
+ 958: hay
1035
+ 959: carbonara
1036
+ 960: chocolate sauce, chocolate syrup
1037
+ 961: dough
1038
+ 962: meat loaf, meatloaf
1039
+ 963: pizza, pizza pie
1040
+ 964: potpie
1041
+ 965: burrito
1042
+ 966: red wine
1043
+ 967: espresso
1044
+ 968: cup
1045
+ 969: eggnog
1046
+ 970: alp
1047
+ 971: bubble
1048
+ 972: cliff, drop, drop-off
1049
+ 973: coral reef
1050
+ 974: geyser
1051
+ 975: lakeside, lakeshore
1052
+ 976: promontory, headland, head, foreland
1053
+ 977: sandbar, sand bar
1054
+ 978: seashore, coast, seacoast, sea-coast
1055
+ 979: valley, vale
1056
+ 980: volcano
1057
+ 981: ballplayer, baseball player
1058
+ 982: groom, bridegroom
1059
+ 983: scuba diver
1060
+ 984: rapeseed
1061
+ 985: daisy
1062
+ 986: yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
1063
+ Cypripedium parviflorum
1064
+ 987: corn
1065
+ 988: acorn
1066
+ 989: hip, rose hip, rosehip
1067
+ 990: buckeye, horse chestnut, conker
1068
+ 991: coral fungus
1069
+ 992: agaric
1070
+ 993: gyromitra
1071
+ 994: stinkhorn, carrion fungus
1072
+ 995: earthstar
1073
+ 996: hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
1074
+ 997: bolete
1075
+ 998: ear, spike, capitulum
1076
+ 999: toilet tissue, toilet paper, bathroom tissue
1077
+ splits:
1078
+ - name: test
1079
+ num_bytes: 13613661561
1080
+ num_examples: 100000
1081
+ - name: train
1082
+ num_bytes: 146956944242
1083
+ num_examples: 1281167
1084
+ - name: validation
1085
+ num_bytes: 6709003386
1086
+ num_examples: 50000
1087
+ download_size: 166009941208
1088
+ dataset_size: 167279609189
1089
+ ---
1090
+
1091
+ # Dataset Card for ImageNet
1092
+
1093
+ ## Table of Contents
1094
+ - [Dataset Description](#dataset-description)
1095
+ - [Dataset Summary](#dataset-summary)
1096
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
1097
+ - [Languages](#languages)
1098
+ - [Dataset Structure](#dataset-structure)
1099
+ - [Data Instances](#data-instances)
1100
+ - [Data Fields](#data-fields)
1101
+ - [Data Splits](#data-splits)
1102
+ - [Dataset Creation](#dataset-creation)
1103
+ - [Curation Rationale](#curation-rationale)
1104
+ - [Source Data](#source-data)
1105
+ - [Annotations](#annotations)
1106
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
1107
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
1108
+ - [Social Impact of Dataset](#social-impact-of-dataset)
1109
+ - [Discussion of Biases](#discussion-of-biases)
1110
+ - [Other Known Limitations](#other-known-limitations)
1111
+ - [Additional Information](#additional-information)
1112
+ - [Dataset Curators](#dataset-curators)
1113
+ - [Licensing Information](#licensing-information)
1114
+ - [Citation Information](#citation-information)
1115
+ - [Contributions](#contributions)
1116
+
1117
+ ## Dataset Description
1118
+
1119
+ - **Homepage:** https://image-net.org/index.php
1120
+ - **Repository:**
1121
+ - **Paper:** https://arxiv.org/abs/1409.0575
1122
+ - **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171
1123
+ - **Point of Contact:** mailto: [email protected]
1124
+
1125
+ ### Dataset Summary
1126
+
1127
+ 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.
1128
+
1129
+ 💡 This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used **subset** of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the [patch](https://drive.google.com/file/d/16RYnHpVOW0XKCsn3G3S9GTHUyoV2-4WX/view) which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](https://ieeexplore.ieee.org/abstract/document/5206848), please check the download section of the [main website](https://image-net.org/download-images.php).
1130
+
1131
+ ### Supported Tasks and Leaderboards
1132
+
1133
+ - `image-classification`: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171).
1134
+
1135
+ 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:
1136
+
1137
+ ```
1138
+ 670 778 794 387 650
1139
+ 217 691 564 909 364
1140
+ 737 369 430 531 124
1141
+ 755 930 755 512 152
1142
+ ```
1143
+
1144
+ 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`.
1145
+
1146
+ ### Languages
1147
+
1148
+ The class labels in the dataset are in English.
1149
+
1150
+ ## Dataset Structure
1151
+
1152
+ ### Data Instances
1153
+
1154
+ An example looks like below:
1155
+
1156
+ ```
1157
+ {
1158
+ 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
1159
+ 'label': 23
1160
+ }
1161
+ ```
1162
+
1163
+ ### Data Fields
1164
+
1165
+ The data instances have the following fields:
1166
+
1167
+ - `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]`.
1168
+ - `label`: an `int` classification label. -1 for `test` set as the labels are missing.
1169
+
1170
+ The labels are indexed based on a sorted list of synset ids such as `n07565083` which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file [LOC_synset_mapping.txt](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt) available on Kaggle challenge page. You can also use `dataset_instance.features["labels"].int2str` function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.
1171
+
1172
+ <details>
1173
+ <summary>
1174
+ Click here to see the full list of ImageNet class labels mapping:
1175
+ </summary>
1176
+
1177
+ |id|Class|
1178
+ |--|-----|
1179
+ |0 | tench, Tinca tinca|
1180
+ |1 | goldfish, Carassius auratus|
1181
+ |2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias|
1182
+ |3 | tiger shark, Galeocerdo cuvieri|
1183
+ |4 | hammerhead, hammerhead shark|
1184
+ |5 | electric ray, crampfish, numbfish, torpedo|
1185
+ |6 | stingray|
1186
+ |7 | cock|
1187
+ |8 | hen|
1188
+ |9 | ostrich, Struthio camelus|
1189
+ |10 | brambling, Fringilla montifringilla|
1190
+ |11 | goldfinch, Carduelis carduelis|
1191
+ |12 | house finch, linnet, Carpodacus mexicanus|
1192
+ |13 | junco, snowbird|
1193
+ |14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea|
1194
+ |15 | robin, American robin, Turdus migratorius|
1195
+ |16 | bulbul|
1196
+ |17 | jay|
1197
+ |18 | magpie|
1198
+ |19 | chickadee|
1199
+ |20 | water ouzel, dipper|
1200
+ |21 | kite|
1201
+ |22 | bald eagle, American eagle, Haliaeetus leucocephalus|
1202
+ |23 | vulture|
1203
+ |24 | great grey owl, great gray owl, Strix nebulosa|
1204
+ |25 | European fire salamander, Salamandra salamandra|
1205
+ |26 | common newt, Triturus vulgaris|
1206
+ |27 | eft|
1207
+ |28 | spotted salamander, Ambystoma maculatum|
1208
+ |29 | axolotl, mud puppy, Ambystoma mexicanum|
1209
+ |30 | bullfrog, Rana catesbeiana|
1210
+ |31 | tree frog, tree-frog|
1211
+ |32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui|
1212
+ |33 | loggerhead, loggerhead turtle, Caretta caretta|
1213
+ |34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea|
1214
+ |35 | mud turtle|
1215
+ |36 | terrapin|
1216
+ |37 | box turtle, box tortoise|
1217
+ |38 | banded gecko|
1218
+ |39 | common iguana, iguana, Iguana iguana|
1219
+ |40 | American chameleon, anole, Anolis carolinensis|
1220
+ |41 | whiptail, whiptail lizard|
1221
+ |42 | agama|
1222
+ |43 | frilled lizard, Chlamydosaurus kingi|
1223
+ |44 | alligator lizard|
1224
+ |45 | Gila monster, Heloderma suspectum|
1225
+ |46 | green lizard, Lacerta viridis|
1226
+ |47 | African chameleon, Chamaeleo chamaeleon|
1227
+ |48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis|
1228
+ |49 | African crocodile, Nile crocodile, Crocodylus niloticus|
1229
+ |50 | American alligator, Alligator mississipiensis|
1230
+ |51 | triceratops|
1231
+ |52 | thunder snake, worm snake, Carphophis amoenus|
1232
+ |53 | ringneck snake, ring-necked snake, ring snake|
1233
+ |54 | hognose snake, puff adder, sand viper|
1234
+ |55 | green snake, grass snake|
1235
+ |56 | king snake, kingsnake|
1236
+ |57 | garter snake, grass snake|
1237
+ |58 | water snake|
1238
+ |59 | vine snake|
1239
+ |60 | night snake, Hypsiglena torquata|
1240
+ |61 | boa constrictor, Constrictor constrictor|
1241
+ |62 | rock python, rock snake, Python sebae|
1242
+ |63 | Indian cobra, Naja naja|
1243
+ |64 | green mamba|
1244
+ |65 | sea snake|
1245
+ |66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus|
1246
+ |67 | diamondback, diamondback rattlesnake, Crotalus adamanteus|
1247
+ |68 | sidewinder, horned rattlesnake, Crotalus cerastes|
1248
+ |69 | trilobite|
1249
+ |70 | harvestman, daddy longlegs, Phalangium opilio|
1250
+ |71 | scorpion|
1251
+ |72 | black and gold garden spider, Argiope aurantia|
1252
+ |73 | barn spider, Araneus cavaticus|
1253
+ |74 | garden spider, Aranea diademata|
1254
+ |75 | black widow, Latrodectus mactans|
1255
+ |76 | tarantula|
1256
+ |77 | wolf spider, hunting spider|
1257
+ |78 | tick|
1258
+ |79 | centipede|
1259
+ |80 | black grouse|
1260
+ |81 | ptarmigan|
1261
+ |82 | ruffed grouse, partridge, Bonasa umbellus|
1262
+ |83 | prairie chicken, prairie grouse, prairie fowl|
1263
+ |84 | peacock|
1264
+ |85 | quail|
1265
+ |86 | partridge|
1266
+ |87 | African grey, African gray, Psittacus erithacus|
1267
+ |88 | macaw|
1268
+ |89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita|
1269
+ |90 | lorikeet|
1270
+ |91 | coucal|
1271
+ |92 | bee eater|
1272
+ |93 | hornbill|
1273
+ |94 | hummingbird|
1274
+ |95 | jacamar|
1275
+ |96 | toucan|
1276
+ |97 | drake|
1277
+ |98 | red-breasted merganser, Mergus serrator|
1278
+ |99 | goose|
1279
+ |100 | black swan, Cygnus atratus|
1280
+ |101 | tusker|
1281
+ |102 | echidna, spiny anteater, anteater|
1282
+ |103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus|
1283
+ |104 | wallaby, brush kangaroo|
1284
+ |105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus|
1285
+ |106 | wombat|
1286
+ |107 | jellyfish|
1287
+ |108 | sea anemone, anemone|
1288
+ |109 | brain coral|
1289
+ |110 | flatworm, platyhelminth|
1290
+ |111 | nematode, nematode worm, roundworm|
1291
+ |112 | conch|
1292
+ |113 | snail|
1293
+ |114 | slug|
1294
+ |115 | sea slug, nudibranch|
1295
+ |116 | chiton, coat-of-mail shell, sea cradle, polyplacophore|
1296
+ |117 | chambered nautilus, pearly nautilus, nautilus|
1297
+ |118 | Dungeness crab, Cancer magister|
1298
+ |119 | rock crab, Cancer irroratus|
1299
+ |120 | fiddler crab|
1300
+ |121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica|
1301
+ |122 | American lobster, Northern lobster, Maine lobster, Homarus americanus|
1302
+ |123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish|
1303
+ |124 | crayfish, crawfish, crawdad, crawdaddy|
1304
+ |125 | hermit crab|
1305
+ |126 | isopod|
1306
+ |127 | white stork, Ciconia ciconia|
1307
+ |128 | black stork, Ciconia nigra|
1308
+ |129 | spoonbill|
1309
+ |130 | flamingo|
1310
+ |131 | little blue heron, Egretta caerulea|
1311
+ |132 | American egret, great white heron, Egretta albus|
1312
+ |133 | bittern|
1313
+ |134 | crane|
1314
+ |135 | limpkin, Aramus pictus|
1315
+ |136 | European gallinule, Porphyrio porphyrio|
1316
+ |137 | American coot, marsh hen, mud hen, water hen, Fulica americana|
1317
+ |138 | bustard|
1318
+ |139 | ruddy turnstone, Arenaria interpres|
1319
+ |140 | red-backed sandpiper, dunlin, Erolia alpina|
1320
+ |141 | redshank, Tringa totanus|
1321
+ |142 | dowitcher|
1322
+ |143 | oystercatcher, oyster catcher|
1323
+ |144 | pelican|
1324
+ |145 | king penguin, Aptenodytes patagonica|
1325
+ |146 | albatross, mollymawk|
1326
+ |147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus|
1327
+ |148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca|
1328
+ |149 | dugong, Dugong dugon|
1329
+ |150 | sea lion|
1330
+ |151 | Chihuahua|
1331
+ |152 | Japanese spaniel|
1332
+ |153 | Maltese dog, Maltese terrier, Maltese|
1333
+ |154 | Pekinese, Pekingese, Peke|
1334
+ |155 | Shih-Tzu|
1335
+ |156 | Blenheim spaniel|
1336
+ |157 | papillon|
1337
+ |158 | toy terrier|
1338
+ |159 | Rhodesian ridgeback|
1339
+ |160 | Afghan hound, Afghan|
1340
+ |161 | basset, basset hound|
1341
+ |162 | beagle|
1342
+ |163 | bloodhound, sleuthhound|
1343
+ |164 | bluetick|
1344
+ |165 | black-and-tan coonhound|
1345
+ |166 | Walker hound, Walker foxhound|
1346
+ |167 | English foxhound|
1347
+ |168 | redbone|
1348
+ |169 | borzoi, Russian wolfhound|
1349
+ |170 | Irish wolfhound|
1350
+ |171 | Italian greyhound|
1351
+ |172 | whippet|
1352
+ |173 | Ibizan hound, Ibizan Podenco|
1353
+ |174 | Norwegian elkhound, elkhound|
1354
+ |175 | otterhound, otter hound|
1355
+ |176 | Saluki, gazelle hound|
1356
+ |177 | Scottish deerhound, deerhound|
1357
+ |178 | Weimaraner|
1358
+ |179 | Staffordshire bullterrier, Staffordshire bull terrier|
1359
+ |180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier|
1360
+ |181 | Bedlington terrier|
1361
+ |182 | Border terrier|
1362
+ |183 | Kerry blue terrier|
1363
+ |184 | Irish terrier|
1364
+ |185 | Norfolk terrier|
1365
+ |186 | Norwich terrier|
1366
+ |187 | Yorkshire terrier|
1367
+ |188 | wire-haired fox terrier|
1368
+ |189 | Lakeland terrier|
1369
+ |190 | Sealyham terrier, Sealyham|
1370
+ |191 | Airedale, Airedale terrier|
1371
+ |192 | cairn, cairn terrier|
1372
+ |193 | Australian terrier|
1373
+ |194 | Dandie Dinmont, Dandie Dinmont terrier|
1374
+ |195 | Boston bull, Boston terrier|
1375
+ |196 | miniature schnauzer|
1376
+ |197 | giant schnauzer|
1377
+ |198 | standard schnauzer|
1378
+ |199 | Scotch terrier, Scottish terrier, Scottie|
1379
+ |200 | Tibetan terrier, chrysanthemum dog|
1380
+ |201 | silky terrier, Sydney silky|
1381
+ |202 | soft-coated wheaten terrier|
1382
+ |203 | West Highland white terrier|
1383
+ |204 | Lhasa, Lhasa apso|
1384
+ |205 | flat-coated retriever|
1385
+ |206 | curly-coated retriever|
1386
+ |207 | golden retriever|
1387
+ |208 | Labrador retriever|
1388
+ |209 | Chesapeake Bay retriever|
1389
+ |210 | German short-haired pointer|
1390
+ |211 | vizsla, Hungarian pointer|
1391
+ |212 | English setter|
1392
+ |213 | Irish setter, red setter|
1393
+ |214 | Gordon setter|
1394
+ |215 | Brittany spaniel|
1395
+ |216 | clumber, clumber spaniel|
1396
+ |217 | English springer, English springer spaniel|
1397
+ |218 | Welsh springer spaniel|
1398
+ |219 | cocker spaniel, English cocker spaniel, cocker|
1399
+ |220 | Sussex spaniel|
1400
+ |221 | Irish water spaniel|
1401
+ |222 | kuvasz|
1402
+ |223 | schipperke|
1403
+ |224 | groenendael|
1404
+ |225 | malinois|
1405
+ |226 | briard|
1406
+ |227 | kelpie|
1407
+ |228 | komondor|
1408
+ |229 | Old English sheepdog, bobtail|
1409
+ |230 | Shetland sheepdog, Shetland sheep dog, Shetland|
1410
+ |231 | collie|
1411
+ |232 | Border collie|
1412
+ |233 | Bouvier des Flandres, Bouviers des Flandres|
1413
+ |234 | Rottweiler|
1414
+ |235 | German shepherd, German shepherd dog, German police dog, alsatian|
1415
+ |236 | Doberman, Doberman pinscher|
1416
+ |237 | miniature pinscher|
1417
+ |238 | Greater Swiss Mountain dog|
1418
+ |239 | Bernese mountain dog|
1419
+ |240 | Appenzeller|
1420
+ |241 | EntleBucher|
1421
+ |242 | boxer|
1422
+ |243 | bull mastiff|
1423
+ |244 | Tibetan mastiff|
1424
+ |245 | French bulldog|
1425
+ |246 | Great Dane|
1426
+ |247 | Saint Bernard, St Bernard|
1427
+ |248 | Eskimo dog, husky|
1428
+ |249 | malamute, malemute, Alaskan malamute|
1429
+ |250 | Siberian husky|
1430
+ |251 | dalmatian, coach dog, carriage dog|
1431
+ |252 | affenpinscher, monkey pinscher, monkey dog|
1432
+ |253 | basenji|
1433
+ |254 | pug, pug-dog|
1434
+ |255 | Leonberg|
1435
+ |256 | Newfoundland, Newfoundland dog|
1436
+ |257 | Great Pyrenees|
1437
+ |258 | Samoyed, Samoyede|
1438
+ |259 | Pomeranian|
1439
+ |260 | chow, chow chow|
1440
+ |261 | keeshond|
1441
+ |262 | Brabancon griffon|
1442
+ |263 | Pembroke, Pembroke Welsh corgi|
1443
+ |264 | Cardigan, Cardigan Welsh corgi|
1444
+ |265 | toy poodle|
1445
+ |266 | miniature poodle|
1446
+ |267 | standard poodle|
1447
+ |268 | Mexican hairless|
1448
+ |269 | timber wolf, grey wolf, gray wolf, Canis lupus|
1449
+ |270 | white wolf, Arctic wolf, Canis lupus tundrarum|
1450
+ |271 | red wolf, maned wolf, Canis rufus, Canis niger|
1451
+ |272 | coyote, prairie wolf, brush wolf, Canis latrans|
1452
+ |273 | dingo, warrigal, warragal, Canis dingo|
1453
+ |274 | dhole, Cuon alpinus|
1454
+ |275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus|
1455
+ |276 | hyena, hyaena|
1456
+ |277 | red fox, Vulpes vulpes|
1457
+ |278 | kit fox, Vulpes macrotis|
1458
+ |279 | Arctic fox, white fox, Alopex lagopus|
1459
+ |280 | grey fox, gray fox, Urocyon cinereoargenteus|
1460
+ |281 | tabby, tabby cat|
1461
+ |282 | tiger cat|
1462
+ |283 | Persian cat|
1463
+ |284 | Siamese cat, Siamese|
1464
+ |285 | Egyptian cat|
1465
+ |286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor|
1466
+ |287 | lynx, catamount|
1467
+ |288 | leopard, Panthera pardus|
1468
+ |289 | snow leopard, ounce, Panthera uncia|
1469
+ |290 | jaguar, panther, Panthera onca, Felis onca|
1470
+ |291 | lion, king of beasts, Panthera leo|
1471
+ |292 | tiger, Panthera tigris|
1472
+ |293 | cheetah, chetah, Acinonyx jubatus|
1473
+ |294 | brown bear, bruin, Ursus arctos|
1474
+ |295 | American black bear, black bear, Ursus americanus, Euarctos americanus|
1475
+ |296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus|
1476
+ |297 | sloth bear, Melursus ursinus, Ursus ursinus|
1477
+ |298 | mongoose|
1478
+ |299 | meerkat, mierkat|
1479
+ |300 | tiger beetle|
1480
+ |301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle|
1481
+ |302 | ground beetle, carabid beetle|
1482
+ |303 | long-horned beetle, longicorn, longicorn beetle|
1483
+ |304 | leaf beetle, chrysomelid|
1484
+ |305 | dung beetle|
1485
+ |306 | rhinoceros beetle|
1486
+ |307 | weevil|
1487
+ |308 | fly|
1488
+ |309 | bee|
1489
+ |310 | ant, emmet, pismire|
1490
+ |311 | grasshopper, hopper|
1491
+ |312 | cricket|
1492
+ |313 | walking stick, walkingstick, stick insect|
1493
+ |314 | cockroach, roach|
1494
+ |315 | mantis, mantid|
1495
+ |316 | cicada, cicala|
1496
+ |317 | leafhopper|
1497
+ |318 | lacewing, lacewing fly|
1498
+ |319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk|
1499
+ |320 | damselfly|
1500
+ |321 | admiral|
1501
+ |322 | ringlet, ringlet butterfly|
1502
+ |323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus|
1503
+ |324 | cabbage butterfly|
1504
+ |325 | sulphur butterfly, sulfur butterfly|
1505
+ |326 | lycaenid, lycaenid butterfly|
1506
+ |327 | starfish, sea star|
1507
+ |328 | sea urchin|
1508
+ |329 | sea cucumber, holothurian|
1509
+ |330 | wood rabbit, cottontail, cottontail rabbit|
1510
+ |331 | hare|
1511
+ |332 | Angora, Angora rabbit|
1512
+ |333 | hamster|
1513
+ |334 | porcupine, hedgehog|
1514
+ |335 | fox squirrel, eastern fox squirrel, Sciurus niger|
1515
+ |336 | marmot|
1516
+ |337 | beaver|
1517
+ |338 | guinea pig, Cavia cobaya|
1518
+ |339 | sorrel|
1519
+ |340 | zebra|
1520
+ |341 | hog, pig, grunter, squealer, Sus scrofa|
1521
+ |342 | wild boar, boar, Sus scrofa|
1522
+ |343 | warthog|
1523
+ |344 | hippopotamus, hippo, river horse, Hippopotamus amphibius|
1524
+ |345 | ox|
1525
+ |346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis|
1526
+ |347 | bison|
1527
+ |348 | ram, tup|
1528
+ |349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis|
1529
+ |350 | ibex, Capra ibex|
1530
+ |351 | hartebeest|
1531
+ |352 | impala, Aepyceros melampus|
1532
+ |353 | gazelle|
1533
+ |354 | Arabian camel, dromedary, Camelus dromedarius|
1534
+ |355 | llama|
1535
+ |356 | weasel|
1536
+ |357 | mink|
1537
+ |358 | polecat, fitch, foulmart, foumart, Mustela putorius|
1538
+ |359 | black-footed ferret, ferret, Mustela nigripes|
1539
+ |360 | otter|
1540
+ |361 | skunk, polecat, wood pussy|
1541
+ |362 | badger|
1542
+ |363 | armadillo|
1543
+ |364 | three-toed sloth, ai, Bradypus tridactylus|
1544
+ |365 | orangutan, orang, orangutang, Pongo pygmaeus|
1545
+ |366 | gorilla, Gorilla gorilla|
1546
+ |367 | chimpanzee, chimp, Pan troglodytes|
1547
+ |368 | gibbon, Hylobates lar|
1548
+ |369 | siamang, Hylobates syndactylus, Symphalangus syndactylus|
1549
+ |370 | guenon, guenon monkey|
1550
+ |371 | patas, hussar monkey, Erythrocebus patas|
1551
+ |372 | baboon|
1552
+ |373 | macaque|
1553
+ |374 | langur|
1554
+ |375 | colobus, colobus monkey|
1555
+ |376 | proboscis monkey, Nasalis larvatus|
1556
+ |377 | marmoset|
1557
+ |378 | capuchin, ringtail, Cebus capucinus|
1558
+ |379 | howler monkey, howler|
1559
+ |380 | titi, titi monkey|
1560
+ |381 | spider monkey, Ateles geoffroyi|
1561
+ |382 | squirrel monkey, Saimiri sciureus|
1562
+ |383 | Madagascar cat, ring-tailed lemur, Lemur catta|
1563
+ |384 | indri, indris, Indri indri, Indri brevicaudatus|
1564
+ |385 | Indian elephant, Elephas maximus|
1565
+ |386 | African elephant, Loxodonta africana|
1566
+ |387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens|
1567
+ |388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca|
1568
+ |389 | barracouta, snoek|
1569
+ |390 | eel|
1570
+ |391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch|
1571
+ |392 | rock beauty, Holocanthus tricolor|
1572
+ |393 | anemone fish|
1573
+ |394 | sturgeon|
1574
+ |395 | gar, garfish, garpike, billfish, Lepisosteus osseus|
1575
+ |396 | lionfish|
1576
+ |397 | puffer, pufferfish, blowfish, globefish|
1577
+ |398 | abacus|
1578
+ |399 | abaya|
1579
+ |400 | academic gown, academic robe, judge's robe|
1580
+ |401 | accordion, piano accordion, squeeze box|
1581
+ |402 | acoustic guitar|
1582
+ |403 | aircraft carrier, carrier, flattop, attack aircraft carrier|
1583
+ |404 | airliner|
1584
+ |405 | airship, dirigible|
1585
+ |406 | altar|
1586
+ |407 | ambulance|
1587
+ |408 | amphibian, amphibious vehicle|
1588
+ |409 | analog clock|
1589
+ |410 | apiary, bee house|
1590
+ |411 | apron|
1591
+ |412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin|
1592
+ |413 | assault rifle, assault gun|
1593
+ |414 | backpack, back pack, knapsack, packsack, rucksack, haversack|
1594
+ |415 | bakery, bakeshop, bakehouse|
1595
+ |416 | balance beam, beam|
1596
+ |417 | balloon|
1597
+ |418 | ballpoint, ballpoint pen, ballpen, Biro|
1598
+ |419 | Band Aid|
1599
+ |420 | banjo|
1600
+ |421 | bannister, banister, balustrade, balusters, handrail|
1601
+ |422 | barbell|
1602
+ |423 | barber chair|
1603
+ |424 | barbershop|
1604
+ |425 | barn|
1605
+ |426 | barometer|
1606
+ |427 | barrel, cask|
1607
+ |428 | barrow, garden cart, lawn cart, wheelbarrow|
1608
+ |429 | baseball|
1609
+ |430 | basketball|
1610
+ |431 | bassinet|
1611
+ |432 | bassoon|
1612
+ |433 | bathing cap, swimming cap|
1613
+ |434 | bath towel|
1614
+ |435 | bathtub, bathing tub, bath, tub|
1615
+ |436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon|
1616
+ |437 | beacon, lighthouse, beacon light, pharos|
1617
+ |438 | beaker|
1618
+ |439 | bearskin, busby, shako|
1619
+ |440 | beer bottle|
1620
+ |441 | beer glass|
1621
+ |442 | bell cote, bell cot|
1622
+ |443 | bib|
1623
+ |444 | bicycle-built-for-two, tandem bicycle, tandem|
1624
+ |445 | bikini, two-piece|
1625
+ |446 | binder, ring-binder|
1626
+ |447 | binoculars, field glasses, opera glasses|
1627
+ |448 | birdhouse|
1628
+ |449 | boathouse|
1629
+ |450 | bobsled, bobsleigh, bob|
1630
+ |451 | bolo tie, bolo, bola tie, bola|
1631
+ |452 | bonnet, poke bonnet|
1632
+ |453 | bookcase|
1633
+ |454 | bookshop, bookstore, bookstall|
1634
+ |455 | bottlecap|
1635
+ |456 | bow|
1636
+ |457 | bow tie, bow-tie, bowtie|
1637
+ |458 | brass, memorial tablet, plaque|
1638
+ |459 | brassiere, bra, bandeau|
1639
+ |460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty|
1640
+ |461 | breastplate, aegis, egis|
1641
+ |462 | broom|
1642
+ |463 | bucket, pail|
1643
+ |464 | buckle|
1644
+ |465 | bulletproof vest|
1645
+ |466 | bullet train, bullet|
1646
+ |467 | butcher shop, meat market|
1647
+ |468 | cab, hack, taxi, taxicab|
1648
+ |469 | caldron, cauldron|
1649
+ |470 | candle, taper, wax light|
1650
+ |471 | cannon|
1651
+ |472 | canoe|
1652
+ |473 | can opener, tin opener|
1653
+ |474 | cardigan|
1654
+ |475 | car mirror|
1655
+ |476 | carousel, carrousel, merry-go-round, roundabout, whirligig|
1656
+ |477 | carpenter's kit, tool kit|
1657
+ |478 | carton|
1658
+ |479 | car wheel|
1659
+ |480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM|
1660
+ |481 | cassette|
1661
+ |482 | cassette player|
1662
+ |483 | castle|
1663
+ |484 | catamaran|
1664
+ |485 | CD player|
1665
+ |486 | cello, violoncello|
1666
+ |487 | cellular telephone, cellular phone, cellphone, cell, mobile phone|
1667
+ |488 | chain|
1668
+ |489 | chainlink fence|
1669
+ |490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour|
1670
+ |491 | chain saw, chainsaw|
1671
+ |492 | chest|
1672
+ |493 | chiffonier, commode|
1673
+ |494 | chime, bell, gong|
1674
+ |495 | china cabinet, china closet|
1675
+ |496 | Christmas stocking|
1676
+ |497 | church, church building|
1677
+ |498 | cinema, movie theater, movie theatre, movie house, picture palace|
1678
+ |499 | cleaver, meat cleaver, chopper|
1679
+ |500 | cliff dwelling|
1680
+ |501 | cloak|
1681
+ |502 | clog, geta, patten, sabot|
1682
+ |503 | cocktail shaker|
1683
+ |504 | coffee mug|
1684
+ |505 | coffeepot|
1685
+ |506 | coil, spiral, volute, whorl, helix|
1686
+ |507 | combination lock|
1687
+ |508 | computer keyboard, keypad|
1688
+ |509 | confectionery, confectionary, candy store|
1689
+ |510 | container ship, containership, container vessel|
1690
+ |511 | convertible|
1691
+ |512 | corkscrew, bottle screw|
1692
+ |513 | cornet, horn, trumpet, trump|
1693
+ |514 | cowboy boot|
1694
+ |515 | cowboy hat, ten-gallon hat|
1695
+ |516 | cradle|
1696
+ |517 | crane_1|
1697
+ |518 | crash helmet|
1698
+ |519 | crate|
1699
+ |520 | crib, cot|
1700
+ |521 | Crock Pot|
1701
+ |522 | croquet ball|
1702
+ |523 | crutch|
1703
+ |524 | cuirass|
1704
+ |525 | dam, dike, dyke|
1705
+ |526 | desk|
1706
+ |527 | desktop computer|
1707
+ |528 | dial telephone, dial phone|
1708
+ |529 | diaper, nappy, napkin|
1709
+ |530 | digital clock|
1710
+ |531 | digital watch|
1711
+ |532 | dining table, board|
1712
+ |533 | dishrag, dishcloth|
1713
+ |534 | dishwasher, dish washer, dishwashing machine|
1714
+ |535 | disk brake, disc brake|
1715
+ |536 | dock, dockage, docking facility|
1716
+ |537 | dogsled, dog sled, dog sleigh|
1717
+ |538 | dome|
1718
+ |539 | doormat, welcome mat|
1719
+ |540 | drilling platform, offshore rig|
1720
+ |541 | drum, membranophone, tympan|
1721
+ |542 | drumstick|
1722
+ |543 | dumbbell|
1723
+ |544 | Dutch oven|
1724
+ |545 | electric fan, blower|
1725
+ |546 | electric guitar|
1726
+ |547 | electric locomotive|
1727
+ |548 | entertainment center|
1728
+ |549 | envelope|
1729
+ |550 | espresso maker|
1730
+ |551 | face powder|
1731
+ |552 | feather boa, boa|
1732
+ |553 | file, file cabinet, filing cabinet|
1733
+ |554 | fireboat|
1734
+ |555 | fire engine, fire truck|
1735
+ |556 | fire screen, fireguard|
1736
+ |557 | flagpole, flagstaff|
1737
+ |558 | flute, transverse flute|
1738
+ |559 | folding chair|
1739
+ |560 | football helmet|
1740
+ |561 | forklift|
1741
+ |562 | fountain|
1742
+ |563 | fountain pen|
1743
+ |564 | four-poster|
1744
+ |565 | freight car|
1745
+ |566 | French horn, horn|
1746
+ |567 | frying pan, frypan, skillet|
1747
+ |568 | fur coat|
1748
+ |569 | garbage truck, dustcart|
1749
+ |570 | gasmask, respirator, gas helmet|
1750
+ |571 | gas pump, gasoline pump, petrol pump, island dispenser|
1751
+ |572 | goblet|
1752
+ |573 | go-kart|
1753
+ |574 | golf ball|
1754
+ |575 | golfcart, golf cart|
1755
+ |576 | gondola|
1756
+ |577 | gong, tam-tam|
1757
+ |578 | gown|
1758
+ |579 | grand piano, grand|
1759
+ |580 | greenhouse, nursery, glasshouse|
1760
+ |581 | grille, radiator grille|
1761
+ |582 | grocery store, grocery, food market, market|
1762
+ |583 | guillotine|
1763
+ |584 | hair slide|
1764
+ |585 | hair spray|
1765
+ |586 | half track|
1766
+ |587 | hammer|
1767
+ |588 | hamper|
1768
+ |589 | hand blower, blow dryer, blow drier, hair dryer, hair drier|
1769
+ |590 | hand-held computer, hand-held microcomputer|
1770
+ |591 | handkerchief, hankie, hanky, hankey|
1771
+ |592 | hard disc, hard disk, fixed disk|
1772
+ |593 | harmonica, mouth organ, harp, mouth harp|
1773
+ |594 | harp|
1774
+ |595 | harvester, reaper|
1775
+ |596 | hatchet|
1776
+ |597 | holster|
1777
+ |598 | home theater, home theatre|
1778
+ |599 | honeycomb|
1779
+ |600 | hook, claw|
1780
+ |601 | hoopskirt, crinoline|
1781
+ |602 | horizontal bar, high bar|
1782
+ |603 | horse cart, horse-cart|
1783
+ |604 | hourglass|
1784
+ |605 | iPod|
1785
+ |606 | iron, smoothing iron|
1786
+ |607 | jack-o'-lantern|
1787
+ |608 | jean, blue jean, denim|
1788
+ |609 | jeep, landrover|
1789
+ |610 | jersey, T-shirt, tee shirt|
1790
+ |611 | jigsaw puzzle|
1791
+ |612 | jinrikisha, ricksha, rickshaw|
1792
+ |613 | joystick|
1793
+ |614 | kimono|
1794
+ |615 | knee pad|
1795
+ |616 | knot|
1796
+ |617 | lab coat, laboratory coat|
1797
+ |618 | ladle|
1798
+ |619 | lampshade, lamp shade|
1799
+ |620 | laptop, laptop computer|
1800
+ |621 | lawn mower, mower|
1801
+ |622 | lens cap, lens cover|
1802
+ |623 | letter opener, paper knife, paperknife|
1803
+ |624 | library|
1804
+ |625 | lifeboat|
1805
+ |626 | lighter, light, igniter, ignitor|
1806
+ |627 | limousine, limo|
1807
+ |628 | liner, ocean liner|
1808
+ |629 | lipstick, lip rouge|
1809
+ |630 | Loafer|
1810
+ |631 | lotion|
1811
+ |632 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system|
1812
+ |633 | loupe, jeweler's loupe|
1813
+ |634 | lumbermill, sawmill|
1814
+ |635 | magnetic compass|
1815
+ |636 | mailbag, postbag|
1816
+ |637 | mailbox, letter box|
1817
+ |638 | maillot|
1818
+ |639 | maillot, tank suit|
1819
+ |640 | manhole cover|
1820
+ |641 | maraca|
1821
+ |642 | marimba, xylophone|
1822
+ |643 | mask|
1823
+ |644 | matchstick|
1824
+ |645 | maypole|
1825
+ |646 | maze, labyrinth|
1826
+ |647 | measuring cup|
1827
+ |648 | medicine chest, medicine cabinet|
1828
+ |649 | megalith, megalithic structure|
1829
+ |650 | microphone, mike|
1830
+ |651 | microwave, microwave oven|
1831
+ |652 | military uniform|
1832
+ |653 | milk can|
1833
+ |654 | minibus|
1834
+ |655 | miniskirt, mini|
1835
+ |656 | minivan|
1836
+ |657 | missile|
1837
+ |658 | mitten|
1838
+ |659 | mixing bowl|
1839
+ |660 | mobile home, manufactured home|
1840
+ |661 | Model T|
1841
+ |662 | modem|
1842
+ |663 | monastery|
1843
+ |664 | monitor|
1844
+ |665 | moped|
1845
+ |666 | mortar|
1846
+ |667 | mortarboard|
1847
+ |668 | mosque|
1848
+ |669 | mosquito net|
1849
+ |670 | motor scooter, scooter|
1850
+ |671 | mountain bike, all-terrain bike, off-roader|
1851
+ |672 | mountain tent|
1852
+ |673 | mouse, computer mouse|
1853
+ |674 | mousetrap|
1854
+ |675 | moving van|
1855
+ |676 | muzzle|
1856
+ |677 | nail|
1857
+ |678 | neck brace|
1858
+ |679 | necklace|
1859
+ |680 | nipple|
1860
+ |681 | notebook, notebook computer|
1861
+ |682 | obelisk|
1862
+ |683 | oboe, hautboy, hautbois|
1863
+ |684 | ocarina, sweet potato|
1864
+ |685 | odometer, hodometer, mileometer, milometer|
1865
+ |686 | oil filter|
1866
+ |687 | organ, pipe organ|
1867
+ |688 | oscilloscope, scope, cathode-ray oscilloscope, CRO|
1868
+ |689 | overskirt|
1869
+ |690 | oxcart|
1870
+ |691 | oxygen mask|
1871
+ |692 | packet|
1872
+ |693 | paddle, boat paddle|
1873
+ |694 | paddlewheel, paddle wheel|
1874
+ |695 | padlock|
1875
+ |696 | paintbrush|
1876
+ |697 | pajama, pyjama, pj's, jammies|
1877
+ |698 | palace|
1878
+ |699 | panpipe, pandean pipe, syrinx|
1879
+ |700 | paper towel|
1880
+ |701 | parachute, chute|
1881
+ |702 | parallel bars, bars|
1882
+ |703 | park bench|
1883
+ |704 | parking meter|
1884
+ |705 | passenger car, coach, carriage|
1885
+ |706 | patio, terrace|
1886
+ |707 | pay-phone, pay-station|
1887
+ |708 | pedestal, plinth, footstall|
1888
+ |709 | pencil box, pencil case|
1889
+ |710 | pencil sharpener|
1890
+ |711 | perfume, essence|
1891
+ |712 | Petri dish|
1892
+ |713 | photocopier|
1893
+ |714 | pick, plectrum, plectron|
1894
+ |715 | pickelhaube|
1895
+ |716 | picket fence, paling|
1896
+ |717 | pickup, pickup truck|
1897
+ |718 | pier|
1898
+ |719 | piggy bank, penny bank|
1899
+ |720 | pill bottle|
1900
+ |721 | pillow|
1901
+ |722 | ping-pong ball|
1902
+ |723 | pinwheel|
1903
+ |724 | pirate, pirate ship|
1904
+ |725 | pitcher, ewer|
1905
+ |726 | plane, carpenter's plane, woodworking plane|
1906
+ |727 | planetarium|
1907
+ |728 | plastic bag|
1908
+ |729 | plate rack|
1909
+ |730 | plow, plough|
1910
+ |731 | plunger, plumber's helper|
1911
+ |732 | Polaroid camera, Polaroid Land camera|
1912
+ |733 | pole|
1913
+ |734 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria|
1914
+ |735 | poncho|
1915
+ |736 | pool table, billiard table, snooker table|
1916
+ |737 | pop bottle, soda bottle|
1917
+ |738 | pot, flowerpot|
1918
+ |739 | potter's wheel|
1919
+ |740 | power drill|
1920
+ |741 | prayer rug, prayer mat|
1921
+ |742 | printer|
1922
+ |743 | prison, prison house|
1923
+ |744 | projectile, missile|
1924
+ |745 | projector|
1925
+ |746 | puck, hockey puck|
1926
+ |747 | punching bag, punch bag, punching ball, punchball|
1927
+ |748 | purse|
1928
+ |749 | quill, quill pen|
1929
+ |750 | quilt, comforter, comfort, puff|
1930
+ |751 | racer, race car, racing car|
1931
+ |752 | racket, racquet|
1932
+ |753 | radiator|
1933
+ |754 | radio, wireless|
1934
+ |755 | radio telescope, radio reflector|
1935
+ |756 | rain barrel|
1936
+ |757 | recreational vehicle, RV, R.V.|
1937
+ |758 | reel|
1938
+ |759 | reflex camera|
1939
+ |760 | refrigerator, icebox|
1940
+ |761 | remote control, remote|
1941
+ |762 | restaurant, eating house, eating place, eatery|
1942
+ |763 | revolver, six-gun, six-shooter|
1943
+ |764 | rifle|
1944
+ |765 | rocking chair, rocker|
1945
+ |766 | rotisserie|
1946
+ |767 | rubber eraser, rubber, pencil eraser|
1947
+ |768 | rugby ball|
1948
+ |769 | rule, ruler|
1949
+ |770 | running shoe|
1950
+ |771 | safe|
1951
+ |772 | safety pin|
1952
+ |773 | saltshaker, salt shaker|
1953
+ |774 | sandal|
1954
+ |775 | sarong|
1955
+ |776 | sax, saxophone|
1956
+ |777 | scabbard|
1957
+ |778 | scale, weighing machine|
1958
+ |779 | school bus|
1959
+ |780 | schooner|
1960
+ |781 | scoreboard|
1961
+ |782 | screen, CRT screen|
1962
+ |783 | screw|
1963
+ |784 | screwdriver|
1964
+ |785 | seat belt, seatbelt|
1965
+ |786 | sewing machine|
1966
+ |787 | shield, buckler|
1967
+ |788 | shoe shop, shoe-shop, shoe store|
1968
+ |789 | shoji|
1969
+ |790 | shopping basket|
1970
+ |791 | shopping cart|
1971
+ |792 | shovel|
1972
+ |793 | shower cap|
1973
+ |794 | shower curtain|
1974
+ |795 | ski|
1975
+ |796 | ski mask|
1976
+ |797 | sleeping bag|
1977
+ |798 | slide rule, slipstick|
1978
+ |799 | sliding door|
1979
+ |800 | slot, one-armed bandit|
1980
+ |801 | snorkel|
1981
+ |802 | snowmobile|
1982
+ |803 | snowplow, snowplough|
1983
+ |804 | soap dispenser|
1984
+ |805 | soccer ball|
1985
+ |806 | sock|
1986
+ |807 | solar dish, solar collector, solar furnace|
1987
+ |808 | sombrero|
1988
+ |809 | soup bowl|
1989
+ |810 | space bar|
1990
+ |811 | space heater|
1991
+ |812 | space shuttle|
1992
+ |813 | spatula|
1993
+ |814 | speedboat|
1994
+ |815 | spider web, spider's web|
1995
+ |816 | spindle|
1996
+ |817 | sports car, sport car|
1997
+ |818 | spotlight, spot|
1998
+ |819 | stage|
1999
+ |820 | steam locomotive|
2000
+ |821 | steel arch bridge|
2001
+ |822 | steel drum|
2002
+ |823 | stethoscope|
2003
+ |824 | stole|
2004
+ |825 | stone wall|
2005
+ |826 | stopwatch, stop watch|
2006
+ |827 | stove|
2007
+ |828 | strainer|
2008
+ |829 | streetcar, tram, tramcar, trolley, trolley car|
2009
+ |830 | stretcher|
2010
+ |831 | studio couch, day bed|
2011
+ |832 | stupa, tope|
2012
+ |833 | submarine, pigboat, sub, U-boat|
2013
+ |834 | suit, suit of clothes|
2014
+ |835 | sundial|
2015
+ |836 | sunglass|
2016
+ |837 | sunglasses, dark glasses, shades|
2017
+ |838 | sunscreen, sunblock, sun blocker|
2018
+ |839 | suspension bridge|
2019
+ |840 | swab, swob, mop|
2020
+ |841 | sweatshirt|
2021
+ |842 | swimming trunks, bathing trunks|
2022
+ |843 | swing|
2023
+ |844 | switch, electric switch, electrical switch|
2024
+ |845 | syringe|
2025
+ |846 | table lamp|
2026
+ |847 | tank, army tank, armored combat vehicle, armoured combat vehicle|
2027
+ |848 | tape player|
2028
+ |849 | teapot|
2029
+ |850 | teddy, teddy bear|
2030
+ |851 | television, television system|
2031
+ |852 | tennis ball|
2032
+ |853 | thatch, thatched roof|
2033
+ |854 | theater curtain, theatre curtain|
2034
+ |855 | thimble|
2035
+ |856 | thresher, thrasher, threshing machine|
2036
+ |857 | throne|
2037
+ |858 | tile roof|
2038
+ |859 | toaster|
2039
+ |860 | tobacco shop, tobacconist shop, tobacconist|
2040
+ |861 | toilet seat|
2041
+ |862 | torch|
2042
+ |863 | totem pole|
2043
+ |864 | tow truck, tow car, wrecker|
2044
+ |865 | toyshop|
2045
+ |866 | tractor|
2046
+ |867 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi|
2047
+ |868 | tray|
2048
+ |869 | trench coat|
2049
+ |870 | tricycle, trike, velocipede|
2050
+ |871 | trimaran|
2051
+ |872 | tripod|
2052
+ |873 | triumphal arch|
2053
+ |874 | trolleybus, trolley coach, trackless trolley|
2054
+ |875 | trombone|
2055
+ |876 | tub, vat|
2056
+ |877 | turnstile|
2057
+ |878 | typewriter keyboard|
2058
+ |879 | umbrella|
2059
+ |880 | unicycle, monocycle|
2060
+ |881 | upright, upright piano|
2061
+ |882 | vacuum, vacuum cleaner|
2062
+ |883 | vase|
2063
+ |884 | vault|
2064
+ |885 | velvet|
2065
+ |886 | vending machine|
2066
+ |887 | vestment|
2067
+ |888 | viaduct|
2068
+ |889 | violin, fiddle|
2069
+ |890 | volleyball|
2070
+ |891 | waffle iron|
2071
+ |892 | wall clock|
2072
+ |893 | wallet, billfold, notecase, pocketbook|
2073
+ |894 | wardrobe, closet, press|
2074
+ |895 | warplane, military plane|
2075
+ |896 | washbasin, handbasin, washbowl, lavabo, wash-hand basin|
2076
+ |897 | washer, automatic washer, washing machine|
2077
+ |898 | water bottle|
2078
+ |899 | water jug|
2079
+ |900 | water tower|
2080
+ |901 | whiskey jug|
2081
+ |902 | whistle|
2082
+ |903 | wig|
2083
+ |904 | window screen|
2084
+ |905 | window shade|
2085
+ |906 | Windsor tie|
2086
+ |907 | wine bottle|
2087
+ |908 | wing|
2088
+ |909 | wok|
2089
+ |910 | wooden spoon|
2090
+ |911 | wool, woolen, woollen|
2091
+ |912 | worm fence, snake fence, snake-rail fence, Virginia fence|
2092
+ |913 | wreck|
2093
+ |914 | yawl|
2094
+ |915 | yurt|
2095
+ |916 | web site, website, internet site, site|
2096
+ |917 | comic book|
2097
+ |918 | crossword puzzle, crossword|
2098
+ |919 | street sign|
2099
+ |920 | traffic light, traffic signal, stoplight|
2100
+ |921 | book jacket, dust cover, dust jacket, dust wrapper|
2101
+ |922 | menu|
2102
+ |923 | plate|
2103
+ |924 | guacamole|
2104
+ |925 | consomme|
2105
+ |926 | hot pot, hotpot|
2106
+ |927 | trifle|
2107
+ |928 | ice cream, icecream|
2108
+ |929 | ice lolly, lolly, lollipop, popsicle|
2109
+ |930 | French loaf|
2110
+ |931 | bagel, beigel|
2111
+ |932 | pretzel|
2112
+ |933 | cheeseburger|
2113
+ |934 | hotdog, hot dog, red hot|
2114
+ |935 | mashed potato|
2115
+ |936 | head cabbage|
2116
+ |937 | broccoli|
2117
+ |938 | cauliflower|
2118
+ |939 | zucchini, courgette|
2119
+ |940 | spaghetti squash|
2120
+ |941 | acorn squash|
2121
+ |942 | butternut squash|
2122
+ |943 | cucumber, cuke|
2123
+ |944 | artichoke, globe artichoke|
2124
+ |945 | bell pepper|
2125
+ |946 | cardoon|
2126
+ |947 | mushroom|
2127
+ |948 | Granny Smith|
2128
+ |949 | strawberry|
2129
+ |950 | orange|
2130
+ |951 | lemon|
2131
+ |952 | fig|
2132
+ |953 | pineapple, ananas|
2133
+ |954 | banana|
2134
+ |955 | jackfruit, jak, jack|
2135
+ |956 | custard apple|
2136
+ |957 | pomegranate|
2137
+ |958 | hay|
2138
+ |959 | carbonara|
2139
+ |960 | chocolate sauce, chocolate syrup|
2140
+ |961 | dough|
2141
+ |962 | meat loaf, meatloaf|
2142
+ |963 | pizza, pizza pie|
2143
+ |964 | potpie|
2144
+ |965 | burrito|
2145
+ |966 | red wine|
2146
+ |967 | espresso|
2147
+ |968 | cup|
2148
+ |969 | eggnog|
2149
+ |970 | alp|
2150
+ |971 | bubble|
2151
+ |972 | cliff, drop, drop-off|
2152
+ |973 | coral reef|
2153
+ |974 | geyser|
2154
+ |975 | lakeside, lakeshore|
2155
+ |976 | promontory, headland, head, foreland|
2156
+ |977 | sandbar, sand bar|
2157
+ |978 | seashore, coast, seacoast, sea-coast|
2158
+ |979 | valley, vale|
2159
+ |980 | volcano|
2160
+ |981 | ballplayer, baseball player|
2161
+ |982 | groom, bridegroom|
2162
+ |983 | scuba diver|
2163
+ |984 | rapeseed|
2164
+ |985 | daisy|
2165
+ |986 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum|
2166
+ |987 | corn|
2167
+ |988 | acorn|
2168
+ |989 | hip, rose hip, rosehip|
2169
+ |990 | buckeye, horse chestnut, conker|
2170
+ |991 | coral fungus|
2171
+ |992 | agaric|
2172
+ |993 | gyromitra|
2173
+ |994 | stinkhorn, carrion fungus|
2174
+ |995 | earthstar|
2175
+ |996 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa|
2176
+ |997 | bolete|
2177
+ |998 | ear, spike, capitulum|
2178
+ |999 | toilet tissue, toilet paper, bathroom tissue|
2179
+ </details>
2180
+
2181
+ ### Data Splits
2182
+
2183
+ | |train |validation| test |
2184
+ |-------------|------:|---------:|------:|
2185
+ |# of examples|1281167|50000 |100000 |
2186
+
2187
+ ## Dataset Creation
2188
+
2189
+ ### Curation Rationale
2190
+
2191
+ 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.
2192
+
2193
+ ### Source Data
2194
+
2195
+ #### Initial Data Collection and Normalization
2196
+
2197
+ Initial data for ImageNet image classification task consists of photographs collected from [Flickr](https://www.flickr.com) and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs [1](https://ieeexplore.ieee.org/abstract/document/5206848). The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.
2198
+
2199
+ #### Who are the source language producers?
2200
+
2201
+ WordNet synsets further quality controlled by human annotators. The images are from Flickr.
2202
+
2203
+ ### Annotations
2204
+
2205
+ #### Annotation process
2206
+
2207
+ The annotation process of collecting ImageNet for image classification task is a three step process.
2208
+
2209
+ 1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
2210
+ 1. Collecting the candidate image for these object categories using a search engine.
2211
+ 1. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.
2212
+
2213
+ See the section 3.1 in [1](https://arxiv.org/abs/1409.0575) for more details on data collection procedure and [2](https://ieeexplore.ieee.org/abstract/document/5206848) for general information on ImageNet.
2214
+
2215
+ #### Who are the annotators?
2216
+
2217
+ Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See [1](https://arxiv.org/abs/1409.0575) for more details.
2218
+
2219
+ ### Personal and Sensitive Information
2220
+
2221
+ The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](https://image-net.org/face-obfuscation/) on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been [an attempt](https://arxiv.org/abs/1912.07726) at filtering and balancing the people subtree in the larger ImageNet.
2222
+
2223
+ ## Considerations for Using the Data
2224
+
2225
+ ### Social Impact of Dataset
2226
+
2227
+ The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in [1](https://arxiv.org/abs/1409.0575) for a discussion on social impact of the dataset.
2228
+
2229
+ ### Discussion of Biases
2230
+
2231
+ 1. A [study](https://image-net.org/update-sep-17-2019.php) of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
2232
+ 1. A [study](https://arxiv.org/abs/1811.12231) has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
2233
+ 1. Another [study](https://arxiv.org/abs/2109.13228) more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
2234
+ 1. ImageNet data with face obfuscation is also provided at [this link](https://image-net.org/face-obfuscation/)
2235
+ 1. A study on genealogy of ImageNet is can be found at [this link](https://journals.sagepub.com/doi/full/10.1177/20539517211035955) about the "norms, values, and assumptions" in ImageNet.
2236
+ 1. See [this study](https://arxiv.org/abs/1912.07726) on filtering and balancing the distribution of people subtree in the larger complete ImageNet.
2237
+
2238
+ ### Other Known Limitations
2239
+
2240
+ 1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](https://arxiv.org/abs/2109.13228) [[2]](https://arxiv.org/abs/1409.0575) [[3]](https://ieeexplore.ieee.org/abstract/document/5206848).
2241
+
2242
+ ## Additional Information
2243
+
2244
+ ### Dataset Curators
2245
+
2246
+ Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):
2247
+
2248
+ - Olga Russakovsky
2249
+ - Jia Deng
2250
+ - Hao Su
2251
+ - Jonathan Krause
2252
+ - Sanjeev Satheesh
2253
+ - Wei Dong
2254
+ - Richard Socher
2255
+ - Li-Jia Li
2256
+ - Kai Li
2257
+ - Sean Ma
2258
+ - Zhiheng Huang
2259
+ - Andrej Karpathy
2260
+ - Aditya Khosla
2261
+ - Michael Bernstein
2262
+ - Alexander C Berg
2263
+ - Li Fei-Fei
2264
+
2265
+ ### Licensing Information
2266
+
2267
+ 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:
2268
+
2269
+ 1. Researcher shall use the Database only for non-commercial research and educational purposes.
2270
+ 1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
2271
+ 1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
2272
+ 1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
2273
+ 1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
2274
+ 1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
2275
+ 1. The law of the State of New Jersey shall apply to all disputes under this agreement.
2276
+
2277
+ ### Citation Information
2278
+
2279
+ ```bibtex
2280
+ @article{imagenet15russakovsky,
2281
+ 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},
2282
+ Title = { {ImageNet Large Scale Visual Recognition Challenge} },
2283
+ Year = {2015},
2284
+ journal = {International Journal of Computer Vision (IJCV)},
2285
+ doi = {10.1007/s11263-015-0816-y},
2286
+ volume={115},
2287
+ number={3},
2288
+ pages={211-252}
2289
+ }
2290
+ ```
2291
+
2292
+ ### Contributions
2293
+
2294
+ Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.
classes.py ADDED
@@ -0,0 +1,1022 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 the HuggingFace Datasets Authors.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ from collections import OrderedDict
17
+
18
+
19
+ IMAGENET2012_CLASSES = OrderedDict(
20
+ {
21
+ "n01440764": "tench, Tinca tinca",
22
+ "n01443537": "goldfish, Carassius auratus",
23
+ "n01484850": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
24
+ "n01491361": "tiger shark, Galeocerdo cuvieri",
25
+ "n01494475": "hammerhead, hammerhead shark",
26
+ "n01496331": "electric ray, crampfish, numbfish, torpedo",
27
+ "n01498041": "stingray",
28
+ "n01514668": "cock",
29
+ "n01514859": "hen",
30
+ "n01518878": "ostrich, Struthio camelus",
31
+ "n01530575": "brambling, Fringilla montifringilla",
32
+ "n01531178": "goldfinch, Carduelis carduelis",
33
+ "n01532829": "house finch, linnet, Carpodacus mexicanus",
34
+ "n01534433": "junco, snowbird",
35
+ "n01537544": "indigo bunting, indigo finch, indigo bird, Passerina cyanea",
36
+ "n01558993": "robin, American robin, Turdus migratorius",
37
+ "n01560419": "bulbul",
38
+ "n01580077": "jay",
39
+ "n01582220": "magpie",
40
+ "n01592084": "chickadee",
41
+ "n01601694": "water ouzel, dipper",
42
+ "n01608432": "kite",
43
+ "n01614925": "bald eagle, American eagle, Haliaeetus leucocephalus",
44
+ "n01616318": "vulture",
45
+ "n01622779": "great grey owl, great gray owl, Strix nebulosa",
46
+ "n01629819": "European fire salamander, Salamandra salamandra",
47
+ "n01630670": "common newt, Triturus vulgaris",
48
+ "n01631663": "eft",
49
+ "n01632458": "spotted salamander, Ambystoma maculatum",
50
+ "n01632777": "axolotl, mud puppy, Ambystoma mexicanum",
51
+ "n01641577": "bullfrog, Rana catesbeiana",
52
+ "n01644373": "tree frog, tree-frog",
53
+ "n01644900": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
54
+ "n01664065": "loggerhead, loggerhead turtle, Caretta caretta",
55
+ "n01665541": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
56
+ "n01667114": "mud turtle",
57
+ "n01667778": "terrapin",
58
+ "n01669191": "box turtle, box tortoise",
59
+ "n01675722": "banded gecko",
60
+ "n01677366": "common iguana, iguana, Iguana iguana",
61
+ "n01682714": "American chameleon, anole, Anolis carolinensis",
62
+ "n01685808": "whiptail, whiptail lizard",
63
+ "n01687978": "agama",
64
+ "n01688243": "frilled lizard, Chlamydosaurus kingi",
65
+ "n01689811": "alligator lizard",
66
+ "n01692333": "Gila monster, Heloderma suspectum",
67
+ "n01693334": "green lizard, Lacerta viridis",
68
+ "n01694178": "African chameleon, Chamaeleo chamaeleon",
69
+ "n01695060": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
70
+ "n01697457": "African crocodile, Nile crocodile, Crocodylus niloticus",
71
+ "n01698640": "American alligator, Alligator mississipiensis",
72
+ "n01704323": "triceratops",
73
+ "n01728572": "thunder snake, worm snake, Carphophis amoenus",
74
+ "n01728920": "ringneck snake, ring-necked snake, ring snake",
75
+ "n01729322": "hognose snake, puff adder, sand viper",
76
+ "n01729977": "green snake, grass snake",
77
+ "n01734418": "king snake, kingsnake",
78
+ "n01735189": "garter snake, grass snake",
79
+ "n01737021": "water snake",
80
+ "n01739381": "vine snake",
81
+ "n01740131": "night snake, Hypsiglena torquata",
82
+ "n01742172": "boa constrictor, Constrictor constrictor",
83
+ "n01744401": "rock python, rock snake, Python sebae",
84
+ "n01748264": "Indian cobra, Naja naja",
85
+ "n01749939": "green mamba",
86
+ "n01751748": "sea snake",
87
+ "n01753488": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
88
+ "n01755581": "diamondback, diamondback rattlesnake, Crotalus adamanteus",
89
+ "n01756291": "sidewinder, horned rattlesnake, Crotalus cerastes",
90
+ "n01768244": "trilobite",
91
+ "n01770081": "harvestman, daddy longlegs, Phalangium opilio",
92
+ "n01770393": "scorpion",
93
+ "n01773157": "black and gold garden spider, Argiope aurantia",
94
+ "n01773549": "barn spider, Araneus cavaticus",
95
+ "n01773797": "garden spider, Aranea diademata",
96
+ "n01774384": "black widow, Latrodectus mactans",
97
+ "n01774750": "tarantula",
98
+ "n01775062": "wolf spider, hunting spider",
99
+ "n01776313": "tick",
100
+ "n01784675": "centipede",
101
+ "n01795545": "black grouse",
102
+ "n01796340": "ptarmigan",
103
+ "n01797886": "ruffed grouse, partridge, Bonasa umbellus",
104
+ "n01798484": "prairie chicken, prairie grouse, prairie fowl",
105
+ "n01806143": "peacock",
106
+ "n01806567": "quail",
107
+ "n01807496": "partridge",
108
+ "n01817953": "African grey, African gray, Psittacus erithacus",
109
+ "n01818515": "macaw",
110
+ "n01819313": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
111
+ "n01820546": "lorikeet",
112
+ "n01824575": "coucal",
113
+ "n01828970": "bee eater",
114
+ "n01829413": "hornbill",
115
+ "n01833805": "hummingbird",
116
+ "n01843065": "jacamar",
117
+ "n01843383": "toucan",
118
+ "n01847000": "drake",
119
+ "n01855032": "red-breasted merganser, Mergus serrator",
120
+ "n01855672": "goose",
121
+ "n01860187": "black swan, Cygnus atratus",
122
+ "n01871265": "tusker",
123
+ "n01872401": "echidna, spiny anteater, anteater",
124
+ "n01873310": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
125
+ "n01877812": "wallaby, brush kangaroo",
126
+ "n01882714": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
127
+ "n01883070": "wombat",
128
+ "n01910747": "jellyfish",
129
+ "n01914609": "sea anemone, anemone",
130
+ "n01917289": "brain coral",
131
+ "n01924916": "flatworm, platyhelminth",
132
+ "n01930112": "nematode, nematode worm, roundworm",
133
+ "n01943899": "conch",
134
+ "n01944390": "snail",
135
+ "n01945685": "slug",
136
+ "n01950731": "sea slug, nudibranch",
137
+ "n01955084": "chiton, coat-of-mail shell, sea cradle, polyplacophore",
138
+ "n01968897": "chambered nautilus, pearly nautilus, nautilus",
139
+ "n01978287": "Dungeness crab, Cancer magister",
140
+ "n01978455": "rock crab, Cancer irroratus",
141
+ "n01980166": "fiddler crab",
142
+ "n01981276": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
143
+ "n01983481": "American lobster, Northern lobster, Maine lobster, Homarus americanus",
144
+ "n01984695": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
145
+ "n01985128": "crayfish, crawfish, crawdad, crawdaddy",
146
+ "n01986214": "hermit crab",
147
+ "n01990800": "isopod",
148
+ "n02002556": "white stork, Ciconia ciconia",
149
+ "n02002724": "black stork, Ciconia nigra",
150
+ "n02006656": "spoonbill",
151
+ "n02007558": "flamingo",
152
+ "n02009229": "little blue heron, Egretta caerulea",
153
+ "n02009912": "American egret, great white heron, Egretta albus",
154
+ "n02011460": "bittern",
155
+ "n02012849": "crane",
156
+ "n02013706": "limpkin, Aramus pictus",
157
+ "n02017213": "European gallinule, Porphyrio porphyrio",
158
+ "n02018207": "American coot, marsh hen, mud hen, water hen, Fulica americana",
159
+ "n02018795": "bustard",
160
+ "n02025239": "ruddy turnstone, Arenaria interpres",
161
+ "n02027492": "red-backed sandpiper, dunlin, Erolia alpina",
162
+ "n02028035": "redshank, Tringa totanus",
163
+ "n02033041": "dowitcher",
164
+ "n02037110": "oystercatcher, oyster catcher",
165
+ "n02051845": "pelican",
166
+ "n02056570": "king penguin, Aptenodytes patagonica",
167
+ "n02058221": "albatross, mollymawk",
168
+ "n02066245": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
169
+ "n02071294": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
170
+ "n02074367": "dugong, Dugong dugon",
171
+ "n02077923": "sea lion",
172
+ "n02085620": "Chihuahua",
173
+ "n02085782": "Japanese spaniel",
174
+ "n02085936": "Maltese dog, Maltese terrier, Maltese",
175
+ "n02086079": "Pekinese, Pekingese, Peke",
176
+ "n02086240": "Shih-Tzu",
177
+ "n02086646": "Blenheim spaniel",
178
+ "n02086910": "papillon",
179
+ "n02087046": "toy terrier",
180
+ "n02087394": "Rhodesian ridgeback",
181
+ "n02088094": "Afghan hound, Afghan",
182
+ "n02088238": "basset, basset hound",
183
+ "n02088364": "beagle",
184
+ "n02088466": "bloodhound, sleuthhound",
185
+ "n02088632": "bluetick",
186
+ "n02089078": "black-and-tan coonhound",
187
+ "n02089867": "Walker hound, Walker foxhound",
188
+ "n02089973": "English foxhound",
189
+ "n02090379": "redbone",
190
+ "n02090622": "borzoi, Russian wolfhound",
191
+ "n02090721": "Irish wolfhound",
192
+ "n02091032": "Italian greyhound",
193
+ "n02091134": "whippet",
194
+ "n02091244": "Ibizan hound, Ibizan Podenco",
195
+ "n02091467": "Norwegian elkhound, elkhound",
196
+ "n02091635": "otterhound, otter hound",
197
+ "n02091831": "Saluki, gazelle hound",
198
+ "n02092002": "Scottish deerhound, deerhound",
199
+ "n02092339": "Weimaraner",
200
+ "n02093256": "Staffordshire bullterrier, Staffordshire bull terrier",
201
+ "n02093428": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
202
+ "n02093647": "Bedlington terrier",
203
+ "n02093754": "Border terrier",
204
+ "n02093859": "Kerry blue terrier",
205
+ "n02093991": "Irish terrier",
206
+ "n02094114": "Norfolk terrier",
207
+ "n02094258": "Norwich terrier",
208
+ "n02094433": "Yorkshire terrier",
209
+ "n02095314": "wire-haired fox terrier",
210
+ "n02095570": "Lakeland terrier",
211
+ "n02095889": "Sealyham terrier, Sealyham",
212
+ "n02096051": "Airedale, Airedale terrier",
213
+ "n02096177": "cairn, cairn terrier",
214
+ "n02096294": "Australian terrier",
215
+ "n02096437": "Dandie Dinmont, Dandie Dinmont terrier",
216
+ "n02096585": "Boston bull, Boston terrier",
217
+ "n02097047": "miniature schnauzer",
218
+ "n02097130": "giant schnauzer",
219
+ "n02097209": "standard schnauzer",
220
+ "n02097298": "Scotch terrier, Scottish terrier, Scottie",
221
+ "n02097474": "Tibetan terrier, chrysanthemum dog",
222
+ "n02097658": "silky terrier, Sydney silky",
223
+ "n02098105": "soft-coated wheaten terrier",
224
+ "n02098286": "West Highland white terrier",
225
+ "n02098413": "Lhasa, Lhasa apso",
226
+ "n02099267": "flat-coated retriever",
227
+ "n02099429": "curly-coated retriever",
228
+ "n02099601": "golden retriever",
229
+ "n02099712": "Labrador retriever",
230
+ "n02099849": "Chesapeake Bay retriever",
231
+ "n02100236": "German short-haired pointer",
232
+ "n02100583": "vizsla, Hungarian pointer",
233
+ "n02100735": "English setter",
234
+ "n02100877": "Irish setter, red setter",
235
+ "n02101006": "Gordon setter",
236
+ "n02101388": "Brittany spaniel",
237
+ "n02101556": "clumber, clumber spaniel",
238
+ "n02102040": "English springer, English springer spaniel",
239
+ "n02102177": "Welsh springer spaniel",
240
+ "n02102318": "cocker spaniel, English cocker spaniel, cocker",
241
+ "n02102480": "Sussex spaniel",
242
+ "n02102973": "Irish water spaniel",
243
+ "n02104029": "kuvasz",
244
+ "n02104365": "schipperke",
245
+ "n02105056": "groenendael",
246
+ "n02105162": "malinois",
247
+ "n02105251": "briard",
248
+ "n02105412": "kelpie",
249
+ "n02105505": "komondor",
250
+ "n02105641": "Old English sheepdog, bobtail",
251
+ "n02105855": "Shetland sheepdog, Shetland sheep dog, Shetland",
252
+ "n02106030": "collie",
253
+ "n02106166": "Border collie",
254
+ "n02106382": "Bouvier des Flandres, Bouviers des Flandres",
255
+ "n02106550": "Rottweiler",
256
+ "n02106662": "German shepherd, German shepherd dog, German police dog, alsatian",
257
+ "n02107142": "Doberman, Doberman pinscher",
258
+ "n02107312": "miniature pinscher",
259
+ "n02107574": "Greater Swiss Mountain dog",
260
+ "n02107683": "Bernese mountain dog",
261
+ "n02107908": "Appenzeller",
262
+ "n02108000": "EntleBucher",
263
+ "n02108089": "boxer",
264
+ "n02108422": "bull mastiff",
265
+ "n02108551": "Tibetan mastiff",
266
+ "n02108915": "French bulldog",
267
+ "n02109047": "Great Dane",
268
+ "n02109525": "Saint Bernard, St Bernard",
269
+ "n02109961": "Eskimo dog, husky",
270
+ "n02110063": "malamute, malemute, Alaskan malamute",
271
+ "n02110185": "Siberian husky",
272
+ "n02110341": "dalmatian, coach dog, carriage dog",
273
+ "n02110627": "affenpinscher, monkey pinscher, monkey dog",
274
+ "n02110806": "basenji",
275
+ "n02110958": "pug, pug-dog",
276
+ "n02111129": "Leonberg",
277
+ "n02111277": "Newfoundland, Newfoundland dog",
278
+ "n02111500": "Great Pyrenees",
279
+ "n02111889": "Samoyed, Samoyede",
280
+ "n02112018": "Pomeranian",
281
+ "n02112137": "chow, chow chow",
282
+ "n02112350": "keeshond",
283
+ "n02112706": "Brabancon griffon",
284
+ "n02113023": "Pembroke, Pembroke Welsh corgi",
285
+ "n02113186": "Cardigan, Cardigan Welsh corgi",
286
+ "n02113624": "toy poodle",
287
+ "n02113712": "miniature poodle",
288
+ "n02113799": "standard poodle",
289
+ "n02113978": "Mexican hairless",
290
+ "n02114367": "timber wolf, grey wolf, gray wolf, Canis lupus",
291
+ "n02114548": "white wolf, Arctic wolf, Canis lupus tundrarum",
292
+ "n02114712": "red wolf, maned wolf, Canis rufus, Canis niger",
293
+ "n02114855": "coyote, prairie wolf, brush wolf, Canis latrans",
294
+ "n02115641": "dingo, warrigal, warragal, Canis dingo",
295
+ "n02115913": "dhole, Cuon alpinus",
296
+ "n02116738": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
297
+ "n02117135": "hyena, hyaena",
298
+ "n02119022": "red fox, Vulpes vulpes",
299
+ "n02119789": "kit fox, Vulpes macrotis",
300
+ "n02120079": "Arctic fox, white fox, Alopex lagopus",
301
+ "n02120505": "grey fox, gray fox, Urocyon cinereoargenteus",
302
+ "n02123045": "tabby, tabby cat",
303
+ "n02123159": "tiger cat",
304
+ "n02123394": "Persian cat",
305
+ "n02123597": "Siamese cat, Siamese",
306
+ "n02124075": "Egyptian cat",
307
+ "n02125311": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
308
+ "n02127052": "lynx, catamount",
309
+ "n02128385": "leopard, Panthera pardus",
310
+ "n02128757": "snow leopard, ounce, Panthera uncia",
311
+ "n02128925": "jaguar, panther, Panthera onca, Felis onca",
312
+ "n02129165": "lion, king of beasts, Panthera leo",
313
+ "n02129604": "tiger, Panthera tigris",
314
+ "n02130308": "cheetah, chetah, Acinonyx jubatus",
315
+ "n02132136": "brown bear, bruin, Ursus arctos",
316
+ "n02133161": "American black bear, black bear, Ursus americanus, Euarctos americanus",
317
+ "n02134084": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
318
+ "n02134418": "sloth bear, Melursus ursinus, Ursus ursinus",
319
+ "n02137549": "mongoose",
320
+ "n02138441": "meerkat, mierkat",
321
+ "n02165105": "tiger beetle",
322
+ "n02165456": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
323
+ "n02167151": "ground beetle, carabid beetle",
324
+ "n02168699": "long-horned beetle, longicorn, longicorn beetle",
325
+ "n02169497": "leaf beetle, chrysomelid",
326
+ "n02172182": "dung beetle",
327
+ "n02174001": "rhinoceros beetle",
328
+ "n02177972": "weevil",
329
+ "n02190166": "fly",
330
+ "n02206856": "bee",
331
+ "n02219486": "ant, emmet, pismire",
332
+ "n02226429": "grasshopper, hopper",
333
+ "n02229544": "cricket",
334
+ "n02231487": "walking stick, walkingstick, stick insect",
335
+ "n02233338": "cockroach, roach",
336
+ "n02236044": "mantis, mantid",
337
+ "n02256656": "cicada, cicala",
338
+ "n02259212": "leafhopper",
339
+ "n02264363": "lacewing, lacewing fly",
340
+ "n02268443": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
341
+ "n02268853": "damselfly",
342
+ "n02276258": "admiral",
343
+ "n02277742": "ringlet, ringlet butterfly",
344
+ "n02279972": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
345
+ "n02280649": "cabbage butterfly",
346
+ "n02281406": "sulphur butterfly, sulfur butterfly",
347
+ "n02281787": "lycaenid, lycaenid butterfly",
348
+ "n02317335": "starfish, sea star",
349
+ "n02319095": "sea urchin",
350
+ "n02321529": "sea cucumber, holothurian",
351
+ "n02325366": "wood rabbit, cottontail, cottontail rabbit",
352
+ "n02326432": "hare",
353
+ "n02328150": "Angora, Angora rabbit",
354
+ "n02342885": "hamster",
355
+ "n02346627": "porcupine, hedgehog",
356
+ "n02356798": "fox squirrel, eastern fox squirrel, Sciurus niger",
357
+ "n02361337": "marmot",
358
+ "n02363005": "beaver",
359
+ "n02364673": "guinea pig, Cavia cobaya",
360
+ "n02389026": "sorrel",
361
+ "n02391049": "zebra",
362
+ "n02395406": "hog, pig, grunter, squealer, Sus scrofa",
363
+ "n02396427": "wild boar, boar, Sus scrofa",
364
+ "n02397096": "warthog",
365
+ "n02398521": "hippopotamus, hippo, river horse, Hippopotamus amphibius",
366
+ "n02403003": "ox",
367
+ "n02408429": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
368
+ "n02410509": "bison",
369
+ "n02412080": "ram, tup",
370
+ "n02415577": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
371
+ "n02417914": "ibex, Capra ibex",
372
+ "n02422106": "hartebeest",
373
+ "n02422699": "impala, Aepyceros melampus",
374
+ "n02423022": "gazelle",
375
+ "n02437312": "Arabian camel, dromedary, Camelus dromedarius",
376
+ "n02437616": "llama",
377
+ "n02441942": "weasel",
378
+ "n02442845": "mink",
379
+ "n02443114": "polecat, fitch, foulmart, foumart, Mustela putorius",
380
+ "n02443484": "black-footed ferret, ferret, Mustela nigripes",
381
+ "n02444819": "otter",
382
+ "n02445715": "skunk, polecat, wood pussy",
383
+ "n02447366": "badger",
384
+ "n02454379": "armadillo",
385
+ "n02457408": "three-toed sloth, ai, Bradypus tridactylus",
386
+ "n02480495": "orangutan, orang, orangutang, Pongo pygmaeus",
387
+ "n02480855": "gorilla, Gorilla gorilla",
388
+ "n02481823": "chimpanzee, chimp, Pan troglodytes",
389
+ "n02483362": "gibbon, Hylobates lar",
390
+ "n02483708": "siamang, Hylobates syndactylus, Symphalangus syndactylus",
391
+ "n02484975": "guenon, guenon monkey",
392
+ "n02486261": "patas, hussar monkey, Erythrocebus patas",
393
+ "n02486410": "baboon",
394
+ "n02487347": "macaque",
395
+ "n02488291": "langur",
396
+ "n02488702": "colobus, colobus monkey",
397
+ "n02489166": "proboscis monkey, Nasalis larvatus",
398
+ "n02490219": "marmoset",
399
+ "n02492035": "capuchin, ringtail, Cebus capucinus",
400
+ "n02492660": "howler monkey, howler",
401
+ "n02493509": "titi, titi monkey",
402
+ "n02493793": "spider monkey, Ateles geoffroyi",
403
+ "n02494079": "squirrel monkey, Saimiri sciureus",
404
+ "n02497673": "Madagascar cat, ring-tailed lemur, Lemur catta",
405
+ "n02500267": "indri, indris, Indri indri, Indri brevicaudatus",
406
+ "n02504013": "Indian elephant, Elephas maximus",
407
+ "n02504458": "African elephant, Loxodonta africana",
408
+ "n02509815": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
409
+ "n02510455": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
410
+ "n02514041": "barracouta, snoek",
411
+ "n02526121": "eel",
412
+ "n02536864": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
413
+ "n02606052": "rock beauty, Holocanthus tricolor",
414
+ "n02607072": "anemone fish",
415
+ "n02640242": "sturgeon",
416
+ "n02641379": "gar, garfish, garpike, billfish, Lepisosteus osseus",
417
+ "n02643566": "lionfish",
418
+ "n02655020": "puffer, pufferfish, blowfish, globefish",
419
+ "n02666196": "abacus",
420
+ "n02667093": "abaya",
421
+ "n02669723": "academic gown, academic robe, judge's robe",
422
+ "n02672831": "accordion, piano accordion, squeeze box",
423
+ "n02676566": "acoustic guitar",
424
+ "n02687172": "aircraft carrier, carrier, flattop, attack aircraft carrier",
425
+ "n02690373": "airliner",
426
+ "n02692877": "airship, dirigible",
427
+ "n02699494": "altar",
428
+ "n02701002": "ambulance",
429
+ "n02704792": "amphibian, amphibious vehicle",
430
+ "n02708093": "analog clock",
431
+ "n02727426": "apiary, bee house",
432
+ "n02730930": "apron",
433
+ "n02747177": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
434
+ "n02749479": "assault rifle, assault gun",
435
+ "n02769748": "backpack, back pack, knapsack, packsack, rucksack, haversack",
436
+ "n02776631": "bakery, bakeshop, bakehouse",
437
+ "n02777292": "balance beam, beam",
438
+ "n02782093": "balloon",
439
+ "n02783161": "ballpoint, ballpoint pen, ballpen, Biro",
440
+ "n02786058": "Band Aid",
441
+ "n02787622": "banjo",
442
+ "n02788148": "bannister, banister, balustrade, balusters, handrail",
443
+ "n02790996": "barbell",
444
+ "n02791124": "barber chair",
445
+ "n02791270": "barbershop",
446
+ "n02793495": "barn",
447
+ "n02794156": "barometer",
448
+ "n02795169": "barrel, cask",
449
+ "n02797295": "barrow, garden cart, lawn cart, wheelbarrow",
450
+ "n02799071": "baseball",
451
+ "n02802426": "basketball",
452
+ "n02804414": "bassinet",
453
+ "n02804610": "bassoon",
454
+ "n02807133": "bathing cap, swimming cap",
455
+ "n02808304": "bath towel",
456
+ "n02808440": "bathtub, bathing tub, bath, tub",
457
+ "n02814533": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
458
+ "n02814860": "beacon, lighthouse, beacon light, pharos",
459
+ "n02815834": "beaker",
460
+ "n02817516": "bearskin, busby, shako",
461
+ "n02823428": "beer bottle",
462
+ "n02823750": "beer glass",
463
+ "n02825657": "bell cote, bell cot",
464
+ "n02834397": "bib",
465
+ "n02835271": "bicycle-built-for-two, tandem bicycle, tandem",
466
+ "n02837789": "bikini, two-piece",
467
+ "n02840245": "binder, ring-binder",
468
+ "n02841315": "binoculars, field glasses, opera glasses",
469
+ "n02843684": "birdhouse",
470
+ "n02859443": "boathouse",
471
+ "n02860847": "bobsled, bobsleigh, bob",
472
+ "n02865351": "bolo tie, bolo, bola tie, bola",
473
+ "n02869837": "bonnet, poke bonnet",
474
+ "n02870880": "bookcase",
475
+ "n02871525": "bookshop, bookstore, bookstall",
476
+ "n02877765": "bottlecap",
477
+ "n02879718": "bow",
478
+ "n02883205": "bow tie, bow-tie, bowtie",
479
+ "n02892201": "brass, memorial tablet, plaque",
480
+ "n02892767": "brassiere, bra, bandeau",
481
+ "n02894605": "breakwater, groin, groyne, mole, bulwark, seawall, jetty",
482
+ "n02895154": "breastplate, aegis, egis",
483
+ "n02906734": "broom",
484
+ "n02909870": "bucket, pail",
485
+ "n02910353": "buckle",
486
+ "n02916936": "bulletproof vest",
487
+ "n02917067": "bullet train, bullet",
488
+ "n02927161": "butcher shop, meat market",
489
+ "n02930766": "cab, hack, taxi, taxicab",
490
+ "n02939185": "caldron, cauldron",
491
+ "n02948072": "candle, taper, wax light",
492
+ "n02950826": "cannon",
493
+ "n02951358": "canoe",
494
+ "n02951585": "can opener, tin opener",
495
+ "n02963159": "cardigan",
496
+ "n02965783": "car mirror",
497
+ "n02966193": "carousel, carrousel, merry-go-round, roundabout, whirligig",
498
+ "n02966687": "carpenter's kit, tool kit",
499
+ "n02971356": "carton",
500
+ "n02974003": "car wheel",
501
+ "n02977058": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
502
+ "n02978881": "cassette",
503
+ "n02979186": "cassette player",
504
+ "n02980441": "castle",
505
+ "n02981792": "catamaran",
506
+ "n02988304": "CD player",
507
+ "n02992211": "cello, violoncello",
508
+ "n02992529": "cellular telephone, cellular phone, cellphone, cell, mobile phone",
509
+ "n02999410": "chain",
510
+ "n03000134": "chainlink fence",
511
+ "n03000247": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
512
+ "n03000684": "chain saw, chainsaw",
513
+ "n03014705": "chest",
514
+ "n03016953": "chiffonier, commode",
515
+ "n03017168": "chime, bell, gong",
516
+ "n03018349": "china cabinet, china closet",
517
+ "n03026506": "Christmas stocking",
518
+ "n03028079": "church, church building",
519
+ "n03032252": "cinema, movie theater, movie theatre, movie house, picture palace",
520
+ "n03041632": "cleaver, meat cleaver, chopper",
521
+ "n03042490": "cliff dwelling",
522
+ "n03045698": "cloak",
523
+ "n03047690": "clog, geta, patten, sabot",
524
+ "n03062245": "cocktail shaker",
525
+ "n03063599": "coffee mug",
526
+ "n03063689": "coffeepot",
527
+ "n03065424": "coil, spiral, volute, whorl, helix",
528
+ "n03075370": "combination lock",
529
+ "n03085013": "computer keyboard, keypad",
530
+ "n03089624": "confectionery, confectionary, candy store",
531
+ "n03095699": "container ship, containership, container vessel",
532
+ "n03100240": "convertible",
533
+ "n03109150": "corkscrew, bottle screw",
534
+ "n03110669": "cornet, horn, trumpet, trump",
535
+ "n03124043": "cowboy boot",
536
+ "n03124170": "cowboy hat, ten-gallon hat",
537
+ "n03125729": "cradle",
538
+ "n03126707": "crane2",
539
+ "n03127747": "crash helmet",
540
+ "n03127925": "crate",
541
+ "n03131574": "crib, cot",
542
+ "n03133878": "Crock Pot",
543
+ "n03134739": "croquet ball",
544
+ "n03141823": "crutch",
545
+ "n03146219": "cuirass",
546
+ "n03160309": "dam, dike, dyke",
547
+ "n03179701": "desk",
548
+ "n03180011": "desktop computer",
549
+ "n03187595": "dial telephone, dial phone",
550
+ "n03188531": "diaper, nappy, napkin",
551
+ "n03196217": "digital clock",
552
+ "n03197337": "digital watch",
553
+ "n03201208": "dining table, board",
554
+ "n03207743": "dishrag, dishcloth",
555
+ "n03207941": "dishwasher, dish washer, dishwashing machine",
556
+ "n03208938": "disk brake, disc brake",
557
+ "n03216828": "dock, dockage, docking facility",
558
+ "n03218198": "dogsled, dog sled, dog sleigh",
559
+ "n03220513": "dome",
560
+ "n03223299": "doormat, welcome mat",
561
+ "n03240683": "drilling platform, offshore rig",
562
+ "n03249569": "drum, membranophone, tympan",
563
+ "n03250847": "drumstick",
564
+ "n03255030": "dumbbell",
565
+ "n03259280": "Dutch oven",
566
+ "n03271574": "electric fan, blower",
567
+ "n03272010": "electric guitar",
568
+ "n03272562": "electric locomotive",
569
+ "n03290653": "entertainment center",
570
+ "n03291819": "envelope",
571
+ "n03297495": "espresso maker",
572
+ "n03314780": "face powder",
573
+ "n03325584": "feather boa, boa",
574
+ "n03337140": "file, file cabinet, filing cabinet",
575
+ "n03344393": "fireboat",
576
+ "n03345487": "fire engine, fire truck",
577
+ "n03347037": "fire screen, fireguard",
578
+ "n03355925": "flagpole, flagstaff",
579
+ "n03372029": "flute, transverse flute",
580
+ "n03376595": "folding chair",
581
+ "n03379051": "football helmet",
582
+ "n03384352": "forklift",
583
+ "n03388043": "fountain",
584
+ "n03388183": "fountain pen",
585
+ "n03388549": "four-poster",
586
+ "n03393912": "freight car",
587
+ "n03394916": "French horn, horn",
588
+ "n03400231": "frying pan, frypan, skillet",
589
+ "n03404251": "fur coat",
590
+ "n03417042": "garbage truck, dustcart",
591
+ "n03424325": "gasmask, respirator, gas helmet",
592
+ "n03425413": "gas pump, gasoline pump, petrol pump, island dispenser",
593
+ "n03443371": "goblet",
594
+ "n03444034": "go-kart",
595
+ "n03445777": "golf ball",
596
+ "n03445924": "golfcart, golf cart",
597
+ "n03447447": "gondola",
598
+ "n03447721": "gong, tam-tam",
599
+ "n03450230": "gown",
600
+ "n03452741": "grand piano, grand",
601
+ "n03457902": "greenhouse, nursery, glasshouse",
602
+ "n03459775": "grille, radiator grille",
603
+ "n03461385": "grocery store, grocery, food market, market",
604
+ "n03467068": "guillotine",
605
+ "n03476684": "hair slide",
606
+ "n03476991": "hair spray",
607
+ "n03478589": "half track",
608
+ "n03481172": "hammer",
609
+ "n03482405": "hamper",
610
+ "n03483316": "hand blower, blow dryer, blow drier, hair dryer, hair drier",
611
+ "n03485407": "hand-held computer, hand-held microcomputer",
612
+ "n03485794": "handkerchief, hankie, hanky, hankey",
613
+ "n03492542": "hard disc, hard disk, fixed disk",
614
+ "n03494278": "harmonica, mouth organ, harp, mouth harp",
615
+ "n03495258": "harp",
616
+ "n03496892": "harvester, reaper",
617
+ "n03498962": "hatchet",
618
+ "n03527444": "holster",
619
+ "n03529860": "home theater, home theatre",
620
+ "n03530642": "honeycomb",
621
+ "n03532672": "hook, claw",
622
+ "n03534580": "hoopskirt, crinoline",
623
+ "n03535780": "horizontal bar, high bar",
624
+ "n03538406": "horse cart, horse-cart",
625
+ "n03544143": "hourglass",
626
+ "n03584254": "iPod",
627
+ "n03584829": "iron, smoothing iron",
628
+ "n03590841": "jack-o'-lantern",
629
+ "n03594734": "jean, blue jean, denim",
630
+ "n03594945": "jeep, landrover",
631
+ "n03595614": "jersey, T-shirt, tee shirt",
632
+ "n03598930": "jigsaw puzzle",
633
+ "n03599486": "jinrikisha, ricksha, rickshaw",
634
+ "n03602883": "joystick",
635
+ "n03617480": "kimono",
636
+ "n03623198": "knee pad",
637
+ "n03627232": "knot",
638
+ "n03630383": "lab coat, laboratory coat",
639
+ "n03633091": "ladle",
640
+ "n03637318": "lampshade, lamp shade",
641
+ "n03642806": "laptop, laptop computer",
642
+ "n03649909": "lawn mower, mower",
643
+ "n03657121": "lens cap, lens cover",
644
+ "n03658185": "letter opener, paper knife, paperknife",
645
+ "n03661043": "library",
646
+ "n03662601": "lifeboat",
647
+ "n03666591": "lighter, light, igniter, ignitor",
648
+ "n03670208": "limousine, limo",
649
+ "n03673027": "liner, ocean liner",
650
+ "n03676483": "lipstick, lip rouge",
651
+ "n03680355": "Loafer",
652
+ "n03690938": "lotion",
653
+ "n03691459": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
654
+ "n03692522": "loupe, jeweler's loupe",
655
+ "n03697007": "lumbermill, sawmill",
656
+ "n03706229": "magnetic compass",
657
+ "n03709823": "mailbag, postbag",
658
+ "n03710193": "mailbox, letter box",
659
+ "n03710637": "maillot",
660
+ "n03710721": "maillot, tank suit",
661
+ "n03717622": "manhole cover",
662
+ "n03720891": "maraca",
663
+ "n03721384": "marimba, xylophone",
664
+ "n03724870": "mask",
665
+ "n03729826": "matchstick",
666
+ "n03733131": "maypole",
667
+ "n03733281": "maze, labyrinth",
668
+ "n03733805": "measuring cup",
669
+ "n03742115": "medicine chest, medicine cabinet",
670
+ "n03743016": "megalith, megalithic structure",
671
+ "n03759954": "microphone, mike",
672
+ "n03761084": "microwave, microwave oven",
673
+ "n03763968": "military uniform",
674
+ "n03764736": "milk can",
675
+ "n03769881": "minibus",
676
+ "n03770439": "miniskirt, mini",
677
+ "n03770679": "minivan",
678
+ "n03773504": "missile",
679
+ "n03775071": "mitten",
680
+ "n03775546": "mixing bowl",
681
+ "n03776460": "mobile home, manufactured home",
682
+ "n03777568": "Model T",
683
+ "n03777754": "modem",
684
+ "n03781244": "monastery",
685
+ "n03782006": "monitor",
686
+ "n03785016": "moped",
687
+ "n03786901": "mortar",
688
+ "n03787032": "mortarboard",
689
+ "n03788195": "mosque",
690
+ "n03788365": "mosquito net",
691
+ "n03791053": "motor scooter, scooter",
692
+ "n03792782": "mountain bike, all-terrain bike, off-roader",
693
+ "n03792972": "mountain tent",
694
+ "n03793489": "mouse, computer mouse",
695
+ "n03794056": "mousetrap",
696
+ "n03796401": "moving van",
697
+ "n03803284": "muzzle",
698
+ "n03804744": "nail",
699
+ "n03814639": "neck brace",
700
+ "n03814906": "necklace",
701
+ "n03825788": "nipple",
702
+ "n03832673": "notebook, notebook computer",
703
+ "n03837869": "obelisk",
704
+ "n03838899": "oboe, hautboy, hautbois",
705
+ "n03840681": "ocarina, sweet potato",
706
+ "n03841143": "odometer, hodometer, mileometer, milometer",
707
+ "n03843555": "oil filter",
708
+ "n03854065": "organ, pipe organ",
709
+ "n03857828": "oscilloscope, scope, cathode-ray oscilloscope, CRO",
710
+ "n03866082": "overskirt",
711
+ "n03868242": "oxcart",
712
+ "n03868863": "oxygen mask",
713
+ "n03871628": "packet",
714
+ "n03873416": "paddle, boat paddle",
715
+ "n03874293": "paddlewheel, paddle wheel",
716
+ "n03874599": "padlock",
717
+ "n03876231": "paintbrush",
718
+ "n03877472": "pajama, pyjama, pj's, jammies",
719
+ "n03877845": "palace",
720
+ "n03884397": "panpipe, pandean pipe, syrinx",
721
+ "n03887697": "paper towel",
722
+ "n03888257": "parachute, chute",
723
+ "n03888605": "parallel bars, bars",
724
+ "n03891251": "park bench",
725
+ "n03891332": "parking meter",
726
+ "n03895866": "passenger car, coach, carriage",
727
+ "n03899768": "patio, terrace",
728
+ "n03902125": "pay-phone, pay-station",
729
+ "n03903868": "pedestal, plinth, footstall",
730
+ "n03908618": "pencil box, pencil case",
731
+ "n03908714": "pencil sharpener",
732
+ "n03916031": "perfume, essence",
733
+ "n03920288": "Petri dish",
734
+ "n03924679": "photocopier",
735
+ "n03929660": "pick, plectrum, plectron",
736
+ "n03929855": "pickelhaube",
737
+ "n03930313": "picket fence, paling",
738
+ "n03930630": "pickup, pickup truck",
739
+ "n03933933": "pier",
740
+ "n03935335": "piggy bank, penny bank",
741
+ "n03937543": "pill bottle",
742
+ "n03938244": "pillow",
743
+ "n03942813": "ping-pong ball",
744
+ "n03944341": "pinwheel",
745
+ "n03947888": "pirate, pirate ship",
746
+ "n03950228": "pitcher, ewer",
747
+ "n03954731": "plane, carpenter's plane, woodworking plane",
748
+ "n03956157": "planetarium",
749
+ "n03958227": "plastic bag",
750
+ "n03961711": "plate rack",
751
+ "n03967562": "plow, plough",
752
+ "n03970156": "plunger, plumber's helper",
753
+ "n03976467": "Polaroid camera, Polaroid Land camera",
754
+ "n03976657": "pole",
755
+ "n03977966": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
756
+ "n03980874": "poncho",
757
+ "n03982430": "pool table, billiard table, snooker table",
758
+ "n03983396": "pop bottle, soda bottle",
759
+ "n03991062": "pot, flowerpot",
760
+ "n03992509": "potter's wheel",
761
+ "n03995372": "power drill",
762
+ "n03998194": "prayer rug, prayer mat",
763
+ "n04004767": "printer",
764
+ "n04005630": "prison, prison house",
765
+ "n04008634": "projectile, missile",
766
+ "n04009552": "projector",
767
+ "n04019541": "puck, hockey puck",
768
+ "n04023962": "punching bag, punch bag, punching ball, punchball",
769
+ "n04026417": "purse",
770
+ "n04033901": "quill, quill pen",
771
+ "n04033995": "quilt, comforter, comfort, puff",
772
+ "n04037443": "racer, race car, racing car",
773
+ "n04039381": "racket, racquet",
774
+ "n04040759": "radiator",
775
+ "n04041544": "radio, wireless",
776
+ "n04044716": "radio telescope, radio reflector",
777
+ "n04049303": "rain barrel",
778
+ "n04065272": "recreational vehicle, RV, R.V.",
779
+ "n04067472": "reel",
780
+ "n04069434": "reflex camera",
781
+ "n04070727": "refrigerator, icebox",
782
+ "n04074963": "remote control, remote",
783
+ "n04081281": "restaurant, eating house, eating place, eatery",
784
+ "n04086273": "revolver, six-gun, six-shooter",
785
+ "n04090263": "rifle",
786
+ "n04099969": "rocking chair, rocker",
787
+ "n04111531": "rotisserie",
788
+ "n04116512": "rubber eraser, rubber, pencil eraser",
789
+ "n04118538": "rugby ball",
790
+ "n04118776": "rule, ruler",
791
+ "n04120489": "running shoe",
792
+ "n04125021": "safe",
793
+ "n04127249": "safety pin",
794
+ "n04131690": "saltshaker, salt shaker",
795
+ "n04133789": "sandal",
796
+ "n04136333": "sarong",
797
+ "n04141076": "sax, saxophone",
798
+ "n04141327": "scabbard",
799
+ "n04141975": "scale, weighing machine",
800
+ "n04146614": "school bus",
801
+ "n04147183": "schooner",
802
+ "n04149813": "scoreboard",
803
+ "n04152593": "screen, CRT screen",
804
+ "n04153751": "screw",
805
+ "n04154565": "screwdriver",
806
+ "n04162706": "seat belt, seatbelt",
807
+ "n04179913": "sewing machine",
808
+ "n04192698": "shield, buckler",
809
+ "n04200800": "shoe shop, shoe-shop, shoe store",
810
+ "n04201297": "shoji",
811
+ "n04204238": "shopping basket",
812
+ "n04204347": "shopping cart",
813
+ "n04208210": "shovel",
814
+ "n04209133": "shower cap",
815
+ "n04209239": "shower curtain",
816
+ "n04228054": "ski",
817
+ "n04229816": "ski mask",
818
+ "n04235860": "sleeping bag",
819
+ "n04238763": "slide rule, slipstick",
820
+ "n04239074": "sliding door",
821
+ "n04243546": "slot, one-armed bandit",
822
+ "n04251144": "snorkel",
823
+ "n04252077": "snowmobile",
824
+ "n04252225": "snowplow, snowplough",
825
+ "n04254120": "soap dispenser",
826
+ "n04254680": "soccer ball",
827
+ "n04254777": "sock",
828
+ "n04258138": "solar dish, solar collector, solar furnace",
829
+ "n04259630": "sombrero",
830
+ "n04263257": "soup bowl",
831
+ "n04264628": "space bar",
832
+ "n04265275": "space heater",
833
+ "n04266014": "space shuttle",
834
+ "n04270147": "spatula",
835
+ "n04273569": "speedboat",
836
+ "n04275548": "spider web, spider's web",
837
+ "n04277352": "spindle",
838
+ "n04285008": "sports car, sport car",
839
+ "n04286575": "spotlight, spot",
840
+ "n04296562": "stage",
841
+ "n04310018": "steam locomotive",
842
+ "n04311004": "steel arch bridge",
843
+ "n04311174": "steel drum",
844
+ "n04317175": "stethoscope",
845
+ "n04325704": "stole",
846
+ "n04326547": "stone wall",
847
+ "n04328186": "stopwatch, stop watch",
848
+ "n04330267": "stove",
849
+ "n04332243": "strainer",
850
+ "n04335435": "streetcar, tram, tramcar, trolley, trolley car",
851
+ "n04336792": "stretcher",
852
+ "n04344873": "studio couch, day bed",
853
+ "n04346328": "stupa, tope",
854
+ "n04347754": "submarine, pigboat, sub, U-boat",
855
+ "n04350905": "suit, suit of clothes",
856
+ "n04355338": "sundial",
857
+ "n04355933": "sunglass",
858
+ "n04356056": "sunglasses, dark glasses, shades",
859
+ "n04357314": "sunscreen, sunblock, sun blocker",
860
+ "n04366367": "suspension bridge",
861
+ "n04367480": "swab, swob, mop",
862
+ "n04370456": "sweatshirt",
863
+ "n04371430": "swimming trunks, bathing trunks",
864
+ "n04371774": "swing",
865
+ "n04372370": "switch, electric switch, electrical switch",
866
+ "n04376876": "syringe",
867
+ "n04380533": "table lamp",
868
+ "n04389033": "tank, army tank, armored combat vehicle, armoured combat vehicle",
869
+ "n04392985": "tape player",
870
+ "n04398044": "teapot",
871
+ "n04399382": "teddy, teddy bear",
872
+ "n04404412": "television, television system",
873
+ "n04409515": "tennis ball",
874
+ "n04417672": "thatch, thatched roof",
875
+ "n04418357": "theater curtain, theatre curtain",
876
+ "n04423845": "thimble",
877
+ "n04428191": "thresher, thrasher, threshing machine",
878
+ "n04429376": "throne",
879
+ "n04435653": "tile roof",
880
+ "n04442312": "toaster",
881
+ "n04443257": "tobacco shop, tobacconist shop, tobacconist",
882
+ "n04447861": "toilet seat",
883
+ "n04456115": "torch",
884
+ "n04458633": "totem pole",
885
+ "n04461696": "tow truck, tow car, wrecker",
886
+ "n04462240": "toyshop",
887
+ "n04465501": "tractor",
888
+ "n04467665": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
889
+ "n04476259": "tray",
890
+ "n04479046": "trench coat",
891
+ "n04482393": "tricycle, trike, velocipede",
892
+ "n04483307": "trimaran",
893
+ "n04485082": "tripod",
894
+ "n04486054": "triumphal arch",
895
+ "n04487081": "trolleybus, trolley coach, trackless trolley",
896
+ "n04487394": "trombone",
897
+ "n04493381": "tub, vat",
898
+ "n04501370": "turnstile",
899
+ "n04505470": "typewriter keyboard",
900
+ "n04507155": "umbrella",
901
+ "n04509417": "unicycle, monocycle",
902
+ "n04515003": "upright, upright piano",
903
+ "n04517823": "vacuum, vacuum cleaner",
904
+ "n04522168": "vase",
905
+ "n04523525": "vault",
906
+ "n04525038": "velvet",
907
+ "n04525305": "vending machine",
908
+ "n04532106": "vestment",
909
+ "n04532670": "viaduct",
910
+ "n04536866": "violin, fiddle",
911
+ "n04540053": "volleyball",
912
+ "n04542943": "waffle iron",
913
+ "n04548280": "wall clock",
914
+ "n04548362": "wallet, billfold, notecase, pocketbook",
915
+ "n04550184": "wardrobe, closet, press",
916
+ "n04552348": "warplane, military plane",
917
+ "n04553703": "washbasin, handbasin, washbowl, lavabo, wash-hand basin",
918
+ "n04554684": "washer, automatic washer, washing machine",
919
+ "n04557648": "water bottle",
920
+ "n04560804": "water jug",
921
+ "n04562935": "water tower",
922
+ "n04579145": "whiskey jug",
923
+ "n04579432": "whistle",
924
+ "n04584207": "wig",
925
+ "n04589890": "window screen",
926
+ "n04590129": "window shade",
927
+ "n04591157": "Windsor tie",
928
+ "n04591713": "wine bottle",
929
+ "n04592741": "wing",
930
+ "n04596742": "wok",
931
+ "n04597913": "wooden spoon",
932
+ "n04599235": "wool, woolen, woollen",
933
+ "n04604644": "worm fence, snake fence, snake-rail fence, Virginia fence",
934
+ "n04606251": "wreck",
935
+ "n04612504": "yawl",
936
+ "n04613696": "yurt",
937
+ "n06359193": "web site, website, internet site, site",
938
+ "n06596364": "comic book",
939
+ "n06785654": "crossword puzzle, crossword",
940
+ "n06794110": "street sign",
941
+ "n06874185": "traffic light, traffic signal, stoplight",
942
+ "n07248320": "book jacket, dust cover, dust jacket, dust wrapper",
943
+ "n07565083": "menu",
944
+ "n07579787": "plate",
945
+ "n07583066": "guacamole",
946
+ "n07584110": "consomme",
947
+ "n07590611": "hot pot, hotpot",
948
+ "n07613480": "trifle",
949
+ "n07614500": "ice cream, icecream",
950
+ "n07615774": "ice lolly, lolly, lollipop, popsicle",
951
+ "n07684084": "French loaf",
952
+ "n07693725": "bagel, beigel",
953
+ "n07695742": "pretzel",
954
+ "n07697313": "cheeseburger",
955
+ "n07697537": "hotdog, hot dog, red hot",
956
+ "n07711569": "mashed potato",
957
+ "n07714571": "head cabbage",
958
+ "n07714990": "broccoli",
959
+ "n07715103": "cauliflower",
960
+ "n07716358": "zucchini, courgette",
961
+ "n07716906": "spaghetti squash",
962
+ "n07717410": "acorn squash",
963
+ "n07717556": "butternut squash",
964
+ "n07718472": "cucumber, cuke",
965
+ "n07718747": "artichoke, globe artichoke",
966
+ "n07720875": "bell pepper",
967
+ "n07730033": "cardoon",
968
+ "n07734744": "mushroom",
969
+ "n07742313": "Granny Smith",
970
+ "n07745940": "strawberry",
971
+ "n07747607": "orange",
972
+ "n07749582": "lemon",
973
+ "n07753113": "fig",
974
+ "n07753275": "pineapple, ananas",
975
+ "n07753592": "banana",
976
+ "n07754684": "jackfruit, jak, jack",
977
+ "n07760859": "custard apple",
978
+ "n07768694": "pomegranate",
979
+ "n07802026": "hay",
980
+ "n07831146": "carbonara",
981
+ "n07836838": "chocolate sauce, chocolate syrup",
982
+ "n07860988": "dough",
983
+ "n07871810": "meat loaf, meatloaf",
984
+ "n07873807": "pizza, pizza pie",
985
+ "n07875152": "potpie",
986
+ "n07880968": "burrito",
987
+ "n07892512": "red wine",
988
+ "n07920052": "espresso",
989
+ "n07930864": "cup",
990
+ "n07932039": "eggnog",
991
+ "n09193705": "alp",
992
+ "n09229709": "bubble",
993
+ "n09246464": "cliff, drop, drop-off",
994
+ "n09256479": "coral reef",
995
+ "n09288635": "geyser",
996
+ "n09332890": "lakeside, lakeshore",
997
+ "n09399592": "promontory, headland, head, foreland",
998
+ "n09421951": "sandbar, sand bar",
999
+ "n09428293": "seashore, coast, seacoast, sea-coast",
1000
+ "n09468604": "valley, vale",
1001
+ "n09472597": "volcano",
1002
+ "n09835506": "ballplayer, baseball player",
1003
+ "n10148035": "groom, bridegroom",
1004
+ "n10565667": "scuba diver",
1005
+ "n11879895": "rapeseed",
1006
+ "n11939491": "daisy",
1007
+ "n12057211": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
1008
+ "n12144580": "corn",
1009
+ "n12267677": "acorn",
1010
+ "n12620546": "hip, rose hip, rosehip",
1011
+ "n12768682": "buckeye, horse chestnut, conker",
1012
+ "n12985857": "coral fungus",
1013
+ "n12998815": "agaric",
1014
+ "n13037406": "gyromitra",
1015
+ "n13040303": "stinkhorn, carrion fungus",
1016
+ "n13044778": "earthstar",
1017
+ "n13052670": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
1018
+ "n13054560": "bolete",
1019
+ "n13133613": "ear, spike, capitulum",
1020
+ "n15075141": "toilet tissue, toilet paper, bathroom tissue",
1021
+ }
1022
+ )
imagenet-1k.py ADDED
@@ -0,0 +1,113 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 the HuggingFace Datasets Authors.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ import os
17
+
18
+ import datasets
19
+ from datasets.tasks import ImageClassification
20
+
21
+ from .classes import IMAGENET2012_CLASSES
22
+
23
+
24
+ _CITATION = """\
25
+ @article{imagenet15russakovsky,
26
+ 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},
27
+ Title = { {ImageNet Large Scale Visual Recognition Challenge} },
28
+ Year = {2015},
29
+ journal = {International Journal of Computer Vision (IJCV)},
30
+ doi = {10.1007/s11263-015-0816-y},
31
+ volume={115},
32
+ number={3},
33
+ pages={211-252}
34
+ }
35
+ """
36
+
37
+ _HOMEPAGE = "https://image-net.org/index.php"
38
+
39
+ _DESCRIPTION = """\
40
+ ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images
41
+ """
42
+
43
+ _DATA_URL = {
44
+ "train": [f"data/train_images_{i}.tar.gz" for i in range(5)],
45
+ "val": ["data/val_images.tar.gz"],
46
+ "test": ["data/test_images.tar.gz"],
47
+ }
48
+
49
+
50
+ class Imagenet1k(datasets.GeneratorBasedBuilder):
51
+ VERSION = datasets.Version("1.0.0")
52
+
53
+ DEFAULT_WRITER_BATCH_SIZE = 1000
54
+
55
+ def _info(self):
56
+ assert len(IMAGENET2012_CLASSES) == 1000
57
+ return datasets.DatasetInfo(
58
+ description=_DESCRIPTION,
59
+ features=datasets.Features(
60
+ {
61
+ "image": datasets.Image(),
62
+ "label": datasets.ClassLabel(names=list(IMAGENET2012_CLASSES.values())),
63
+ }
64
+ ),
65
+ homepage=_HOMEPAGE,
66
+ citation=_CITATION,
67
+ task_templates=[ImageClassification(image_column="image", label_column="label")],
68
+ )
69
+
70
+ def _split_generators(self, dl_manager):
71
+ """Returns SplitGenerators."""
72
+ archives = dl_manager.download(_DATA_URL)
73
+
74
+ return [
75
+ datasets.SplitGenerator(
76
+ name=datasets.Split.TRAIN,
77
+ gen_kwargs={
78
+ "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
79
+ "split": "train",
80
+ },
81
+ ),
82
+ datasets.SplitGenerator(
83
+ name=datasets.Split.VALIDATION,
84
+ gen_kwargs={
85
+ "archives": [dl_manager.iter_archive(archive) for archive in archives["val"]],
86
+ "split": "validation",
87
+ },
88
+ ),
89
+ datasets.SplitGenerator(
90
+ name=datasets.Split.TEST,
91
+ gen_kwargs={
92
+ "archives": [dl_manager.iter_archive(archive) for archive in archives["test"]],
93
+ "split": "test",
94
+ },
95
+ ),
96
+ ]
97
+
98
+ def _generate_examples(self, archives, split):
99
+ """Yields examples."""
100
+ idx = 0
101
+ for archive in archives:
102
+ for path, file in archive:
103
+ if path.endswith(".JPEG"):
104
+ if split != "test":
105
+ # image filepath format: <IMAGE_FILENAME>_<SYNSET_ID>.JPEG
106
+ root, _ = os.path.splitext(path)
107
+ _, synset_id = os.path.basename(root).rsplit("_", 1)
108
+ label = IMAGENET2012_CLASSES[synset_id]
109
+ else:
110
+ label = -1
111
+ ex = {"image": {"path": path, "bytes": file.read()}, "label": label}
112
+ yield idx, ex
113
+ idx += 1
val_images.tar.gz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3b2c352b8b39579e10d5904bb0a6ec3ac807199ea0f6a7be0559a89bf44620f7
3
+ size 6667094731