顔の画像認識で認知症判別⁇【コラム】 ＃005
こんにちは！こーたろーです。
今日のコラムは、昨日ledge AIのニュース記事であった気になる話についてです。
AIの画像認識は、応用に向けて益々加速しているイメージが強いですが、医療への応用はとても気になります。
診察も医師のAIに代替できるところがあると、医療も進んでいきますね。
続きを読む
BigGANで画像生成やってみた！【図解速習DeepLearning】#010
こんにちは！こーたろーです。
本日も【図解速習DEEP LEARNING】やっていきます。
本日はBIG-GANやります。
BIG-GANは2018年にDeepMindのA.Brockらが提案したもので、バリエーションが多く複雑なデータセットを使って作られたもので、過去最大規模で学習させたモデルです。
参考図書にならって、ImageNetの1000カテゴリーの画像の生成を行います。（※一部だけやります）
いろいろバラメータを変更することで、加工可能なソースにしていますので、よかったら皆さんも試してみてください。
これまでで一番サンプルコードを修正した気がします。
疲れた・・・
そのうち、どういうエラーがでて、どう対処したかをまとめていきたいと思っています。
１．BIG-GANのモデルpathを設定
以下の提供されているものを使用します。※お好きなものを選んでみてください。
今回は256×256のBigGANを使用します。
# module_path = 'https://tfhub.dev/deepmind/biggan-128/2' # 128x128 BigGAN module_path = 'https://tfhub.dev/deepmind/biggan-256/2' # 256x256 BigGAN # module_path = 'https://tfhub.dev/deepmind/biggan-512/2' # 512x512 BigGAN
２．ライブラリーのインポート
1行目は、「from io import StringIO」ではなく、「from io import BytesIO」としてください。
Python3では、対応していないので、サンプル通りにしてもエラーが返ってきます。
それと、「eager_execution()」も「disable」にしておきましょう。
from io import BytesIO import IPython.display import numpy as np import PIL.Image from scipy.stats import truncnorm import tensorflow as tf import tensorflow_hub as hub tf.compat.v1.disable_eager_execution()
３．TF_HubからGigGANジェネレータモジュールを読み込む
tf.compat.v1.reset_default_graph() print ('Loading BigGAN module from:', module_path) module = hub.Module(module_path) inputs = {k: tf.compat.v1.placeholder(v.dtype, v.get_shape().as_list(), k) for k, v in module.get_input_info_dict().items()} output = module(inputs) print print ('Inputs:\n', '\n'.join( ' {}: {}'.format(*kv) for kv in inputs.items())) print print ('Output:', output)
４．BigGAN生成画像の表示用関数を定義
input_z = inputs['z'] input_y = inputs['y'] input_trunc = inputs['truncation'] dim_z = input_z.shape.as_list()[1] vocab_size = input_y.shape.as_list()[1] def truncated_z_sample(batch_size, truncation=1., seed=None): state = None if seed is None else np.random.RandomState(seed) values = truncnorm.rvs(-2, 2, size=(batch_size, dim_z), random_state=state) return truncation * values def one_hot(index, vocab_size=vocab_size): index = np.asarray(index) if len(index.shape) == 0: index = np.asarray([index]) assert len(index.shape) == 1 num = index.shape[0] output = np.zeros((num, vocab_size), dtype=np.float32) output[np.arange(num), index] = 1 return output def one_hot_if_needed(label, vocab_size=vocab_size): label = np.asarray(label) if len(label.shape) <= 1: label = one_hot(label, vocab_size) assert len(label.shape) == 2 return label def sample(sess, noise, label, truncation=1., batch_size=4, vocab_size=vocab_size): noise = np.asarray(noise) label = np.asarray(label) num = noise.shape[0] if len(label.shape) == 0: label = np.asarray([label] * num) if label.shape[0] != num: raise ValueError('Got # noise samples ({}) != # label samples ({})' .format(noise.shape[0], label.shape[0])) label = one_hot_if_needed(label, vocab_size) ims = [] for batch_start in range(0, num, batch_size): s = slice(batch_start, min(num, batch_start + batch_size)) feed_dict = {input_z: noise[s], input_y: label[s], input_trunc: truncation} ims.append(sess.run(output, feed_dict=feed_dict)) ims = np.concatenate(ims, axis=0) assert ims.shape[0] == num ims = np.clip(((ims + 1) / 2.0) * 256, 0, 255) ims = np.uint8(ims) return ims def interpolate(A, B, num_interps): alphas = np.linspace(0, 1, num_interps) if A.shape != B.shape: raise ValueError('A and B must have the same shape to interpolate.') return np.array([(1-a)*A + a*B for a in alphas]) def imgrid(imarray, cols=5, pad=1): if imarray.dtype != np.uint8: raise ValueError('imgrid input imarray must be uint8') pad = int(pad) assert pad >= 0 cols = int(cols) assert cols >= 1 N, H, W, C = imarray.shape rows = int(np.ceil(N / float(cols))) batch_pad = rows * cols - N assert batch_pad >= 0 post_pad = [batch_pad, pad, pad, 0] pad_arg = [[0, p] for p in post_pad] imarray = np.pad(imarray, pad_arg, 'constant', constant_values=255) H += pad W += pad grid = (imarray .reshape(rows, cols, H, W, C) .transpose(0, 2, 1, 3, 4) .reshape(rows*H, cols*W, C)) if pad: grid = grid[:-pad, :-pad] return grid def imshow(a, format='png', jpeg_fallback=True): a = np.asarray(a, dtype=np.uint8) str_file = BytesIO() PIL.Image.fromarray(a).save(str_file, format) im_data = str_file.getvalue() try: disp = IPython.display.display(IPython.display.Image(im_data)) except IOError: if jpeg_fallback and format != 'jpeg': print ('Warning: image was too large to display in format "{}"; ' 'trying jpeg instead.').format(format) return imshow(a, format='jpeg') else: raise return disp
５．Tensorflowセッションを作成、変数を初期化
initializer = tf.compat.v1.global_variables_initializer() sess = tf.compat.v1.Session() sess.run(initializer)
６．カテゴリを決めるBigGAN画像を生成
#@title Category-conditional sampling { display-mode: "form", run: "auto" } num_samples = 10 #@param {type:"slider", min:1, max:20, step:1} truncation = 0.4 #@param {type:"slider", min:0.02, max:1, step:0.02} noise_seed = 0 #@param {type:"slider", min:0, max:100, step:1} category = "933) cheeseburger" #@param ["0) tench, Tinca tinca", "1) goldfish, Carassius auratus", "2) great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3) tiger shark, Galeocerdo cuvieri", "4) hammerhead, hammerhead shark", "5) electric ray, crampfish, numbfish, torpedo", "6) stingray", "7) cock", "8) hen", "9) ostrich, Struthio camelus", "10) brambling, Fringilla montifringilla", "11) goldfinch, Carduelis carduelis", "12) house finch, linnet, Carpodacus mexicanus", "13) junco, snowbird", "14) indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15) robin, American robin, Turdus migratorius", "16) bulbul", "17) jay", "18) magpie", "19) chickadee", "20) water ouzel, dipper", "21) kite", "22) bald eagle, American eagle, Haliaeetus leucocephalus", "23) vulture", "24) great grey owl, great gray owl, Strix nebulosa", "25) European fire salamander, Salamandra salamandra", "26) common newt, Triturus vulgaris", "27) eft", "28) spotted salamander, Ambystoma maculatum", "29) axolotl, mud puppy, Ambystoma mexicanum", "30) bullfrog, Rana catesbeiana", "31) tree frog, tree-frog", "32) tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33) loggerhead, loggerhead turtle, Caretta caretta", "34) leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35) mud turtle", "36) terrapin", "37) box turtle, box tortoise", "38) banded gecko", "39) common iguana, iguana, Iguana iguana", "40) American chameleon, anole, Anolis carolinensis", "41) whiptail, whiptail lizard", "42) agama", "43) frilled lizard, Chlamydosaurus kingi", "44) alligator lizard", "45) Gila monster, Heloderma suspectum", "46) green lizard, Lacerta viridis", "47) African chameleon, Chamaeleo chamaeleon", "48) Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49) African crocodile, Nile crocodile, Crocodylus niloticus", "50) American alligator, Alligator mississipiensis", "51) triceratops", "52) thunder snake, worm snake, Carphophis amoenus", "53) ringneck snake, ring-necked snake, ring snake", "54) hognose snake, puff adder, sand viper", "55) green snake, grass snake", "56) king snake, kingsnake", "57) garter snake, grass snake", "58) water snake", "59) vine snake", "60) night snake, Hypsiglena torquata", "61) boa constrictor, Constrictor constrictor", "62) rock python, rock snake, Python sebae", "63) Indian cobra, Naja naja", "64) green mamba", "65) sea snake", "66) horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67) diamondback, diamondback rattlesnake, Crotalus adamanteus", "68) sidewinder, horned rattlesnake, Crotalus cerastes", "69) trilobite", "70) harvestman, daddy longlegs, Phalangium opilio", "71) scorpion", "72) black and gold garden spider, Argiope aurantia", "73) barn spider, Araneus cavaticus", "74) garden spider, Aranea diademata", "75) black widow, Latrodectus mactans", "76) tarantula", "77) wolf spider, hunting spider", "78) tick", "79) centipede", "80) black grouse", "81) ptarmigan", "82) ruffed grouse, partridge, Bonasa umbellus", "83) prairie chicken, prairie grouse, prairie fowl", "84) peacock", "85) quail", "86) partridge", "87) African grey, African gray, Psittacus erithacus", "88) macaw", "89) sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90) lorikeet", "91) coucal", "92) bee eater", "93) hornbill", "94) hummingbird", "95) jacamar", "96) toucan", "97) drake", "98) red-breasted merganser, Mergus serrator", "99) goose", "100) black swan, Cygnus atratus", "101) tusker", "102) echidna, spiny anteater, anteater", "103) platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104) wallaby, brush kangaroo", "105) koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106) wombat", "107) jellyfish", "108) sea anemone, anemone", "109) brain coral", "110) flatworm, platyhelminth", "111) nematode, nematode worm, roundworm", "112) conch", "113) snail", "114) slug", "115) sea slug, nudibranch", "116) chiton, coat-of-mail shell, sea cradle, polyplacophore", "117) chambered nautilus, pearly nautilus, nautilus", "118) Dungeness crab, Cancer magister", "119) rock crab, Cancer irroratus", "120) fiddler crab", "121) king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122) American lobster, Northern lobster, Maine lobster, Homarus americanus", "123) spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124) crayfish, crawfish, crawdad, crawdaddy", "125) hermit crab", "126) isopod", "127) white stork, Ciconia ciconia", "128) black stork, Ciconia nigra", "129) spoonbill", "130) flamingo", "131) little blue heron, Egretta caerulea", "132) American egret, great white heron, Egretta albus", "133) bittern", "134) crane", "135) limpkin, Aramus pictus", "136) European gallinule, Porphyrio porphyrio", "137) American coot, marsh hen, mud hen, water hen, Fulica americana", "138) bustard", "139) ruddy turnstone, Arenaria interpres", "140) red-backed sandpiper, dunlin, Erolia alpina", "141) redshank, Tringa totanus", "142) dowitcher", "143) oystercatcher, oyster catcher", "144) pelican", "145) king penguin, Aptenodytes patagonica", "146) albatross, mollymawk", "147) grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148) killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149) dugong, Dugong dugon", "150) sea lion", "151) Chihuahua", "152) Japanese spaniel", "153) Maltese dog, Maltese terrier, Maltese", "154) Pekinese, Pekingese, Peke", "155) Shih-Tzu", "156) Blenheim spaniel", "157) papillon", "158) toy terrier", "159) Rhodesian ridgeback", "160) Afghan hound, Afghan", "161) basset, basset hound", "162) beagle", "163) bloodhound, sleuthhound", "164) bluetick", "165) black-and-tan coonhound", "166) Walker hound, Walker foxhound", "167) English foxhound", "168) redbone", "169) borzoi, Russian wolfhound", "170) Irish wolfhound", "171) Italian greyhound", "172) whippet", "173) Ibizan hound, Ibizan Podenco", "174) Norwegian elkhound, elkhound", "175) otterhound, otter hound", "176) Saluki, gazelle hound", "177) Scottish deerhound, deerhound", "178) Weimaraner", "179) Staffordshire bullterrier, Staffordshire bull terrier", "180) American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181) Bedlington terrier", "182) Border terrier", "183) Kerry blue terrier", "184) Irish terrier", "185) Norfolk terrier", "186) Norwich terrier", "187) Yorkshire terrier", "188) wire-haired fox terrier", "189) Lakeland terrier", "190) Sealyham terrier, Sealyham", "191) Airedale, Airedale terrier", "192) cairn, cairn terrier", "193) Australian terrier", "194) Dandie Dinmont, Dandie Dinmont terrier", "195) Boston bull, Boston terrier", "196) miniature schnauzer", "197) giant schnauzer", "198) standard schnauzer", "199) Scotch terrier, Scottish terrier, Scottie", "200) Tibetan terrier, chrysanthemum dog", "201) silky terrier, Sydney silky", "202) soft-coated wheaten terrier", "203) West Highland white terrier", "204) Lhasa, Lhasa apso", "205) flat-coated retriever", "206) curly-coated retriever", "207) golden retriever", "208) Labrador retriever", "209) Chesapeake Bay retriever", "210) German short-haired pointer", "211) vizsla, Hungarian pointer", "212) English setter", "213) Irish setter, red setter", "214) Gordon setter", "215) Brittany spaniel", "216) clumber, clumber spaniel", "217) English springer, English springer spaniel", "218) Welsh springer spaniel", "219) cocker spaniel, English cocker spaniel, cocker", "220) Sussex spaniel", "221) Irish water spaniel", "222) kuvasz", "223) schipperke", "224) groenendael", "225) malinois", "226) briard", "227) kelpie", "228) komondor", "229) Old English sheepdog, bobtail", "230) Shetland sheepdog, Shetland sheep dog, Shetland", "231) collie", "232) Border collie", "233) Bouvier des Flandres, Bouviers des Flandres", "234) Rottweiler", "235) German shepherd, German shepherd dog, German police dog, alsatian", "236) Doberman, Doberman pinscher", "237) miniature pinscher", "238) Greater Swiss Mountain dog", "239) Bernese mountain dog", "240) Appenzeller", "241) EntleBucher", "242) boxer", "243) bull mastiff", "244) Tibetan mastiff", "245) French bulldog", "246) Great Dane", "247) Saint Bernard, St Bernard", "248) Eskimo dog, husky", "249) malamute, malemute, Alaskan malamute", "250) Siberian husky", "251) dalmatian, coach dog, carriage dog", "252) affenpinscher, monkey pinscher, monkey dog", "253) basenji", "254) pug, pug-dog", "255) Leonberg", "256) Newfoundland, Newfoundland dog", "257) Great Pyrenees", "258) Samoyed, Samoyede", "259) Pomeranian", "260) chow, chow chow", "261) keeshond", "262) Brabancon griffon", "263) Pembroke, Pembroke Welsh corgi", "264) Cardigan, Cardigan Welsh corgi", "265) toy poodle", "266) miniature poodle", "267) standard poodle", "268) Mexican hairless", "269) timber wolf, grey wolf, gray wolf, Canis lupus", "270) white wolf, Arctic wolf, Canis lupus tundrarum", "271) red wolf, maned wolf, Canis rufus, Canis niger", "272) coyote, prairie wolf, brush wolf, Canis latrans", "273) dingo, warrigal, warragal, Canis dingo", "274) dhole, Cuon alpinus", "275) African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276) hyena, hyaena", "277) red fox, Vulpes vulpes", "278) kit fox, Vulpes macrotis", "279) Arctic fox, white fox, Alopex lagopus", "280) grey fox, gray fox, Urocyon cinereoargenteus", "281) tabby, tabby cat", "282) tiger cat", "283) Persian cat", "284) Siamese cat, Siamese", "285) Egyptian cat", "286) cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287) lynx, catamount", "288) leopard, Panthera pardus", "289) snow leopard, ounce, Panthera uncia", "290) jaguar, panther, Panthera onca, Felis onca", "291) lion, king of beasts, Panthera leo", "292) tiger, Panthera tigris", "293) cheetah, chetah, Acinonyx jubatus", "294) brown bear, bruin, Ursus arctos", "295) American black bear, black bear, Ursus americanus, Euarctos americanus", "296) ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297) sloth bear, Melursus ursinus, Ursus ursinus", "298) mongoose", "299) meerkat, mierkat", "300) tiger beetle", "301) ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302) ground beetle, carabid beetle", "303) long-horned beetle, longicorn, longicorn beetle", "304) leaf beetle, chrysomelid", "305) dung beetle", "306) rhinoceros beetle", "307) weevil", "308) fly", "309) bee", "310) ant, emmet, pismire", "311) grasshopper, hopper", "312) cricket", "313) walking stick, walkingstick, stick insect", "314) cockroach, roach", "315) mantis, mantid", "316) cicada, cicala", "317) leafhopper", "318) lacewing, lacewing fly", "319) dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320) damselfly", "321) admiral", "322) ringlet, ringlet butterfly", "323) monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324) cabbage butterfly", "325) sulphur butterfly, sulfur butterfly", "326) lycaenid, lycaenid butterfly", "327) starfish, sea star", "328) sea urchin", "329) sea cucumber, holothurian", "330) wood rabbit, cottontail, cottontail rabbit", "331) hare", "332) Angora, Angora rabbit", "333) hamster", "334) porcupine, hedgehog", "335) fox squirrel, eastern fox squirrel, Sciurus niger", "336) marmot", "337) beaver", "338) guinea pig, Cavia cobaya", "339) sorrel", "340) zebra", "341) hog, pig, grunter, squealer, Sus scrofa", "342) wild boar, boar, Sus scrofa", "343) warthog", "344) hippopotamus, hippo, river horse, Hippopotamus amphibius", "345) ox", "346) water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347) bison", "348) ram, tup", "349) bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350) ibex, Capra ibex", "351) hartebeest", "352) impala, Aepyceros melampus", "353) gazelle", "354) Arabian camel, dromedary, Camelus dromedarius", "355) llama", "356) weasel", "357) mink", "358) polecat, fitch, foulmart, foumart, Mustela putorius", "359) black-footed ferret, ferret, Mustela nigripes", "360) otter", "361) skunk, polecat, wood pussy", "362) badger", "363) armadillo", "364) three-toed sloth, ai, Bradypus tridactylus", "365) orangutan, orang, orangutang, Pongo pygmaeus", "366) gorilla, Gorilla gorilla", "367) chimpanzee, chimp, Pan troglodytes", "368) gibbon, Hylobates lar", "369) siamang, Hylobates syndactylus, Symphalangus syndactylus", "370) guenon, guenon monkey", "371) patas, hussar monkey, Erythrocebus patas", "372) baboon", "373) macaque", "374) langur", "375) colobus, colobus monkey", "376) proboscis monkey, Nasalis larvatus", "377) marmoset", "378) capuchin, ringtail, Cebus capucinus", "379) howler monkey, howler", "380) titi, titi monkey", "381) spider monkey, Ateles geoffroyi", "382) squirrel monkey, Saimiri sciureus", "383) Madagascar cat, ring-tailed lemur, Lemur catta", "384) indri, indris, Indri indri, Indri brevicaudatus", "385) Indian elephant, Elephas maximus", "386) African elephant, Loxodonta africana", "387) lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388) giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389) barracouta, snoek", "390) eel", "391) coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392) rock beauty, Holocanthus tricolor", "393) anemone fish", "394) sturgeon", "395) gar, garfish, garpike, billfish, Lepisosteus osseus", "396) lionfish", "397) puffer, pufferfish, blowfish, globefish", "398) abacus", "399) abaya", "400) academic gown, academic robe, judge's robe", "401) accordion, piano accordion, squeeze box", "402) acoustic guitar", "403) aircraft carrier, carrier, flattop, attack aircraft carrier", "404) airliner", "405) airship, dirigible", "406) altar", "407) ambulance", "408) amphibian, amphibious vehicle", "409) analog clock", "410) apiary, bee house", "411) apron", "412) ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413) assault rifle, assault gun", "414) backpack, back pack, knapsack, packsack, rucksack, haversack", "415) bakery, bakeshop, bakehouse", "416) balance beam, beam", "417) balloon", "418) ballpoint, ballpoint pen, ballpen, Biro", "419) Band Aid", "420) banjo", "421) bannister, banister, balustrade, balusters, handrail", "422) barbell", "423) barber chair", "424) barbershop", "425) barn", "426) barometer", "427) barrel, cask", "428) barrow, garden cart, lawn cart, wheelbarrow", "429) baseball", "430) basketball", "431) bassinet", "432) bassoon", "433) bathing cap, swimming cap", "434) bath towel", "435) bathtub, bathing tub, bath, tub", "436) beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437) beacon, lighthouse, beacon light, pharos", "438) beaker", "439) bearskin, busby, shako", "440) beer bottle", "441) beer glass", "442) bell cote, bell cot", "443) bib", "444) bicycle-built-for-two, tandem bicycle, tandem", "445) bikini, two-piece", "446) binder, ring-binder", "447) binoculars, field glasses, opera glasses", "448) birdhouse", "449) boathouse", "450) bobsled, bobsleigh, bob", "451) bolo tie, bolo, bola tie, bola", "452) bonnet, poke bonnet", "453) bookcase", "454) bookshop, bookstore, bookstall", "455) bottlecap", "456) bow", "457) bow tie, bow-tie, bowtie", "458) brass, memorial tablet, plaque", "459) brassiere, bra, bandeau", "460) breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461) breastplate, aegis, egis", "462) broom", "463) bucket, pail", "464) buckle", "465) bulletproof vest", "466) bullet train, bullet", "467) butcher shop, meat market", "468) cab, hack, taxi, taxicab", "469) caldron, cauldron", "470) candle, taper, wax light", "471) cannon", "472) canoe", "473) can opener, tin opener", "474) cardigan", "475) car mirror", "476) carousel, carrousel, merry-go-round, roundabout, whirligig", "477) carpenter's kit, tool kit", "478) carton", "479) car wheel", "480) cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481) cassette", "482) cassette player", "483) castle", "484) catamaran", "485) CD player", "486) cello, violoncello", "487) cellular telephone, cellular phone, cellphone, cell, mobile phone", "488) chain", "489) chainlink fence", "490) chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491) chain saw, chainsaw", "492) chest", "493) chiffonier, commode", "494) chime, bell, gong", "495) china cabinet, china closet", "496) Christmas stocking", "497) church, church building", "498) cinema, movie theater, movie theatre, movie house, picture palace", "499) cleaver, meat cleaver, chopper", "500) cliff dwelling", "501) cloak", "502) clog, geta, patten, sabot", "503) cocktail shaker", "504) coffee mug", "505) coffeepot", "506) coil, spiral, volute, whorl, helix", "507) combination lock", "508) computer keyboard, keypad", "509) confectionery, confectionary, candy store", "510) container ship, containership, container vessel", "511) convertible", "512) corkscrew, bottle screw", "513) cornet, horn, trumpet, trump", "514) cowboy boot", "515) cowboy hat, ten-gallon hat", "516) cradle", "517) crane", "518) crash helmet", "519) crate", "520) crib, cot", "521) Crock Pot", "522) croquet ball", "523) crutch", "524) cuirass", "525) dam, dike, dyke", "526) desk", "527) desktop computer", "528) dial telephone, dial phone", "529) diaper, nappy, napkin", "530) digital clock", "531) digital watch", "532) dining table, board", "533) dishrag, dishcloth", "534) dishwasher, dish washer, dishwashing machine", "535) disk brake, disc brake", "536) dock, dockage, docking facility", "537) dogsled, dog sled, dog sleigh", "538) dome", "539) doormat, welcome mat", "540) drilling platform, offshore rig", "541) drum, membranophone, tympan", "542) drumstick", "543) dumbbell", "544) Dutch oven", "545) electric fan, blower", "546) electric guitar", "547) electric locomotive", "548) entertainment center", "549) envelope", "550) espresso maker", "551) face powder", "552) feather boa, boa", "553) file, file cabinet, filing cabinet", "554) fireboat", "555) fire engine, fire truck", "556) fire screen, fireguard", "557) flagpole, flagstaff", "558) flute, transverse flute", "559) folding chair", "560) football helmet", "561) forklift", "562) fountain", "563) fountain pen", "564) four-poster", "565) freight car", "566) French horn, horn", "567) frying pan, frypan, skillet", "568) fur coat", "569) garbage truck, dustcart", "570) gasmask, respirator, gas helmet", "571) gas pump, gasoline pump, petrol pump, island dispenser", "572) goblet", "573) go-kart", "574) golf ball", "575) golfcart, golf cart", "576) gondola", "577) gong, tam-tam", "578) gown", "579) grand piano, grand", "580) greenhouse, nursery, glasshouse", "581) grille, radiator grille", "582) grocery store, grocery, food market, market", "583) guillotine", "584) hair slide", "585) hair spray", "586) half track", "587) hammer", "588) hamper", "589) hand blower, blow dryer, blow drier, hair dryer, hair drier", "590) hand-held computer, hand-held microcomputer", "591) handkerchief, hankie, hanky, hankey", "592) hard disc, hard disk, fixed disk", "593) harmonica, mouth organ, harp, mouth harp", "594) harp", "595) harvester, reaper", "596) hatchet", "597) holster", "598) home theater, home theatre", "599) honeycomb", "600) hook, claw", "601) hoopskirt, crinoline", "602) horizontal bar, high bar", "603) horse cart, horse-cart", "604) hourglass", "605) iPod", "606) iron, smoothing iron", "607) jack-o'-lantern", "608) jean, blue jean, denim", "609) jeep, landrover", "610) jersey, T-shirt, tee shirt", "611) jigsaw puzzle", "612) jinrikisha, ricksha, rickshaw", "613) joystick", "614) kimono", "615) knee pad", "616) knot", "617) lab coat, laboratory coat", "618) ladle", "619) lampshade, lamp shade", "620) laptop, laptop computer", "621) lawn mower, mower", "622) lens cap, lens cover", "623) letter opener, paper knife, paperknife", "624) library", "625) lifeboat", "626) lighter, light, igniter, ignitor", "627) limousine, limo", "628) liner, ocean liner", "629) lipstick, lip rouge", "630) Loafer", "631) lotion", "632) loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633) loupe, jeweler's loupe", "634) lumbermill, sawmill", "635) magnetic compass", "636) mailbag, postbag", "637) mailbox, letter box", "638) maillot", "639) maillot, tank suit", "640) manhole cover", "641) maraca", "642) marimba, xylophone", "643) mask", "644) matchstick", "645) maypole", "646) maze, labyrinth", "647) measuring cup", "648) medicine chest, medicine cabinet", "649) megalith, megalithic structure", "650) microphone, mike", "651) microwave, microwave oven", "652) military uniform", "653) milk can", "654) minibus", "655) miniskirt, mini", "656) minivan", "657) missile", "658) mitten", "659) mixing bowl", "660) mobile home, manufactured home", "661) Model T", "662) modem", "663) monastery", "664) monitor", "665) moped", "666) mortar", "667) mortarboard", "668) mosque", "669) mosquito net", "670) motor scooter, scooter", "671) mountain bike, all-terrain bike, off-roader", "672) mountain tent", "673) mouse, computer mouse", "674) mousetrap", "675) moving van", "676) muzzle", "677) nail", "678) neck brace", "679) necklace", "680) nipple", "681) notebook, notebook computer", "682) obelisk", "683) oboe, hautboy, hautbois", "684) ocarina, sweet potato", "685) odometer, hodometer, mileometer, milometer", "686) oil filter", "687) organ, pipe organ", "688) oscilloscope, scope, cathode-ray oscilloscope, CRO", "689) overskirt", "690) oxcart", "691) oxygen mask", "692) packet", "693) paddle, boat paddle", "694) paddlewheel, paddle wheel", "695) padlock", "696) paintbrush", "697) pajama, pyjama, pj's, jammies", "698) palace", "699) panpipe, pandean pipe, syrinx", "700) paper towel", "701) parachute, chute", "702) parallel bars, bars", "703) park bench", "704) parking meter", "705) passenger car, coach, carriage", "706) patio, terrace", "707) pay-phone, pay-station", "708) pedestal, plinth, footstall", "709) pencil box, pencil case", "710) pencil sharpener", "711) perfume, essence", "712) Petri dish", "713) photocopier", "714) pick, plectrum, plectron", "715) pickelhaube", "716) picket fence, paling", "717) pickup, pickup truck", "718) pier", "719) piggy bank, penny bank", "720) pill bottle", "721) pillow", "722) ping-pong ball", "723) pinwheel", "724) pirate, pirate ship", "725) pitcher, ewer", "726) plane, carpenter's plane, woodworking plane", "727) planetarium", "728) plastic bag", "729) plate rack", "730) plow, plough", "731) plunger, plumber's helper", "732) Polaroid camera, Polaroid Land camera", "733) pole", "734) police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735) poncho", "736) pool table, billiard table, snooker table", "737) pop bottle, soda bottle", "738) pot, flowerpot", "739) potter's wheel", "740) power drill", "741) prayer rug, prayer mat", "742) printer", "743) prison, prison house", "744) projectile, missile", "745) projector", "746) puck, hockey puck", "747) punching bag, punch bag, punching ball, punchball", "748) purse", "749) quill, quill pen", "750) quilt, comforter, comfort, puff", "751) racer, race car, racing car", "752) racket, racquet", "753) radiator", "754) radio, wireless", "755) radio telescope, radio reflector", "756) rain barrel", "757) recreational vehicle, RV, R.V.", "758) reel", "759) reflex camera", "760) refrigerator, icebox", "761) remote control, remote", "762) restaurant, eating house, eating place, eatery", "763) revolver, six-gun, six-shooter", "764) rifle", "765) rocking chair, rocker", "766) rotisserie", "767) rubber eraser, rubber, pencil eraser", "768) rugby ball", "769) rule, ruler", "770) running shoe", "771) safe", "772) safety pin", "773) saltshaker, salt shaker", "774) sandal", "775) sarong", "776) sax, saxophone", "777) scabbard", "778) scale, weighing machine", "779) school bus", "780) schooner", "781) scoreboard", "782) screen, CRT screen", "783) screw", "784) screwdriver", "785) seat belt, seatbelt", "786) sewing machine", "787) shield, buckler", "788) shoe shop, shoe-shop, shoe store", "789) shoji", "790) shopping basket", "791) shopping cart", "792) shovel", "793) shower cap", "794) shower curtain", "795) ski", "796) ski mask", "797) sleeping bag", "798) slide rule, slipstick", "799) sliding door", "800) slot, one-armed bandit", "801) snorkel", "802) snowmobile", "803) snowplow, snowplough", "804) soap dispenser", "805) soccer ball", "806) sock", "807) solar dish, solar collector, solar furnace", "808) sombrero", "809) soup bowl", "810) space bar", "811) space heater", "812) space shuttle", "813) spatula", "814) speedboat", "815) spider web, spider's web", "816) spindle", "817) sports car, sport car", "818) spotlight, spot", "819) stage", "820) steam locomotive", "821) steel arch bridge", "822) steel drum", "823) stethoscope", "824) stole", "825) stone wall", "826) stopwatch, stop watch", "827) stove", "828) strainer", "829) streetcar, tram, tramcar, trolley, trolley car", "830) stretcher", "831) studio couch, day bed", "832) stupa, tope", "833) submarine, pigboat, sub, U-boat", "834) suit, suit of clothes", "835) sundial", "836) sunglass", "837) sunglasses, dark glasses, shades", "838) sunscreen, sunblock, sun blocker", "839) suspension bridge", "840) swab, swob, mop", "841) sweatshirt", "842) swimming trunks, bathing trunks", "843) swing", "844) switch, electric switch, electrical switch", "845) syringe", "846) table lamp", "847) tank, army tank, armored combat vehicle, armoured combat vehicle", "848) tape player", "849) teapot", "850) teddy, teddy bear", "851) television, television system", "852) tennis ball", "853) thatch, thatched roof", "854) theater curtain, theatre curtain", "855) thimble", "856) thresher, thrasher, threshing machine", "857) throne", "858) tile roof", "859) toaster", "860) tobacco shop, tobacconist shop, tobacconist", "861) toilet seat", "862) torch", "863) totem pole", "864) tow truck, tow car, wrecker", "865) toyshop", "866) tractor", "867) trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868) tray", "869) trench coat", "870) tricycle, trike, velocipede", "871) trimaran", "872) tripod", "873) triumphal arch", "874) trolleybus, trolley coach, trackless trolley", "875) trombone", "876) tub, vat", "877) turnstile", "878) typewriter keyboard", "879) umbrella", "880) unicycle, monocycle", "881) upright, upright piano", "882) vacuum, vacuum cleaner", "883) vase", "884) vault", "885) velvet", "886) vending machine", "887) vestment", "888) viaduct", "889) violin, fiddle", "890) volleyball", "891) waffle iron", "892) wall clock", "893) wallet, billfold, notecase, pocketbook", "894) wardrobe, closet, press", "895) warplane, military plane", "896) washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897) washer, automatic washer, washing machine", "898) water bottle", "899) water jug", "900) water tower", "901) whiskey jug", "902) whistle", "903) wig", "904) window screen", "905) window shade", "906) Windsor tie", "907) wine bottle", "908) wing", "909) wok", "910) wooden spoon", "911) wool, woolen, woollen", "912) worm fence, snake fence, snake-rail fence, Virginia fence", "913) wreck", "914) yawl", "915) yurt", "916) web site, website, internet site, site", "917) comic book", "918) crossword puzzle, crossword", "919) street sign", "920) traffic light, traffic signal, stoplight", "921) book jacket, dust cover, dust jacket, dust wrapper", "922) menu", "923) plate", "924) guacamole", "925) consomme", "926) hot pot, hotpot", "927) trifle", "928) ice cream, icecream", "929) ice lolly, lolly, lollipop, popsicle", "930) French loaf", "931) bagel, beigel", "932) pretzel", "933) cheeseburger", "934) hotdog, hot dog, red hot", "935) mashed potato", "936) head cabbage", "937) broccoli", "938) cauliflower", "939) zucchini, courgette", "940) spaghetti squash", "941) acorn squash", "942) butternut squash", "943) cucumber, cuke", "944) artichoke, globe artichoke", "945) bell pepper", "946) cardoon", "947) mushroom", "948) Granny Smith", "949) strawberry", "950) orange", "951) lemon", "952) fig", "953) pineapple, ananas", "954) banana", "955) jackfruit, jak, jack", "956) custard apple", "957) pomegranate", "958) hay", "959) carbonara", "960) chocolate sauce, chocolate syrup", "961) dough", "962) meat loaf, meatloaf", "963) pizza, pizza pie", "964) potpie", "965) burrito", "966) red wine", "967) espresso", "968) cup", "969) eggnog", "970) alp", "971) bubble", "972) cliff, drop, drop-off", "973) coral reef", "974) geyser", "975) lakeside, lakeshore", "976) promontory, headland, head, foreland", "977) sandbar, sand bar", "978) seashore, coast, seacoast, sea-coast", "979) valley, vale", "980) volcano", "981) ballplayer, baseball player", "982) groom, bridegroom", "983) scuba diver", "984) rapeseed", "985) daisy", "986) yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987) corn", "988) acorn", "989) hip, rose hip, rosehip", "990) buckeye, horse chestnut, conker", "991) coral fungus", "992) agaric", "993) gyromitra", "994) stinkhorn, carrion fungus", "995) earthstar", "996) hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997) bolete", "998) ear, spike, capitulum", "999) toilet tissue, toilet paper, bathroom tissue"] z = truncated_z_sample(num_samples, truncation, noise_seed) y = int(category.split(')')[0]) ims = sample(sess, z, y, truncation=truncation) imshow(imgrid(ims, cols=min(num_samples, 5)))
スライダー表示させて、可変にしておきます。
いろいろ変更させて試してみましょう。
ちなみに、上図の条件だと
このような画像が生成されます。
７．GigGAN生成画像間を補完
設定でカテゴリーを設定したり、noise_seedを使用して、結果を見てみましょう。
#@title Interpolation { display-mode: "form", run: "auto" } num_samples = 2 #@param {type:"slider", min:1, max:5, step:1} num_interps = 5 #@param {type:"slider", min:2, max:10, step:1} truncation = 0.2 #@param {type:"slider", min:0.02, max:1, step:0.02} noise_seed_A = 0 #@param {type:"slider", min:0, max:100, step:1} category_A = "207) golden retriever" #@param ["0) tench, Tinca tinca", "1) goldfish, Carassius auratus", "2) great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3) tiger shark, Galeocerdo cuvieri", "4) hammerhead, hammerhead shark", "5) electric ray, crampfish, numbfish, torpedo", "6) stingray", "7) cock", "8) hen", "9) ostrich, Struthio camelus", "10) brambling, Fringilla montifringilla", "11) goldfinch, Carduelis carduelis", "12) house finch, linnet, Carpodacus mexicanus", "13) junco, snowbird", "14) indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15) robin, American robin, Turdus migratorius", "16) bulbul", "17) jay", "18) magpie", "19) chickadee", "20) water ouzel, dipper", "21) kite", "22) bald eagle, American eagle, Haliaeetus leucocephalus", "23) vulture", "24) great grey owl, great gray owl, Strix nebulosa", "25) European fire salamander, Salamandra salamandra", "26) common newt, Triturus vulgaris", "27) eft", "28) spotted salamander, Ambystoma maculatum", "29) axolotl, mud puppy, Ambystoma mexicanum", "30) bullfrog, Rana catesbeiana", "31) tree frog, tree-frog", "32) tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33) loggerhead, loggerhead turtle, Caretta caretta", "34) leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35) mud turtle", "36) terrapin", "37) box turtle, box tortoise", "38) banded gecko", "39) common iguana, iguana, Iguana iguana", "40) American chameleon, anole, Anolis carolinensis", "41) whiptail, whiptail lizard", "42) agama", "43) frilled lizard, Chlamydosaurus kingi", "44) alligator lizard", "45) Gila monster, Heloderma suspectum", "46) green lizard, Lacerta viridis", "47) African chameleon, Chamaeleo chamaeleon", "48) Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49) African crocodile, Nile crocodile, Crocodylus niloticus", "50) American alligator, Alligator mississipiensis", "51) triceratops", "52) thunder snake, worm snake, Carphophis amoenus", "53) ringneck snake, ring-necked snake, ring snake", "54) hognose snake, puff adder, sand viper", "55) green snake, grass snake", "56) king snake, kingsnake", "57) garter snake, grass snake", "58) water snake", "59) vine snake", "60) night snake, Hypsiglena torquata", "61) boa constrictor, Constrictor constrictor", "62) rock python, rock snake, Python sebae", "63) Indian cobra, Naja naja", "64) green mamba", "65) sea snake", "66) horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67) diamondback, diamondback rattlesnake, Crotalus adamanteus", "68) sidewinder, horned rattlesnake, Crotalus cerastes", "69) trilobite", "70) harvestman, daddy longlegs, Phalangium opilio", "71) scorpion", "72) black and gold garden spider, Argiope aurantia", "73) barn spider, Araneus cavaticus", "74) garden spider, Aranea diademata", "75) black widow, Latrodectus mactans", "76) tarantula", "77) wolf spider, hunting spider", "78) tick", "79) centipede", "80) black grouse", "81) ptarmigan", "82) ruffed grouse, partridge, Bonasa umbellus", "83) prairie chicken, prairie grouse, prairie fowl", "84) peacock", "85) quail", "86) partridge", "87) African grey, African gray, Psittacus erithacus", "88) macaw", "89) sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90) lorikeet", "91) coucal", "92) bee eater", "93) hornbill", "94) hummingbird", "95) jacamar", "96) toucan", "97) drake", "98) red-breasted merganser, Mergus serrator", "99) goose", "100) black swan, Cygnus atratus", "101) tusker", "102) echidna, spiny anteater, anteater", "103) platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104) wallaby, brush kangaroo", "105) koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106) wombat", "107) jellyfish", "108) sea anemone, anemone", "109) brain coral", "110) flatworm, platyhelminth", "111) nematode, nematode worm, roundworm", "112) conch", "113) snail", "114) slug", "115) sea slug, nudibranch", "116) chiton, coat-of-mail shell, sea cradle, polyplacophore", "117) chambered nautilus, pearly nautilus, nautilus", "118) Dungeness crab, Cancer magister", "119) rock crab, Cancer irroratus", "120) fiddler crab", "121) king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122) American lobster, Northern lobster, Maine lobster, Homarus americanus", "123) spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124) crayfish, crawfish, crawdad, crawdaddy", "125) hermit crab", "126) isopod", "127) white stork, Ciconia ciconia", "128) black stork, Ciconia nigra", "129) spoonbill", "130) flamingo", "131) little blue heron, Egretta caerulea", "132) American egret, great white heron, Egretta albus", "133) bittern", "134) crane", "135) limpkin, Aramus pictus", "136) European gallinule, Porphyrio porphyrio", "137) American coot, marsh hen, mud hen, water hen, Fulica americana", "138) bustard", "139) ruddy turnstone, Arenaria interpres", "140) red-backed sandpiper, dunlin, Erolia alpina", "141) redshank, Tringa totanus", "142) dowitcher", "143) oystercatcher, oyster catcher", "144) pelican", "145) king penguin, Aptenodytes patagonica", "146) albatross, mollymawk", "147) grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148) killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149) dugong, Dugong dugon", "150) sea lion", "151) Chihuahua", "152) Japanese spaniel", "153) Maltese dog, Maltese terrier, Maltese", "154) Pekinese, Pekingese, Peke", "155) Shih-Tzu", "156) Blenheim spaniel", "157) papillon", "158) toy terrier", "159) Rhodesian ridgeback", "160) Afghan hound, Afghan", "161) basset, basset hound", "162) beagle", "163) bloodhound, sleuthhound", "164) bluetick", "165) black-and-tan coonhound", "166) Walker hound, Walker foxhound", "167) English foxhound", "168) redbone", "169) borzoi, Russian wolfhound", "170) Irish wolfhound", "171) Italian greyhound", "172) whippet", "173) Ibizan hound, Ibizan Podenco", "174) Norwegian elkhound, elkhound", "175) otterhound, otter hound", "176) Saluki, gazelle hound", "177) Scottish deerhound, deerhound", "178) Weimaraner", "179) Staffordshire bullterrier, Staffordshire bull terrier", "180) American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181) Bedlington terrier", "182) Border terrier", "183) Kerry blue terrier", "184) Irish terrier", "185) Norfolk terrier", "186) Norwich terrier", "187) Yorkshire terrier", "188) wire-haired fox terrier", "189) Lakeland terrier", "190) Sealyham terrier, Sealyham", "191) Airedale, Airedale terrier", "192) cairn, cairn terrier", "193) Australian terrier", "194) Dandie Dinmont, Dandie Dinmont terrier", "195) Boston bull, Boston terrier", "196) miniature schnauzer", "197) giant schnauzer", "198) standard schnauzer", "199) Scotch terrier, Scottish terrier, Scottie", "200) Tibetan terrier, chrysanthemum dog", "201) silky terrier, Sydney silky", "202) soft-coated wheaten terrier", "203) West Highland white terrier", "204) Lhasa, Lhasa apso", "205) flat-coated retriever", "206) curly-coated retriever", "207) golden retriever", "208) Labrador retriever", "209) Chesapeake Bay retriever", "210) German short-haired pointer", "211) vizsla, Hungarian pointer", "212) English setter", "213) Irish setter, red setter", "214) Gordon setter", "215) Brittany spaniel", "216) clumber, clumber spaniel", "217) English springer, English springer spaniel", "218) Welsh springer spaniel", "219) cocker spaniel, English cocker spaniel, cocker", "220) Sussex spaniel", "221) Irish water spaniel", "222) kuvasz", "223) schipperke", "224) groenendael", "225) malinois", "226) briard", "227) kelpie", "228) komondor", "229) Old English sheepdog, bobtail", "230) Shetland sheepdog, Shetland sheep dog, Shetland", "231) collie", "232) Border collie", "233) Bouvier des Flandres, Bouviers des Flandres", "234) Rottweiler", "235) German shepherd, German shepherd dog, German police dog, alsatian", "236) Doberman, Doberman pinscher", "237) miniature pinscher", "238) Greater Swiss Mountain dog", "239) Bernese mountain dog", "240) Appenzeller", "241) EntleBucher", "242) boxer", "243) bull mastiff", "244) Tibetan mastiff", "245) French bulldog", "246) Great Dane", "247) Saint Bernard, St Bernard", "248) Eskimo dog, husky", "249) malamute, malemute, Alaskan malamute", "250) Siberian husky", "251) dalmatian, coach dog, carriage dog", "252) affenpinscher, monkey pinscher, monkey dog", "253) basenji", "254) pug, pug-dog", "255) Leonberg", "256) Newfoundland, Newfoundland dog", "257) Great Pyrenees", "258) Samoyed, Samoyede", "259) Pomeranian", "260) chow, chow chow", "261) keeshond", "262) Brabancon griffon", "263) Pembroke, Pembroke Welsh corgi", "264) Cardigan, Cardigan Welsh corgi", "265) toy poodle", "266) miniature poodle", "267) standard poodle", "268) Mexican hairless", "269) timber wolf, grey wolf, gray wolf, Canis lupus", "270) white wolf, Arctic wolf, Canis lupus tundrarum", "271) red wolf, maned wolf, Canis rufus, Canis niger", "272) coyote, prairie wolf, brush wolf, Canis latrans", "273) dingo, warrigal, warragal, Canis dingo", "274) dhole, Cuon alpinus", "275) African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276) hyena, hyaena", "277) red fox, Vulpes vulpes", "278) kit fox, Vulpes macrotis", "279) Arctic fox, white fox, Alopex lagopus", "280) grey fox, gray fox, Urocyon cinereoargenteus", "281) tabby, tabby cat", "282) tiger cat", "283) Persian cat", "284) Siamese cat, Siamese", "285) Egyptian cat", "286) cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287) lynx, catamount", "288) leopard, Panthera pardus", "289) snow leopard, ounce, Panthera uncia", "290) jaguar, panther, Panthera onca, Felis onca", "291) lion, king of beasts, Panthera leo", "292) tiger, Panthera tigris", "293) cheetah, chetah, Acinonyx jubatus", "294) brown bear, bruin, Ursus arctos", "295) American black bear, black bear, Ursus americanus, Euarctos americanus", "296) ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297) sloth bear, Melursus ursinus, Ursus ursinus", "298) mongoose", "299) meerkat, mierkat", "300) tiger beetle", "301) ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302) ground beetle, carabid beetle", "303) long-horned beetle, longicorn, longicorn beetle", "304) leaf beetle, chrysomelid", "305) dung beetle", "306) rhinoceros beetle", "307) weevil", "308) fly", "309) bee", "310) ant, emmet, pismire", "311) grasshopper, hopper", "312) cricket", "313) walking stick, walkingstick, stick insect", "314) cockroach, roach", "315) mantis, mantid", "316) cicada, cicala", "317) leafhopper", "318) lacewing, lacewing fly", "319) dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320) damselfly", "321) admiral", "322) ringlet, ringlet butterfly", "323) monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324) cabbage butterfly", "325) sulphur butterfly, sulfur butterfly", "326) lycaenid, lycaenid butterfly", "327) starfish, sea star", "328) sea urchin", "329) sea cucumber, holothurian", "330) wood rabbit, cottontail, cottontail rabbit", "331) hare", "332) Angora, Angora rabbit", "333) hamster", "334) porcupine, hedgehog", "335) fox squirrel, eastern fox squirrel, Sciurus niger", "336) marmot", "337) beaver", "338) guinea pig, Cavia cobaya", "339) sorrel", "340) zebra", "341) hog, pig, grunter, squealer, Sus scrofa", "342) wild boar, boar, Sus scrofa", "343) warthog", "344) hippopotamus, hippo, river horse, Hippopotamus amphibius", "345) ox", "346) water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347) bison", "348) ram, tup", "349) bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350) ibex, Capra ibex", "351) hartebeest", "352) impala, Aepyceros melampus", "353) gazelle", "354) Arabian camel, dromedary, Camelus dromedarius", "355) llama", "356) weasel", "357) mink", "358) polecat, fitch, foulmart, foumart, Mustela putorius", "359) black-footed ferret, ferret, Mustela nigripes", "360) otter", "361) skunk, polecat, wood pussy", "362) badger", "363) armadillo", "364) three-toed sloth, ai, Bradypus tridactylus", "365) orangutan, orang, orangutang, Pongo pygmaeus", "366) gorilla, Gorilla gorilla", "367) chimpanzee, chimp, Pan troglodytes", "368) gibbon, Hylobates lar", "369) siamang, Hylobates syndactylus, Symphalangus syndactylus", "370) guenon, guenon monkey", "371) patas, hussar monkey, Erythrocebus patas", "372) baboon", "373) macaque", "374) langur", "375) colobus, colobus monkey", "376) proboscis monkey, Nasalis larvatus", "377) marmoset", "378) capuchin, ringtail, Cebus capucinus", "379) howler monkey, howler", "380) titi, titi monkey", "381) spider monkey, Ateles geoffroyi", "382) squirrel monkey, Saimiri sciureus", "383) Madagascar cat, ring-tailed lemur, Lemur catta", "384) indri, indris, Indri indri, Indri brevicaudatus", "385) Indian elephant, Elephas maximus", "386) African elephant, Loxodonta africana", "387) lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388) giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389) barracouta, snoek", "390) eel", "391) coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392) rock beauty, Holocanthus tricolor", "393) anemone fish", "394) sturgeon", "395) gar, garfish, garpike, billfish, Lepisosteus osseus", "396) lionfish", "397) puffer, pufferfish, blowfish, globefish", "398) abacus", "399) abaya", "400) academic gown, academic robe, judge's robe", "401) accordion, piano accordion, squeeze box", "402) acoustic guitar", "403) aircraft carrier, carrier, flattop, attack aircraft carrier", "404) airliner", "405) airship, dirigible", "406) altar", "407) ambulance", "408) amphibian, amphibious vehicle", "409) analog clock", "410) apiary, bee house", "411) apron", "412) ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413) assault rifle, assault gun", "414) backpack, back pack, knapsack, packsack, rucksack, haversack", "415) bakery, bakeshop, bakehouse", "416) balance beam, beam", "417) balloon", "418) ballpoint, ballpoint pen, ballpen, Biro", "419) Band Aid", "420) banjo", "421) bannister, banister, balustrade, balusters, handrail", "422) barbell", "423) barber chair", "424) barbershop", "425) barn", "426) barometer", "427) barrel, cask", "428) barrow, garden cart, lawn cart, wheelbarrow", "429) baseball", "430) basketball", "431) bassinet", "432) bassoon", "433) bathing cap, swimming cap", "434) bath towel", "435) bathtub, bathing tub, bath, tub", "436) beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437) beacon, lighthouse, beacon light, pharos", "438) beaker", "439) bearskin, busby, shako", "440) beer bottle", "441) beer glass", "442) bell cote, bell cot", "443) bib", "444) bicycle-built-for-two, tandem bicycle, tandem", "445) bikini, two-piece", "446) binder, ring-binder", "447) binoculars, field glasses, opera glasses", "448) birdhouse", "449) boathouse", "450) bobsled, bobsleigh, bob", "451) bolo tie, bolo, bola tie, bola", "452) bonnet, poke bonnet", "453) bookcase", "454) bookshop, bookstore, bookstall", "455) bottlecap", "456) bow", "457) bow tie, bow-tie, bowtie", "458) brass, memorial tablet, plaque", "459) brassiere, bra, bandeau", "460) breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461) breastplate, aegis, egis", "462) broom", "463) bucket, pail", "464) buckle", "465) bulletproof vest", "466) bullet train, bullet", "467) butcher shop, meat market", "468) cab, hack, taxi, taxicab", "469) caldron, cauldron", "470) candle, taper, wax light", "471) cannon", "472) canoe", "473) can opener, tin opener", "474) cardigan", "475) car mirror", "476) carousel, carrousel, merry-go-round, roundabout, whirligig", "477) carpenter's kit, tool kit", "478) carton", "479) car wheel", "480) cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481) cassette", "482) cassette player", "483) castle", "484) catamaran", "485) CD player", "486) cello, violoncello", "487) cellular telephone, cellular phone, cellphone, cell, mobile phone", "488) chain", "489) chainlink fence", "490) chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491) chain saw, chainsaw", "492) chest", "493) chiffonier, commode", "494) chime, bell, gong", "495) china cabinet, china closet", "496) Christmas stocking", "497) church, church building", "498) cinema, movie theater, movie theatre, movie house, picture palace", "499) cleaver, meat cleaver, chopper", "500) cliff dwelling", "501) cloak", "502) clog, geta, patten, sabot", "503) cocktail shaker", "504) coffee mug", "505) coffeepot", "506) coil, spiral, volute, whorl, helix", "507) combination lock", "508) computer keyboard, keypad", "509) confectionery, confectionary, candy store", "510) container ship, containership, container vessel", "511) convertible", "512) corkscrew, bottle screw", "513) cornet, horn, trumpet, trump", "514) cowboy boot", "515) cowboy hat, ten-gallon hat", "516) cradle", "517) crane", "518) crash helmet", "519) crate", "520) crib, cot", "521) Crock Pot", "522) croquet ball", "523) crutch", "524) cuirass", "525) dam, dike, dyke", "526) desk", "527) desktop computer", "528) dial telephone, dial phone", "529) diaper, nappy, napkin", "530) digital clock", "531) digital watch", "532) dining table, board", "533) dishrag, dishcloth", "534) dishwasher, dish washer, dishwashing machine", "535) disk brake, disc brake", "536) dock, dockage, docking facility", "537) dogsled, dog sled, dog sleigh", "538) dome", "539) doormat, welcome mat", "540) drilling platform, offshore rig", "541) drum, membranophone, tympan", "542) drumstick", "543) dumbbell", "544) Dutch oven", "545) electric fan, blower", "546) electric guitar", "547) electric locomotive", "548) entertainment center", "549) envelope", "550) espresso maker", "551) face powder", "552) feather boa, boa", "553) file, file cabinet, filing cabinet", "554) fireboat", "555) fire engine, fire truck", "556) fire screen, fireguard", "557) flagpole, flagstaff", "558) flute, transverse flute", "559) folding chair", "560) football helmet", "561) forklift", "562) fountain", "563) fountain pen", "564) four-poster", "565) freight car", "566) French horn, horn", "567) frying pan, frypan, skillet", "568) fur coat", "569) garbage truck, dustcart", "570) gasmask, respirator, gas helmet", "571) gas pump, gasoline pump, petrol pump, island dispenser", "572) goblet", "573) go-kart", "574) golf ball", "575) golfcart, golf cart", "576) gondola", "577) gong, tam-tam", "578) gown", "579) grand piano, grand", "580) greenhouse, nursery, glasshouse", "581) grille, radiator grille", "582) grocery store, grocery, food market, market", "583) guillotine", "584) hair slide", "585) hair spray", "586) half track", "587) hammer", "588) hamper", "589) hand blower, blow dryer, blow drier, hair dryer, hair drier", "590) hand-held computer, hand-held microcomputer", "591) handkerchief, hankie, hanky, hankey", "592) hard disc, hard disk, fixed disk", "593) harmonica, mouth organ, harp, mouth harp", "594) harp", "595) harvester, reaper", "596) hatchet", "597) holster", "598) home theater, home theatre", "599) honeycomb", "600) hook, claw", "601) hoopskirt, crinoline", "602) horizontal bar, high bar", "603) horse cart, horse-cart", "604) hourglass", "605) iPod", "606) iron, smoothing iron", "607) jack-o'-lantern", "608) jean, blue jean, denim", "609) jeep, landrover", "610) jersey, T-shirt, tee shirt", "611) jigsaw puzzle", "612) jinrikisha, ricksha, rickshaw", "613) joystick", "614) kimono", "615) knee pad", "616) knot", "617) lab coat, laboratory coat", "618) ladle", "619) lampshade, lamp shade", "620) laptop, laptop computer", "621) lawn mower, mower", "622) lens cap, lens cover", "623) letter opener, paper knife, paperknife", "624) library", "625) lifeboat", "626) lighter, light, igniter, ignitor", "627) limousine, limo", "628) liner, ocean liner", "629) lipstick, lip rouge", "630) Loafer", "631) lotion", "632) loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633) loupe, jeweler's loupe", "634) lumbermill, sawmill", "635) magnetic compass", "636) mailbag, postbag", "637) mailbox, letter box", "638) maillot", "639) maillot, tank suit", "640) manhole cover", "641) maraca", "642) marimba, xylophone", "643) mask", "644) matchstick", "645) maypole", "646) maze, labyrinth", "647) measuring cup", "648) medicine chest, medicine cabinet", "649) megalith, megalithic structure", "650) microphone, mike", "651) microwave, microwave oven", "652) military uniform", "653) milk can", "654) minibus", "655) miniskirt, mini", "656) minivan", "657) missile", "658) mitten", "659) mixing bowl", "660) mobile home, manufactured home", "661) Model T", "662) modem", "663) monastery", "664) monitor", "665) moped", "666) mortar", "667) mortarboard", "668) mosque", "669) mosquito net", "670) motor scooter, scooter", "671) mountain bike, all-terrain bike, off-roader", "672) mountain tent", "673) mouse, computer mouse", "674) mousetrap", "675) moving van", "676) muzzle", "677) nail", "678) neck brace", "679) necklace", "680) nipple", "681) notebook, notebook computer", "682) obelisk", "683) oboe, hautboy, hautbois", "684) ocarina, sweet potato", "685) odometer, hodometer, mileometer, milometer", "686) oil filter", "687) organ, pipe organ", "688) oscilloscope, scope, cathode-ray oscilloscope, CRO", "689) overskirt", "690) oxcart", "691) oxygen mask", "692) packet", "693) paddle, boat paddle", "694) paddlewheel, paddle wheel", "695) padlock", "696) paintbrush", "697) pajama, pyjama, pj's, jammies", "698) palace", "699) panpipe, pandean pipe, syrinx", "700) paper towel", "701) parachute, chute", "702) parallel bars, bars", "703) park bench", "704) parking meter", "705) passenger car, coach, carriage", "706) patio, terrace", "707) pay-phone, pay-station", "708) pedestal, plinth, footstall", "709) pencil box, pencil case", "710) pencil sharpener", "711) perfume, essence", "712) Petri dish", "713) photocopier", "714) pick, plectrum, plectron", "715) pickelhaube", "716) picket fence, paling", "717) pickup, pickup truck", "718) pier", "719) piggy bank, penny bank", "720) pill bottle", "721) pillow", "722) ping-pong ball", "723) pinwheel", "724) pirate, pirate ship", "725) pitcher, ewer", "726) plane, carpenter's plane, woodworking plane", "727) planetarium", "728) plastic bag", "729) plate rack", "730) plow, plough", "731) plunger, plumber's helper", "732) Polaroid camera, Polaroid Land camera", "733) pole", "734) police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735) poncho", "736) pool table, billiard table, snooker table", "737) pop bottle, soda bottle", "738) pot, flowerpot", "739) potter's wheel", "740) power drill", "741) prayer rug, prayer mat", "742) printer", "743) prison, prison house", "744) projectile, missile", "745) projector", "746) puck, hockey puck", "747) punching bag, punch bag, punching ball, punchball", "748) purse", "749) quill, quill pen", "750) quilt, comforter, comfort, puff", "751) racer, race car, racing car", "752) racket, racquet", "753) radiator", "754) radio, wireless", "755) radio telescope, radio reflector", "756) rain barrel", "757) recreational vehicle, RV, R.V.", "758) reel", "759) reflex camera", "760) refrigerator, icebox", "761) remote control, remote", "762) restaurant, eating house, eating place, eatery", "763) revolver, six-gun, six-shooter", "764) rifle", "765) rocking chair, rocker", "766) rotisserie", "767) rubber eraser, rubber, pencil eraser", "768) rugby ball", "769) rule, ruler", "770) running shoe", "771) safe", "772) safety pin", "773) saltshaker, salt shaker", "774) sandal", "775) sarong", "776) sax, saxophone", "777) scabbard", "778) scale, weighing machine", "779) school bus", "780) schooner", "781) scoreboard", "782) screen, CRT screen", "783) screw", "784) screwdriver", "785) seat belt, seatbelt", "786) sewing machine", "787) shield, buckler", "788) shoe shop, shoe-shop, shoe store", "789) shoji", "790) shopping basket", "791) shopping cart", "792) shovel", "793) shower cap", "794) shower curtain", "795) ski", "796) ski mask", "797) sleeping bag", "798) slide rule, slipstick", "799) sliding door", "800) slot, one-armed bandit", "801) snorkel", "802) snowmobile", "803) snowplow, snowplough", "804) soap dispenser", "805) soccer ball", "806) sock", "807) solar dish, solar collector, solar furnace", "808) sombrero", "809) soup bowl", "810) space bar", "811) space heater", "812) space shuttle", "813) spatula", "814) speedboat", "815) spider web, spider's web", "816) spindle", "817) sports car, sport car", "818) spotlight, spot", "819) stage", "820) steam locomotive", "821) steel arch bridge", "822) steel drum", "823) stethoscope", "824) stole", "825) stone wall", "826) stopwatch, stop watch", "827) stove", "828) strainer", "829) streetcar, tram, tramcar, trolley, trolley car", "830) stretcher", "831) studio couch, day bed", "832) stupa, tope", "833) submarine, pigboat, sub, U-boat", "834) suit, suit of clothes", "835) sundial", "836) sunglass", "837) sunglasses, dark glasses, shades", "838) sunscreen, sunblock, sun blocker", "839) suspension bridge", "840) swab, swob, mop", "841) sweatshirt", "842) swimming trunks, bathing trunks", "843) swing", "844) switch, electric switch, electrical switch", "845) syringe", "846) table lamp", "847) tank, army tank, armored combat vehicle, armoured combat vehicle", "848) tape player", "849) teapot", "850) teddy, teddy bear", "851) television, television system", "852) tennis ball", "853) thatch, thatched roof", "854) theater curtain, theatre curtain", "855) thimble", "856) thresher, thrasher, threshing machine", "857) throne", "858) tile roof", "859) toaster", "860) tobacco shop, tobacconist shop, tobacconist", "861) toilet seat", "862) torch", "863) totem pole", "864) tow truck, tow car, wrecker", "865) toyshop", "866) tractor", "867) trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868) tray", "869) trench coat", "870) tricycle, trike, velocipede", "871) trimaran", "872) tripod", "873) triumphal arch", "874) trolleybus, trolley coach, trackless trolley", "875) trombone", "876) tub, vat", "877) turnstile", "878) typewriter keyboard", "879) umbrella", "880) unicycle, monocycle", "881) upright, upright piano", "882) vacuum, vacuum cleaner", "883) vase", "884) vault", "885) velvet", "886) vending machine", "887) vestment", "888) viaduct", "889) violin, fiddle", "890) volleyball", "891) waffle iron", "892) wall clock", "893) wallet, billfold, notecase, pocketbook", "894) wardrobe, closet, press", "895) warplane, military plane", "896) washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897) washer, automatic washer, washing machine", "898) water bottle", "899) water jug", "900) water tower", "901) whiskey jug", "902) whistle", "903) wig", "904) window screen", "905) window shade", "906) Windsor tie", "907) wine bottle", "908) wing", "909) wok", "910) wooden spoon", "911) wool, woolen, woollen", "912) worm fence, snake fence, snake-rail fence, Virginia fence", "913) wreck", "914) yawl", "915) yurt", "916) web site, website, internet site, site", "917) comic book", "918) crossword puzzle, crossword", "919) street sign", "920) traffic light, traffic signal, stoplight", "921) book jacket, dust cover, dust jacket, dust wrapper", "922) menu", "923) plate", "924) guacamole", "925) consomme", "926) hot pot, hotpot", "927) trifle", "928) ice cream, icecream", "929) ice lolly, lolly, lollipop, popsicle", "930) French loaf", "931) bagel, beigel", "932) pretzel", "933) cheeseburger", "934) hotdog, hot dog, red hot", "935) mashed potato", "936) head cabbage", "937) broccoli", "938) cauliflower", "939) zucchini, courgette", "940) spaghetti squash", "941) acorn squash", "942) butternut squash", "943) cucumber, cuke", "944) artichoke, globe artichoke", "945) bell pepper", "946) cardoon", "947) mushroom", "948) Granny Smith", "949) strawberry", "950) orange", "951) lemon", "952) fig", "953) pineapple, ananas", "954) banana", "955) jackfruit, jak, jack", "956) custard apple", "957) pomegranate", "958) hay", "959) carbonara", "960) chocolate sauce, chocolate syrup", "961) dough", "962) meat loaf, meatloaf", "963) pizza, pizza pie", "964) potpie", "965) burrito", "966) red wine", "967) espresso", "968) cup", "969) eggnog", "970) alp", "971) bubble", "972) cliff, drop, drop-off", "973) coral reef", "974) geyser", "975) lakeside, lakeshore", "976) promontory, headland, head, foreland", "977) sandbar, sand bar", "978) seashore, coast, seacoast, sea-coast", "979) valley, vale", "980) volcano", "981) ballplayer, baseball player", "982) groom, bridegroom", "983) scuba diver", "984) rapeseed", "985) daisy", "986) yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987) corn", "988) acorn", "989) hip, rose hip, rosehip", "990) buckeye, horse chestnut, conker", "991) coral fungus", "992) agaric", "993) gyromitra", "994) stinkhorn, carrion fungus", "995) earthstar", "996) hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997) bolete", "998) ear, spike, capitulum", "999) toilet tissue, toilet paper, bathroom tissue"] noise_seed_B = 0 #@param {type:"slider", min:0, max:100, step:1} category_B = "8) hen" #@param ["0) tench, Tinca tinca", "1) goldfish, Carassius auratus", "2) great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3) tiger shark, Galeocerdo cuvieri", "4) hammerhead, hammerhead shark", "5) electric ray, crampfish, numbfish, torpedo", "6) stingray", "7) cock", "8) hen", "9) ostrich, Struthio camelus", "10) brambling, Fringilla montifringilla", "11) goldfinch, Carduelis carduelis", "12) house finch, linnet, Carpodacus mexicanus", "13) junco, snowbird", "14) indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15) robin, American robin, Turdus migratorius", "16) bulbul", "17) jay", "18) magpie", "19) chickadee", "20) water ouzel, dipper", "21) kite", "22) bald eagle, American eagle, Haliaeetus leucocephalus", "23) vulture", "24) great grey owl, great gray owl, Strix nebulosa", "25) European fire salamander, Salamandra salamandra", "26) common newt, Triturus vulgaris", "27) eft", "28) spotted salamander, Ambystoma maculatum", "29) axolotl, mud puppy, Ambystoma mexicanum", "30) bullfrog, Rana catesbeiana", "31) tree frog, tree-frog", "32) tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33) loggerhead, loggerhead turtle, Caretta caretta", "34) leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35) mud turtle", "36) terrapin", "37) box turtle, box tortoise", "38) banded gecko", "39) common iguana, iguana, Iguana iguana", "40) American chameleon, anole, Anolis carolinensis", "41) whiptail, whiptail lizard", "42) agama", "43) frilled lizard, Chlamydosaurus kingi", "44) alligator lizard", "45) Gila monster, Heloderma suspectum", "46) green lizard, Lacerta viridis", "47) African chameleon, Chamaeleo chamaeleon", "48) Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49) African crocodile, Nile crocodile, Crocodylus niloticus", "50) American alligator, Alligator mississipiensis", "51) triceratops", "52) thunder snake, worm snake, Carphophis amoenus", "53) ringneck snake, ring-necked snake, ring snake", "54) hognose snake, puff adder, sand viper", "55) green snake, grass snake", "56) king snake, kingsnake", "57) garter snake, grass snake", "58) water snake", "59) vine snake", "60) night snake, Hypsiglena torquata", "61) boa constrictor, Constrictor constrictor", "62) rock python, rock snake, Python sebae", "63) Indian cobra, Naja naja", "64) green mamba", "65) sea snake", "66) horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67) diamondback, diamondback rattlesnake, Crotalus adamanteus", "68) sidewinder, horned rattlesnake, Crotalus cerastes", "69) trilobite", "70) harvestman, daddy longlegs, Phalangium opilio", "71) scorpion", "72) black and gold garden spider, Argiope aurantia", "73) barn spider, Araneus cavaticus", "74) garden spider, Aranea diademata", "75) black widow, Latrodectus mactans", "76) tarantula", "77) wolf spider, hunting spider", "78) tick", "79) centipede", "80) black grouse", "81) ptarmigan", "82) ruffed grouse, partridge, Bonasa umbellus", "83) prairie chicken, prairie grouse, prairie fowl", "84) peacock", "85) quail", "86) partridge", "87) African grey, African gray, Psittacus erithacus", "88) macaw", "89) sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90) lorikeet", "91) coucal", "92) bee eater", "93) hornbill", "94) hummingbird", "95) jacamar", "96) toucan", "97) drake", "98) red-breasted merganser, Mergus serrator", "99) goose", "100) black swan, Cygnus atratus", "101) tusker", "102) echidna, spiny anteater, anteater", "103) platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104) wallaby, brush kangaroo", "105) koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106) wombat", "107) jellyfish", "108) sea anemone, anemone", "109) brain coral", "110) flatworm, platyhelminth", "111) nematode, nematode worm, roundworm", "112) conch", "113) snail", "114) slug", "115) sea slug, nudibranch", "116) chiton, coat-of-mail shell, sea cradle, polyplacophore", "117) chambered nautilus, pearly nautilus, nautilus", "118) Dungeness crab, Cancer magister", "119) rock crab, Cancer irroratus", "120) fiddler crab", "121) king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122) American lobster, Northern lobster, Maine lobster, Homarus americanus", "123) spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124) crayfish, crawfish, crawdad, crawdaddy", "125) hermit crab", "126) isopod", "127) white stork, Ciconia ciconia", "128) black stork, Ciconia nigra", "129) spoonbill", "130) flamingo", "131) little blue heron, Egretta caerulea", "132) American egret, great white heron, Egretta albus", "133) bittern", "134) crane", "135) limpkin, Aramus pictus", "136) European gallinule, Porphyrio porphyrio", "137) American coot, marsh hen, mud hen, water hen, Fulica americana", "138) bustard", "139) ruddy turnstone, Arenaria interpres", "140) red-backed sandpiper, dunlin, Erolia alpina", "141) redshank, Tringa totanus", "142) dowitcher", "143) oystercatcher, oyster catcher", "144) pelican", "145) king penguin, Aptenodytes patagonica", "146) albatross, mollymawk", "147) grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148) killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149) dugong, Dugong dugon", "150) sea lion", "151) Chihuahua", "152) Japanese spaniel", "153) Maltese dog, Maltese terrier, Maltese", "154) Pekinese, Pekingese, Peke", "155) Shih-Tzu", "156) Blenheim spaniel", "157) papillon", "158) toy terrier", "159) Rhodesian ridgeback", "160) Afghan hound, Afghan", "161) basset, basset hound", "162) beagle", "163) bloodhound, sleuthhound", "164) bluetick", "165) black-and-tan coonhound", "166) Walker hound, Walker foxhound", "167) English foxhound", "168) redbone", "169) borzoi, Russian wolfhound", "170) Irish wolfhound", "171) Italian greyhound", "172) whippet", "173) Ibizan hound, Ibizan Podenco", "174) Norwegian elkhound, elkhound", "175) otterhound, otter hound", "176) Saluki, gazelle hound", "177) Scottish deerhound, deerhound", "178) Weimaraner", "179) Staffordshire bullterrier, Staffordshire bull terrier", "180) American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181) Bedlington terrier", "182) Border terrier", "183) Kerry blue terrier", "184) Irish terrier", "185) Norfolk terrier", "186) Norwich terrier", "187) Yorkshire terrier", "188) wire-haired fox terrier", "189) Lakeland terrier", "190) Sealyham terrier, Sealyham", "191) Airedale, Airedale terrier", "192) cairn, cairn terrier", "193) Australian terrier", "194) Dandie Dinmont, Dandie Dinmont terrier", "195) Boston bull, Boston terrier", "196) miniature schnauzer", "197) giant schnauzer", "198) standard schnauzer", "199) Scotch terrier, Scottish terrier, Scottie", "200) Tibetan terrier, chrysanthemum dog", "201) silky terrier, Sydney silky", "202) soft-coated wheaten terrier", "203) West Highland white terrier", "204) Lhasa, Lhasa apso", "205) flat-coated retriever", "206) curly-coated retriever", "207) golden retriever", "208) Labrador retriever", "209) Chesapeake Bay retriever", "210) German short-haired pointer", "211) vizsla, Hungarian pointer", "212) English setter", "213) Irish setter, red setter", "214) Gordon setter", "215) Brittany spaniel", "216) clumber, clumber spaniel", "217) English springer, English springer spaniel", "218) Welsh springer spaniel", "219) cocker spaniel, English cocker spaniel, cocker", "220) Sussex spaniel", "221) Irish water spaniel", "222) kuvasz", "223) schipperke", "224) groenendael", "225) malinois", "226) briard", "227) kelpie", "228) komondor", "229) Old English sheepdog, bobtail", "230) Shetland sheepdog, Shetland sheep dog, Shetland", "231) collie", "232) Border collie", "233) Bouvier des Flandres, Bouviers des Flandres", "234) Rottweiler", "235) German shepherd, German shepherd dog, German police dog, alsatian", "236) Doberman, Doberman pinscher", "237) miniature pinscher", "238) Greater Swiss Mountain dog", "239) Bernese mountain dog", "240) Appenzeller", "241) EntleBucher", "242) boxer", "243) bull mastiff", "244) Tibetan mastiff", "245) French bulldog", "246) Great Dane", "247) Saint Bernard, St Bernard", "248) Eskimo dog, husky", "249) malamute, malemute, Alaskan malamute", "250) Siberian husky", "251) dalmatian, coach dog, carriage dog", "252) affenpinscher, monkey pinscher, monkey dog", "253) basenji", "254) pug, pug-dog", "255) Leonberg", "256) Newfoundland, Newfoundland dog", "257) Great Pyrenees", "258) Samoyed, Samoyede", "259) Pomeranian", "260) chow, chow chow", "261) keeshond", "262) Brabancon griffon", "263) Pembroke, Pembroke Welsh corgi", "264) Cardigan, Cardigan Welsh corgi", "265) toy poodle", "266) miniature poodle", "267) standard poodle", "268) Mexican hairless", "269) timber wolf, grey wolf, gray wolf, Canis lupus", "270) white wolf, Arctic wolf, Canis lupus tundrarum", "271) red wolf, maned wolf, Canis rufus, Canis niger", "272) coyote, prairie wolf, brush wolf, Canis latrans", "273) dingo, warrigal, warragal, Canis dingo", "274) dhole, Cuon alpinus", "275) African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276) hyena, hyaena", "277) red fox, Vulpes vulpes", "278) kit fox, Vulpes macrotis", "279) Arctic fox, white fox, Alopex lagopus", "280) grey fox, gray fox, Urocyon cinereoargenteus", "281) tabby, tabby cat", "282) tiger cat", "283) Persian cat", "284) Siamese cat, Siamese", "285) Egyptian cat", "286) cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287) lynx, catamount", "288) leopard, Panthera pardus", "289) snow leopard, ounce, Panthera uncia", "290) jaguar, panther, Panthera onca, Felis onca", "291) lion, king of beasts, Panthera leo", "292) tiger, Panthera tigris", "293) cheetah, chetah, Acinonyx jubatus", "294) brown bear, bruin, Ursus arctos", "295) American black bear, black bear, Ursus americanus, Euarctos americanus", "296) ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297) sloth bear, Melursus ursinus, Ursus ursinus", "298) mongoose", "299) meerkat, mierkat", "300) tiger beetle", "301) ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302) ground beetle, carabid beetle", "303) long-horned beetle, longicorn, longicorn beetle", "304) leaf beetle, chrysomelid", "305) dung beetle", "306) rhinoceros beetle", "307) weevil", "308) fly", "309) bee", "310) ant, emmet, pismire", "311) grasshopper, hopper", "312) cricket", "313) walking stick, walkingstick, stick insect", "314) cockroach, roach", "315) mantis, mantid", "316) cicada, cicala", "317) leafhopper", "318) lacewing, lacewing fly", "319) dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320) damselfly", "321) admiral", "322) ringlet, ringlet butterfly", "323) monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324) cabbage butterfly", "325) sulphur butterfly, sulfur butterfly", "326) lycaenid, lycaenid butterfly", "327) starfish, sea star", "328) sea urchin", "329) sea cucumber, holothurian", "330) wood rabbit, cottontail, cottontail rabbit", "331) hare", "332) Angora, Angora rabbit", "333) hamster", "334) porcupine, hedgehog", "335) fox squirrel, eastern fox squirrel, Sciurus niger", "336) marmot", "337) beaver", "338) guinea pig, Cavia cobaya", "339) sorrel", "340) zebra", "341) hog, pig, grunter, squealer, Sus scrofa", "342) wild boar, boar, Sus scrofa", "343) warthog", "344) hippopotamus, hippo, river horse, Hippopotamus amphibius", "345) ox", "346) water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347) bison", "348) ram, tup", "349) bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350) ibex, Capra ibex", "351) hartebeest", "352) impala, Aepyceros melampus", "353) gazelle", "354) Arabian camel, dromedary, Camelus dromedarius", "355) llama", "356) weasel", "357) mink", "358) polecat, fitch, foulmart, foumart, Mustela putorius", "359) black-footed ferret, ferret, Mustela nigripes", "360) otter", "361) skunk, polecat, wood pussy", "362) badger", "363) armadillo", "364) three-toed sloth, ai, Bradypus tridactylus", "365) orangutan, orang, orangutang, Pongo pygmaeus", "366) gorilla, Gorilla gorilla", "367) chimpanzee, chimp, Pan troglodytes", "368) gibbon, Hylobates lar", "369) siamang, Hylobates syndactylus, Symphalangus syndactylus", "370) guenon, guenon monkey", "371) patas, hussar monkey, Erythrocebus patas", "372) baboon", "373) macaque", "374) langur", "375) colobus, colobus monkey", "376) proboscis monkey, Nasalis larvatus", "377) marmoset", "378) capuchin, ringtail, Cebus capucinus", "379) howler monkey, howler", "380) titi, titi monkey", "381) spider monkey, Ateles geoffroyi", "382) squirrel monkey, Saimiri sciureus", "383) Madagascar cat, ring-tailed lemur, Lemur catta", "384) indri, indris, Indri indri, Indri brevicaudatus", "385) Indian elephant, Elephas maximus", "386) African elephant, Loxodonta africana", "387) lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388) giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389) barracouta, snoek", "390) eel", "391) coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392) rock beauty, Holocanthus tricolor", "393) anemone fish", "394) sturgeon", "395) gar, garfish, garpike, billfish, Lepisosteus osseus", "396) lionfish", "397) puffer, pufferfish, blowfish, globefish", "398) abacus", "399) abaya", "400) academic gown, academic robe, judge's robe", "401) accordion, piano accordion, squeeze box", "402) acoustic guitar", "403) aircraft carrier, carrier, flattop, attack aircraft carrier", "404) airliner", "405) airship, dirigible", "406) altar", "407) ambulance", "408) amphibian, amphibious vehicle", "409) analog clock", "410) apiary, bee house", "411) apron", "412) ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413) assault rifle, assault gun", "414) backpack, back pack, knapsack, packsack, rucksack, haversack", "415) bakery, bakeshop, bakehouse", "416) balance beam, beam", "417) balloon", "418) ballpoint, ballpoint pen, ballpen, Biro", "419) Band Aid", "420) banjo", "421) bannister, banister, balustrade, balusters, handrail", "422) barbell", "423) barber chair", "424) barbershop", "425) barn", "426) barometer", "427) barrel, cask", "428) barrow, garden cart, lawn cart, wheelbarrow", "429) baseball", "430) basketball", "431) bassinet", "432) bassoon", "433) bathing cap, swimming cap", "434) bath towel", "435) bathtub, bathing tub, bath, tub", "436) beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437) beacon, lighthouse, beacon light, pharos", "438) beaker", "439) bearskin, busby, shako", "440) beer bottle", "441) beer glass", "442) bell cote, bell cot", "443) bib", "444) bicycle-built-for-two, tandem bicycle, tandem", "445) bikini, two-piece", "446) binder, ring-binder", "447) binoculars, field glasses, opera glasses", "448) birdhouse", "449) boathouse", "450) bobsled, bobsleigh, bob", "451) bolo tie, bolo, bola tie, bola", "452) bonnet, poke bonnet", "453) bookcase", "454) bookshop, bookstore, bookstall", "455) bottlecap", "456) bow", "457) bow tie, bow-tie, bowtie", "458) brass, memorial tablet, plaque", "459) brassiere, bra, bandeau", "460) breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461) breastplate, aegis, egis", "462) broom", "463) bucket, pail", "464) buckle", "465) bulletproof vest", "466) bullet train, bullet", "467) butcher shop, meat market", "468) cab, hack, taxi, taxicab", "469) caldron, cauldron", "470) candle, taper, wax light", "471) cannon", "472) canoe", "473) can opener, tin opener", "474) cardigan", "475) car mirror", "476) carousel, carrousel, merry-go-round, roundabout, whirligig", "477) carpenter's kit, tool kit", "478) carton", "479) car wheel", "480) cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481) cassette", "482) cassette player", "483) castle", "484) catamaran", "485) CD player", "486) cello, violoncello", "487) cellular telephone, cellular phone, cellphone, cell, mobile phone", "488) chain", "489) chainlink fence", "490) chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491) chain saw, chainsaw", "492) chest", "493) chiffonier, commode", "494) chime, bell, gong", "495) china cabinet, china closet", "496) Christmas stocking", "497) church, church building", "498) cinema, movie theater, movie theatre, movie house, picture palace", "499) cleaver, meat cleaver, chopper", "500) cliff dwelling", "501) cloak", "502) clog, geta, patten, sabot", "503) cocktail shaker", "504) coffee mug", "505) coffeepot", "506) coil, spiral, volute, whorl, helix", "507) combination lock", "508) computer keyboard, keypad", "509) confectionery, confectionary, candy store", "510) container ship, containership, container vessel", "511) convertible", "512) corkscrew, bottle screw", "513) cornet, horn, trumpet, trump", "514) cowboy boot", "515) cowboy hat, ten-gallon hat", "516) cradle", "517) crane", "518) crash helmet", "519) crate", "520) crib, cot", "521) Crock Pot", "522) croquet ball", "523) crutch", "524) cuirass", "525) dam, dike, dyke", "526) desk", "527) desktop computer", "528) dial telephone, dial phone", "529) diaper, nappy, napkin", "530) digital clock", "531) digital watch", "532) dining table, board", "533) dishrag, dishcloth", "534) dishwasher, dish washer, dishwashing machine", "535) disk brake, disc brake", "536) dock, dockage, docking facility", "537) dogsled, dog sled, dog sleigh", "538) dome", "539) doormat, welcome mat", "540) drilling platform, offshore rig", "541) drum, membranophone, tympan", "542) drumstick", "543) dumbbell", "544) Dutch oven", "545) electric fan, blower", "546) electric guitar", "547) electric locomotive", "548) entertainment center", "549) envelope", "550) espresso maker", "551) face powder", "552) feather boa, boa", "553) file, file cabinet, filing cabinet", "554) fireboat", "555) fire engine, fire truck", "556) fire screen, fireguard", "557) flagpole, flagstaff", "558) flute, transverse flute", "559) folding chair", "560) football helmet", "561) forklift", "562) fountain", "563) fountain pen", "564) four-poster", "565) freight car", "566) French horn, horn", "567) frying pan, frypan, skillet", "568) fur coat", "569) garbage truck, dustcart", "570) gasmask, respirator, gas helmet", "571) gas pump, gasoline pump, petrol pump, island dispenser", "572) goblet", "573) go-kart", "574) golf ball", "575) golfcart, golf cart", "576) gondola", "577) gong, tam-tam", "578) gown", "579) grand piano, grand", "580) greenhouse, nursery, glasshouse", "581) grille, radiator grille", "582) grocery store, grocery, food market, market", "583) guillotine", "584) hair slide", "585) hair spray", "586) half track", "587) hammer", "588) hamper", "589) hand blower, blow dryer, blow drier, hair dryer, hair drier", "590) hand-held computer, hand-held microcomputer", "591) handkerchief, hankie, hanky, hankey", "592) hard disc, hard disk, fixed disk", "593) harmonica, mouth organ, harp, mouth harp", "594) harp", "595) harvester, reaper", "596) hatchet", "597) holster", "598) home theater, home theatre", "599) honeycomb", "600) hook, claw", "601) hoopskirt, crinoline", "602) horizontal bar, high bar", "603) horse cart, horse-cart", "604) hourglass", "605) iPod", "606) iron, smoothing iron", "607) jack-o'-lantern", "608) jean, blue jean, denim", "609) jeep, landrover", "610) jersey, T-shirt, tee shirt", "611) jigsaw puzzle", "612) jinrikisha, ricksha, rickshaw", "613) joystick", "614) kimono", "615) knee pad", "616) knot", "617) lab coat, laboratory coat", "618) ladle", "619) lampshade, lamp shade", "620) laptop, laptop computer", "621) lawn mower, mower", "622) lens cap, lens cover", "623) letter opener, paper knife, paperknife", "624) library", "625) lifeboat", "626) lighter, light, igniter, ignitor", "627) limousine, limo", "628) liner, ocean liner", "629) lipstick, lip rouge", "630) Loafer", "631) lotion", "632) loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633) loupe, jeweler's loupe", "634) lumbermill, sawmill", "635) magnetic compass", "636) mailbag, postbag", "637) mailbox, letter box", "638) maillot", "639) maillot, tank suit", "640) manhole cover", "641) maraca", "642) marimba, xylophone", "643) mask", "644) matchstick", "645) maypole", "646) maze, labyrinth", "647) measuring cup", "648) medicine chest, medicine cabinet", "649) megalith, megalithic structure", "650) microphone, mike", "651) microwave, microwave oven", "652) military uniform", "653) milk can", "654) minibus", "655) miniskirt, mini", "656) minivan", "657) missile", "658) mitten", "659) mixing bowl", "660) mobile home, manufactured home", "661) Model T", "662) modem", "663) monastery", "664) monitor", "665) moped", "666) mortar", "667) mortarboard", "668) mosque", "669) mosquito net", "670) motor scooter, scooter", "671) mountain bike, all-terrain bike, off-roader", "672) mountain tent", "673) mouse, computer mouse", "674) mousetrap", "675) moving van", "676) muzzle", "677) nail", "678) neck brace", "679) necklace", "680) nipple", "681) notebook, notebook computer", "682) obelisk", "683) oboe, hautboy, hautbois", "684) ocarina, sweet potato", "685) odometer, hodometer, mileometer, milometer", "686) oil filter", "687) organ, pipe organ", "688) oscilloscope, scope, cathode-ray oscilloscope, CRO", "689) overskirt", "690) oxcart", "691) oxygen mask", "692) packet", "693) paddle, boat paddle", "694) paddlewheel, paddle wheel", "695) padlock", "696) paintbrush", "697) pajama, pyjama, pj's, jammies", "698) palace", "699) panpipe, pandean pipe, syrinx", "700) paper towel", "701) parachute, chute", "702) parallel bars, bars", "703) park bench", "704) parking meter", "705) passenger car, coach, carriage", "706) patio, terrace", "707) pay-phone, pay-station", "708) pedestal, plinth, footstall", "709) pencil box, pencil case", "710) pencil sharpener", "711) perfume, essence", "712) Petri dish", "713) photocopier", "714) pick, plectrum, plectron", "715) pickelhaube", "716) picket fence, paling", "717) pickup, pickup truck", "718) pier", "719) piggy bank, penny bank", "720) pill bottle", "721) pillow", "722) ping-pong ball", "723) pinwheel", "724) pirate, pirate ship", "725) pitcher, ewer", "726) plane, carpenter's plane, woodworking plane", "727) planetarium", "728) plastic bag", "729) plate rack", "730) plow, plough", "731) plunger, plumber's helper", "732) Polaroid camera, Polaroid Land camera", "733) pole", "734) police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735) poncho", "736) pool table, billiard table, snooker table", "737) pop bottle, soda bottle", "738) pot, flowerpot", "739) potter's wheel", "740) power drill", "741) prayer rug, prayer mat", "742) printer", "743) prison, prison house", "744) projectile, missile", "745) projector", "746) puck, hockey puck", "747) punching bag, punch bag, punching ball, punchball", "748) purse", "749) quill, quill pen", "750) quilt, comforter, comfort, puff", "751) racer, race car, racing car", "752) racket, racquet", "753) radiator", "754) radio, wireless", "755) radio telescope, radio reflector", "756) rain barrel", "757) recreational vehicle, RV, R.V.", "758) reel", "759) reflex camera", "760) refrigerator, icebox", "761) remote control, remote", "762) restaurant, eating house, eating place, eatery", "763) revolver, six-gun, six-shooter", "764) rifle", "765) rocking chair, rocker", "766) rotisserie", "767) rubber eraser, rubber, pencil eraser", "768) rugby ball", "769) rule, ruler", "770) running shoe", "771) safe", "772) safety pin", "773) saltshaker, salt shaker", "774) sandal", "775) sarong", "776) sax, saxophone", "777) scabbard", "778) scale, weighing machine", "779) school bus", "780) schooner", "781) scoreboard", "782) screen, CRT screen", "783) screw", "784) screwdriver", "785) seat belt, seatbelt", "786) sewing machine", "787) shield, buckler", "788) shoe shop, shoe-shop, shoe store", "789) shoji", "790) shopping basket", "791) shopping cart", "792) shovel", "793) shower cap", "794) shower curtain", "795) ski", "796) ski mask", "797) sleeping bag", "798) slide rule, slipstick", "799) sliding door", "800) slot, one-armed bandit", "801) snorkel", "802) snowmobile", "803) snowplow, snowplough", "804) soap dispenser", "805) soccer ball", "806) sock", "807) solar dish, solar collector, solar furnace", "808) sombrero", "809) soup bowl", "810) space bar", "811) space heater", "812) space shuttle", "813) spatula", "814) speedboat", "815) spider web, spider's web", "816) spindle", "817) sports car, sport car", "818) spotlight, spot", "819) stage", "820) steam locomotive", "821) steel arch bridge", "822) steel drum", "823) stethoscope", "824) stole", "825) stone wall", "826) stopwatch, stop watch", "827) stove", "828) strainer", "829) streetcar, tram, tramcar, trolley, trolley car", "830) stretcher", "831) studio couch, day bed", "832) stupa, tope", "833) submarine, pigboat, sub, U-boat", "834) suit, suit of clothes", "835) sundial", "836) sunglass", "837) sunglasses, dark glasses, shades", "838) sunscreen, sunblock, sun blocker", "839) suspension bridge", "840) swab, swob, mop", "841) sweatshirt", "842) swimming trunks, bathing trunks", "843) swing", "844) switch, electric switch, electrical switch", "845) syringe", "846) table lamp", "847) tank, army tank, armored combat vehicle, armoured combat vehicle", "848) tape player", "849) teapot", "850) teddy, teddy bear", "851) television, television system", "852) tennis ball", "853) thatch, thatched roof", "854) theater curtain, theatre curtain", "855) thimble", "856) thresher, thrasher, threshing machine", "857) throne", "858) tile roof", "859) toaster", "860) tobacco shop, tobacconist shop, tobacconist", "861) toilet seat", "862) torch", "863) totem pole", "864) tow truck, tow car, wrecker", "865) toyshop", "866) tractor", "867) trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868) tray", "869) trench coat", "870) tricycle, trike, velocipede", "871) trimaran", "872) tripod", "873) triumphal arch", "874) trolleybus, trolley coach, trackless trolley", "875) trombone", "876) tub, vat", "877) turnstile", "878) typewriter keyboard", "879) umbrella", "880) unicycle, monocycle", "881) upright, upright piano", "882) vacuum, vacuum cleaner", "883) vase", "884) vault", "885) velvet", "886) vending machine", "887) vestment", "888) viaduct", "889) violin, fiddle", "890) volleyball", "891) waffle iron", "892) wall clock", "893) wallet, billfold, notecase, pocketbook", "894) wardrobe, closet, press", "895) warplane, military plane", "896) washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897) washer, automatic washer, washing machine", "898) water bottle", "899) water jug", "900) water tower", "901) whiskey jug", "902) whistle", "903) wig", "904) window screen", "905) window shade", "906) Windsor tie", "907) wine bottle", "908) wing", "909) wok", "910) wooden spoon", "911) wool, woolen, woollen", "912) worm fence, snake fence, snake-rail fence, Virginia fence", "913) wreck", "914) yawl", "915) yurt", "916) web site, website, internet site, site", "917) comic book", "918) crossword puzzle, crossword", "919) street sign", "920) traffic light, traffic signal, stoplight", "921) book jacket, dust cover, dust jacket, dust wrapper", "922) menu", "923) plate", "924) guacamole", "925) consomme", "926) hot pot, hotpot", "927) trifle", "928) ice cream, icecream", "929) ice lolly, lolly, lollipop, popsicle", "930) French loaf", "931) bagel, beigel", "932) pretzel", "933) cheeseburger", "934) hotdog, hot dog, red hot", "935) mashed potato", "936) head cabbage", "937) broccoli", "938) cauliflower", "939) zucchini, courgette", "940) spaghetti squash", "941) acorn squash", "942) butternut squash", "943) cucumber, cuke", "944) artichoke, globe artichoke", "945) bell pepper", "946) cardoon", "947) mushroom", "948) Granny Smith", "949) strawberry", "950) orange", "951) lemon", "952) fig", "953) pineapple, ananas", "954) banana", "955) jackfruit, jak, jack", "956) custard apple", "957) pomegranate", "958) hay", "959) carbonara", "960) chocolate sauce, chocolate syrup", "961) dough", "962) meat loaf, meatloaf", "963) pizza, pizza pie", "964) potpie", "965) burrito", "966) red wine", "967) espresso", "968) cup", "969) eggnog", "970) alp", "971) bubble", "972) cliff, drop, drop-off", "973) coral reef", "974) geyser", "975) lakeside, lakeshore", "976) promontory, headland, head, foreland", "977) sandbar, sand bar", "978) seashore, coast, seacoast, sea-coast", "979) valley, vale", "980) volcano", "981) ballplayer, baseball player", "982) groom, bridegroom", "983) scuba diver", "984) rapeseed", "985) daisy", "986) yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987) corn", "988) acorn", "989) hip, rose hip, rosehip", "990) buckeye, horse chestnut, conker", "991) coral fungus", "992) agaric", "993) gyromitra", "994) stinkhorn, carrion fungus", "995) earthstar", "996) hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997) bolete", "998) ear, spike, capitulum", "999) toilet tissue, toilet paper, bathroom tissue"] def interpolate_and_shape(A, B, num_interps): interps = interpolate(A, B, num_interps) return (interps.transpose(1, 0, *range(2, len(interps.shape))) .reshape(num_samples * num_interps, *interps.shape[2:])) z_A, z_B = [truncated_z_sample(num_samples, truncation, noise_seed) for noise_seed in [noise_seed_A, noise_seed_B]] y_A, y_B = [one_hot([int(category.split(')')[0])] * num_samples) for category in [category_A, category_B]] z_interp = interpolate_and_shape(z_A, z_B, num_interps) y_interp = interpolate_and_shape(y_A, y_B, num_interps) ims = sample(sess, z_interp, y_interp, truncation=truncation) imshow(imgrid(ims, cols=num_interps))
BigGANでの画像生成いかがでしたでしょうか。
皆さんも是非やってみてください！
ではでは。
亡くなった人をチャットボットに？？？【コラム】＃004
こんにちは！こーたろーです。
先日こんな記事を見かけました。
【マイクロソフト、亡くなった人をチャットボットにできる特許を取得 | ギズモード・ジャパン】
とうやら、マイクロソフトの方で、アメリカの特許商標庁に提出された特許では、亡くなった人の画像や音声データ、SNSへの投稿、電子メッセージなどの情報からチャットボットを作成するというものが公開されている。
これを見たとき、まず思い出したのは、葬儀の時の遺影の表情が変化するサービス。
【進化する日本の葬儀業、ついに遺影が・・・動き出す！＝中国メディア (2019年12月9日) - エキサイトニュース 】
故人情報が後世に残って、あたかも生きているときのようにふるまうという発想。
これは、昔見たアニメ攻殻機動隊のように、電脳化の世界へ向けて前進したような気がするのは私だけでしょうか。
AIもシンギュラリティを迎え、さらに進化を遂げていくと、意思を持ち始めるといったことが可能になるかもしれません。
電脳化して生き続ける世界。皆さんはどう思いますか？
攻殻機動隊を見ながらいろいろ考えさせられることもあったなーと懐かしく思っています。
ネットの世界で生き続けるというのは、どういった感情になるのか。。。
GHOSTがささやくのか！？！？
ではでは。
![GHOST IN THE SHELL/攻殻機動隊 [Blu-ray]](https://m.media-amazon.com/images/I/51zQhaRZOLL.jpg "GHOST IN THE SHELL/攻殻機動隊 [Blu-ray]")
![攻殻機動隊 Stand Alone Complex 1st & 2nd Gig [import][DVD][PAL]](https://m.media-amazon.com/images/I/511WXqgJuKL.jpg "攻殻機動隊 Stand Alone Complex 1st & 2nd Gig [import][DVD][PAL]")
学習済みGAN使ってみた【図解速習Deep Learning】#009
こんにちは！こーたろーです。
本日はついにGANに取り掛かっていきます。
今日はdemoですが（笑
本日も【図解速習DEEP LEARNING】をやっていきます。
皆さんもう買いました？ すべてを理解するのは結構難しいですよね。。汗
それでは早速本日分！
サンプルコードではTensorFlow1.Xですが、今回もTensorFlow2.4.0でやっていきます。
コードが変わっていますのでご注意ください。
この辺のバグ取りなんかは、Python、TensorFlowの勉強になって、とても為になっています。
GANをやっていきますが、今回は学習済みのGANのコレクションを使って、画像生成を行います。
洗剤空間上のランダムなベクトルを選び、それをGANへ入力し、画像を生成するという流れです。
ではソースコードを見ていきましょう。
１．ライブラリのインポート
from google.colab import output import matplotlib.pyplot as plt import numpy as np import pandas as pd import tensorflow as tf import tensorflow_hub as hub tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
２．モデルを取得
辞書型で入れているGANモデルを選択すると、Tensorflow_hubからモデルをダウンロードします。
module_metadata_dict = {'dataset': ['CelebA HQ (128x128)', 'CelebA HQ (128x128)', 'LSUN Bedroom', 'LSUN Bedroom', 'CelebA HQ (128x128)', 'CelebA HQ (128x128)', 'LSUN Bedroom', 'LSUN Bedroom', 'CelebA HQ (128x128)', 'LSUN Bedroom', 'CIFAR10', 'CIFAR10', 'CIFAR10', 'CIFAR10', 'CIFAR10'], 'penalty': ['-', '-', '-', '-', '-', '-', '-', '-', 'DRAGAN (lambda=1.000)', 'WGAN (lambda=0.145)', '-', '-', '-', '-', 'WGAN (lambda=1.000)'], 'architecture': ['ResNet19', 'ResNet19', 'ResNet19', 'ResNet19', 'ResNet19', 'ResNet19', 'ResNet19', 'ResNet19', 'ResNet19', 'ResNet19', 'ResNet CIFAR', 'ResNet CIFAR', 'ResNet CIFAR', 'ResNet CIFAR', 'ResNet CIFAR'], 'beta1': ['0.375', '0.500', '0.585', '0.195', '0.500', '0.500', '0.500', '0.102', '0.500', '0.711', '0.500', '0.500', '0.500', '0.500', '0.500'], 'beta2': ['0.998', '0.999', '0.990', '0.882', '0.999', '0.999', '0.999', '0.998', '0.900', '0.979', '0.999', '0.999', '0.999', '0.999', '0.999'], 'module_url': ['https://tfhub.dev/google/compare_gan/model_1_celebahq128_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_2_celebahq128_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_3_lsun_bedroom_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_4_lsun_bedroom_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_5_celebahq128_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_6_celebahq128_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_7_lsun_bedroom_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_8_lsun_bedroom_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_9_celebahq128_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_10_lsun_bedroom_resnet19/1', 'https://tfhub.dev/google/compare_gan/model_11_cifar10_resnet_cifar/1', 'https://tfhub.dev/google/compare_gan/model_12_cifar10_resnet_cifar/1', 'https://tfhub.dev/google/compare_gan/model_13_cifar10_resnet_cifar/1', 'https://tfhub.dev/google/compare_gan/model_14_cifar10_resnet_cifar/1', 'https://tfhub.dev/google/compare_gan/model_15_cifar10_resnet_cifar/1'], 'disc_iters': [1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 5, 5, 5, 5, 5], 'model': ['Non-saturating GAN', 'Non-saturating GAN', 'Least-squares GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Least-squares GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Non-saturating GAN', 'Non-saturating GAN'], 'inception_score': ['2.38', '2.59', '4.23', '4.10', '2.38', '2.54', '3.64', '3.58', '2.34', '3.92', '7.57', '7.47', '7.74', '7.74', '7.70'], 'disc_norm': ['none', 'none', 'none', 'none', 'layer_norm', 'layer_norm', 'spectral_norm', 'spectral_norm', 'layer_norm', 'layer_norm', 'none', 'none', 'spectral_norm', 'spectral_norm', 'spectral_norm'], 'fid': ['34.29', '35.85', '102.74', '112.92', '30.02', '32.05', '41.60', '42.51', '29.13', '40.36', '28.12', '30.08', '22.91', '23.22', '22.73'], 'ms_ssim_score': ['0.32', '0.29', 'N/A', 'N/A', '0.29', '0.28', 'N/A', 'N/A', '0.30', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A'], 'learning_rate': ['3.381e-05', '1.000e-04', '3.220e-05', '1.927e-05', '1.000e-04', '1.000e-04', '2.000e-04', '2.851e-04', '1.000e-04', '1.281e-04', '2.000e-04', '1.000e-04', '2.000e-04', '2.000e-04', '2.000e-04']}
MODULE_METADATA = pd.DataFrame.from_dict(module_metadata_dict)
MIN_FID_MODULE = MODULE_METADATA.loc[
MODULE_METADATA['fid'].astype(float).idxmin()]
SELECTED_MODULE = MIN_FID_MODULE['module_url']
SELECTED_MODULE_DATASET = MIN_FID_MODULE['dataset']
def display_images(images, captions=None):
batch_size, dim1, dim2, channels = images.shape
num_horizontally = 8
figsize = (20, 20) if dim1 > 32 else (10, 10)
f, axes = plt.subplots(
len(images) // num_horizontally, num_horizontally, figsize=figsize)
for i in range(len(images)):
axes[i // num_horizontally, i % num_horizontally].axis("off")
if captions is not None:
axes[i // num_horizontally, i % num_horizontally].text(0, -3, captions[i])
axes[i // num_horizontally, i % num_horizontally].imshow(images[i])
f.tight_layout()
class ShowModuleTable(object):
def __init__(self, callback):
self._callback = callback
def _repr_html_(self):
template = """
<style>
table {
font-size: 15px;
font-family: Inconsolata, monospace;
border-collapse: collapse;
border: 1px solid #444444;
}
th {
font-size: 18px;
background-color: #DDDDDD;
border: 1px solid #AAAAAA;
white-space: nowrap;
}
tr {
cursor: pointer;
white-space: nowrap;
}
td {
padding: 6px 6px 6px 6px;
border: 1px solid #AAAAAA;
}
.selected-row {
font-weight: bold;
background-color: #B0BED9;
}
</style>
<table>"""
table_headers = [
('dataset', 'Dataset'),
('architecture', 'Architecture'),
('fid', 'FID'),
('inception_score', 'IS'),
('ms_ssim_score', 'MS-SSIM'),
('model', 'Model'),
('learning_rate', 'Learning rate'),
('beta1', 'β<sub>1</sub>'),
('beta2', 'β<sub>2</sub>'),
('disc_iters', 'n<sub>disc</sub>'),
('disc_norm', 'Disc norm'),
('penalty', 'Penalty'),
('module_url', 'Module name'),
]
header_template = "<tr>"
for _, header_name in table_headers:
header_template += "<th>" + header_name + "</th>"
header_template += "</tr>"
template += header_template
for i, (_, row) in enumerate(MODULE_METADATA.iterrows()):
uuid = "row-%s" % i
output.register_callback(uuid, self._callback)
selected_class = ""
if row['module_url'] == MIN_FID_MODULE['module_url']:
selected_class = "class=\"selected-row\""
row_template = "<tr id=\"" + uuid + "\"" + selected_class + ">"
for key, _ in table_headers:
row_template += "<td>" + str(row[key]) + "</td>"
row_template += "</tr>"
template += row_template
template += """
</table>
<script>"""
for i, (_, row) in enumerate(MODULE_METADATA.iterrows()):
uuid = "row-%s" % i
m = row['module_url']
d = row['dataset']
template += """
document.querySelector(\"#""" + uuid + """\").onclick = function() {
google.colab.kernel.invokeFunction('""" + uuid + """', ['""" + m +"""', '""" + d + """'], {});
var selected = document.getElementsByClassName('selected-row');
for (var i = 0; i < selected.length; i++) {
selected[i].classList.remove('selected-row');
}
this.classList.toggle("selected-row");
e.preventDefault();
};
"""
template += """</script>"""
return template
def set_selected_module(module_name, dataset):
global SELECTED_MODULE
SELECTED_MODULE = module_name
global SELECTED_MODULE_DATASET
SELECTED_MODULE_DATASET = dataset
ShowModuleTable(set_selected_module)
一覧表示したらこんな感じです。
下記の「assert」の使い方は覚えておいた方がいいです！
今度解説したいと思います。
assert SELECTED_MODULE is not None and SELECTED_MODULE_DATASET is not None, \ 'You must run the above cell and select a module from the table to generate images.' print('Using module: "%s"' % SELECTED_MODULE) print('Generating images like dataset: "%s"' % SELECTED_MODULE_DATASET) batch_size = 64 z_dim = 128 with tf.Graph().as_default(): gan = hub.Module(SELECTED_MODULE) z_input = tf.compat.v1.placeholder(dtype=tf.float32, shape=(batch_size, z_dim)) image_output = gan(z_input, signature="generator") with tf.compat.v1.train.MonitoredSession() as session: z_values = np.random.uniform(-1, 1, size=(batch_size, z_dim)) images = session.run(image_output, feed_dict={z_input: z_values}) display_images(images)
Z_input という潜在空間ベクトルを定義しています。
そこからGANを使って画像を生成しています。
結果がこちら↓↓↓↓↓
メタデータから別の学習済みGANを選択するには、上記のコードのうち、
MIN_FID_MODULE = MODULE_METADATA.loc[
MODULE_METADATA['fid'].astype(float).idxmin()]
の部分を変更してみてください。
モジュールダイレクト入力なんかでも大丈夫です。
一覧表示のコードの記述が面倒なので、一つずつ使ってみてもいいかもしれませんね。
「Dataset : LSUN Bedroom」、「model : model_4_lsun_bedroom_resnet19」の場合
こんな感じです。
いかがでしたでしょうか。
よくわかりませんよね。。。汗
入力から画像を生成しただけなので、GAN本来の特性が出ていない感じがします。
入門編なのでこんなもんなのかな？ GANも後々作成できたらと思っています。
ではでは。
DELF使って特徴くらべてみた【図解速習Deep Learning】#008
こんにちは！こーたろーです。
今日は【図解速習DEEP LEARNING】の続きです！！
実践はどんどん続き、本日は「DELF」を使ってみます。
DELFは、画像を処理し、特徴点とそれらの特徴量記述を識別するロジックです。
説明を忘れていましたが、最近使っていた「Tensorflow Hub」は、事前に学習されたモジュール群を再利用可能なリソースとしてパッケージ化したものです。
学習を済ませているため、学習データを準備する必要がなく、今後活用例などが出てくるかもしれません。
しかし、特徴を抽出するにとどまっているので、そのあとどう処理するかというところは、開発しなければならないです。
それでは始めます。
１．ライブラリのインストール
!pip install -q 'tensorflow-hub' !pip install -q 'scikit-image'
２．ライブラリのインポート
from absl import logging import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np from PIL import Image, ImageOps from scipy.spatial import cKDTree from skimage.feature import plot_matches from skimage.measure import ransac from skimage.transform import AffineTransform from six import BytesIO import tensorflow as tf import tensorflow_hub as hub from six.moves.urllib.request import urlopen
３．比較したい２つの画像のURLを指定
IMAGE_1_URL = 'https://upload.wikimedia.org/wikipedia/commons/d/d8/Eiffel_Tower%2C_November_15%2C_2011.jpg' IMAGE_2_URL = 'https://upload.wikimedia.org/wikipedia/commons/a/a8/Eiffel_Tower_from_immediately_beside_it%2C_Paris_May_2008.jpg'
４．画像のダウンロードを行い、resizeを行う
ダウンロードとresizeを行う関数を定義
def download_and_resize(name, url, new_width=256, new_height=256): path = tf.keras.utils.get_file(url.split('/')[-1], url) image = Image.open(path) image = ImageOps.fit(image, (new_width, new_height), Image.ANTIALIAS) return image
各URLに対して、画像を取得する
image1 = download_and_resize('image_1.jpg', IMAGE_1_URL) image2 = download_and_resize('image_2.jpg', IMAGE_2_URL) plt.subplot(1,2,1) plt.imshow(image1) plt.subplot(1,2,2) plt.imshow(image2)
チュートリアルの中のこちらの画像を使用しました。
５．DELFモジュールのロード
delf = hub.load('https://tfhub.dev/google/delf/1').signatures['default']
６．各イメージにDELFを実行
DELFモジュールは、画像を入力とし、特徴点をベクトル形式で出力します。
DELFを実行するための関数を定義（前処理部分の記述）
def run_delf(image): np_image = np.array(image) float_image = tf.image.convert_image_dtype(np_image, tf.float32) return delf( image=float_image, score_threshold=tf.constant(100.0), image_scales=tf.constant([0.25, 0.3536, 0.5, 0.7071, 1.0, 1.4142, 2.0]), max_feature_num=tf.constant(1000))
各画像に対してDELFを実行
result1 = run_delf(image1) result2 = run_delf(image2)
７．比較結果の描画
def match_images(image1, image2, result1, result2): distance_threshold = 0.8 num_features_1 = result1['locations'].shape[0] print("Loaded image 1's %d features" % num_features_1) num_features_2 = result2['locations'].shape[0] print("Loaded image 2's %d features" % num_features_2) d1_tree = cKDTree(result1['descriptors']) _, indices = d1_tree.query( result2['descriptors'], distance_upper_bound=distance_threshold) locations_2_to_use = np.array([ result2['locations'][i,] for i in range(num_features_2) if indices[i] != num_features_1 ]) locations_1_to_use = np.array([ result1['locations'][indices[i],] for i in range(num_features_2) if indices[i] != num_features_1 ]) _, inliers = ransac( (locations_1_to_use, locations_2_to_use), AffineTransform, min_samples=3, residual_threshold=20, max_trials=1000) print('Found %d inliers' % sum(inliers)) _, ax = plt.subplots() inlier_idxs = np.nonzero(inliers)[0] plot_matches( ax, image1, image2, locations_1_to_use, locations_2_to_use, np.column_stack((inlier_idxs, inlier_idxs)), matches_color='b') ax.axis('off') ax.set_title('DELF correspondences')
match_images(image1, image2, result1, result2)
他のはこんな感じになりました。
どんどん難しくなっている気がしますね。。
Tensorflow極めないと。。。そして、メソッドも覚えないと。。。
勉強することはかなりありそうですね。
ではでは。。
DLフレームワーク「SmallTrain 0.2.1」リリース【コラム】＃003
こんにちは！こーたろーです。
Geek Guild社がディープラーニングのフレームワークをオープンソースソフトウェアとしてリリースしたようです。
どうやら学習済みモデルが内包されていて、そのまま活用可能なパッケージがあるとか。
しかも「Jupyter Notebook」対応。
利点は記事にも書いていますが、引用させていただいて、
【利点】
- 最小限のデータサイエンスのバックグラウンドで、PoCだけでなく本運用を見据えた開発が可能 ⇐今度中身をみてみましょう！
- TensorFlowおよびPyTorchラッパーとして利用できる ⇐なるほど！いいね☆
- AI研究論文の先端のアルゴリズムを使用して学習済みモデルを構築している ⇐学習ソースが問題かな？
- 最小限のデータと学習時間で精度が向上 ⇐転移学習が簡単にできるってことかな
- 「SmallTrain」は様々なデータを事前学習している ⇐ 学習済みがあるって素敵💛
- MITライセンスに準拠したオープンソースのため、バグ修正や改善について心配が不要 ⇐MITライセンスの信頼度！！
参考URL（記事） 、出典
ディープラーニングフレームワーク「SmallTrain 0.2.1」がリリース：CodeZine（コードジン）
こういったパッケージが大量にあふれだすと、こちらもどれを使っていいのか・・・ってなりそう。。。（汗
基本はTensorFlow、PyTorch、OpenCVとかそのあたりを使えればいいのかな？
皆さんの意見が聞いてみたいです。
ではでは。。
AIブームから５年。・・・・【コラム】＃002
こんにちは！コータローです。
情報発信として、ニュースなどをピックアップして、私の考えを述べるコラムもブログに投稿したいと考えています。
日々情報収集する中から、気になるニュースをチェック！ということで。
本日はこちらです。
ベンチャー企業では、AIブームを捉えて、AIをビジネスに役立てるためにいろいろな思考錯誤が行われているようです。
特に取り掛かりやすい画像認識関連のビジネス。
最近ではパン屋のレジで商品をかざすだけで、どの商品かを見分けて、料金計算をおこなう、といった形で実用化されたケースもニュースでみました。
コロナ禍においては、画像で様々な認識を行っているものもよく目にしますね。
体温もそうですし、マスクの着用の有無など。
この記事では、概念実証（PoC）などをやって受注を受けているが、データのクレンジング業務に追われる企業が多く、本番（中・長期的な契約）までたどり着けていないといったことが記されている。
私が働いていた職場でも、こういったデジタル技術の導入に意欲はみせるけれども、費用対効果がどうなるかといった、経営面の課題や経営陣の説得が難しく、また価格がいくらが妥当かなどの議論をする中で、PoCを超えることができなかったケースが沢山有ります。
記事では、製造業についても書いていますが、AI・データ分析をソリューションとする課題は多くあるものの、活用するためのデータを取得する部分や分析インフラを構築することろに壁がある模様。
工場関係でIoTを行う（IIoT）と呼ばれる技術を導入しなければならないが、センサーやカメラなどの大幅な投資が必要となってくる。
最近ではPoCなどのスモールスタートで、どんどんスケーリングさせていくということは考えられるが、上記に書いたように技術進歩の早さも相まって、一つのソリューションで一気通貫に行うことが難しいのも実際のところのようだ。
ベンチャービジネスは、こういった壁を乗り越えて、導入に漕ぎつけた企業がユニコーンとなる。その難しさは、これまでベンチャー企業を３社経験した私はよく知っている。
コラムでは、自分の経験なども踏まえて、コメントできたらなと思います。
今後ともコラムも宜しくお願いします。
ではでは。
