├── README.md ├── categories ├── categories_imagenet.txt └── categories_places2.txt ├── data ├── __init__.py └── loader.py ├── eval_esc50.py ├── main_train.py └── main_train_small.py /README.md: -------------------------------------------------------------------------------- 1 | # SoundNet with PyTorch 2 | 3 | ## Introduction 4 | PyTorch implementation for SoundNet [paper link](http://web.mit.edu/vondrick/soundnet.pdf).[1] 5 | SoundNet is a model to classify sounds using transfer learning with visual knowledges on large size of unlabeled videos. 6 | 7 | ![By soundnet](https://camo.githubusercontent.com/0b88af5c13ba987a17dcf90cd58816cf8ef04554/687474703a2f2f70726f6a656374732e637361696c2e6d69742e6564752f736f756e646e65742f736f756e646e65742e6a7067) 8 | 9 | The original implementation by cvondrick [link](https://github.com/cvondrick/soundnet) was using Torch. Alternative implementation with TensorFlow was created by eborboihuc [link](https://github.com/eborboihuc/SoundNet-tensorflow) . 10 | 11 | 12 | ## Prerequisites 13 | - PyTorch [website](http://pytorch.org/) 14 | - NVIDIA GPU + CUDA 8 + CuDNN v5 15 | - Python 2.7 16 | 17 | .... To be Continued .... 18 | 19 | Reference 20 | [1] SoundNet: Learning Sound Representations from Unlabeled Video : By Yusuf Aytar, Carl Vondrick, Antonio Torralba. NIPS 2016 21 | -------------------------------------------------------------------------------- /categories/categories_imagenet.txt: -------------------------------------------------------------------------------- 1 | n01440764 tench, Tinca tinca 2 | n01443537 goldfish, Carassius auratus 3 | n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias 4 | n01491361 tiger shark, Galeocerdo cuvieri 5 | n01494475 hammerhead, hammerhead shark 6 | n01496331 electric ray, crampfish, numbfish, torpedo 7 | n01498041 stingray 8 | n01514668 cock 9 | n01514859 hen 10 | n01518878 ostrich, Struthio camelus 11 | n01530575 brambling, Fringilla montifringilla 12 | n01531178 goldfinch, Carduelis carduelis 13 | n01532829 house finch, linnet, Carpodacus mexicanus 14 | n01534433 junco, snowbird 15 | n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea 16 | n01558993 robin, American robin, Turdus migratorius 17 | n01560419 bulbul 18 | n01580077 jay 19 | n01582220 magpie 20 | n01592084 chickadee 21 | n01601694 water ouzel, dipper 22 | n01608432 kite 23 | n01614925 bald eagle, American eagle, Haliaeetus leucocephalus 24 | n01616318 vulture 25 | n01622779 great grey owl, great gray owl, Strix nebulosa 26 | n01629819 European fire salamander, Salamandra salamandra 27 | n01630670 common newt, Triturus vulgaris 28 | n01631663 eft 29 | n01632458 spotted salamander, Ambystoma maculatum 30 | n01632777 axolotl, mud puppy, Ambystoma mexicanum 31 | n01641577 bullfrog, Rana catesbeiana 32 | n01644373 tree frog, tree-frog 33 | n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui 34 | n01664065 loggerhead, loggerhead turtle, Caretta caretta 35 | n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea 36 | n01667114 mud turtle 37 | n01667778 terrapin 38 | n01669191 box turtle, box tortoise 39 | n01675722 banded gecko 40 | n01677366 common iguana, iguana, Iguana iguana 41 | n01682714 American chameleon, anole, Anolis carolinensis 42 | n01685808 whiptail, whiptail lizard 43 | n01687978 agama 44 | n01688243 frilled lizard, Chlamydosaurus kingi 45 | n01689811 alligator lizard 46 | n01692333 Gila monster, Heloderma suspectum 47 | n01693334 green lizard, Lacerta viridis 48 | n01694178 African chameleon, Chamaeleo chamaeleon 49 | n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis 50 | n01697457 African crocodile, Nile crocodile, Crocodylus niloticus 51 | n01698640 American alligator, Alligator mississipiensis 52 | n01704323 triceratops 53 | n01728572 thunder snake, worm snake, Carphophis amoenus 54 | n01728920 ringneck snake, ring-necked snake, ring snake 55 | n01729322 hognose snake, puff adder, sand viper 56 | n01729977 green snake, grass snake 57 | n01734418 king snake, kingsnake 58 | n01735189 garter snake, grass snake 59 | n01737021 water snake 60 | n01739381 vine snake 61 | n01740131 night snake, Hypsiglena torquata 62 | n01742172 boa constrictor, Constrictor constrictor 63 | n01744401 rock python, rock snake, Python sebae 64 | n01748264 Indian cobra, Naja naja 65 | n01749939 green mamba 66 | n01751748 sea snake 67 | n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus 68 | n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus 69 | n01756291 sidewinder, horned rattlesnake, Crotalus cerastes 70 | n01768244 trilobite 71 | n01770081 harvestman, daddy longlegs, Phalangium opilio 72 | n01770393 scorpion 73 | n01773157 black and gold garden spider, Argiope aurantia 74 | n01773549 barn spider, Araneus cavaticus 75 | n01773797 garden spider, Aranea diademata 76 | n01774384 black widow, Latrodectus mactans 77 | n01774750 tarantula 78 | n01775062 wolf spider, hunting spider 79 | n01776313 tick 80 | n01784675 centipede 81 | n01795545 black grouse 82 | n01796340 ptarmigan 83 | n01797886 ruffed grouse, partridge, Bonasa umbellus 84 | n01798484 prairie chicken, prairie grouse, prairie fowl 85 | n01806143 peacock 86 | n01806567 quail 87 | n01807496 partridge 88 | n01817953 African grey, African gray, Psittacus erithacus 89 | n01818515 macaw 90 | n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita 91 | n01820546 lorikeet 92 | n01824575 coucal 93 | n01828970 bee eater 94 | n01829413 hornbill 95 | n01833805 hummingbird 96 | n01843065 jacamar 97 | n01843383 toucan 98 | n01847000 drake 99 | n01855032 red-breasted merganser, Mergus serrator 100 | n01855672 goose 101 | n01860187 black swan, Cygnus atratus 102 | n01871265 tusker 103 | n01872401 echidna, spiny anteater, anteater 104 | n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus 105 | n01877812 wallaby, brush kangaroo 106 | n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus 107 | n01883070 wombat 108 | n01910747 jellyfish 109 | n01914609 sea anemone, anemone 110 | n01917289 brain coral 111 | n01924916 flatworm, platyhelminth 112 | n01930112 nematode, nematode worm, roundworm 113 | n01943899 conch 114 | n01944390 snail 115 | n01945685 slug 116 | n01950731 sea slug, nudibranch 117 | n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore 118 | n01968897 chambered nautilus, pearly nautilus, nautilus 119 | n01978287 Dungeness crab, Cancer magister 120 | n01978455 rock crab, Cancer irroratus 121 | n01980166 fiddler crab 122 | n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica 123 | n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus 124 | n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish 125 | n01985128 crayfish, crawfish, crawdad, crawdaddy 126 | n01986214 hermit crab 127 | n01990800 isopod 128 | n02002556 white stork, Ciconia ciconia 129 | n02002724 black stork, Ciconia nigra 130 | n02006656 spoonbill 131 | n02007558 flamingo 132 | n02009229 little blue heron, Egretta caerulea 133 | n02009912 American egret, great white heron, Egretta albus 134 | n02011460 bittern 135 | n02012849 crane 136 | n02013706 limpkin, Aramus pictus 137 | n02017213 European gallinule, Porphyrio porphyrio 138 | n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana 139 | n02018795 bustard 140 | n02025239 ruddy turnstone, Arenaria interpres 141 | n02027492 red-backed sandpiper, dunlin, Erolia alpina 142 | n02028035 redshank, Tringa totanus 143 | n02033041 dowitcher 144 | n02037110 oystercatcher, oyster catcher 145 | n02051845 pelican 146 | n02056570 king penguin, Aptenodytes patagonica 147 | n02058221 albatross, mollymawk 148 | n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus 149 | n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca 150 | n02074367 dugong, Dugong dugon 151 | n02077923 sea lion 152 | n02085620 Chihuahua 153 | n02085782 Japanese spaniel 154 | n02085936 Maltese dog, Maltese terrier, Maltese 155 | n02086079 Pekinese, Pekingese, Peke 156 | n02086240 Shih-Tzu 157 | n02086646 Blenheim spaniel 158 | n02086910 papillon 159 | n02087046 toy terrier 160 | n02087394 Rhodesian ridgeback 161 | n02088094 Afghan hound, Afghan 162 | n02088238 basset, basset hound 163 | n02088364 beagle 164 | n02088466 bloodhound, sleuthhound 165 | n02088632 bluetick 166 | n02089078 black-and-tan coonhound 167 | n02089867 Walker hound, Walker foxhound 168 | n02089973 English foxhound 169 | n02090379 redbone 170 | n02090622 borzoi, Russian wolfhound 171 | n02090721 Irish wolfhound 172 | n02091032 Italian greyhound 173 | n02091134 whippet 174 | n02091244 Ibizan hound, Ibizan Podenco 175 | n02091467 Norwegian elkhound, elkhound 176 | n02091635 otterhound, otter hound 177 | n02091831 Saluki, gazelle hound 178 | n02092002 Scottish deerhound, deerhound 179 | n02092339 Weimaraner 180 | n02093256 Staffordshire bullterrier, Staffordshire bull terrier 181 | n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier 182 | n02093647 Bedlington terrier 183 | n02093754 Border terrier 184 | n02093859 Kerry blue terrier 185 | n02093991 Irish terrier 186 | n02094114 Norfolk terrier 187 | n02094258 Norwich terrier 188 | n02094433 Yorkshire terrier 189 | n02095314 wire-haired fox terrier 190 | n02095570 Lakeland terrier 191 | n02095889 Sealyham terrier, Sealyham 192 | n02096051 Airedale, Airedale terrier 193 | n02096177 cairn, cairn terrier 194 | n02096294 Australian terrier 195 | n02096437 Dandie Dinmont, Dandie Dinmont terrier 196 | n02096585 Boston bull, Boston terrier 197 | n02097047 miniature schnauzer 198 | n02097130 giant schnauzer 199 | n02097209 standard schnauzer 200 | n02097298 Scotch terrier, Scottish terrier, Scottie 201 | n02097474 Tibetan terrier, chrysanthemum dog 202 | n02097658 silky terrier, Sydney silky 203 | n02098105 soft-coated wheaten terrier 204 | n02098286 West Highland white terrier 205 | n02098413 Lhasa, Lhasa apso 206 | n02099267 flat-coated retriever 207 | n02099429 curly-coated retriever 208 | n02099601 golden retriever 209 | n02099712 Labrador retriever 210 | n02099849 Chesapeake Bay retriever 211 | n02100236 German short-haired pointer 212 | n02100583 vizsla, Hungarian pointer 213 | n02100735 English setter 214 | n02100877 Irish setter, red setter 215 | n02101006 Gordon setter 216 | n02101388 Brittany spaniel 217 | n02101556 clumber, clumber spaniel 218 | n02102040 English springer, English springer spaniel 219 | n02102177 Welsh springer spaniel 220 | n02102318 cocker spaniel, English cocker spaniel, cocker 221 | n02102480 Sussex spaniel 222 | n02102973 Irish water spaniel 223 | n02104029 kuvasz 224 | n02104365 schipperke 225 | n02105056 groenendael 226 | n02105162 malinois 227 | n02105251 briard 228 | n02105412 kelpie 229 | n02105505 komondor 230 | n02105641 Old English sheepdog, bobtail 231 | n02105855 Shetland sheepdog, Shetland sheep dog, Shetland 232 | n02106030 collie 233 | n02106166 Border collie 234 | n02106382 Bouvier des Flandres, Bouviers des Flandres 235 | n02106550 Rottweiler 236 | n02106662 German shepherd, German shepherd dog, German police dog, alsatian 237 | n02107142 Doberman, Doberman pinscher 238 | n02107312 miniature pinscher 239 | n02107574 Greater Swiss Mountain dog 240 | n02107683 Bernese mountain dog 241 | n02107908 Appenzeller 242 | n02108000 EntleBucher 243 | n02108089 boxer 244 | n02108422 bull mastiff 245 | n02108551 Tibetan mastiff 246 | n02108915 French bulldog 247 | n02109047 Great Dane 248 | n02109525 Saint Bernard, St Bernard 249 | n02109961 Eskimo dog, husky 250 | n02110063 malamute, malemute, Alaskan malamute 251 | n02110185 Siberian husky 252 | n02110341 dalmatian, coach dog, carriage dog 253 | n02110627 affenpinscher, monkey pinscher, monkey dog 254 | n02110806 basenji 255 | n02110958 pug, pug-dog 256 | n02111129 Leonberg 257 | n02111277 Newfoundland, Newfoundland dog 258 | n02111500 Great Pyrenees 259 | n02111889 Samoyed, Samoyede 260 | n02112018 Pomeranian 261 | n02112137 chow, chow chow 262 | n02112350 keeshond 263 | n02112706 Brabancon griffon 264 | n02113023 Pembroke, Pembroke Welsh corgi 265 | n02113186 Cardigan, Cardigan Welsh corgi 266 | n02113624 toy poodle 267 | n02113712 miniature poodle 268 | n02113799 standard poodle 269 | n02113978 Mexican hairless 270 | n02114367 timber wolf, grey wolf, gray wolf, Canis lupus 271 | n02114548 white wolf, Arctic wolf, Canis lupus tundrarum 272 | n02114712 red wolf, maned wolf, Canis rufus, Canis niger 273 | n02114855 coyote, prairie wolf, brush wolf, Canis latrans 274 | n02115641 dingo, warrigal, warragal, Canis dingo 275 | n02115913 dhole, Cuon alpinus 276 | n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus 277 | n02117135 hyena, hyaena 278 | n02119022 red fox, Vulpes vulpes 279 | n02119789 kit fox, Vulpes macrotis 280 | n02120079 Arctic fox, white fox, Alopex lagopus 281 | n02120505 grey fox, gray fox, Urocyon cinereoargenteus 282 | n02123045 tabby, tabby cat 283 | n02123159 tiger cat 284 | n02123394 Persian cat 285 | n02123597 Siamese cat, Siamese 286 | n02124075 Egyptian cat 287 | n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor 288 | n02127052 lynx, catamount 289 | n02128385 leopard, Panthera pardus 290 | n02128757 snow leopard, ounce, Panthera uncia 291 | n02128925 jaguar, panther, Panthera onca, Felis onca 292 | n02129165 lion, king of beasts, Panthera leo 293 | n02129604 tiger, Panthera tigris 294 | n02130308 cheetah, chetah, Acinonyx jubatus 295 | n02132136 brown bear, bruin, Ursus arctos 296 | n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus 297 | n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus 298 | n02134418 sloth bear, Melursus ursinus, Ursus ursinus 299 | n02137549 mongoose 300 | n02138441 meerkat, mierkat 301 | n02165105 tiger beetle 302 | n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle 303 | n02167151 ground beetle, carabid beetle 304 | n02168699 long-horned beetle, longicorn, longicorn beetle 305 | n02169497 leaf beetle, chrysomelid 306 | n02172182 dung beetle 307 | n02174001 rhinoceros beetle 308 | n02177972 weevil 309 | n02190166 fly 310 | n02206856 bee 311 | n02219486 ant, emmet, pismire 312 | n02226429 grasshopper, hopper 313 | n02229544 cricket 314 | n02231487 walking stick, walkingstick, stick insect 315 | n02233338 cockroach, roach 316 | n02236044 mantis, mantid 317 | n02256656 cicada, cicala 318 | n02259212 leafhopper 319 | n02264363 lacewing, lacewing fly 320 | n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk 321 | n02268853 damselfly 322 | n02276258 admiral 323 | n02277742 ringlet, ringlet butterfly 324 | n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus 325 | n02280649 cabbage butterfly 326 | n02281406 sulphur butterfly, sulfur butterfly 327 | n02281787 lycaenid, lycaenid butterfly 328 | n02317335 starfish, sea star 329 | n02319095 sea urchin 330 | n02321529 sea cucumber, holothurian 331 | n02325366 wood rabbit, cottontail, cottontail rabbit 332 | n02326432 hare 333 | n02328150 Angora, Angora rabbit 334 | n02342885 hamster 335 | n02346627 porcupine, hedgehog 336 | n02356798 fox squirrel, eastern fox squirrel, Sciurus niger 337 | n02361337 marmot 338 | n02363005 beaver 339 | n02364673 guinea pig, Cavia cobaya 340 | n02389026 sorrel 341 | n02391049 zebra 342 | n02395406 hog, pig, grunter, squealer, Sus scrofa 343 | n02396427 wild boar, boar, Sus scrofa 344 | n02397096 warthog 345 | n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius 346 | n02403003 ox 347 | n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis 348 | n02410509 bison 349 | n02412080 ram, tup 350 | n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis 351 | n02417914 ibex, Capra ibex 352 | n02422106 hartebeest 353 | n02422699 impala, Aepyceros melampus 354 | n02423022 gazelle 355 | n02437312 Arabian camel, dromedary, Camelus dromedarius 356 | n02437616 llama 357 | n02441942 weasel 358 | n02442845 mink 359 | n02443114 polecat, fitch, foulmart, foumart, Mustela putorius 360 | n02443484 black-footed ferret, ferret, Mustela nigripes 361 | n02444819 otter 362 | n02445715 skunk, polecat, wood pussy 363 | n02447366 badger 364 | n02454379 armadillo 365 | n02457408 three-toed sloth, ai, Bradypus tridactylus 366 | n02480495 orangutan, orang, orangutang, Pongo pygmaeus 367 | n02480855 gorilla, Gorilla gorilla 368 | n02481823 chimpanzee, chimp, Pan troglodytes 369 | n02483362 gibbon, Hylobates lar 370 | n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus 371 | n02484975 guenon, guenon monkey 372 | n02486261 patas, hussar monkey, Erythrocebus patas 373 | n02486410 baboon 374 | n02487347 macaque 375 | n02488291 langur 376 | n02488702 colobus, colobus monkey 377 | n02489166 proboscis monkey, Nasalis larvatus 378 | n02490219 marmoset 379 | n02492035 capuchin, ringtail, Cebus capucinus 380 | n02492660 howler monkey, howler 381 | n02493509 titi, titi monkey 382 | n02493793 spider monkey, Ateles geoffroyi 383 | n02494079 squirrel monkey, Saimiri sciureus 384 | n02497673 Madagascar cat, ring-tailed lemur, Lemur catta 385 | n02500267 indri, indris, Indri indri, Indri brevicaudatus 386 | n02504013 Indian elephant, Elephas maximus 387 | n02504458 African elephant, Loxodonta africana 388 | n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens 389 | n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 390 | n02514041 barracouta, snoek 391 | n02526121 eel 392 | n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch 393 | n02606052 rock beauty, Holocanthus tricolor 394 | n02607072 anemone fish 395 | n02640242 sturgeon 396 | n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus 397 | n02643566 lionfish 398 | n02655020 puffer, pufferfish, blowfish, globefish 399 | n02666196 abacus 400 | n02667093 abaya 401 | n02669723 academic gown, academic robe, judge's robe 402 | n02672831 accordion, piano accordion, squeeze box 403 | n02676566 acoustic guitar 404 | n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier 405 | n02690373 airliner 406 | n02692877 airship, dirigible 407 | n02699494 altar 408 | n02701002 ambulance 409 | n02704792 amphibian, amphibious vehicle 410 | n02708093 analog clock 411 | n02727426 apiary, bee house 412 | n02730930 apron 413 | n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin 414 | n02749479 assault rifle, assault gun 415 | n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack 416 | n02776631 bakery, bakeshop, bakehouse 417 | n02777292 balance beam, beam 418 | n02782093 balloon 419 | n02783161 ballpoint, ballpoint pen, ballpen, Biro 420 | n02786058 Band Aid 421 | n02787622 banjo 422 | n02788148 bannister, banister, balustrade, balusters, handrail 423 | n02790996 barbell 424 | n02791124 barber chair 425 | n02791270 barbershop 426 | n02793495 barn 427 | n02794156 barometer 428 | n02795169 barrel, cask 429 | n02797295 barrow, garden cart, lawn cart, wheelbarrow 430 | n02799071 baseball 431 | n02802426 basketball 432 | n02804414 bassinet 433 | n02804610 bassoon 434 | n02807133 bathing cap, swimming cap 435 | n02808304 bath towel 436 | n02808440 bathtub, bathing tub, bath, tub 437 | n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 438 | n02814860 beacon, lighthouse, beacon light, pharos 439 | n02815834 beaker 440 | n02817516 bearskin, busby, shako 441 | n02823428 beer bottle 442 | n02823750 beer glass 443 | n02825657 bell cote, bell cot 444 | n02834397 bib 445 | n02835271 bicycle-built-for-two, tandem bicycle, tandem 446 | n02837789 bikini, two-piece 447 | n02840245 binder, ring-binder 448 | n02841315 binoculars, field glasses, opera glasses 449 | n02843684 birdhouse 450 | n02859443 boathouse 451 | n02860847 bobsled, bobsleigh, bob 452 | n02865351 bolo tie, bolo, bola tie, bola 453 | n02869837 bonnet, poke bonnet 454 | n02870880 bookcase 455 | n02871525 bookshop, bookstore, bookstall 456 | n02877765 bottlecap 457 | n02879718 bow 458 | n02883205 bow tie, bow-tie, bowtie 459 | n02892201 brass, memorial tablet, plaque 460 | n02892767 brassiere, bra, bandeau 461 | n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty 462 | n02895154 breastplate, aegis, egis 463 | n02906734 broom 464 | n02909870 bucket, pail 465 | n02910353 buckle 466 | n02916936 bulletproof vest 467 | n02917067 bullet train, bullet 468 | n02927161 butcher shop, meat market 469 | n02930766 cab, hack, taxi, taxicab 470 | n02939185 caldron, cauldron 471 | n02948072 candle, taper, wax light 472 | n02950826 cannon 473 | n02951358 canoe 474 | n02951585 can opener, tin opener 475 | n02963159 cardigan 476 | n02965783 car mirror 477 | n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig 478 | n02966687 carpenter's kit, tool kit 479 | n02971356 carton 480 | n02974003 car wheel 481 | n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM 482 | n02978881 cassette 483 | n02979186 cassette player 484 | n02980441 castle 485 | n02981792 catamaran 486 | n02988304 CD player 487 | n02992211 cello, violoncello 488 | n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone 489 | n02999410 chain 490 | n03000134 chainlink fence 491 | n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour 492 | n03000684 chain saw, chainsaw 493 | n03014705 chest 494 | n03016953 chiffonier, commode 495 | n03017168 chime, bell, gong 496 | n03018349 china cabinet, china closet 497 | n03026506 Christmas stocking 498 | n03028079 church, church building 499 | n03032252 cinema, movie theater, movie theatre, movie house, picture palace 500 | n03041632 cleaver, meat cleaver, chopper 501 | n03042490 cliff dwelling 502 | n03045698 cloak 503 | n03047690 clog, geta, patten, sabot 504 | n03062245 cocktail shaker 505 | n03063599 coffee mug 506 | n03063689 coffeepot 507 | n03065424 coil, spiral, volute, whorl, helix 508 | n03075370 combination lock 509 | n03085013 computer keyboard, keypad 510 | n03089624 confectionery, confectionary, candy store 511 | n03095699 container ship, containership, container vessel 512 | n03100240 convertible 513 | n03109150 corkscrew, bottle screw 514 | n03110669 cornet, horn, trumpet, trump 515 | n03124043 cowboy boot 516 | n03124170 cowboy hat, ten-gallon hat 517 | n03125729 cradle 518 | n03126707 crane 519 | n03127747 crash helmet 520 | n03127925 crate 521 | n03131574 crib, cot 522 | n03133878 Crock Pot 523 | n03134739 croquet ball 524 | n03141823 crutch 525 | n03146219 cuirass 526 | n03160309 dam, dike, dyke 527 | n03179701 desk 528 | n03180011 desktop computer 529 | n03187595 dial telephone, dial phone 530 | n03188531 diaper, nappy, napkin 531 | n03196217 digital clock 532 | n03197337 digital watch 533 | n03201208 dining table, board 534 | n03207743 dishrag, dishcloth 535 | n03207941 dishwasher, dish washer, dishwashing machine 536 | n03208938 disk brake, disc brake 537 | n03216828 dock, dockage, docking facility 538 | n03218198 dogsled, dog sled, dog sleigh 539 | n03220513 dome 540 | n03223299 doormat, welcome mat 541 | n03240683 drilling platform, offshore rig 542 | n03249569 drum, membranophone, tympan 543 | n03250847 drumstick 544 | n03255030 dumbbell 545 | n03259280 Dutch oven 546 | n03271574 electric fan, blower 547 | n03272010 electric guitar 548 | n03272562 electric locomotive 549 | n03290653 entertainment center 550 | n03291819 envelope 551 | n03297495 espresso maker 552 | n03314780 face powder 553 | n03325584 feather boa, boa 554 | n03337140 file, file cabinet, filing cabinet 555 | n03344393 fireboat 556 | n03345487 fire engine, fire truck 557 | n03347037 fire screen, fireguard 558 | n03355925 flagpole, flagstaff 559 | n03372029 flute, transverse flute 560 | n03376595 folding chair 561 | n03379051 football helmet 562 | n03384352 forklift 563 | n03388043 fountain 564 | n03388183 fountain pen 565 | n03388549 four-poster 566 | n03393912 freight car 567 | n03394916 French horn, horn 568 | n03400231 frying pan, frypan, skillet 569 | n03404251 fur coat 570 | n03417042 garbage truck, dustcart 571 | n03424325 gasmask, respirator, gas helmet 572 | n03425413 gas pump, gasoline pump, petrol pump, island dispenser 573 | n03443371 goblet 574 | n03444034 go-kart 575 | n03445777 golf ball 576 | n03445924 golfcart, golf cart 577 | n03447447 gondola 578 | n03447721 gong, tam-tam 579 | n03450230 gown 580 | n03452741 grand piano, grand 581 | n03457902 greenhouse, nursery, glasshouse 582 | n03459775 grille, radiator grille 583 | n03461385 grocery store, grocery, food market, market 584 | n03467068 guillotine 585 | n03476684 hair slide 586 | n03476991 hair spray 587 | n03478589 half track 588 | n03481172 hammer 589 | n03482405 hamper 590 | n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier 591 | n03485407 hand-held computer, hand-held microcomputer 592 | n03485794 handkerchief, hankie, hanky, hankey 593 | n03492542 hard disc, hard disk, fixed disk 594 | n03494278 harmonica, mouth organ, harp, mouth harp 595 | n03495258 harp 596 | n03496892 harvester, reaper 597 | n03498962 hatchet 598 | n03527444 holster 599 | n03529860 home theater, home theatre 600 | n03530642 honeycomb 601 | n03532672 hook, claw 602 | n03534580 hoopskirt, crinoline 603 | n03535780 horizontal bar, high bar 604 | n03538406 horse cart, horse-cart 605 | n03544143 hourglass 606 | n03584254 iPod 607 | n03584829 iron, smoothing iron 608 | n03590841 jack-o'-lantern 609 | n03594734 jean, blue jean, denim 610 | n03594945 jeep, landrover 611 | n03595614 jersey, T-shirt, tee shirt 612 | n03598930 jigsaw puzzle 613 | n03599486 jinrikisha, ricksha, rickshaw 614 | n03602883 joystick 615 | n03617480 kimono 616 | n03623198 knee pad 617 | n03627232 knot 618 | n03630383 lab coat, laboratory coat 619 | n03633091 ladle 620 | n03637318 lampshade, lamp shade 621 | n03642806 laptop, laptop computer 622 | n03649909 lawn mower, mower 623 | n03657121 lens cap, lens cover 624 | n03658185 letter opener, paper knife, paperknife 625 | n03661043 library 626 | n03662601 lifeboat 627 | n03666591 lighter, light, igniter, ignitor 628 | n03670208 limousine, limo 629 | n03673027 liner, ocean liner 630 | n03676483 lipstick, lip rouge 631 | n03680355 Loafer 632 | n03690938 lotion 633 | n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system 634 | n03692522 loupe, jeweler's loupe 635 | n03697007 lumbermill, sawmill 636 | n03706229 magnetic compass 637 | n03709823 mailbag, postbag 638 | n03710193 mailbox, letter box 639 | n03710637 maillot 640 | n03710721 maillot, tank suit 641 | n03717622 manhole cover 642 | n03720891 maraca 643 | n03721384 marimba, xylophone 644 | n03724870 mask 645 | n03729826 matchstick 646 | n03733131 maypole 647 | n03733281 maze, labyrinth 648 | n03733805 measuring cup 649 | n03742115 medicine chest, medicine cabinet 650 | n03743016 megalith, megalithic structure 651 | n03759954 microphone, mike 652 | n03761084 microwave, microwave oven 653 | n03763968 military uniform 654 | n03764736 milk can 655 | n03769881 minibus 656 | n03770439 miniskirt, mini 657 | n03770679 minivan 658 | n03773504 missile 659 | n03775071 mitten 660 | n03775546 mixing bowl 661 | n03776460 mobile home, manufactured home 662 | n03777568 Model T 663 | n03777754 modem 664 | n03781244 monastery 665 | n03782006 monitor 666 | n03785016 moped 667 | n03786901 mortar 668 | n03787032 mortarboard 669 | n03788195 mosque 670 | n03788365 mosquito net 671 | n03791053 motor scooter, scooter 672 | n03792782 mountain bike, all-terrain bike, off-roader 673 | n03792972 mountain tent 674 | n03793489 mouse, computer mouse 675 | n03794056 mousetrap 676 | n03796401 moving van 677 | n03803284 muzzle 678 | n03804744 nail 679 | n03814639 neck brace 680 | n03814906 necklace 681 | n03825788 nipple 682 | n03832673 notebook, notebook computer 683 | n03837869 obelisk 684 | n03838899 oboe, hautboy, hautbois 685 | n03840681 ocarina, sweet potato 686 | n03841143 odometer, hodometer, mileometer, milometer 687 | n03843555 oil filter 688 | n03854065 organ, pipe organ 689 | n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO 690 | n03866082 overskirt 691 | n03868242 oxcart 692 | n03868863 oxygen mask 693 | n03871628 packet 694 | n03873416 paddle, boat paddle 695 | n03874293 paddlewheel, paddle wheel 696 | n03874599 padlock 697 | n03876231 paintbrush 698 | n03877472 pajama, pyjama, pj's, jammies 699 | n03877845 palace 700 | n03884397 panpipe, pandean pipe, syrinx 701 | n03887697 paper towel 702 | n03888257 parachute, chute 703 | n03888605 parallel bars, bars 704 | n03891251 park bench 705 | n03891332 parking meter 706 | n03895866 passenger car, coach, carriage 707 | n03899768 patio, terrace 708 | n03902125 pay-phone, pay-station 709 | n03903868 pedestal, plinth, footstall 710 | n03908618 pencil box, pencil case 711 | n03908714 pencil sharpener 712 | n03916031 perfume, essence 713 | n03920288 Petri dish 714 | n03924679 photocopier 715 | n03929660 pick, plectrum, plectron 716 | n03929855 pickelhaube 717 | n03930313 picket fence, paling 718 | n03930630 pickup, pickup truck 719 | n03933933 pier 720 | n03935335 piggy bank, penny bank 721 | n03937543 pill bottle 722 | n03938244 pillow 723 | n03942813 ping-pong ball 724 | n03944341 pinwheel 725 | n03947888 pirate, pirate ship 726 | n03950228 pitcher, ewer 727 | n03954731 plane, carpenter's plane, woodworking plane 728 | n03956157 planetarium 729 | n03958227 plastic bag 730 | n03961711 plate rack 731 | n03967562 plow, plough 732 | n03970156 plunger, plumber's helper 733 | n03976467 Polaroid camera, Polaroid Land camera 734 | n03976657 pole 735 | n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria 736 | n03980874 poncho 737 | n03982430 pool table, billiard table, snooker table 738 | n03983396 pop bottle, soda bottle 739 | n03991062 pot, flowerpot 740 | n03992509 potter's wheel 741 | n03995372 power drill 742 | n03998194 prayer rug, prayer mat 743 | n04004767 printer 744 | n04005630 prison, prison house 745 | n04008634 projectile, missile 746 | n04009552 projector 747 | n04019541 puck, hockey puck 748 | n04023962 punching bag, punch bag, punching ball, punchball 749 | n04026417 purse 750 | n04033901 quill, quill pen 751 | n04033995 quilt, comforter, comfort, puff 752 | n04037443 racer, race car, racing car 753 | n04039381 racket, racquet 754 | n04040759 radiator 755 | n04041544 radio, wireless 756 | n04044716 radio telescope, radio reflector 757 | n04049303 rain barrel 758 | n04065272 recreational vehicle, RV, R.V. 759 | n04067472 reel 760 | n04069434 reflex camera 761 | n04070727 refrigerator, icebox 762 | n04074963 remote control, remote 763 | n04081281 restaurant, eating house, eating place, eatery 764 | n04086273 revolver, six-gun, six-shooter 765 | n04090263 rifle 766 | n04099969 rocking chair, rocker 767 | n04111531 rotisserie 768 | n04116512 rubber eraser, rubber, pencil eraser 769 | n04118538 rugby ball 770 | n04118776 rule, ruler 771 | n04120489 running shoe 772 | n04125021 safe 773 | n04127249 safety pin 774 | n04131690 saltshaker, salt shaker 775 | n04133789 sandal 776 | n04136333 sarong 777 | n04141076 sax, saxophone 778 | n04141327 scabbard 779 | n04141975 scale, weighing machine 780 | n04146614 school bus 781 | n04147183 schooner 782 | n04149813 scoreboard 783 | n04152593 screen, CRT screen 784 | n04153751 screw 785 | n04154565 screwdriver 786 | n04162706 seat belt, seatbelt 787 | n04179913 sewing machine 788 | n04192698 shield, buckler 789 | n04200800 shoe shop, shoe-shop, shoe store 790 | n04201297 shoji 791 | n04204238 shopping basket 792 | n04204347 shopping cart 793 | n04208210 shovel 794 | n04209133 shower cap 795 | n04209239 shower curtain 796 | n04228054 ski 797 | n04229816 ski mask 798 | n04235860 sleeping bag 799 | n04238763 slide rule, slipstick 800 | n04239074 sliding door 801 | n04243546 slot, one-armed bandit 802 | n04251144 snorkel 803 | n04252077 snowmobile 804 | n04252225 snowplow, snowplough 805 | n04254120 soap dispenser 806 | n04254680 soccer ball 807 | n04254777 sock 808 | n04258138 solar dish, solar collector, solar furnace 809 | n04259630 sombrero 810 | n04263257 soup bowl 811 | n04264628 space bar 812 | n04265275 space heater 813 | n04266014 space shuttle 814 | n04270147 spatula 815 | n04273569 speedboat 816 | n04275548 spider web, spider's web 817 | n04277352 spindle 818 | n04285008 sports car, sport car 819 | n04286575 spotlight, spot 820 | n04296562 stage 821 | n04310018 steam locomotive 822 | n04311004 steel arch bridge 823 | n04311174 steel drum 824 | n04317175 stethoscope 825 | n04325704 stole 826 | n04326547 stone wall 827 | n04328186 stopwatch, stop watch 828 | n04330267 stove 829 | n04332243 strainer 830 | n04335435 streetcar, tram, tramcar, trolley, trolley car 831 | n04336792 stretcher 832 | n04344873 studio couch, day bed 833 | n04346328 stupa, tope 834 | n04347754 submarine, pigboat, sub, U-boat 835 | n04350905 suit, suit of clothes 836 | n04355338 sundial 837 | n04355933 sunglass 838 | n04356056 sunglasses, dark glasses, shades 839 | n04357314 sunscreen, sunblock, sun blocker 840 | n04366367 suspension bridge 841 | n04367480 swab, swob, mop 842 | n04370456 sweatshirt 843 | n04371430 swimming trunks, bathing trunks 844 | n04371774 swing 845 | n04372370 switch, electric switch, electrical switch 846 | n04376876 syringe 847 | n04380533 table lamp 848 | n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle 849 | n04392985 tape player 850 | n04398044 teapot 851 | n04399382 teddy, teddy bear 852 | n04404412 television, television system 853 | n04409515 tennis ball 854 | n04417672 thatch, thatched roof 855 | n04418357 theater curtain, theatre curtain 856 | n04423845 thimble 857 | n04428191 thresher, thrasher, threshing machine 858 | n04429376 throne 859 | n04435653 tile roof 860 | n04442312 toaster 861 | n04443257 tobacco shop, tobacconist shop, tobacconist 862 | n04447861 toilet seat 863 | n04456115 torch 864 | n04458633 totem pole 865 | n04461696 tow truck, tow car, wrecker 866 | n04462240 toyshop 867 | n04465501 tractor 868 | n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi 869 | n04476259 tray 870 | n04479046 trench coat 871 | n04482393 tricycle, trike, velocipede 872 | n04483307 trimaran 873 | n04485082 tripod 874 | n04486054 triumphal arch 875 | n04487081 trolleybus, trolley coach, trackless trolley 876 | n04487394 trombone 877 | n04493381 tub, vat 878 | n04501370 turnstile 879 | n04505470 typewriter keyboard 880 | n04507155 umbrella 881 | n04509417 unicycle, monocycle 882 | n04515003 upright, upright piano 883 | n04517823 vacuum, vacuum cleaner 884 | n04522168 vase 885 | n04523525 vault 886 | n04525038 velvet 887 | n04525305 vending machine 888 | n04532106 vestment 889 | n04532670 viaduct 890 | n04536866 violin, fiddle 891 | n04540053 volleyball 892 | n04542943 waffle iron 893 | n04548280 wall clock 894 | n04548362 wallet, billfold, notecase, pocketbook 895 | n04550184 wardrobe, closet, press 896 | n04552348 warplane, military plane 897 | n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin 898 | n04554684 washer, automatic washer, washing machine 899 | n04557648 water bottle 900 | n04560804 water jug 901 | n04562935 water tower 902 | n04579145 whiskey jug 903 | n04579432 whistle 904 | n04584207 wig 905 | n04589890 window screen 906 | n04590129 window shade 907 | n04591157 Windsor tie 908 | n04591713 wine bottle 909 | n04592741 wing 910 | n04596742 wok 911 | n04597913 wooden spoon 912 | n04599235 wool, woolen, woollen 913 | n04604644 worm fence, snake fence, snake-rail fence, Virginia fence 914 | n04606251 wreck 915 | n04612504 yawl 916 | n04613696 yurt 917 | n06359193 web site, website, internet site, site 918 | n06596364 comic book 919 | n06785654 crossword puzzle, crossword 920 | n06794110 street sign 921 | n06874185 traffic light, traffic signal, stoplight 922 | n07248320 book jacket, dust cover, dust jacket, dust wrapper 923 | n07565083 menu 924 | n07579787 plate 925 | n07583066 guacamole 926 | n07584110 consomme 927 | n07590611 hot pot, hotpot 928 | n07613480 trifle 929 | n07614500 ice cream, icecream 930 | n07615774 ice lolly, lolly, lollipop, popsicle 931 | n07684084 French loaf 932 | n07693725 bagel, beigel 933 | n07695742 pretzel 934 | n07697313 cheeseburger 935 | n07697537 hotdog, hot dog, red hot 936 | n07711569 mashed potato 937 | n07714571 head cabbage 938 | n07714990 broccoli 939 | n07715103 cauliflower 940 | n07716358 zucchini, courgette 941 | n07716906 spaghetti squash 942 | n07717410 acorn squash 943 | n07717556 butternut squash 944 | n07718472 cucumber, cuke 945 | n07718747 artichoke, globe artichoke 946 | n07720875 bell pepper 947 | n07730033 cardoon 948 | n07734744 mushroom 949 | n07742313 Granny Smith 950 | n07745940 strawberry 951 | n07747607 orange 952 | n07749582 lemon 953 | n07753113 fig 954 | n07753275 pineapple, ananas 955 | n07753592 banana 956 | n07754684 jackfruit, jak, jack 957 | n07760859 custard apple 958 | n07768694 pomegranate 959 | n07802026 hay 960 | n07831146 carbonara 961 | n07836838 chocolate sauce, chocolate syrup 962 | n07860988 dough 963 | n07871810 meat loaf, meatloaf 964 | n07873807 pizza, pizza pie 965 | n07875152 potpie 966 | n07880968 burrito 967 | n07892512 red wine 968 | n07920052 espresso 969 | n07930864 cup 970 | n07932039 eggnog 971 | n09193705 alp 972 | n09229709 bubble 973 | n09246464 cliff, drop, drop-off 974 | n09256479 coral reef 975 | n09288635 geyser 976 | n09332890 lakeside, lakeshore 977 | n09399592 promontory, headland, head, foreland 978 | n09421951 sandbar, sand bar 979 | n09428293 seashore, coast, seacoast, sea-coast 980 | n09468604 valley, vale 981 | n09472597 volcano 982 | n09835506 ballplayer, baseball player 983 | n10148035 groom, bridegroom 984 | n10565667 scuba diver 985 | n11879895 rapeseed 986 | n11939491 daisy 987 | n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum 988 | n12144580 corn 989 | n12267677 acorn 990 | n12620546 hip, rose hip, rosehip 991 | n12768682 buckeye, horse chestnut, conker 992 | n12985857 coral fungus 993 | n12998815 agaric 994 | n13037406 gyromitra 995 | n13040303 stinkhorn, carrion fungus 996 | n13044778 earthstar 997 | n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa 998 | n13054560 bolete 999 | n13133613 ear, spike, capitulum 1000 | n15075141 toilet tissue, toilet paper, bathroom tissue 1001 | -------------------------------------------------------------------------------- /categories/categories_places2.txt: -------------------------------------------------------------------------------- 1 | /a/abbey 2 | /a/airfield 3 | /a/airplane_cabin 4 | /a/airport_terminal 5 | /a/alcove 6 | /a/alley 7 | /a/amphitheater 8 | /a/amusement_arcade 9 | /a/amusement_park 10 | /a/apartment_building/outdoor 11 | /a/aquarium 12 | /a/aqueduct 13 | /a/arcade 14 | /a/arch 15 | /a/archaelogical_excavation 16 | /a/archive 17 | /a/arena/hockey 18 | /a/arena/performance 19 | /a/arena/rodeo 20 | /a/army_base 21 | /a/art_gallery 22 | /a/art_school 23 | /a/art_studio 24 | /a/artists_loft 25 | /a/assembly_line 26 | /a/athletic_field/outdoor 27 | /a/atrium/public 28 | /a/attic 29 | /a/auditorium 30 | /a/auto_factory 31 | /a/auto_showroom 32 | /b/badlands 33 | /b/bakery/shop 34 | /b/balcony/exterior 35 | /b/balcony/interior 36 | /b/ball_pit 37 | /b/ballroom 38 | /b/bamboo_forest 39 | /b/banquet_hall 40 | /b/bar 41 | /b/barn 42 | /b/barndoor 43 | /b/baseball_field 44 | /b/basement 45 | /b/basilica 46 | /b/basketball_court/indoor 47 | /b/bathroom 48 | /b/bayou 49 | /b/bazaar/indoor 50 | /b/bazaar/outdoor 51 | /b/beach 52 | /b/beach_house 53 | /b/beauty_salon 54 | /b/bedchamber 55 | /b/bedroom 56 | /b/beer_garden 57 | /b/beer_hall 58 | /b/berth 59 | /b/bistro/indoor 60 | /b/bistro/outdoor 61 | /b/boardwalk 62 | /b/boat_deck 63 | /b/boathouse 64 | /b/bookstore 65 | /b/booth/indoor 66 | /b/botanical_garden 67 | /b/bow_window/indoor 68 | /b/bowling_alley 69 | /b/boxing_ring 70 | /b/bridge 71 | /b/building_facade 72 | /b/bullring 73 | /b/burial_chamber 74 | /b/bus_interior 75 | /b/bus_station/indoor 76 | /b/butchers_shop 77 | /b/butte 78 | /c/cabin/outdoor 79 | /c/cafeteria 80 | /c/campsite 81 | /c/campus 82 | /c/canal/natural 83 | /c/canal/urban 84 | /c/candy_store 85 | /c/canyon 86 | /c/car_interior/backseat 87 | /c/car_interior/frontseat 88 | /c/carrousel 89 | /c/castle 90 | /c/catacomb 91 | /c/cathedral/indoor 92 | /c/cathedral/outdoor 93 | /c/cemetery 94 | /c/chalet 95 | /c/chapel 96 | /c/childs_room 97 | /c/church/indoor 98 | /c/church/outdoor 99 | /c/classroom 100 | /c/cliff 101 | /c/closet 102 | /c/clothing_store 103 | /c/coast 104 | /c/cockpit 105 | /c/coffee_shop 106 | /c/conference_center 107 | /c/conference_room 108 | /c/construction_site 109 | /c/corn_field 110 | /c/corral 111 | /c/corridor 112 | /c/cottage 113 | /c/cottage_garden 114 | /c/courthouse 115 | /c/courtyard 116 | /c/creek 117 | /c/crevasse 118 | /c/crosswalk 119 | /c/cubicle/office 120 | /d/dam 121 | /d/delicatessen 122 | /d/department_store 123 | /d/desert/sand 124 | /d/desert/vegetation 125 | /d/desert_road 126 | /d/diner/outdoor 127 | /d/dinette/home 128 | /d/dining_hall 129 | /d/dining_room 130 | /d/discotheque 131 | /d/dock 132 | /d/doorway/outdoor 133 | /d/dorm_room 134 | /d/downtown 135 | /d/driveway 136 | /e/elevator/door 137 | /e/elevator/interior 138 | /e/elevator_lobby 139 | /e/elevator_shaft 140 | /e/embassy 141 | /e/engine_room 142 | /e/entrance_hall 143 | /e/escalator/indoor 144 | /e/excavation 145 | /f/fairway 146 | /f/farm 147 | /f/fastfood_restaurant 148 | /f/field/cultivated 149 | /f/field/wild 150 | /f/field_road 151 | /f/fire_escape 152 | /f/fire_station 153 | /f/fishpond 154 | /f/flea_market/indoor 155 | /f/florist_shop/indoor 156 | /f/food_court 157 | /f/football_field 158 | /f/forest/broadleaf 159 | /f/forest_path 160 | /f/forest_road 161 | /f/formal_garden 162 | /f/fountain 163 | /f/freeway 164 | /g/galley 165 | /g/game_room 166 | /g/garage/indoor 167 | /g/garage/outdoor 168 | /g/garbage_dump 169 | /g/gas_station 170 | /g/gazebo/exterior 171 | /g/general_store/indoor 172 | /g/general_store/outdoor 173 | /g/gift_shop 174 | /g/glacier 175 | /g/golf_course 176 | /g/gorge 177 | /g/greenhouse/indoor 178 | /g/greenhouse/outdoor 179 | /g/grotto 180 | /g/gymnasium/indoor 181 | /h/hallway 182 | /h/hangar/indoor 183 | /h/hangar/outdoor 184 | /h/harbor 185 | /h/hardware_store 186 | /h/hayfield 187 | /h/heliport 188 | /h/herb_garden 189 | /h/highway 190 | /h/home_office 191 | /h/home_theater 192 | /h/hospital 193 | /h/hospital_room 194 | /h/hot_spring 195 | /h/hotel/outdoor 196 | /h/hotel_room 197 | /h/house 198 | /h/hunting_lodge/outdoor 199 | /i/ice_cream_parlor 200 | /i/ice_floe 201 | /i/ice_shelf 202 | /i/ice_skating_rink/indoor 203 | /i/ice_skating_rink/outdoor 204 | /i/iceberg 205 | /i/igloo 206 | /i/industrial_area 207 | /i/industrial_park 208 | /i/inn/outdoor 209 | /i/islet 210 | /j/jacuzzi/indoor 211 | /j/jail_cell 212 | /j/japanese_garden 213 | /j/jewelry_shop 214 | /j/junkyard 215 | /k/kasbah 216 | /k/kennel/outdoor 217 | /k/kindergarden_classroom 218 | /k/kitchen 219 | /k/kitchenette 220 | /l/lagoon 221 | /l/lake/natural 222 | /l/landfill 223 | /l/landing_deck 224 | /l/laundromat 225 | /l/lavatory 226 | /l/lawn 227 | /l/lecture_room 228 | /l/legislative_chamber 229 | /l/library/indoor 230 | /l/library/outdoor 231 | /l/lift_bridge 232 | /l/lighthouse 233 | /l/limousine_interior 234 | /l/living_room 235 | /l/loading_dock 236 | /l/lobby 237 | /l/lock_chamber 238 | /l/locker_room 239 | /m/mansion 240 | /m/manufactured_home 241 | /m/market/indoor 242 | /m/market/outdoor 243 | /m/marsh 244 | /m/martial_arts_gym 245 | /m/mausoleum 246 | /m/medina 247 | /m/mezzanine 248 | /m/moat/water 249 | /m/monastery/outdoor 250 | /m/mosque/outdoor 251 | /m/motel 252 | /m/mountain 253 | /m/mountain_path 254 | /m/mountain_snowy 255 | /m/movie_theater/indoor 256 | /m/museum/indoor 257 | /m/museum/outdoor 258 | /m/music_studio 259 | /n/natural_history_museum 260 | /n/nursery 261 | /n/nursing_home 262 | /o/oast_house 263 | /o/ocean 264 | /o/office 265 | /o/office_building 266 | /o/office_cubicles 267 | /o/oilrig 268 | /o/operating_room 269 | /o/orchard 270 | /o/orchestra_pit 271 | /p/pagoda 272 | /p/palace 273 | /p/pantry 274 | /p/park 275 | /p/parking_garage/indoor 276 | /p/parking_garage/outdoor 277 | /p/parking_lot 278 | /p/parlor 279 | /p/pasture 280 | /p/patio 281 | /p/pavilion 282 | /p/pet_shop 283 | /p/pharmacy 284 | /p/phone_booth 285 | /p/physics_laboratory 286 | /p/picnic_area 287 | /p/pier 288 | /p/pizzeria 289 | /p/playground 290 | /p/playroom 291 | /p/plaza 292 | /p/pond 293 | /p/porch 294 | /p/promenade 295 | /p/pub/indoor 296 | /p/pulpit 297 | /p/putting_green 298 | /r/racecourse 299 | /r/raceway 300 | /r/raft 301 | /r/railroad_track 302 | /r/rainforest 303 | /r/reading_room 304 | /r/reception 305 | /r/recreation_room 306 | /r/repair_shop 307 | /r/residential_neighborhood 308 | /r/restaurant 309 | /r/restaurant_kitchen 310 | /r/restaurant_patio 311 | /r/rice_paddy 312 | /r/riding_arena 313 | /r/river 314 | /r/rock_arch 315 | /r/roof_garden 316 | /r/rope_bridge 317 | /r/ruin 318 | /r/runway 319 | /s/sandbar 320 | /s/sandbox 321 | /s/sauna 322 | /s/schoolhouse 323 | /s/science_museum 324 | /s/sea_cliff 325 | /s/server_room 326 | /s/shed 327 | /s/shoe_shop 328 | /s/shopfront 329 | /s/shopping_mall/indoor 330 | /s/shower 331 | /s/ski_lodge 332 | /s/ski_resort 333 | /s/ski_slope 334 | /s/sky 335 | /s/skyscraper 336 | /s/slum 337 | /s/snowfield 338 | /s/soccer_field 339 | /s/stable 340 | /s/stadium/baseball 341 | /s/stadium/football 342 | /s/stadium/soccer 343 | /s/stage/indoor 344 | /s/stage/outdoor 345 | /s/staircase 346 | /s/storage_room 347 | /s/street 348 | /s/subway_station/platform 349 | /s/supermarket 350 | /s/sushi_bar 351 | /s/swamp 352 | /s/swimming_hole 353 | /s/swimming_pool/indoor 354 | /s/swimming_pool/outdoor 355 | /s/synagogue/outdoor 356 | /t/television_room 357 | /t/television_studio 358 | /t/temple/east_asia 359 | /t/temple/south_asia 360 | /t/tent/outdoor 361 | /t/throne_room 362 | /t/ticket_booth 363 | /t/topiary_garden 364 | /t/tower 365 | /t/toyshop 366 | /t/track/outdoor 367 | /t/train_interior 368 | /t/train_railway 369 | /t/train_station/platform 370 | /t/tree_farm 371 | /t/tree_house 372 | /t/trench 373 | /t/tundra 374 | /u/underwater/coral_reef 375 | /u/underwater/ocean_deep 376 | /u/utility_room 377 | /v/valley 378 | /v/vegetable_garden 379 | /v/veranda 380 | /v/veterinarians_office 381 | /v/viaduct 382 | /v/village 383 | /v/vineyard 384 | /v/volcano 385 | /v/volleyball_court/outdoor 386 | /w/waiting_room 387 | /w/water_park 388 | /w/water_tower 389 | /w/waterfall/block 390 | /w/waterfall/fan 391 | /w/waterfall/plunge 392 | /w/watering_hole 393 | /w/wave 394 | /w/wet_bar 395 | /w/wheat_field 396 | /w/wind_farm 397 | /w/windmill 398 | /w/woodland 399 | /y/yard 400 | /y/youth_hostel 401 | /z/zen_garden 402 | -------------------------------------------------------------------------------- /data/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /data/loader.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /eval_esc50.py: -------------------------------------------------------------------------------- 1 | ### evaluation on ESC50 2 | -------------------------------------------------------------------------------- /main_train.py: -------------------------------------------------------------------------------- 1 | ### main script for training the whole dataset 2 | 3 | from torch.legacy import nn 4 | from torch.utils.serialization import load_lua 5 | import torch 6 | opt = { 7 | 'dataset' : 'audio', # indicates what dataset load to use (in data.lua) 8 | 'nThreads' : 40, # how many threads to pre-fetch data 9 | 'batchSize' : 64, # self-explanatory 10 | 'loadSize' : 22050*20, # when loading images, resize first to this size 11 | 'fineSize' : 22050*20, # crop this size from the loaded image 12 | 'lr' : 0.001, # learning rate 13 | 'lambda' : 250, 14 | 'beta1' : 0.9, # momentum term for adam 15 | 'meanIter' : 0, # how many iterations to retrieve for mean estimation 16 | 'saveIter' : 5000, # write check point on this interval 17 | 'niter' : 10000, # number of iterations through dataset 18 | 'ntrain' : float('inf'), # how big one epoch should be 19 | 'gpu' : 0, # which GPU to use; consider using CUDA_VISIBLE_DEVICES instead 20 | 'cudnn' : 1, # whether to use cudnn or not 21 | 'finetune' : '', # if set, will load this network instead of starting from scratch 22 | 'name' : 'soundnet', # the name of the experiment 23 | 'randomize' : 1, # whether to shuffle the data file or not 24 | 'data_root' : '/data/vision/torralba/crossmodal/flickr_videos/soundnet/mp3', 25 | 'label_binary_file' : '/data/vision/torralba/crossmodal/soundnet/features/VGG16_IMNET_TRAIN_B%04d/prob', 26 | 'label2_binary_file' : '/data/vision/torralba/crossmodal/soundnet/features/VGG16_PLACES2_TRAIN_B%04d/prob', 27 | 'label_text_file' : '/data/vision/torralba/crossmodal/soundnet/lmdbs/train_frames4_%04d.txt', 28 | 'label_dim' : 1000, 29 | 'label2_dim' : 401, 30 | 'label_time_steps' : 4, 31 | 'video_frame_time' : 5, # 5 seconds 32 | 'sample_rate' : 22050, 33 | 'mean' : 0, 34 | } 35 | 36 | torch.manual_seed(0) 37 | torch.set_num_threads(1) 38 | torch.set_default_tensor_type('torch.FloatTensor') 39 | 40 | #### Create data loader 41 | 42 | ##### create net work 43 | ## initialize the model 44 | def weights_init(layer): 45 | name = torch.typename(layer) 46 | if name.find('Convolution') > 0 : 47 | layer.weight.normal_(0.0, 0.01) 48 | layer.bias.fill_(0) 49 | #print name, name.find('Convolution') 50 | elif name.find('BatchNormalization') > 0: 51 | if layer.weight is not None: 52 | layer.weight.normal_(1.0, 0.02) 53 | if layer.bias is not None: 54 | layer.bias.fill_(0) 55 | 56 | ## create network 57 | def create_network(): 58 | net = nn.Sequential() 59 | 60 | net.add(nn.SpatialConvolution(1, 16, 1,64, 1,2, 0, 32)) 61 | net.add(nn.SpatialBatchNormalization(16)) 62 | net.add(nn.ReLU(True)) 63 | net.add(nn.SpatialMaxPooling(1,8, 1,8)) 64 | 65 | net.add(nn.SpatialConvolution(16, 32, 1,32, 1,2, 0, 16)) 66 | net.add(nn.SpatialBatchNormalization(32)) 67 | net.add(nn.ReLU(True)) 68 | net.add(nn.SpatialMaxPooling(1,8, 1,8)) 69 | 70 | net.add(nn.SpatialConvolution(32, 64, 1,16, 1,2, 0, 8)) 71 | net.add(nn.SpatialBatchNormalization(64)) 72 | net.add(nn.ReLU(True)) 73 | 74 | net.add(nn.SpatialConvolution(64, 128, 1,8, 1,2, 0, 4)) 75 | net.add(nn.SpatialBatchNormalization(128)) 76 | net.add(nn.ReLU(True)) 77 | 78 | net.add(nn.SpatialConvolution(128, 256, 1, 4, 1, 2, 0, 2)) 79 | net.add(nn.SpatialBatchNormalization(256)) 80 | net.add(nn.ReLU(True)) 81 | 82 | net.add(nn.SpatialMaxPooling(1,4, 1,4)) 83 | 84 | net.add(nn.SpatialConvolution(256, 512, 1,4, 1,2, 0,2)) 85 | net.add(nn.SpatialBatchNormalization(512)) 86 | net.add(nn.ReLU(True)) 87 | 88 | net.add(nn.SpatialConvolution(512, 1024, 1,4, 1,2, 0,2)) 89 | net.add(nn.SpatialBatchNormalization(1024)) 90 | net.add(nn.ReLU(True)) 91 | 92 | 93 | net.add(nn.ConcatTable().add(nn.SpatialConvolution(1024, 1000, 1,8, 1,2, 0,0)) 94 | .add(nn.SpatialConvolution(1024, 401, 1,8, 1,2, 0,0))) 95 | 96 | net.add(nn.ParallelTable().add(nn.SplitTable(3)).add(nn.SplitTable(3))) 97 | net.add(nn.FlattenTable()) 98 | return net 99 | -------------------------------------------------------------------------------- /main_train_small.py: -------------------------------------------------------------------------------- 1 | from torch.legacy import nn, optim 2 | from torch.utils.serialization import load_lua 3 | import torch 4 | import gc 5 | opt = { 6 | 'dataset' : 'audio', # indicates what dataset load to use (in data.lua) 7 | 'nThreads' : 40, # how many threads to pre-fetch data 8 | 'batchSize' : 64, # self-explanatory 9 | 'loadSize' : 22050*20, # when loading images, resize first to this size 10 | 'fineSize' : 22050*20, # crop this size from the loaded image 11 | 'lr' : 0.001, # learning rate 12 | 'lambda' : 250, 13 | 'beta1' : 0.9, # momentum term for adam 14 | 'meanIter' : 0, # how many iterations to retrieve for mean estimation 15 | 'saveIter' : 5000, # write check point on this interval 16 | 'niter' : 10000, # number of iterations through dataset 17 | 'ntrain' : float('inf'), # how big one epoch should be 18 | 'gpu' : 0, # which GPU to use; consider using CUDA_VISIBLE_DEVICES instead 19 | 'cudnn' : 1, # whether to use cudnn or not 20 | 'finetune' : '', # if set, will load this network instead of starting from scratch 21 | 'name' : 'soundnet', # the name of the experiment 22 | 'randomize' : 1, # whether to shuffle the data file or not 23 | 'data_root' : '/data/vision/torralba/crossmodal/flickr_videos/soundnet/mp3', 24 | 'label_binary_file' : '/data/vision/torralba/crossmodal/soundnet/features/VGG16_IMNET_TRAIN_B%04d/prob', 25 | 'label2_binary_file' : '/data/vision/torralba/crossmodal/soundnet/features/VGG16_PLACES2_TRAIN_B%04d/prob', 26 | 'label_text_file' : '/data/vision/torralba/crossmodal/soundnet/lmdbs/train_frames4_%04d.txt', 27 | 'label_dim' : 1000, 28 | 'label2_dim' : 401, 29 | 'label_time_steps' : 4, 30 | 'video_frame_time' : 5, # 5 seconds 31 | 'sample_rate' : 22050, 32 | 'mean' : 0, 33 | } 34 | 35 | torch.manual_seed(0) 36 | torch.set_num_threads(1) 37 | torch.set_default_tensor_type('torch.FloatTensor') 38 | 39 | 40 | 41 | #### Create data loader 42 | 43 | ##### create net work 44 | 45 | ## initialize the model 46 | def weights_init(layer): 47 | name = torch.typename(layer) 48 | if name.find('Convolution') > 0 : 49 | layer.weight.normal_(0.0, 0.01) 50 | layer.bias.fill_(0) 51 | #print name, name.find('Convolution') 52 | elif name.find('BatchNormalization') > 0: 53 | if layer.weight is not None: 54 | layer.weight.normal_(1.0, 0.02) 55 | if layer.bias is not None: 56 | layer.bias.fill_(0) 57 | 58 | ## create network 59 | def create_network(): 60 | net = nn.Sequential() 61 | 62 | net.add(nn.SpatialConvolution(1, 32, 1,64, 1,2, 0,32)) 63 | net.add(nn.SpatialBatchNormalization(32)) 64 | net.add(nn.ReLU(True)) 65 | net.add(nn.SpatialMaxPooling(1,8, 1,8)) 66 | 67 | net.add(nn.SpatialConvolution(32, 64, 1,32, 1,2, 0,16)) 68 | net.add(nn.SpatialBatchNormalization(64)) 69 | net.add(nn.ReLU(True)) 70 | net.add(nn.SpatialMaxPooling(1,8, 1,8)) 71 | 72 | net.add(nn.SpatialConvolution(64, 128, 1,16, 1,2, 0,8)) 73 | net.add(nn.SpatialBatchNormalization(128)) 74 | net.add(nn.ReLU(True)) 75 | net.add(nn.SpatialMaxPooling(1, 8, 1,8)) 76 | # net.add(nn.SpatialDropout(0.5)) 77 | 78 | net.add(nn.SpatialConvolution(128, 256, 1,8, 1,2, 0,4)) 79 | net.add(nn.SpatialBatchNormalization(256)) 80 | net.add(nn.ReLU(True)) 81 | # net.add(nn.SpatialDropout(0.5)) 82 | 83 | net.add(nn.ConcatTable().add(nn.SpatialConvolution(256, 1000, 1,16, 1,12, 0,4)) 84 | .add(nn.SpatialConvolution(256, 401, 1,16, 1,12, 0,4))) 85 | 86 | net.add(nn.ParallelTable().add(nn.SplitTable(3)).add(nn.SplitTable(3))) 87 | net.add(nn.FlattenTable()) 88 | return net 89 | 90 | ## -- optimization closure 91 | ## the optimizer will call this function to get the gradients 92 | def closure(x): 93 | global gradParameters 94 | gradParameters = gradParameters.zero_() 95 | ## data_im,data_label,data_label2,data_extra = data:getBatch() 96 | inputTensor.copy_(data_im.view(opt['batchSize'], 1, opt['fineSize'], 1)) 97 | for i in xrange(opt['label_time_steps']): 98 | labels[i] = data_label.select(2, i) #labels[i]:copy(data_label:select(3,i)) 99 | 100 | for i in xrange(opt['label_time_steps']): 101 | labels[opt['label_time_steps'] - 1 + i] = data_label2.select(2, i) 102 | 103 | output = net.forward(inputTensor) 104 | err = criterion.forward(output, labels) / float(len(labels)) * opt['lambda'] 105 | df_do = criterion.backward(output, labels) 106 | for i in range(len(labels)): 107 | df_do.mul(opt['lambda'] / len(labels)) 108 | net.backward(inputTensor, df_do) 109 | return err, gradParameters # todo : Test this method 110 | 111 | def main(): 112 | if opt['gpu'] >= 0 : 113 | torch.cuda.set_device(opt['gpu']) 114 | # create or load network 115 | net = None 116 | if opt['finetune'] == '' : 117 | net = create_network() 118 | output_net = nn.ParallelTable() 119 | ### output net 120 | for i in range(8): 121 | output_net.add(nn.Sequential().add(nn.Contiguous()) 122 | .add(nn.LogSoftMax()).add(nn.Squeeze())) 123 | 124 | net.applyToModules(weights_init) 125 | else : 126 | print ('loading :' + opt['finetune']) 127 | net = load_lua(opt['finetune']) 128 | #print(net) 129 | # defind criterion with KL div 130 | criterion = nn.ParallelCriterion(False) 131 | for i in range(8): 132 | criterion.add(nn.DistKLDivCriterion()) 133 | inputs = torch.Tensor(opt['batchSize'], 1, opt['fineSize'], 1).double() 134 | labels = {} 135 | for i in range(opt['label_time_steps']): 136 | labels[i] = torch.Tensor(opt['batchSize'], 1000) 137 | labels[i + opt['label_time_steps']] = torch.Tensor(opt['batchSize'], 401) 138 | ## ship everything to GPU if needed 139 | if opt['gpu'] >= 0 : 140 | inputs = inputs.cuda() 141 | for i in range(len(labels)): 142 | labels[i] = labels[i].cuda() 143 | net.cuda() 144 | criterion.cuda() 145 | ## is not able to conver to cudnn in pytorch so far 146 | """ 147 | if opt.gpu > 0 and opt.cudnn > 0 then 148 | require 'cudnn' 149 | net = cudnn.convert(net, cudnn) 150 | end 151 | 152 | """ 153 | parameters, gradParameters = net.flattenParameters() 154 | optimConfig = { 155 | 'learningRate':opt['lr'], 156 | 'beta1':opt['beta1'] 157 | } 158 | 159 | counter, history = 0, {} 160 | for epoch in range(opt['niter']): 161 | for i in range(0, min(data.size(), opt['ntrain']), opt['batchSize']): 162 | gc.collect() 163 | optim.adam(closure, parameters, config = optimConfig) 164 | 165 | ## logging 166 | if counter % 10 == 0 : 167 | w = net.modules[1].weight.clone().float().squeeze() 168 | counter += 1 169 | print "-"*10 170 | --------------------------------------------------------------------------------