├── .gitignore ├── LICENSE ├── assets ├── Imagenet-Patch-schema.png ├── Imagenet_Patch.png ├── Imagenet_Patch_Predictions.png ├── LICENSE ├── data │ ├── n01440764 │ │ ├── ILSVRC2012_val_00009111.JPEG │ │ ├── ILSVRC2012_val_00021740.JPEG │ │ ├── n01440764_2751.JPEG │ │ ├── n01440764_341.JPEG │ │ ├── n01440764_490.JPEG │ │ ├── n01440764_5911.JPEG │ │ ├── n01440764_650.JPEG │ │ ├── n01440764_6500.JPEG │ │ ├── n01440764_6570.JPEG │ │ └── n01440764_910.JPEG │ ├── n02102040 │ │ ├── ILSVRC2012_val_00004650.JPEG │ │ ├── ILSVRC2012_val_00017370.JPEG │ │ ├── ILSVRC2012_val_00036282.JPEG │ │ ├── ILSVRC2012_val_00037182.JPEG │ │ ├── n02102040_171.JPEG │ │ ├── n02102040_202.JPEG │ │ ├── n02102040_352.JPEG │ │ ├── n02102040_371.JPEG │ │ ├── n02102040_451.JPEG │ │ └── n02102040_460.JPEG │ ├── n02979186 │ │ ├── ILSVRC2012_val_00008651.JPEG │ │ ├── ILSVRC2012_val_00020400.JPEG │ │ ├── ILSVRC2012_val_00028911.JPEG │ │ ├── ILSVRC2012_val_00035160.JPEG │ │ ├── ILSVRC2012_val_00042482.JPEG │ │ ├── ILSVRC2012_val_00042982.JPEG │ │ ├── ILSVRC2012_val_00043121.JPEG │ │ ├── ILSVRC2012_val_00043731.JPEG │ │ ├── ILSVRC2012_val_00045110.JPEG │ │ └── n02979186_40.JPEG │ ├── n03000684 │ │ ├── ILSVRC2012_val_00025890.JPEG │ │ ├── ILSVRC2012_val_00029211.JPEG │ │ ├── ILSVRC2012_val_00045501.JPEG │ │ ├── n03000684_2102.JPEG │ │ ├── n03000684_410.JPEG │ │ ├── n03000684_590.JPEG │ │ ├── n03000684_711.JPEG │ │ ├── n03000684_731.JPEG │ │ ├── n03000684_900.JPEG │ │ └── n03000684_931.JPEG │ └── n03028079 │ │ ├── ILSVRC2012_val_00023291.JPEG │ │ ├── ILSVRC2012_val_00026451.JPEG │ │ ├── ILSVRC2012_val_00028310.JPEG │ │ ├── ILSVRC2012_val_00029870.JPEG │ │ ├── ILSVRC2012_val_00034220.JPEG │ │ ├── ILSVRC2012_val_00046232.JPEG │ │ ├── ILSVRC2012_val_00046550.JPEG │ │ ├── n03028079_102.JPEG │ │ ├── n03028079_151.JPEG │ │ └── n03028079_80.JPEG ├── imagenet1000_clsidx_to_labels.txt └── imagenet_patch.gz ├── example.py ├── imagenet_patch_example.ipynb ├── readme.md ├── requirements.txt ├── transforms ├── __init__.py ├── apply_patch.py └── my_random_affine.py └── utils ├── __init__.py ├── utils.py └── visualization.py /.gitignore: -------------------------------------------------------------------------------- 1 | /venv/ 2 | /.idea 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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/assets/imagenet1000_clsidx_to_labels.txt: -------------------------------------------------------------------------------- 1 | {0: 'tench, Tinca tinca', 2 | 1: 'goldfish, Carassius auratus', 3 | 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 4 | 3: 'tiger shark, Galeocerdo cuvieri', 5 | 4: 'hammerhead, hammerhead shark', 6 | 5: 'electric ray, crampfish, numbfish, torpedo', 7 | 6: 'stingray', 8 | 7: 'cock', 9 | 8: 'hen', 10 | 9: 'ostrich, Struthio camelus', 11 | 10: 'brambling, Fringilla montifringilla', 12 | 11: 'goldfinch, Carduelis carduelis', 13 | 12: 'house finch, linnet, Carpodacus mexicanus', 14 | 13: 'junco, snowbird', 15 | 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', 16 | 15: 'robin, American robin, Turdus migratorius', 17 | 16: 'bulbul', 18 | 17: 'jay', 19 | 18: 'magpie', 20 | 19: 'chickadee', 21 | 20: 'water ouzel, dipper', 22 | 21: 'kite', 23 | 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', 24 | 23: 'vulture', 25 | 24: 'great grey owl, great gray owl, Strix nebulosa', 26 | 25: 'European fire salamander, Salamandra salamandra', 27 | 26: 'common newt, Triturus vulgaris', 28 | 27: 'eft', 29 | 28: 'spotted salamander, Ambystoma maculatum', 30 | 29: 'axolotl, mud puppy, Ambystoma mexicanum', 31 | 30: 'bullfrog, Rana catesbeiana', 32 | 31: 'tree frog, tree-frog', 33 | 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', 34 | 33: 'loggerhead, loggerhead turtle, Caretta caretta', 35 | 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', 36 | 35: 'mud turtle', 37 | 36: 'terrapin', 38 | 37: 'box turtle, box tortoise', 39 | 38: 'banded gecko', 40 | 39: 'common iguana, iguana, Iguana iguana', 41 | 40: 'American chameleon, anole, Anolis carolinensis', 42 | 41: 'whiptail, whiptail lizard', 43 | 42: 'agama', 44 | 43: 'frilled lizard, Chlamydosaurus kingi', 45 | 44: 'alligator lizard', 46 | 45: 'Gila monster, Heloderma suspectum', 47 | 46: 'green lizard, Lacerta viridis', 48 | 47: 'African chameleon, Chamaeleo chamaeleon', 49 | 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', 50 | 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', 51 | 50: 'American alligator, Alligator mississipiensis', 52 | 51: 'triceratops', 53 | 52: 'thunder snake, worm snake, Carphophis amoenus', 54 | 53: 'ringneck snake, ring-necked snake, ring snake', 55 | 54: 'hognose snake, puff adder, sand viper', 56 | 55: 'green snake, grass snake', 57 | 56: 'king snake, kingsnake', 58 | 57: 'garter snake, grass snake', 59 | 58: 'water snake', 60 | 59: 'vine snake', 61 | 60: 'night snake, Hypsiglena torquata', 62 | 61: 'boa constrictor, Constrictor constrictor', 63 | 62: 'rock python, rock snake, Python sebae', 64 | 63: 'Indian cobra, Naja naja', 65 | 64: 'green mamba', 66 | 65: 'sea snake', 67 | 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', 68 | 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', 69 | 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', 70 | 69: 'trilobite', 71 | 70: 'harvestman, daddy longlegs, Phalangium opilio', 72 | 71: 'scorpion', 73 | 72: 'black and gold garden spider, Argiope aurantia', 74 | 73: 'barn spider, Araneus cavaticus', 75 | 74: 'garden spider, Aranea diademata', 76 | 75: 'black widow, Latrodectus mactans', 77 | 76: 'tarantula', 78 | 77: 'wolf spider, hunting spider', 79 | 78: 'tick', 80 | 79: 'centipede', 81 | 80: 'black grouse', 82 | 81: 'ptarmigan', 83 | 82: 'ruffed grouse, partridge, Bonasa umbellus', 84 | 83: 'prairie chicken, prairie grouse, prairie fowl', 85 | 84: 'peacock', 86 | 85: 'quail', 87 | 86: 'partridge', 88 | 87: 'African grey, African gray, Psittacus erithacus', 89 | 88: 'macaw', 90 | 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', 91 | 90: 'lorikeet', 92 | 91: 'coucal', 93 | 92: 'bee eater', 94 | 93: 'hornbill', 95 | 94: 'hummingbird', 96 | 95: 'jacamar', 97 | 96: 'toucan', 98 | 97: 'drake', 99 | 98: 'red-breasted merganser, Mergus serrator', 100 | 99: 'goose', 101 | 100: 'black swan, Cygnus atratus', 102 | 101: 'tusker', 103 | 102: 'echidna, spiny anteater, anteater', 104 | 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', 105 | 104: 'wallaby, brush kangaroo', 106 | 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', 107 | 106: 'wombat', 108 | 107: 'jellyfish', 109 | 108: 'sea anemone, anemone', 110 | 109: 'brain coral', 111 | 110: 'flatworm, platyhelminth', 112 | 111: 'nematode, nematode worm, roundworm', 113 | 112: 'conch', 114 | 113: 'snail', 115 | 114: 'slug', 116 | 115: 'sea slug, nudibranch', 117 | 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', 118 | 117: 'chambered nautilus, pearly nautilus, nautilus', 119 | 118: 'Dungeness crab, Cancer magister', 120 | 119: 'rock crab, Cancer irroratus', 121 | 120: 'fiddler crab', 122 | 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', 123 | 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', 124 | 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', 125 | 124: 'crayfish, crawfish, crawdad, crawdaddy', 126 | 125: 'hermit crab', 127 | 126: 'isopod', 128 | 127: 'white stork, Ciconia ciconia', 129 | 128: 'black stork, Ciconia nigra', 130 | 129: 'spoonbill', 131 | 130: 'flamingo', 132 | 131: 'little blue heron, Egretta caerulea', 133 | 132: 'American egret, great white heron, Egretta albus', 134 | 133: 'bittern', 135 | 134: 'crane', 136 | 135: 'limpkin, Aramus pictus', 137 | 136: 'European gallinule, Porphyrio porphyrio', 138 | 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', 139 | 138: 'bustard', 140 | 139: 'ruddy turnstone, Arenaria interpres', 141 | 140: 'red-backed sandpiper, dunlin, Erolia alpina', 142 | 141: 'redshank, Tringa totanus', 143 | 142: 'dowitcher', 144 | 143: 'oystercatcher, oyster catcher', 145 | 144: 'pelican', 146 | 145: 'king penguin, Aptenodytes patagonica', 147 | 146: 'albatross, mollymawk', 148 | 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', 149 | 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', 150 | 149: 'dugong, Dugong dugon', 151 | 150: 'sea lion', 152 | 151: 'Chihuahua', 153 | 152: 'Japanese spaniel', 154 | 153: 'Maltese dog, Maltese terrier, Maltese', 155 | 154: 'Pekinese, Pekingese, Peke', 156 | 155: 'Shih-Tzu', 157 | 156: 'Blenheim spaniel', 158 | 157: 'papillon', 159 | 158: 'toy terrier', 160 | 159: 'Rhodesian ridgeback', 161 | 160: 'Afghan hound, Afghan', 162 | 161: 'basset, basset hound', 163 | 162: 'beagle', 164 | 163: 'bloodhound, sleuthhound', 165 | 164: 'bluetick', 166 | 165: 'black-and-tan coonhound', 167 | 166: 'Walker hound, Walker foxhound', 168 | 167: 'English foxhound', 169 | 168: 'redbone', 170 | 169: 'borzoi, Russian wolfhound', 171 | 170: 'Irish wolfhound', 172 | 171: 'Italian greyhound', 173 | 172: 'whippet', 174 | 173: 'Ibizan hound, Ibizan Podenco', 175 | 174: 'Norwegian elkhound, elkhound', 176 | 175: 'otterhound, otter hound', 177 | 176: 'Saluki, gazelle hound', 178 | 177: 'Scottish deerhound, deerhound', 179 | 178: 'Weimaraner', 180 | 179: 'Staffordshire bullterrier, Staffordshire bull terrier', 181 | 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', 182 | 181: 'Bedlington terrier', 183 | 182: 'Border terrier', 184 | 183: 'Kerry blue terrier', 185 | 184: 'Irish terrier', 186 | 185: 'Norfolk terrier', 187 | 186: 'Norwich terrier', 188 | 187: 'Yorkshire terrier', 189 | 188: 'wire-haired fox terrier', 190 | 189: 'Lakeland terrier', 191 | 190: 'Sealyham terrier, Sealyham', 192 | 191: 'Airedale, Airedale terrier', 193 | 192: 'cairn, cairn terrier', 194 | 193: 'Australian terrier', 195 | 194: 'Dandie Dinmont, Dandie Dinmont terrier', 196 | 195: 'Boston bull, Boston terrier', 197 | 196: 'miniature schnauzer', 198 | 197: 'giant schnauzer', 199 | 198: 'standard schnauzer', 200 | 199: 'Scotch terrier, Scottish terrier, Scottie', 201 | 200: 'Tibetan terrier, chrysanthemum dog', 202 | 201: 'silky terrier, Sydney silky', 203 | 202: 'soft-coated wheaten terrier', 204 | 203: 'West Highland white terrier', 205 | 204: 'Lhasa, Lhasa apso', 206 | 205: 'flat-coated retriever', 207 | 206: 'curly-coated retriever', 208 | 207: 'golden retriever', 209 | 208: 'Labrador retriever', 210 | 209: 'Chesapeake Bay retriever', 211 | 210: 'German short-haired pointer', 212 | 211: 'vizsla, Hungarian pointer', 213 | 212: 'English setter', 214 | 213: 'Irish setter, red setter', 215 | 214: 'Gordon setter', 216 | 215: 'Brittany spaniel', 217 | 216: 'clumber, clumber spaniel', 218 | 217: 'English springer, English springer spaniel', 219 | 218: 'Welsh springer spaniel', 220 | 219: 'cocker spaniel, English cocker spaniel, cocker', 221 | 220: 'Sussex spaniel', 222 | 221: 'Irish water spaniel', 223 | 222: 'kuvasz', 224 | 223: 'schipperke', 225 | 224: 'groenendael', 226 | 225: 'malinois', 227 | 226: 'briard', 228 | 227: 'kelpie', 229 | 228: 'komondor', 230 | 229: 'Old English sheepdog, bobtail', 231 | 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', 232 | 231: 'collie', 233 | 232: 'Border collie', 234 | 233: 'Bouvier des Flandres, Bouviers des Flandres', 235 | 234: 'Rottweiler', 236 | 235: 'German shepherd, German shepherd dog, German police dog, alsatian', 237 | 236: 'Doberman, Doberman pinscher', 238 | 237: 'miniature pinscher', 239 | 238: 'Greater Swiss Mountain dog', 240 | 239: 'Bernese mountain dog', 241 | 240: 'Appenzeller', 242 | 241: 'EntleBucher', 243 | 242: 'boxer', 244 | 243: 'bull mastiff', 245 | 244: 'Tibetan mastiff', 246 | 245: 'French bulldog', 247 | 246: 'Great Dane', 248 | 247: 'Saint Bernard, St Bernard', 249 | 248: 'Eskimo dog, husky', 250 | 249: 'malamute, malemute, Alaskan malamute', 251 | 250: 'Siberian husky', 252 | 251: 'dalmatian, coach dog, carriage dog', 253 | 252: 'affenpinscher, monkey pinscher, monkey dog', 254 | 253: 'basenji', 255 | 254: 'pug, pug-dog', 256 | 255: 'Leonberg', 257 | 256: 'Newfoundland, Newfoundland dog', 258 | 257: 'Great Pyrenees', 259 | 258: 'Samoyed, Samoyede', 260 | 259: 'Pomeranian', 261 | 260: 'chow, chow chow', 262 | 261: 'keeshond', 263 | 262: 'Brabancon griffon', 264 | 263: 'Pembroke, Pembroke Welsh corgi', 265 | 264: 'Cardigan, Cardigan Welsh corgi', 266 | 265: 'toy poodle', 267 | 266: 'miniature poodle', 268 | 267: 'standard poodle', 269 | 268: 'Mexican hairless', 270 | 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', 271 | 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', 272 | 271: 'red wolf, maned wolf, Canis rufus, Canis niger', 273 | 272: 'coyote, prairie wolf, brush wolf, Canis latrans', 274 | 273: 'dingo, warrigal, warragal, Canis dingo', 275 | 274: 'dhole, Cuon alpinus', 276 | 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', 277 | 276: 'hyena, hyaena', 278 | 277: 'red fox, Vulpes vulpes', 279 | 278: 'kit fox, Vulpes macrotis', 280 | 279: 'Arctic fox, white fox, Alopex lagopus', 281 | 280: 'grey fox, gray fox, Urocyon cinereoargenteus', 282 | 281: 'tabby, tabby cat', 283 | 282: 'tiger cat', 284 | 283: 'Persian cat', 285 | 284: 'Siamese cat, Siamese', 286 | 285: 'Egyptian cat', 287 | 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', 288 | 287: 'lynx, catamount', 289 | 288: 'leopard, Panthera pardus', 290 | 289: 'snow leopard, ounce, Panthera uncia', 291 | 290: 'jaguar, panther, Panthera onca, Felis onca', 292 | 291: 'lion, king of beasts, Panthera leo', 293 | 292: 'tiger, Panthera tigris', 294 | 293: 'cheetah, chetah, Acinonyx jubatus', 295 | 294: 'brown bear, bruin, Ursus arctos', 296 | 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', 297 | 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', 298 | 297: 'sloth bear, Melursus ursinus, Ursus ursinus', 299 | 298: 'mongoose', 300 | 299: 'meerkat, mierkat', 301 | 300: 'tiger beetle', 302 | 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', 303 | 302: 'ground beetle, carabid beetle', 304 | 303: 'long-horned beetle, longicorn, longicorn beetle', 305 | 304: 'leaf beetle, chrysomelid', 306 | 305: 'dung beetle', 307 | 306: 'rhinoceros beetle', 308 | 307: 'weevil', 309 | 308: 'fly', 310 | 309: 'bee', 311 | 310: 'ant, emmet, pismire', 312 | 311: 'grasshopper, hopper', 313 | 312: 'cricket', 314 | 313: 'walking stick, walkingstick, stick insect', 315 | 314: 'cockroach, roach', 316 | 315: 'mantis, mantid', 317 | 316: 'cicada, cicala', 318 | 317: 'leafhopper', 319 | 318: 'lacewing, lacewing fly', 320 | 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", 321 | 320: 'damselfly', 322 | 321: 'admiral', 323 | 322: 'ringlet, ringlet butterfly', 324 | 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', 325 | 324: 'cabbage butterfly', 326 | 325: 'sulphur butterfly, sulfur butterfly', 327 | 326: 'lycaenid, lycaenid butterfly', 328 | 327: 'starfish, sea star', 329 | 328: 'sea urchin', 330 | 329: 'sea cucumber, holothurian', 331 | 330: 'wood rabbit, cottontail, cottontail rabbit', 332 | 331: 'hare', 333 | 332: 'Angora, Angora rabbit', 334 | 333: 'hamster', 335 | 334: 'porcupine, hedgehog', 336 | 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', 337 | 336: 'marmot', 338 | 337: 'beaver', 339 | 338: 'guinea pig, Cavia cobaya', 340 | 339: 'sorrel', 341 | 340: 'zebra', 342 | 341: 'hog, pig, grunter, squealer, Sus scrofa', 343 | 342: 'wild boar, boar, Sus scrofa', 344 | 343: 'warthog', 345 | 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', 346 | 345: 'ox', 347 | 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', 348 | 347: 'bison', 349 | 348: 'ram, tup', 350 | 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', 351 | 350: 'ibex, Capra ibex', 352 | 351: 'hartebeest', 353 | 352: 'impala, Aepyceros melampus', 354 | 353: 'gazelle', 355 | 354: 'Arabian camel, dromedary, Camelus dromedarius', 356 | 355: 'llama', 357 | 356: 'weasel', 358 | 357: 'mink', 359 | 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', 360 | 359: 'black-footed ferret, ferret, Mustela nigripes', 361 | 360: 'otter', 362 | 361: 'skunk, polecat, wood pussy', 363 | 362: 'badger', 364 | 363: 'armadillo', 365 | 364: 'three-toed sloth, ai, Bradypus tridactylus', 366 | 365: 'orangutan, orang, orangutang, Pongo pygmaeus', 367 | 366: 'gorilla, Gorilla gorilla', 368 | 367: 'chimpanzee, chimp, Pan troglodytes', 369 | 368: 'gibbon, Hylobates lar', 370 | 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', 371 | 370: 'guenon, guenon monkey', 372 | 371: 'patas, hussar monkey, Erythrocebus patas', 373 | 372: 'baboon', 374 | 373: 'macaque', 375 | 374: 'langur', 376 | 375: 'colobus, colobus monkey', 377 | 376: 'proboscis monkey, Nasalis larvatus', 378 | 377: 'marmoset', 379 | 378: 'capuchin, ringtail, Cebus capucinus', 380 | 379: 'howler monkey, howler', 381 | 380: 'titi, titi monkey', 382 | 381: 'spider monkey, Ateles geoffroyi', 383 | 382: 'squirrel monkey, Saimiri sciureus', 384 | 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', 385 | 384: 'indri, indris, Indri indri, Indri brevicaudatus', 386 | 385: 'Indian elephant, Elephas maximus', 387 | 386: 'African elephant, Loxodonta africana', 388 | 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', 389 | 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', 390 | 389: 'barracouta, snoek', 391 | 390: 'eel', 392 | 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', 393 | 392: 'rock beauty, Holocanthus tricolor', 394 | 393: 'anemone fish', 395 | 394: 'sturgeon', 396 | 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', 397 | 396: 'lionfish', 398 | 397: 'puffer, pufferfish, blowfish, globefish', 399 | 398: 'abacus', 400 | 399: 'abaya', 401 | 400: "academic gown, academic robe, judge's robe", 402 | 401: 'accordion, piano accordion, squeeze box', 403 | 402: 'acoustic guitar', 404 | 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', 405 | 404: 'airliner', 406 | 405: 'airship, dirigible', 407 | 406: 'altar', 408 | 407: 'ambulance', 409 | 408: 'amphibian, amphibious vehicle', 410 | 409: 'analog clock', 411 | 410: 'apiary, bee house', 412 | 411: 'apron', 413 | 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', 414 | 413: 'assault rifle, assault gun', 415 | 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', 416 | 415: 'bakery, bakeshop, bakehouse', 417 | 416: 'balance beam, beam', 418 | 417: 'balloon', 419 | 418: 'ballpoint, ballpoint pen, ballpen, Biro', 420 | 419: 'Band Aid', 421 | 420: 'banjo', 422 | 421: 'bannister, banister, balustrade, balusters, handrail', 423 | 422: 'barbell', 424 | 423: 'barber chair', 425 | 424: 'barbershop', 426 | 425: 'barn', 427 | 426: 'barometer', 428 | 427: 'barrel, cask', 429 | 428: 'barrow, garden cart, lawn cart, wheelbarrow', 430 | 429: 'baseball', 431 | 430: 'basketball', 432 | 431: 'bassinet', 433 | 432: 'bassoon', 434 | 433: 'bathing cap, swimming cap', 435 | 434: 'bath towel', 436 | 435: 'bathtub, bathing tub, bath, tub', 437 | 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', 438 | 437: 'beacon, lighthouse, beacon light, pharos', 439 | 438: 'beaker', 440 | 439: 'bearskin, busby, shako', 441 | 440: 'beer bottle', 442 | 441: 'beer glass', 443 | 442: 'bell cote, bell cot', 444 | 443: 'bib', 445 | 444: 'bicycle-built-for-two, tandem bicycle, tandem', 446 | 445: 'bikini, two-piece', 447 | 446: 'binder, ring-binder', 448 | 447: 'binoculars, field glasses, opera glasses', 449 | 448: 'birdhouse', 450 | 449: 'boathouse', 451 | 450: 'bobsled, bobsleigh, bob', 452 | 451: 'bolo tie, bolo, bola tie, bola', 453 | 452: 'bonnet, poke bonnet', 454 | 453: 'bookcase', 455 | 454: 'bookshop, bookstore, bookstall', 456 | 455: 'bottlecap', 457 | 456: 'bow', 458 | 457: 'bow tie, bow-tie, bowtie', 459 | 458: 'brass, memorial tablet, plaque', 460 | 459: 'brassiere, bra, bandeau', 461 | 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', 462 | 461: 'breastplate, aegis, egis', 463 | 462: 'broom', 464 | 463: 'bucket, pail', 465 | 464: 'buckle', 466 | 465: 'bulletproof vest', 467 | 466: 'bullet train, bullet', 468 | 467: 'butcher shop, meat market', 469 | 468: 'cab, hack, taxi, taxicab', 470 | 469: 'caldron, cauldron', 471 | 470: 'candle, taper, wax light', 472 | 471: 'cannon', 473 | 472: 'canoe', 474 | 473: 'can opener, tin opener', 475 | 474: 'cardigan', 476 | 475: 'car mirror', 477 | 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', 478 | 477: "carpenter's kit, tool kit", 479 | 478: 'carton', 480 | 479: 'car wheel', 481 | 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', 482 | 481: 'cassette', 483 | 482: 'cassette player', 484 | 483: 'castle', 485 | 484: 'catamaran', 486 | 485: 'CD player', 487 | 486: 'cello, violoncello', 488 | 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', 489 | 488: 'chain', 490 | 489: 'chainlink fence', 491 | 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', 492 | 491: 'chain saw, chainsaw', 493 | 492: 'chest', 494 | 493: 'chiffonier, commode', 495 | 494: 'chime, bell, gong', 496 | 495: 'china cabinet, china closet', 497 | 496: 'Christmas stocking', 498 | 497: 'church, church building', 499 | 498: 'cinema, movie theater, movie theatre, movie house, picture palace', 500 | 499: 'cleaver, meat cleaver, chopper', 501 | 500: 'cliff dwelling', 502 | 501: 'cloak', 503 | 502: 'clog, geta, patten, sabot', 504 | 503: 'cocktail shaker', 505 | 504: 'coffee mug', 506 | 505: 'coffeepot', 507 | 506: 'coil, spiral, volute, whorl, helix', 508 | 507: 'combination lock', 509 | 508: 'computer keyboard, keypad', 510 | 509: 'confectionery, confectionary, candy store', 511 | 510: 'container ship, containership, container vessel', 512 | 511: 'convertible', 513 | 512: 'corkscrew, bottle screw', 514 | 513: 'cornet, horn, trumpet, trump', 515 | 514: 'cowboy boot', 516 | 515: 'cowboy hat, ten-gallon hat', 517 | 516: 'cradle', 518 | 517: 'crane', 519 | 518: 'crash helmet', 520 | 519: 'crate', 521 | 520: 'crib, cot', 522 | 521: 'Crock Pot', 523 | 522: 'croquet ball', 524 | 523: 'crutch', 525 | 524: 'cuirass', 526 | 525: 'dam, dike, dyke', 527 | 526: 'desk', 528 | 527: 'desktop computer', 529 | 528: 'dial telephone, dial phone', 530 | 529: 'diaper, nappy, napkin', 531 | 530: 'digital clock', 532 | 531: 'digital watch', 533 | 532: 'dining table, board', 534 | 533: 'dishrag, dishcloth', 535 | 534: 'dishwasher, dish washer, dishwashing machine', 536 | 535: 'disk brake, disc brake', 537 | 536: 'dock, dockage, docking facility', 538 | 537: 'dogsled, dog sled, dog sleigh', 539 | 538: 'dome', 540 | 539: 'doormat, welcome mat', 541 | 540: 'drilling platform, offshore rig', 542 | 541: 'drum, membranophone, tympan', 543 | 542: 'drumstick', 544 | 543: 'dumbbell', 545 | 544: 'Dutch oven', 546 | 545: 'electric fan, blower', 547 | 546: 'electric guitar', 548 | 547: 'electric locomotive', 549 | 548: 'entertainment center', 550 | 549: 'envelope', 551 | 550: 'espresso maker', 552 | 551: 'face powder', 553 | 552: 'feather boa, boa', 554 | 553: 'file, file cabinet, filing cabinet', 555 | 554: 'fireboat', 556 | 555: 'fire engine, fire truck', 557 | 556: 'fire screen, fireguard', 558 | 557: 'flagpole, flagstaff', 559 | 558: 'flute, transverse flute', 560 | 559: 'folding chair', 561 | 560: 'football helmet', 562 | 561: 'forklift', 563 | 562: 'fountain', 564 | 563: 'fountain pen', 565 | 564: 'four-poster', 566 | 565: 'freight car', 567 | 566: 'French horn, horn', 568 | 567: 'frying pan, frypan, skillet', 569 | 568: 'fur coat', 570 | 569: 'garbage truck, dustcart', 571 | 570: 'gasmask, respirator, gas helmet', 572 | 571: 'gas pump, gasoline pump, petrol pump, island dispenser', 573 | 572: 'goblet', 574 | 573: 'go-kart', 575 | 574: 'golf ball', 576 | 575: 'golfcart, golf cart', 577 | 576: 'gondola', 578 | 577: 'gong, tam-tam', 579 | 578: 'gown', 580 | 579: 'grand piano, grand', 581 | 580: 'greenhouse, nursery, glasshouse', 582 | 581: 'grille, radiator grille', 583 | 582: 'grocery store, grocery, food market, market', 584 | 583: 'guillotine', 585 | 584: 'hair slide', 586 | 585: 'hair spray', 587 | 586: 'half track', 588 | 587: 'hammer', 589 | 588: 'hamper', 590 | 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', 591 | 590: 'hand-held computer, hand-held microcomputer', 592 | 591: 'handkerchief, hankie, hanky, hankey', 593 | 592: 'hard disc, hard disk, fixed disk', 594 | 593: 'harmonica, mouth organ, harp, mouth harp', 595 | 594: 'harp', 596 | 595: 'harvester, reaper', 597 | 596: 'hatchet', 598 | 597: 'holster', 599 | 598: 'home theater, home theatre', 600 | 599: 'honeycomb', 601 | 600: 'hook, claw', 602 | 601: 'hoopskirt, crinoline', 603 | 602: 'horizontal bar, high bar', 604 | 603: 'horse cart, horse-cart', 605 | 604: 'hourglass', 606 | 605: 'iPod', 607 | 606: 'iron, smoothing iron', 608 | 607: "jack-o'-lantern", 609 | 608: 'jean, blue jean, denim', 610 | 609: 'jeep, landrover', 611 | 610: 'jersey, T-shirt, tee shirt', 612 | 611: 'jigsaw puzzle', 613 | 612: 'jinrikisha, ricksha, rickshaw', 614 | 613: 'joystick', 615 | 614: 'kimono', 616 | 615: 'knee pad', 617 | 616: 'knot', 618 | 617: 'lab coat, laboratory coat', 619 | 618: 'ladle', 620 | 619: 'lampshade, lamp shade', 621 | 620: 'laptop, laptop computer', 622 | 621: 'lawn mower, mower', 623 | 622: 'lens cap, lens cover', 624 | 623: 'letter opener, paper knife, paperknife', 625 | 624: 'library', 626 | 625: 'lifeboat', 627 | 626: 'lighter, light, igniter, ignitor', 628 | 627: 'limousine, limo', 629 | 628: 'liner, ocean liner', 630 | 629: 'lipstick, lip rouge', 631 | 630: 'Loafer', 632 | 631: 'lotion', 633 | 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', 634 | 633: "loupe, jeweler's loupe", 635 | 634: 'lumbermill, sawmill', 636 | 635: 'magnetic compass', 637 | 636: 'mailbag, postbag', 638 | 637: 'mailbox, letter box', 639 | 638: 'maillot', 640 | 639: 'maillot, tank suit', 641 | 640: 'manhole cover', 642 | 641: 'maraca', 643 | 642: 'marimba, xylophone', 644 | 643: 'mask', 645 | 644: 'matchstick', 646 | 645: 'maypole', 647 | 646: 'maze, labyrinth', 648 | 647: 'measuring cup', 649 | 648: 'medicine chest, medicine cabinet', 650 | 649: 'megalith, megalithic structure', 651 | 650: 'microphone, mike', 652 | 651: 'microwave, microwave oven', 653 | 652: 'military uniform', 654 | 653: 'milk can', 655 | 654: 'minibus', 656 | 655: 'miniskirt, mini', 657 | 656: 'minivan', 658 | 657: 'missile', 659 | 658: 'mitten', 660 | 659: 'mixing bowl', 661 | 660: 'mobile home, manufactured home', 662 | 661: 'Model T', 663 | 662: 'modem', 664 | 663: 'monastery', 665 | 664: 'monitor', 666 | 665: 'moped', 667 | 666: 'mortar', 668 | 667: 'mortarboard', 669 | 668: 'mosque', 670 | 669: 'mosquito net', 671 | 670: 'motor scooter, scooter', 672 | 671: 'mountain bike, all-terrain bike, off-roader', 673 | 672: 'mountain tent', 674 | 673: 'mouse, computer mouse', 675 | 674: 'mousetrap', 676 | 675: 'moving van', 677 | 676: 'muzzle', 678 | 677: 'nail', 679 | 678: 'neck brace', 680 | 679: 'necklace', 681 | 680: 'nipple', 682 | 681: 'notebook, notebook computer', 683 | 682: 'obelisk', 684 | 683: 'oboe, hautboy, hautbois', 685 | 684: 'ocarina, sweet potato', 686 | 685: 'odometer, hodometer, mileometer, milometer', 687 | 686: 'oil filter', 688 | 687: 'organ, pipe organ', 689 | 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', 690 | 689: 'overskirt', 691 | 690: 'oxcart', 692 | 691: 'oxygen mask', 693 | 692: 'packet', 694 | 693: 'paddle, boat paddle', 695 | 694: 'paddlewheel, paddle wheel', 696 | 695: 'padlock', 697 | 696: 'paintbrush', 698 | 697: "pajama, pyjama, pj's, jammies", 699 | 698: 'palace', 700 | 699: 'panpipe, pandean pipe, syrinx', 701 | 700: 'paper towel', 702 | 701: 'parachute, chute', 703 | 702: 'parallel bars, bars', 704 | 703: 'park bench', 705 | 704: 'parking meter', 706 | 705: 'passenger car, coach, carriage', 707 | 706: 'patio, terrace', 708 | 707: 'pay-phone, pay-station', 709 | 708: 'pedestal, plinth, footstall', 710 | 709: 'pencil box, pencil case', 711 | 710: 'pencil sharpener', 712 | 711: 'perfume, essence', 713 | 712: 'Petri dish', 714 | 713: 'photocopier', 715 | 714: 'pick, plectrum, plectron', 716 | 715: 'pickelhaube', 717 | 716: 'picket fence, paling', 718 | 717: 'pickup, pickup truck', 719 | 718: 'pier', 720 | 719: 'piggy bank, penny bank', 721 | 720: 'pill bottle', 722 | 721: 'pillow', 723 | 722: 'ping-pong ball', 724 | 723: 'pinwheel', 725 | 724: 'pirate, pirate ship', 726 | 725: 'pitcher, ewer', 727 | 726: "plane, carpenter's plane, woodworking plane", 728 | 727: 'planetarium', 729 | 728: 'plastic bag', 730 | 729: 'plate rack', 731 | 730: 'plow, plough', 732 | 731: "plunger, plumber's helper", 733 | 732: 'Polaroid camera, Polaroid Land camera', 734 | 733: 'pole', 735 | 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', 736 | 735: 'poncho', 737 | 736: 'pool table, billiard table, snooker table', 738 | 737: 'pop bottle, soda bottle', 739 | 738: 'pot, flowerpot', 740 | 739: "potter's wheel", 741 | 740: 'power drill', 742 | 741: 'prayer rug, prayer mat', 743 | 742: 'printer', 744 | 743: 'prison, prison house', 745 | 744: 'projectile, missile', 746 | 745: 'projector', 747 | 746: 'puck, hockey puck', 748 | 747: 'punching bag, punch bag, punching ball, punchball', 749 | 748: 'purse', 750 | 749: 'quill, quill pen', 751 | 750: 'quilt, comforter, comfort, puff', 752 | 751: 'racer, race car, racing car', 753 | 752: 'racket, racquet', 754 | 753: 'radiator', 755 | 754: 'radio, wireless', 756 | 755: 'radio telescope, radio reflector', 757 | 756: 'rain barrel', 758 | 757: 'recreational vehicle, RV, R.V.', 759 | 758: 'reel', 760 | 759: 'reflex camera', 761 | 760: 'refrigerator, icebox', 762 | 761: 'remote control, remote', 763 | 762: 'restaurant, eating house, eating place, eatery', 764 | 763: 'revolver, six-gun, six-shooter', 765 | 764: 'rifle', 766 | 765: 'rocking chair, rocker', 767 | 766: 'rotisserie', 768 | 767: 'rubber eraser, rubber, pencil eraser', 769 | 768: 'rugby ball', 770 | 769: 'rule, ruler', 771 | 770: 'running shoe', 772 | 771: 'safe', 773 | 772: 'safety pin', 774 | 773: 'saltshaker, salt shaker', 775 | 774: 'sandal', 776 | 775: 'sarong', 777 | 776: 'sax, saxophone', 778 | 777: 'scabbard', 779 | 778: 'scale, weighing machine', 780 | 779: 'school bus', 781 | 780: 'schooner', 782 | 781: 'scoreboard', 783 | 782: 'screen, CRT screen', 784 | 783: 'screw', 785 | 784: 'screwdriver', 786 | 785: 'seat belt, seatbelt', 787 | 786: 'sewing machine', 788 | 787: 'shield, buckler', 789 | 788: 'shoe shop, shoe-shop, shoe store', 790 | 789: 'shoji', 791 | 790: 'shopping basket', 792 | 791: 'shopping cart', 793 | 792: 'shovel', 794 | 793: 'shower cap', 795 | 794: 'shower curtain', 796 | 795: 'ski', 797 | 796: 'ski mask', 798 | 797: 'sleeping bag', 799 | 798: 'slide rule, slipstick', 800 | 799: 'sliding door', 801 | 800: 'slot, one-armed bandit', 802 | 801: 'snorkel', 803 | 802: 'snowmobile', 804 | 803: 'snowplow, snowplough', 805 | 804: 'soap dispenser', 806 | 805: 'soccer ball', 807 | 806: 'sock', 808 | 807: 'solar dish, solar collector, solar furnace', 809 | 808: 'sombrero', 810 | 809: 'soup bowl', 811 | 810: 'space bar', 812 | 811: 'space heater', 813 | 812: 'space shuttle', 814 | 813: 'spatula', 815 | 814: 'speedboat', 816 | 815: "spider web, spider's web", 817 | 816: 'spindle', 818 | 817: 'sports car, sport car', 819 | 818: 'spotlight, spot', 820 | 819: 'stage', 821 | 820: 'steam locomotive', 822 | 821: 'steel arch bridge', 823 | 822: 'steel drum', 824 | 823: 'stethoscope', 825 | 824: 'stole', 826 | 825: 'stone wall', 827 | 826: 'stopwatch, stop watch', 828 | 827: 'stove', 829 | 828: 'strainer', 830 | 829: 'streetcar, tram, tramcar, trolley, trolley car', 831 | 830: 'stretcher', 832 | 831: 'studio couch, day bed', 833 | 832: 'stupa, tope', 834 | 833: 'submarine, pigboat, sub, U-boat', 835 | 834: 'suit, suit of clothes', 836 | 835: 'sundial', 837 | 836: 'sunglass', 838 | 837: 'sunglasses, dark glasses, shades', 839 | 838: 'sunscreen, sunblock, sun blocker', 840 | 839: 'suspension bridge', 841 | 840: 'swab, swob, mop', 842 | 841: 'sweatshirt', 843 | 842: 'swimming trunks, bathing trunks', 844 | 843: 'swing', 845 | 844: 'switch, electric switch, electrical switch', 846 | 845: 'syringe', 847 | 846: 'table lamp', 848 | 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', 849 | 848: 'tape player', 850 | 849: 'teapot', 851 | 850: 'teddy, teddy bear', 852 | 851: 'television, television system', 853 | 852: 'tennis ball', 854 | 853: 'thatch, thatched roof', 855 | 854: 'theater curtain, theatre curtain', 856 | 855: 'thimble', 857 | 856: 'thresher, thrasher, threshing machine', 858 | 857: 'throne', 859 | 858: 'tile roof', 860 | 859: 'toaster', 861 | 860: 'tobacco shop, tobacconist shop, tobacconist', 862 | 861: 'toilet seat', 863 | 862: 'torch', 864 | 863: 'totem pole', 865 | 864: 'tow truck, tow car, wrecker', 866 | 865: 'toyshop', 867 | 866: 'tractor', 868 | 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', 869 | 868: 'tray', 870 | 869: 'trench coat', 871 | 870: 'tricycle, trike, velocipede', 872 | 871: 'trimaran', 873 | 872: 'tripod', 874 | 873: 'triumphal arch', 875 | 874: 'trolleybus, trolley coach, trackless trolley', 876 | 875: 'trombone', 877 | 876: 'tub, vat', 878 | 877: 'turnstile', 879 | 878: 'typewriter keyboard', 880 | 879: 'umbrella', 881 | 880: 'unicycle, monocycle', 882 | 881: 'upright, upright piano', 883 | 882: 'vacuum, vacuum cleaner', 884 | 883: 'vase', 885 | 884: 'vault', 886 | 885: 'velvet', 887 | 886: 'vending machine', 888 | 887: 'vestment', 889 | 888: 'viaduct', 890 | 889: 'violin, fiddle', 891 | 890: 'volleyball', 892 | 891: 'waffle iron', 893 | 892: 'wall clock', 894 | 893: 'wallet, billfold, notecase, pocketbook', 895 | 894: 'wardrobe, closet, press', 896 | 895: 'warplane, military plane', 897 | 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', 898 | 897: 'washer, automatic washer, washing machine', 899 | 898: 'water bottle', 900 | 899: 'water jug', 901 | 900: 'water tower', 902 | 901: 'whiskey jug', 903 | 902: 'whistle', 904 | 903: 'wig', 905 | 904: 'window screen', 906 | 905: 'window shade', 907 | 906: 'Windsor tie', 908 | 907: 'wine bottle', 909 | 908: 'wing', 910 | 909: 'wok', 911 | 910: 'wooden spoon', 912 | 911: 'wool, woolen, woollen', 913 | 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', 914 | 913: 'wreck', 915 | 914: 'yawl', 916 | 915: 'yurt', 917 | 916: 'web site, website, internet site, site', 918 | 917: 'comic book', 919 | 918: 'crossword puzzle, crossword', 920 | 919: 'street sign', 921 | 920: 'traffic light, traffic signal, stoplight', 922 | 921: 'book jacket, dust cover, dust jacket, dust wrapper', 923 | 922: 'menu', 924 | 923: 'plate', 925 | 924: 'guacamole', 926 | 925: 'consomme', 927 | 926: 'hot pot, hotpot', 928 | 927: 'trifle', 929 | 928: 'ice cream, icecream', 930 | 929: 'ice lolly, lolly, lollipop, popsicle', 931 | 930: 'French loaf', 932 | 931: 'bagel, beigel', 933 | 932: 'pretzel', 934 | 933: 'cheeseburger', 935 | 934: 'hotdog, hot dog, red hot', 936 | 935: 'mashed potato', 937 | 936: 'head cabbage', 938 | 937: 'broccoli', 939 | 938: 'cauliflower', 940 | 939: 'zucchini, courgette', 941 | 940: 'spaghetti squash', 942 | 941: 'acorn squash', 943 | 942: 'butternut squash', 944 | 943: 'cucumber, cuke', 945 | 944: 'artichoke, globe artichoke', 946 | 945: 'bell pepper', 947 | 946: 'cardoon', 948 | 947: 'mushroom', 949 | 948: 'Granny Smith', 950 | 949: 'strawberry', 951 | 950: 'orange', 952 | 951: 'lemon', 953 | 952: 'fig', 954 | 953: 'pineapple, ananas', 955 | 954: 'banana', 956 | 955: 'jackfruit, jak, jack', 957 | 956: 'custard apple', 958 | 957: 'pomegranate', 959 | 958: 'hay', 960 | 959: 'carbonara', 961 | 960: 'chocolate sauce, chocolate syrup', 962 | 961: 'dough', 963 | 962: 'meat loaf, meatloaf', 964 | 963: 'pizza, pizza pie', 965 | 964: 'potpie', 966 | 965: 'burrito', 967 | 966: 'red wine', 968 | 967: 'espresso', 969 | 968: 'cup', 970 | 969: 'eggnog', 971 | 970: 'alp', 972 | 971: 'bubble', 973 | 972: 'cliff, drop, drop-off', 974 | 973: 'coral reef', 975 | 974: 'geyser', 976 | 975: 'lakeside, lakeshore', 977 | 976: 'promontory, headland, head, foreland', 978 | 977: 'sandbar, sand bar', 979 | 978: 'seashore, coast, seacoast, sea-coast', 980 | 979: 'valley, vale', 981 | 980: 'volcano', 982 | 981: 'ballplayer, baseball player', 983 | 982: 'groom, bridegroom', 984 | 983: 'scuba diver', 985 | 984: 'rapeseed', 986 | 985: 'daisy', 987 | 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", 988 | 987: 'corn', 989 | 988: 'acorn', 990 | 989: 'hip, rose hip, rosehip', 991 | 990: 'buckeye, horse chestnut, conker', 992 | 991: 'coral fungus', 993 | 992: 'agaric', 994 | 993: 'gyromitra', 995 | 994: 'stinkhorn, carrion fungus', 996 | 995: 'earthstar', 997 | 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', 998 | 997: 'bolete', 999 | 998: 'ear, spike, capitulum', 1000 | 999: 'toilet tissue, toilet paper, bathroom tissue'} -------------------------------------------------------------------------------- /assets/imagenet_patch.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pralab/ImageNet-Patch/1a8600c6aa20b640bc179cbb812b20fb5b11ae29/assets/imagenet_patch.gz -------------------------------------------------------------------------------- /example.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.utils.data import DataLoader 3 | 4 | import torchvision 5 | from torchvision.datasets import ImageFolder 6 | 7 | from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, \ 8 | Normalize 9 | import torchvision.models as models 10 | 11 | from utils.visualization import imshow 12 | from utils.utils import set_all_seed 13 | from utils.utils import target_transforms 14 | 15 | from transforms.apply_patch import ApplyPatch 16 | 17 | import gzip 18 | import pickle 19 | 20 | import os 21 | 22 | 23 | set_all_seed(0) 24 | 25 | # Choose an integer in the range 0-9 to select the patch 26 | patch_id = 1 27 | 28 | # dictionary with the ImageNet label names 29 | with open(os.path.join(os.getcwd(), "assets/imagenet1000_clsidx_to_labels.txt")) as f: 30 | target_to_classname = eval(f.read()) 31 | 32 | # Load the patches 33 | with gzip.open(os.path.join(os.getcwd(), "assets/imagenet_patch.gz"), 'rb') as f: 34 | imagenet_patch = pickle.load(f) 35 | patches, targets, info = imagenet_patch 36 | patch = patches[patch_id] # get the patch with id=1 37 | 38 | print(f"Target class: {target_to_classname[targets[patch_id].item()].split(',')[0]}") 39 | 40 | # Instantiate the ApplyPatch module setting the patch and the affine transformation that will be applied 41 | apply_patch = ApplyPatch(patch, patch_size=info['patch_size'], 42 | translation_range=(.2, .2), # translation fraction wrt image dimensions 43 | rotation_range=45, # maximum absolute value of the rotation in degree 44 | scale_range=(0.7, 1) # scale range wrt image dimensions 45 | ) 46 | 47 | # For convenience the preprocessing steps are splitted to compute also the clean predictions 48 | normalizer = Normalize(mean=[0.485, 0.456, 0.406], 49 | std=[0.229, 0.224, 0.225]) 50 | patch_normalizer = Compose([apply_patch, normalizer]) 51 | 52 | # Load the data 53 | preprocess = Compose([Resize(256), CenterCrop(224), ToTensor()]) # ensure images are 224x224 54 | dataset = ImageFolder(os.path.join(os.getcwd(), "assets/data"), 55 | transform=preprocess, 56 | target_transform=target_transforms) 57 | data_loader = DataLoader(dataset, batch_size=10, shuffle=True) 58 | x, y = next(iter(data_loader)) # load a mini-batch 59 | x_clean = normalizer(x) 60 | x_adv = patch_normalizer(x) 61 | 62 | imshow(torchvision.utils.make_grid(x_adv.cpu().detach(), nrow=5), transforms=patch_normalizer) 63 | 64 | # Load model 65 | model = models.alexnet(pretrained=True) 66 | model.eval() 67 | 68 | # Test the model with the clean images 69 | output_clean = model(x_clean) 70 | clean_predictions = torch.argmax(output_clean, dim=1).cpu().detach().numpy() 71 | 72 | # Test the model with the images corrupted by the patch 73 | output_adv = model(x_adv) 74 | adv_predictions = torch.argmax(output_adv, dim=1).cpu().detach().numpy() 75 | 76 | print("\nPredictions:") 77 | for true_label, clean_pred, adv_pred in list(zip(y, clean_predictions, adv_predictions)): 78 | print(f"True label: {target_to_classname[true_label.item()].split(',')[0]} -> " 79 | f"Clean: {target_to_classname[clean_pred].split(',')[0]} -> " 80 | f"Adv.: {target_to_classname[adv_pred].split(',')[0]}") 81 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # **ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches** 2 | 3 | The demo code for the application of the generated patches on a batch from the Imagenet dataset. 4 | 5 |

6 | 9 |

10 | 11 | ## :world_map: Documentation 12 | 13 | **Paper**: Preprint available at https://arxiv.org/abs/2203.04412 14 | 15 | **Tutorial**: You can find a detailed example of how to use ImageNet-Patch in your model predictions in [this script](./example.py) and our [tutorial notebook](./imagenet_patch_example.ipynb). 16 | 17 | # :joystick: Example 18 | 19 | ### How can I load the patches? 20 | 21 |

22 | 25 |

26 | 27 | You can find the patches saved in assets/patches.gz in the form (patches, targets), 28 | where patches and targets are pytorch tensors respectively with shape (10, 3, 224, 224) and (10,). 29 | To load them you can simply run the following code snippet and select your favourite one :) 30 | 31 | ```python 32 | # Load the patches 33 | with gzip.open(os.path.join(os.getcwd(), "assets/patches.gz"), 'rb') as f: 34 | patches, targets = pickle.load(f) 35 | patch = patches[1] # get the patch with id=1 36 | target = targets[1] 37 | ``` 38 | 39 | 40 | ### How can I use them to make predictions? 41 | 42 | Once a patch is selected it can be used 43 | instantiating the ApplyPatch module in the preprocessing stack, just before the normalizer! 44 | To do that you have to specify the patch tensor and the affine transformations 45 | ranges (translation_range, rotation_range, scale_range), for which the parameters will be random sampled when an image is taken. 46 | 47 | 48 | ```python 49 | from transforms.apply_patch import ApplyPatch 50 | from torchvision.transforms import Resize, CenterCrop, ToTensor, Normalize, Compose 51 | 52 | # Instantiate the ApplyPatch layer, setting patch, target and transforms 53 | apply_patch = ApplyPatch(patch, 54 | translation_range=(.2, .2), # translation fraction wrt image dimensions 55 | rotation_range=45, # maximum absolute value of the rotation in degree 56 | scale_range=(0.5, 1) # scale range wrt image dimensions 57 | ) 58 | 59 | # Build the preprocessing stack with ApplyPatch before the normalization step 60 | preprocess = Compose([Resize(256), CenterCrop(224), ToTensor(), 61 | apply_patch, 62 | Normalize(mean=[0.485, 0.456, 0.406], 63 | std=[0.229, 0.224, 0.225]) 64 | ]) 65 | ``` 66 | 67 | 68 | 69 |

70 | 73 |

74 | 75 | Note: for our experiments we used the same subset of images used in RobustBench, extracted from the ImageNet validation set. The list of images can be found [here](https://github.com/RobustBench/robustbench/blob/master/robustbench/data/imagenet_test_image_ids.txt). 76 | 77 | # :printer: Printed Patches applied to Real-World Objects 78 | As an additional experiment, we print and apply our 10 adversarial patches on 3 different real-world objects, with 3 different roto-translations, thus acquiring 90 photos that can be [downloaded here](https://www.dropbox.com/scl/fo/6pin19joxsu3yp5sjzm0u/h?dl=0&rlkey=60eor85ral2bmytnuty397en6). 79 | 80 | 81 | # :newspaper: Citation 82 | 83 | If you use Imagenet-Patch in your work, please cite us using the following BibTeX entry: 84 | 85 | ``` 86 | @article{pintor2023imagenet, 87 | title={ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches}, 88 | author={Pintor, Maura and Angioni, Daniele and Sotgiu, Angelo and Demetrio, Luca and Demontis, Ambra and Biggio, Battista and Roli, Fabio}, 89 | journal={Pattern Recognition}, 90 | volume={134}, 91 | pages={109064}, 92 | year={2023}, 93 | publisher={Elsevier} 94 | } 95 | ``` 96 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | cycler==0.11.0 2 | fonttools==4.30.0 3 | kiwisolver==1.3.2 4 | matplotlib==3.5.1 5 | numpy==1.22.3 6 | packaging==21.3 7 | Pillow==9.0.1 8 | pyparsing==3.0.7 9 | python-dateutil==2.8.2 10 | six==1.16.0 11 | torch==1.10.2 12 | torchvision==0.11.3 13 | typing_extensions==4.1.1 14 | -------------------------------------------------------------------------------- /transforms/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pralab/ImageNet-Patch/1a8600c6aa20b640bc179cbb812b20fb5b11ae29/transforms/__init__.py -------------------------------------------------------------------------------- /transforms/apply_patch.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from transforms.my_random_affine import MyRandomAffine 3 | 4 | 5 | class ApplyPatch(torch.nn.Module): 6 | 7 | def __init__(self, patch, translation_range=(.2, .2), rotation_range=45, 8 | scale_range=(0.5, 1), patch_size=50): 9 | super().__init__() 10 | self.patch_size = patch_size 11 | self.translation_range = translation_range 12 | self.rotation_range = rotation_range 13 | self.scale_range = scale_range 14 | 15 | self._transforms = None 16 | self._patch = None 17 | self._input_shape = None 18 | self._mask = None 19 | 20 | self.set_transforms(translation_range, rotation_range, scale_range) 21 | self.set_patch(patch) 22 | 23 | @property 24 | def mask(self): 25 | return self._mask 26 | 27 | @property 28 | def transforms(self): 29 | return self._transforms 30 | 31 | def set_patch(self, patch): 32 | self._patch = patch 33 | self._input_shape = self._patch.shape 34 | self._mask = self._generate_mask() 35 | 36 | def _generate_mask(self): 37 | mask = torch.ones(self._input_shape) 38 | upp_l_x = self._input_shape[2] // 2 - self.patch_size // 2 39 | upp_l_y = self._input_shape[1] // 2 - self.patch_size // 2 40 | bott_r_x = self._input_shape[2] // 2 + self.patch_size // 2 41 | bott_r_y = self._input_shape[1] // 2 + self.patch_size // 2 42 | mask[:, :upp_l_x, :] = 0 43 | mask[:, :, :upp_l_y] = 0 44 | mask[:, bott_r_x:, :] = 0 45 | mask[:, :, bott_r_y:] = 0 46 | 47 | return mask 48 | 49 | def set_transforms(self, translation_range, rotation_range, 50 | scale_range): 51 | self._transforms = MyRandomAffine( 52 | rotation_range, translation_range, scale_range) 53 | 54 | def forward(self, img): 55 | patch, mask = self.transforms(self._patch, self._mask) 56 | inv_mask = torch.zeros_like(mask) 57 | inv_mask[mask == 0] = 1 58 | return img * inv_mask + patch * mask 59 | -------------------------------------------------------------------------------- /transforms/my_random_affine.py: -------------------------------------------------------------------------------- 1 | import torchvision 2 | from torch import Tensor 3 | from torchvision.transforms import functional as F 4 | 5 | 6 | class MyRandomAffine(torchvision.transforms.RandomAffine): 7 | def forward(self, img, mask): 8 | fill = self.fill 9 | if isinstance(img, Tensor): 10 | if isinstance(fill, (int, float)): 11 | try: 12 | fill = [float(fill)] * F.get_image_num_channels(img) 13 | except: 14 | fill = [float(fill)] * F._get_image_num_channels(img) 15 | else: 16 | fill = [float(f) for f in fill] 17 | try: 18 | img_size = F.get_image_size(img) 19 | except: 20 | img_size = F._get_image_size(img) 21 | ret = self.get_params(self.degrees, self.translate, self.scale, 22 | self.shear, img_size) 23 | transf_img = F.affine(img, *ret, interpolation=self.interpolation, 24 | fill=fill) 25 | transf_mask = F.affine(mask, *ret, interpolation=self.interpolation, 26 | fill=fill) 27 | return transf_img, transf_mask 28 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pralab/ImageNet-Patch/1a8600c6aa20b640bc179cbb812b20fb5b11ae29/utils/__init__.py -------------------------------------------------------------------------------- /utils/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | import random 4 | 5 | def set_all_seed(seed): 6 | torch.manual_seed(seed) 7 | torch.cuda.manual_seed(seed) 8 | torch.backends.cudnn.benchmark = False 9 | np.random.seed(seed) 10 | random.seed(seed) 11 | 12 | _imagenette_classes = [0, 217, 482, 491, 497] 13 | target_transforms = lambda y: _imagenette_classes[y] -------------------------------------------------------------------------------- /utils/visualization.py: -------------------------------------------------------------------------------- 1 | from torchvision.transforms import Normalize 2 | from torchvision.utils import make_grid 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | import os 6 | 7 | class InvNormalize(Normalize): 8 | def __init__(self, normalizer): 9 | inv_mean = [-mean / std for mean, std in list(zip(normalizer.mean, normalizer.std))] 10 | inv_std = [1 / std for std in normalizer.std] 11 | super().__init__(inv_mean, inv_std) 12 | 13 | def _tensor_to_show(img, transforms=None): 14 | if transforms is not None: 15 | for transform in transforms.transforms: 16 | if isinstance(transform, Normalize): 17 | normalizer = transform 18 | break 19 | inverse_transform = InvNormalize(normalizer) 20 | img = inverse_transform(img) 21 | 22 | npimg = img.numpy() 23 | npimg = np.transpose(npimg, (1, 2, 0)) 24 | return npimg 25 | 26 | def imshow(img, transforms=None, figsize=(10, 20)): 27 | npimg = _tensor_to_show(img, transforms) 28 | plt.figure(figsize=figsize) 29 | plt.imshow(npimg, interpolation=None) 30 | 31 | upp_l_x = 224 // 2 - 50 // 2 32 | upp_l_y = 224 // 2 - 50 // 2 33 | bott_r_x = 224 // 2 + 50 // 2 34 | bott_r_y = 224 // 2 + 50 // 2 35 | 36 | # dictionary with the ImageNet label names 37 | with open(os.path.join(os.getcwd(), "assets/imagenet1000_clsidx_to_labels.txt")) as f: 38 | target_to_classname = eval(f.read()) 39 | 40 | def show_imagenet_patch(patches, targets): 41 | fig = plt.figure(figsize=(10, 5)) 42 | 43 | for i, (patch, target) in enumerate(list(zip(patches, targets))): 44 | patch = np.transpose(patch, (1, 2, 0)) 45 | patch = patch[upp_l_x:bott_r_x, upp_l_y:bott_r_y, :] 46 | 47 | plt.subplot(2, 5, i+1) 48 | plt.title(target_to_classname[int(target.item())].split(",")[0]) 49 | plt.imshow(patch) 50 | plt.axis('off') 51 | plt.show() 52 | 53 | 54 | def show_batch_with_patch(x, transforms=None, figsize=(10, 20)): 55 | imshow(make_grid(x.cpu().detach(), nrow=5), 56 | transforms=transforms, figsize=figsize) 57 | plt.axis('off') 58 | plt.show() 59 | 60 | 61 | def plot_patch_predictions(x_clean, x_adv, clean_pred, adv_pred, 62 | true_label, target, figsize=(5, 20), 63 | normalizer=None): 64 | N_IMAGES = x_clean.shape[0] 65 | 66 | fig, ax = plt.subplots(2, N_IMAGES, figsize=figsize) 67 | 68 | if normalizer is not None: 69 | inverse_transform = InvNormalize(normalizer) 70 | x_clean = inverse_transform(x_clean) 71 | x_adv = inverse_transform(x_adv) 72 | x_clean_img = np.transpose(x_clean.detach().cpu().numpy(), (0, 2, 3, 1)) 73 | x_adv_img = np.transpose(x_adv.detach().cpu().numpy(), (0, 2, 3, 1)) 74 | 75 | # plot clean samples in first row 76 | for j in range(N_IMAGES): 77 | true_j = true_label[j] 78 | true = target_to_classname[true_j].split(",")[0] 79 | clean_j = clean_pred[j] 80 | clean = target_to_classname[clean_j].split(",")[0] 81 | ax[0, j].imshow(x_clean_img[j]) 82 | ax[0, j].set_title(f"Pred.: {clean}\nTrue: {true}") 83 | ax[0, j].set_xticks([]) 84 | ax[0, j].set_yticks([]) 85 | 86 | for j in range(N_IMAGES): 87 | ax[1, j].imshow(x_adv_img[j]) 88 | p_i = adv_pred[j].item() 89 | p = target_to_classname[p_i].split(",")[0] 90 | true_i = true_label[j].item() 91 | true = target_to_classname[true_i].split(",")[0] 92 | 93 | if p_i == target: 94 | color = 'r' 95 | elif p_i != true_i: 96 | color = 'b' 97 | else: 98 | color = 'g' 99 | ax[1, j].set_title(f"Pred.: {p}", color=color) 100 | ax[1, j].set_xticks([]) 101 | ax[1, j].set_yticks([]) 102 | 103 | ax[1, 0].set_ylabel(target_to_classname[target].split(",")[0]) 104 | ax[0, 0].set_ylabel("Clean") 105 | plt.show() --------------------------------------------------------------------------------