Upload folder using huggingface_hub
Browse files- config.json +2268 -0
- generation_config.json +6 -0
- modelling_medicap.py +518 -0
- pytorch_model.bin +3 -0
config.json
ADDED
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@@ -0,0 +1,2268 @@
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|
| 1 |
+
{
|
| 2 |
+
"_commit_hash": null,
|
| 3 |
+
"architectures": [
|
| 4 |
+
"MedICapEncoderDecoderModel"
|
| 5 |
+
],
|
| 6 |
+
"auto_map": {
|
| 7 |
+
"AutoModel": "modelling_medicap.MedICapEncoderDecoderModel"
|
| 8 |
+
},
|
| 9 |
+
"decoder": {
|
| 10 |
+
"_name_or_path": "",
|
| 11 |
+
"_num_labels": 1,
|
| 12 |
+
"activation_function": "gelu_new",
|
| 13 |
+
"add_cross_attention": false,
|
| 14 |
+
"architectures": [
|
| 15 |
+
"GPT2LMHeadModel"
|
| 16 |
+
],
|
| 17 |
+
"attn_pdrop": 0.1,
|
| 18 |
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"bad_words_ids": null,
|
| 19 |
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"begin_suppress_tokens": null,
|
| 20 |
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"bos_token_id": 50256,
|
| 21 |
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"chunk_size_feed_forward": 0,
|
| 22 |
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"cross_attention_hidden_size": null,
|
| 23 |
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"decoder_start_token_id": null,
|
| 24 |
+
"diversity_penalty": 0.0,
|
| 25 |
+
"do_sample": false,
|
| 26 |
+
"early_stopping": false,
|
| 27 |
+
"embd_pdrop": 0.1,
|
| 28 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 29 |
+
"eos_token_id": 50256,
|
| 30 |
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"exponential_decay_length_penalty": null,
|
| 31 |
+
"finetuning_task": null,
|
| 32 |
+
"forced_bos_token_id": null,
|
| 33 |
+
"forced_eos_token_id": null,
|
| 34 |
+
"id2label": {
|
| 35 |
+
"0": "LABEL_0"
|
| 36 |
+
},
|
| 37 |
+
"initializer_range": 0.02,
|
| 38 |
+
"is_decoder": true,
|
| 39 |
+
"is_encoder_decoder": false,
|
| 40 |
+
"label2id": {
|
| 41 |
+
"LABEL_0": 0
|
| 42 |
+
},
|
| 43 |
+
"layer_norm_epsilon": 1e-05,
|
| 44 |
+
"length_penalty": 1.0,
|
| 45 |
+
"max_length": 20,
|
| 46 |
+
"min_length": 0,
|
| 47 |
+
"model_type": "gpt2",
|
| 48 |
+
"n_ctx": 1024,
|
| 49 |
+
"n_embd": 768,
|
| 50 |
+
"n_head": 12,
|
| 51 |
+
"n_inner": null,
|
| 52 |
+
"n_layer": 6,
|
| 53 |
+
"n_positions": 1024,
|
| 54 |
+
"no_repeat_ngram_size": 0,
|
| 55 |
+
"num_beam_groups": 1,
|
| 56 |
+
"num_beams": 1,
|
| 57 |
+
"num_return_sequences": 1,
|
| 58 |
+
"output_attentions": false,
|
| 59 |
+
"output_hidden_states": false,
|
| 60 |
+
"output_scores": false,
|
| 61 |
+
"pad_token_id": null,
|
| 62 |
+
"prefix": null,
|
| 63 |
+
"problem_type": null,
|
| 64 |
+
"pruned_heads": {},
|
| 65 |
+
"remove_invalid_values": false,
|
| 66 |
+
"reorder_and_upcast_attn": false,
|
| 67 |
+
"repetition_penalty": 1.0,
|
| 68 |
+
"resid_pdrop": 0.1,
|
| 69 |
+
"return_dict": true,
|
| 70 |
+
"return_dict_in_generate": false,
|
| 71 |
+
"scale_attn_by_inverse_layer_idx": false,
|
| 72 |
+
"scale_attn_weights": true,
|
| 73 |
+
"sep_token_id": null,
|
| 74 |
+
"summary_activation": null,
|
| 75 |
+
"summary_first_dropout": 0.1,
|
| 76 |
+
"summary_proj_to_labels": true,
|
| 77 |
+
"summary_type": "cls_index",
|
| 78 |
+
"summary_use_proj": true,
|
| 79 |
+
"suppress_tokens": null,
|
| 80 |
+
"task_specific_params": {
|
| 81 |
+
"text-generation": {
|
| 82 |
+
"do_sample": true,
|
| 83 |
+
"max_length": 50
|
| 84 |
+
}
|
| 85 |
+
},
|
| 86 |
+
"temperature": 1.0,
|
| 87 |
+
"tf_legacy_loss": false,
|
| 88 |
+
"tie_encoder_decoder": false,
|
| 89 |
+
"tie_word_embeddings": true,
|
| 90 |
+
"tokenizer_class": null,
|
| 91 |
+
"top_k": 50,
|
| 92 |
+
"top_p": 1.0,
|
| 93 |
+
"torch_dtype": null,
|
| 94 |
+
"torchscript": false,
|
| 95 |
+
"transformers_version": "4.31.0",
|
| 96 |
+
"typical_p": 1.0,
|
| 97 |
+
"use_bfloat16": false,
|
| 98 |
+
"use_cache": true,
|
| 99 |
+
"vocab_size": 50259
|
| 100 |
+
},
|
| 101 |
+
"encoder": {
|
| 102 |
+
"_name_or_path": "",
|
| 103 |
+
"add_cross_attention": false,
|
| 104 |
+
"architectures": [
|
| 105 |
+
"CvtForImageClassification"
|
| 106 |
+
],
|
| 107 |
+
"attention_drop_rate": [
|
| 108 |
+
0.0,
|
| 109 |
+
0.0,
|
| 110 |
+
0.0
|
| 111 |
+
],
|
| 112 |
+
"bad_words_ids": null,
|
| 113 |
+
"begin_suppress_tokens": null,
|
| 114 |
+
"bos_token_id": null,
|
| 115 |
+
"chunk_size_feed_forward": 0,
|
| 116 |
+
"cls_token": [
|
| 117 |
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false,
|
| 118 |
+
false,
|
| 119 |
+
true
|
| 120 |
+
],
|
| 121 |
+
"cross_attention_hidden_size": null,
|
| 122 |
+
"decoder_start_token_id": null,
|
| 123 |
+
"depth": [
|
| 124 |
+
1,
|
| 125 |
+
4,
|
| 126 |
+
16
|
| 127 |
+
],
|
| 128 |
+
"diversity_penalty": 0.0,
|
| 129 |
+
"do_sample": false,
|
| 130 |
+
"drop_path_rate": [
|
| 131 |
+
0.0,
|
| 132 |
+
0.0,
|
| 133 |
+
0.1
|
| 134 |
+
],
|
| 135 |
+
"drop_rate": [
|
| 136 |
+
0.0,
|
| 137 |
+
0.0,
|
| 138 |
+
0.0
|
| 139 |
+
],
|
| 140 |
+
"early_stopping": false,
|
| 141 |
+
"embed_dim": [
|
| 142 |
+
64,
|
| 143 |
+
192,
|
| 144 |
+
384
|
| 145 |
+
],
|
| 146 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 147 |
+
"eos_token_id": null,
|
| 148 |
+
"exponential_decay_length_penalty": null,
|
| 149 |
+
"finetuning_task": null,
|
| 150 |
+
"forced_bos_token_id": null,
|
| 151 |
+
"forced_eos_token_id": null,
|
| 152 |
+
"id2label": {
|
| 153 |
+
"0": "tench, Tinca tinca",
|
| 154 |
+
"1": "goldfish, Carassius auratus",
|
| 155 |
+
"2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
|
| 156 |
+
"3": "tiger shark, Galeocerdo cuvieri",
|
| 157 |
+
"4": "hammerhead, hammerhead shark",
|
| 158 |
+
"5": "electric ray, crampfish, numbfish, torpedo",
|
| 159 |
+
"6": "stingray",
|
| 160 |
+
"7": "cock",
|
| 161 |
+
"8": "hen",
|
| 162 |
+
"9": "ostrich, Struthio camelus",
|
| 163 |
+
"10": "brambling, Fringilla montifringilla",
|
| 164 |
+
"11": "goldfinch, Carduelis carduelis",
|
| 165 |
+
"12": "house finch, linnet, Carpodacus mexicanus",
|
| 166 |
+
"13": "junco, snowbird",
|
| 167 |
+
"14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea",
|
| 168 |
+
"15": "robin, American robin, Turdus migratorius",
|
| 169 |
+
"16": "bulbul",
|
| 170 |
+
"17": "jay",
|
| 171 |
+
"18": "magpie",
|
| 172 |
+
"19": "chickadee",
|
| 173 |
+
"20": "water ouzel, dipper",
|
| 174 |
+
"21": "kite",
|
| 175 |
+
"22": "bald eagle, American eagle, Haliaeetus leucocephalus",
|
| 176 |
+
"23": "vulture",
|
| 177 |
+
"24": "great grey owl, great gray owl, Strix nebulosa",
|
| 178 |
+
"25": "European fire salamander, Salamandra salamandra",
|
| 179 |
+
"26": "common newt, Triturus vulgaris",
|
| 180 |
+
"27": "eft",
|
| 181 |
+
"28": "spotted salamander, Ambystoma maculatum",
|
| 182 |
+
"29": "axolotl, mud puppy, Ambystoma mexicanum",
|
| 183 |
+
"30": "bullfrog, Rana catesbeiana",
|
| 184 |
+
"31": "tree frog, tree-frog",
|
| 185 |
+
"32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
|
| 186 |
+
"33": "loggerhead, loggerhead turtle, Caretta caretta",
|
| 187 |
+
"34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
|
| 188 |
+
"35": "mud turtle",
|
| 189 |
+
"36": "terrapin",
|
| 190 |
+
"37": "box turtle, box tortoise",
|
| 191 |
+
"38": "banded gecko",
|
| 192 |
+
"39": "common iguana, iguana, Iguana iguana",
|
| 193 |
+
"40": "American chameleon, anole, Anolis carolinensis",
|
| 194 |
+
"41": "whiptail, whiptail lizard",
|
| 195 |
+
"42": "agama",
|
| 196 |
+
"43": "frilled lizard, Chlamydosaurus kingi",
|
| 197 |
+
"44": "alligator lizard",
|
| 198 |
+
"45": "Gila monster, Heloderma suspectum",
|
| 199 |
+
"46": "green lizard, Lacerta viridis",
|
| 200 |
+
"47": "African chameleon, Chamaeleo chamaeleon",
|
| 201 |
+
"48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
|
| 202 |
+
"49": "African crocodile, Nile crocodile, Crocodylus niloticus",
|
| 203 |
+
"50": "American alligator, Alligator mississipiensis",
|
| 204 |
+
"51": "triceratops",
|
| 205 |
+
"52": "thunder snake, worm snake, Carphophis amoenus",
|
| 206 |
+
"53": "ringneck snake, ring-necked snake, ring snake",
|
| 207 |
+
"54": "hognose snake, puff adder, sand viper",
|
| 208 |
+
"55": "green snake, grass snake",
|
| 209 |
+
"56": "king snake, kingsnake",
|
| 210 |
+
"57": "garter snake, grass snake",
|
| 211 |
+
"58": "water snake",
|
| 212 |
+
"59": "vine snake",
|
| 213 |
+
"60": "night snake, Hypsiglena torquata",
|
| 214 |
+
"61": "boa constrictor, Constrictor constrictor",
|
| 215 |
+
"62": "rock python, rock snake, Python sebae",
|
| 216 |
+
"63": "Indian cobra, Naja naja",
|
| 217 |
+
"64": "green mamba",
|
| 218 |
+
"65": "sea snake",
|
| 219 |
+
"66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
|
| 220 |
+
"67": "diamondback, diamondback rattlesnake, Crotalus adamanteus",
|
| 221 |
+
"68": "sidewinder, horned rattlesnake, Crotalus cerastes",
|
| 222 |
+
"69": "trilobite",
|
| 223 |
+
"70": "harvestman, daddy longlegs, Phalangium opilio",
|
| 224 |
+
"71": "scorpion",
|
| 225 |
+
"72": "black and gold garden spider, Argiope aurantia",
|
| 226 |
+
"73": "barn spider, Araneus cavaticus",
|
| 227 |
+
"74": "garden spider, Aranea diademata",
|
| 228 |
+
"75": "black widow, Latrodectus mactans",
|
| 229 |
+
"76": "tarantula",
|
| 230 |
+
"77": "wolf spider, hunting spider",
|
| 231 |
+
"78": "tick",
|
| 232 |
+
"79": "centipede",
|
| 233 |
+
"80": "black grouse",
|
| 234 |
+
"81": "ptarmigan",
|
| 235 |
+
"82": "ruffed grouse, partridge, Bonasa umbellus",
|
| 236 |
+
"83": "prairie chicken, prairie grouse, prairie fowl",
|
| 237 |
+
"84": "peacock",
|
| 238 |
+
"85": "quail",
|
| 239 |
+
"86": "partridge",
|
| 240 |
+
"87": "African grey, African gray, Psittacus erithacus",
|
| 241 |
+
"88": "macaw",
|
| 242 |
+
"89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
|
| 243 |
+
"90": "lorikeet",
|
| 244 |
+
"91": "coucal",
|
| 245 |
+
"92": "bee eater",
|
| 246 |
+
"93": "hornbill",
|
| 247 |
+
"94": "hummingbird",
|
| 248 |
+
"95": "jacamar",
|
| 249 |
+
"96": "toucan",
|
| 250 |
+
"97": "drake",
|
| 251 |
+
"98": "red-breasted merganser, Mergus serrator",
|
| 252 |
+
"99": "goose",
|
| 253 |
+
"100": "black swan, Cygnus atratus",
|
| 254 |
+
"101": "tusker",
|
| 255 |
+
"102": "echidna, spiny anteater, anteater",
|
| 256 |
+
"103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
|
| 257 |
+
"104": "wallaby, brush kangaroo",
|
| 258 |
+
"105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
|
| 259 |
+
"106": "wombat",
|
| 260 |
+
"107": "jellyfish",
|
| 261 |
+
"108": "sea anemone, anemone",
|
| 262 |
+
"109": "brain coral",
|
| 263 |
+
"110": "flatworm, platyhelminth",
|
| 264 |
+
"111": "nematode, nematode worm, roundworm",
|
| 265 |
+
"112": "conch",
|
| 266 |
+
"113": "snail",
|
| 267 |
+
"114": "slug",
|
| 268 |
+
"115": "sea slug, nudibranch",
|
| 269 |
+
"116": "chiton, coat-of-mail shell, sea cradle, polyplacophore",
|
| 270 |
+
"117": "chambered nautilus, pearly nautilus, nautilus",
|
| 271 |
+
"118": "Dungeness crab, Cancer magister",
|
| 272 |
+
"119": "rock crab, Cancer irroratus",
|
| 273 |
+
"120": "fiddler crab",
|
| 274 |
+
"121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
|
| 275 |
+
"122": "American lobster, Northern lobster, Maine lobster, Homarus americanus",
|
| 276 |
+
"123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
|
| 277 |
+
"124": "crayfish, crawfish, crawdad, crawdaddy",
|
| 278 |
+
"125": "hermit crab",
|
| 279 |
+
"126": "isopod",
|
| 280 |
+
"127": "white stork, Ciconia ciconia",
|
| 281 |
+
"128": "black stork, Ciconia nigra",
|
| 282 |
+
"129": "spoonbill",
|
| 283 |
+
"130": "flamingo",
|
| 284 |
+
"131": "little blue heron, Egretta caerulea",
|
| 285 |
+
"132": "American egret, great white heron, Egretta albus",
|
| 286 |
+
"133": "bittern",
|
| 287 |
+
"134": "crane",
|
| 288 |
+
"135": "limpkin, Aramus pictus",
|
| 289 |
+
"136": "European gallinule, Porphyrio porphyrio",
|
| 290 |
+
"137": "American coot, marsh hen, mud hen, water hen, Fulica americana",
|
| 291 |
+
"138": "bustard",
|
| 292 |
+
"139": "ruddy turnstone, Arenaria interpres",
|
| 293 |
+
"140": "red-backed sandpiper, dunlin, Erolia alpina",
|
| 294 |
+
"141": "redshank, Tringa totanus",
|
| 295 |
+
"142": "dowitcher",
|
| 296 |
+
"143": "oystercatcher, oyster catcher",
|
| 297 |
+
"144": "pelican",
|
| 298 |
+
"145": "king penguin, Aptenodytes patagonica",
|
| 299 |
+
"146": "albatross, mollymawk",
|
| 300 |
+
"147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
|
| 301 |
+
"148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
|
| 302 |
+
"149": "dugong, Dugong dugon",
|
| 303 |
+
"150": "sea lion",
|
| 304 |
+
"151": "Chihuahua",
|
| 305 |
+
"152": "Japanese spaniel",
|
| 306 |
+
"153": "Maltese dog, Maltese terrier, Maltese",
|
| 307 |
+
"154": "Pekinese, Pekingese, Peke",
|
| 308 |
+
"155": "Shih-Tzu",
|
| 309 |
+
"156": "Blenheim spaniel",
|
| 310 |
+
"157": "papillon",
|
| 311 |
+
"158": "toy terrier",
|
| 312 |
+
"159": "Rhodesian ridgeback",
|
| 313 |
+
"160": "Afghan hound, Afghan",
|
| 314 |
+
"161": "basset, basset hound",
|
| 315 |
+
"162": "beagle",
|
| 316 |
+
"163": "bloodhound, sleuthhound",
|
| 317 |
+
"164": "bluetick",
|
| 318 |
+
"165": "black-and-tan coonhound",
|
| 319 |
+
"166": "Walker hound, Walker foxhound",
|
| 320 |
+
"167": "English foxhound",
|
| 321 |
+
"168": "redbone",
|
| 322 |
+
"169": "borzoi, Russian wolfhound",
|
| 323 |
+
"170": "Irish wolfhound",
|
| 324 |
+
"171": "Italian greyhound",
|
| 325 |
+
"172": "whippet",
|
| 326 |
+
"173": "Ibizan hound, Ibizan Podenco",
|
| 327 |
+
"174": "Norwegian elkhound, elkhound",
|
| 328 |
+
"175": "otterhound, otter hound",
|
| 329 |
+
"176": "Saluki, gazelle hound",
|
| 330 |
+
"177": "Scottish deerhound, deerhound",
|
| 331 |
+
"178": "Weimaraner",
|
| 332 |
+
"179": "Staffordshire bullterrier, Staffordshire bull terrier",
|
| 333 |
+
"180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
|
| 334 |
+
"181": "Bedlington terrier",
|
| 335 |
+
"182": "Border terrier",
|
| 336 |
+
"183": "Kerry blue terrier",
|
| 337 |
+
"184": "Irish terrier",
|
| 338 |
+
"185": "Norfolk terrier",
|
| 339 |
+
"186": "Norwich terrier",
|
| 340 |
+
"187": "Yorkshire terrier",
|
| 341 |
+
"188": "wire-haired fox terrier",
|
| 342 |
+
"189": "Lakeland terrier",
|
| 343 |
+
"190": "Sealyham terrier, Sealyham",
|
| 344 |
+
"191": "Airedale, Airedale terrier",
|
| 345 |
+
"192": "cairn, cairn terrier",
|
| 346 |
+
"193": "Australian terrier",
|
| 347 |
+
"194": "Dandie Dinmont, Dandie Dinmont terrier",
|
| 348 |
+
"195": "Boston bull, Boston terrier",
|
| 349 |
+
"196": "miniature schnauzer",
|
| 350 |
+
"197": "giant schnauzer",
|
| 351 |
+
"198": "standard schnauzer",
|
| 352 |
+
"199": "Scotch terrier, Scottish terrier, Scottie",
|
| 353 |
+
"200": "Tibetan terrier, chrysanthemum dog",
|
| 354 |
+
"201": "silky terrier, Sydney silky",
|
| 355 |
+
"202": "soft-coated wheaten terrier",
|
| 356 |
+
"203": "West Highland white terrier",
|
| 357 |
+
"204": "Lhasa, Lhasa apso",
|
| 358 |
+
"205": "flat-coated retriever",
|
| 359 |
+
"206": "curly-coated retriever",
|
| 360 |
+
"207": "golden retriever",
|
| 361 |
+
"208": "Labrador retriever",
|
| 362 |
+
"209": "Chesapeake Bay retriever",
|
| 363 |
+
"210": "German short-haired pointer",
|
| 364 |
+
"211": "vizsla, Hungarian pointer",
|
| 365 |
+
"212": "English setter",
|
| 366 |
+
"213": "Irish setter, red setter",
|
| 367 |
+
"214": "Gordon setter",
|
| 368 |
+
"215": "Brittany spaniel",
|
| 369 |
+
"216": "clumber, clumber spaniel",
|
| 370 |
+
"217": "English springer, English springer spaniel",
|
| 371 |
+
"218": "Welsh springer spaniel",
|
| 372 |
+
"219": "cocker spaniel, English cocker spaniel, cocker",
|
| 373 |
+
"220": "Sussex spaniel",
|
| 374 |
+
"221": "Irish water spaniel",
|
| 375 |
+
"222": "kuvasz",
|
| 376 |
+
"223": "schipperke",
|
| 377 |
+
"224": "groenendael",
|
| 378 |
+
"225": "malinois",
|
| 379 |
+
"226": "briard",
|
| 380 |
+
"227": "kelpie",
|
| 381 |
+
"228": "komondor",
|
| 382 |
+
"229": "Old English sheepdog, bobtail",
|
| 383 |
+
"230": "Shetland sheepdog, Shetland sheep dog, Shetland",
|
| 384 |
+
"231": "collie",
|
| 385 |
+
"232": "Border collie",
|
| 386 |
+
"233": "Bouvier des Flandres, Bouviers des Flandres",
|
| 387 |
+
"234": "Rottweiler",
|
| 388 |
+
"235": "German shepherd, German shepherd dog, German police dog, alsatian",
|
| 389 |
+
"236": "Doberman, Doberman pinscher",
|
| 390 |
+
"237": "miniature pinscher",
|
| 391 |
+
"238": "Greater Swiss Mountain dog",
|
| 392 |
+
"239": "Bernese mountain dog",
|
| 393 |
+
"240": "Appenzeller",
|
| 394 |
+
"241": "EntleBucher",
|
| 395 |
+
"242": "boxer",
|
| 396 |
+
"243": "bull mastiff",
|
| 397 |
+
"244": "Tibetan mastiff",
|
| 398 |
+
"245": "French bulldog",
|
| 399 |
+
"246": "Great Dane",
|
| 400 |
+
"247": "Saint Bernard, St Bernard",
|
| 401 |
+
"248": "Eskimo dog, husky",
|
| 402 |
+
"249": "malamute, malemute, Alaskan malamute",
|
| 403 |
+
"250": "Siberian husky",
|
| 404 |
+
"251": "dalmatian, coach dog, carriage dog",
|
| 405 |
+
"252": "affenpinscher, monkey pinscher, monkey dog",
|
| 406 |
+
"253": "basenji",
|
| 407 |
+
"254": "pug, pug-dog",
|
| 408 |
+
"255": "Leonberg",
|
| 409 |
+
"256": "Newfoundland, Newfoundland dog",
|
| 410 |
+
"257": "Great Pyrenees",
|
| 411 |
+
"258": "Samoyed, Samoyede",
|
| 412 |
+
"259": "Pomeranian",
|
| 413 |
+
"260": "chow, chow chow",
|
| 414 |
+
"261": "keeshond",
|
| 415 |
+
"262": "Brabancon griffon",
|
| 416 |
+
"263": "Pembroke, Pembroke Welsh corgi",
|
| 417 |
+
"264": "Cardigan, Cardigan Welsh corgi",
|
| 418 |
+
"265": "toy poodle",
|
| 419 |
+
"266": "miniature poodle",
|
| 420 |
+
"267": "standard poodle",
|
| 421 |
+
"268": "Mexican hairless",
|
| 422 |
+
"269": "timber wolf, grey wolf, gray wolf, Canis lupus",
|
| 423 |
+
"270": "white wolf, Arctic wolf, Canis lupus tundrarum",
|
| 424 |
+
"271": "red wolf, maned wolf, Canis rufus, Canis niger",
|
| 425 |
+
"272": "coyote, prairie wolf, brush wolf, Canis latrans",
|
| 426 |
+
"273": "dingo, warrigal, warragal, Canis dingo",
|
| 427 |
+
"274": "dhole, Cuon alpinus",
|
| 428 |
+
"275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
|
| 429 |
+
"276": "hyena, hyaena",
|
| 430 |
+
"277": "red fox, Vulpes vulpes",
|
| 431 |
+
"278": "kit fox, Vulpes macrotis",
|
| 432 |
+
"279": "Arctic fox, white fox, Alopex lagopus",
|
| 433 |
+
"280": "grey fox, gray fox, Urocyon cinereoargenteus",
|
| 434 |
+
"281": "tabby, tabby cat",
|
| 435 |
+
"282": "tiger cat",
|
| 436 |
+
"283": "Persian cat",
|
| 437 |
+
"284": "Siamese cat, Siamese",
|
| 438 |
+
"285": "Egyptian cat",
|
| 439 |
+
"286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
|
| 440 |
+
"287": "lynx, catamount",
|
| 441 |
+
"288": "leopard, Panthera pardus",
|
| 442 |
+
"289": "snow leopard, ounce, Panthera uncia",
|
| 443 |
+
"290": "jaguar, panther, Panthera onca, Felis onca",
|
| 444 |
+
"291": "lion, king of beasts, Panthera leo",
|
| 445 |
+
"292": "tiger, Panthera tigris",
|
| 446 |
+
"293": "cheetah, chetah, Acinonyx jubatus",
|
| 447 |
+
"294": "brown bear, bruin, Ursus arctos",
|
| 448 |
+
"295": "American black bear, black bear, Ursus americanus, Euarctos americanus",
|
| 449 |
+
"296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
|
| 450 |
+
"297": "sloth bear, Melursus ursinus, Ursus ursinus",
|
| 451 |
+
"298": "mongoose",
|
| 452 |
+
"299": "meerkat, mierkat",
|
| 453 |
+
"300": "tiger beetle",
|
| 454 |
+
"301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
|
| 455 |
+
"302": "ground beetle, carabid beetle",
|
| 456 |
+
"303": "long-horned beetle, longicorn, longicorn beetle",
|
| 457 |
+
"304": "leaf beetle, chrysomelid",
|
| 458 |
+
"305": "dung beetle",
|
| 459 |
+
"306": "rhinoceros beetle",
|
| 460 |
+
"307": "weevil",
|
| 461 |
+
"308": "fly",
|
| 462 |
+
"309": "bee",
|
| 463 |
+
"310": "ant, emmet, pismire",
|
| 464 |
+
"311": "grasshopper, hopper",
|
| 465 |
+
"312": "cricket",
|
| 466 |
+
"313": "walking stick, walkingstick, stick insect",
|
| 467 |
+
"314": "cockroach, roach",
|
| 468 |
+
"315": "mantis, mantid",
|
| 469 |
+
"316": "cicada, cicala",
|
| 470 |
+
"317": "leafhopper",
|
| 471 |
+
"318": "lacewing, lacewing fly",
|
| 472 |
+
"319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
|
| 473 |
+
"320": "damselfly",
|
| 474 |
+
"321": "admiral",
|
| 475 |
+
"322": "ringlet, ringlet butterfly",
|
| 476 |
+
"323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
|
| 477 |
+
"324": "cabbage butterfly",
|
| 478 |
+
"325": "sulphur butterfly, sulfur butterfly",
|
| 479 |
+
"326": "lycaenid, lycaenid butterfly",
|
| 480 |
+
"327": "starfish, sea star",
|
| 481 |
+
"328": "sea urchin",
|
| 482 |
+
"329": "sea cucumber, holothurian",
|
| 483 |
+
"330": "wood rabbit, cottontail, cottontail rabbit",
|
| 484 |
+
"331": "hare",
|
| 485 |
+
"332": "Angora, Angora rabbit",
|
| 486 |
+
"333": "hamster",
|
| 487 |
+
"334": "porcupine, hedgehog",
|
| 488 |
+
"335": "fox squirrel, eastern fox squirrel, Sciurus niger",
|
| 489 |
+
"336": "marmot",
|
| 490 |
+
"337": "beaver",
|
| 491 |
+
"338": "guinea pig, Cavia cobaya",
|
| 492 |
+
"339": "sorrel",
|
| 493 |
+
"340": "zebra",
|
| 494 |
+
"341": "hog, pig, grunter, squealer, Sus scrofa",
|
| 495 |
+
"342": "wild boar, boar, Sus scrofa",
|
| 496 |
+
"343": "warthog",
|
| 497 |
+
"344": "hippopotamus, hippo, river horse, Hippopotamus amphibius",
|
| 498 |
+
"345": "ox",
|
| 499 |
+
"346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
|
| 500 |
+
"347": "bison",
|
| 501 |
+
"348": "ram, tup",
|
| 502 |
+
"349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
|
| 503 |
+
"350": "ibex, Capra ibex",
|
| 504 |
+
"351": "hartebeest",
|
| 505 |
+
"352": "impala, Aepyceros melampus",
|
| 506 |
+
"353": "gazelle",
|
| 507 |
+
"354": "Arabian camel, dromedary, Camelus dromedarius",
|
| 508 |
+
"355": "llama",
|
| 509 |
+
"356": "weasel",
|
| 510 |
+
"357": "mink",
|
| 511 |
+
"358": "polecat, fitch, foulmart, foumart, Mustela putorius",
|
| 512 |
+
"359": "black-footed ferret, ferret, Mustela nigripes",
|
| 513 |
+
"360": "otter",
|
| 514 |
+
"361": "skunk, polecat, wood pussy",
|
| 515 |
+
"362": "badger",
|
| 516 |
+
"363": "armadillo",
|
| 517 |
+
"364": "three-toed sloth, ai, Bradypus tridactylus",
|
| 518 |
+
"365": "orangutan, orang, orangutang, Pongo pygmaeus",
|
| 519 |
+
"366": "gorilla, Gorilla gorilla",
|
| 520 |
+
"367": "chimpanzee, chimp, Pan troglodytes",
|
| 521 |
+
"368": "gibbon, Hylobates lar",
|
| 522 |
+
"369": "siamang, Hylobates syndactylus, Symphalangus syndactylus",
|
| 523 |
+
"370": "guenon, guenon monkey",
|
| 524 |
+
"371": "patas, hussar monkey, Erythrocebus patas",
|
| 525 |
+
"372": "baboon",
|
| 526 |
+
"373": "macaque",
|
| 527 |
+
"374": "langur",
|
| 528 |
+
"375": "colobus, colobus monkey",
|
| 529 |
+
"376": "proboscis monkey, Nasalis larvatus",
|
| 530 |
+
"377": "marmoset",
|
| 531 |
+
"378": "capuchin, ringtail, Cebus capucinus",
|
| 532 |
+
"379": "howler monkey, howler",
|
| 533 |
+
"380": "titi, titi monkey",
|
| 534 |
+
"381": "spider monkey, Ateles geoffroyi",
|
| 535 |
+
"382": "squirrel monkey, Saimiri sciureus",
|
| 536 |
+
"383": "Madagascar cat, ring-tailed lemur, Lemur catta",
|
| 537 |
+
"384": "indri, indris, Indri indri, Indri brevicaudatus",
|
| 538 |
+
"385": "Indian elephant, Elephas maximus",
|
| 539 |
+
"386": "African elephant, Loxodonta africana",
|
| 540 |
+
"387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
|
| 541 |
+
"388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
|
| 542 |
+
"389": "barracouta, snoek",
|
| 543 |
+
"390": "eel",
|
| 544 |
+
"391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
|
| 545 |
+
"392": "rock beauty, Holocanthus tricolor",
|
| 546 |
+
"393": "anemone fish",
|
| 547 |
+
"394": "sturgeon",
|
| 548 |
+
"395": "gar, garfish, garpike, billfish, Lepisosteus osseus",
|
| 549 |
+
"396": "lionfish",
|
| 550 |
+
"397": "puffer, pufferfish, blowfish, globefish",
|
| 551 |
+
"398": "abacus",
|
| 552 |
+
"399": "abaya",
|
| 553 |
+
"400": "academic gown, academic robe, judge's robe",
|
| 554 |
+
"401": "accordion, piano accordion, squeeze box",
|
| 555 |
+
"402": "acoustic guitar",
|
| 556 |
+
"403": "aircraft carrier, carrier, flattop, attack aircraft carrier",
|
| 557 |
+
"404": "airliner",
|
| 558 |
+
"405": "airship, dirigible",
|
| 559 |
+
"406": "altar",
|
| 560 |
+
"407": "ambulance",
|
| 561 |
+
"408": "amphibian, amphibious vehicle",
|
| 562 |
+
"409": "analog clock",
|
| 563 |
+
"410": "apiary, bee house",
|
| 564 |
+
"411": "apron",
|
| 565 |
+
"412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
|
| 566 |
+
"413": "assault rifle, assault gun",
|
| 567 |
+
"414": "backpack, back pack, knapsack, packsack, rucksack, haversack",
|
| 568 |
+
"415": "bakery, bakeshop, bakehouse",
|
| 569 |
+
"416": "balance beam, beam",
|
| 570 |
+
"417": "balloon",
|
| 571 |
+
"418": "ballpoint, ballpoint pen, ballpen, Biro",
|
| 572 |
+
"419": "Band Aid",
|
| 573 |
+
"420": "banjo",
|
| 574 |
+
"421": "bannister, banister, balustrade, balusters, handrail",
|
| 575 |
+
"422": "barbell",
|
| 576 |
+
"423": "barber chair",
|
| 577 |
+
"424": "barbershop",
|
| 578 |
+
"425": "barn",
|
| 579 |
+
"426": "barometer",
|
| 580 |
+
"427": "barrel, cask",
|
| 581 |
+
"428": "barrow, garden cart, lawn cart, wheelbarrow",
|
| 582 |
+
"429": "baseball",
|
| 583 |
+
"430": "basketball",
|
| 584 |
+
"431": "bassinet",
|
| 585 |
+
"432": "bassoon",
|
| 586 |
+
"433": "bathing cap, swimming cap",
|
| 587 |
+
"434": "bath towel",
|
| 588 |
+
"435": "bathtub, bathing tub, bath, tub",
|
| 589 |
+
"436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
|
| 590 |
+
"437": "beacon, lighthouse, beacon light, pharos",
|
| 591 |
+
"438": "beaker",
|
| 592 |
+
"439": "bearskin, busby, shako",
|
| 593 |
+
"440": "beer bottle",
|
| 594 |
+
"441": "beer glass",
|
| 595 |
+
"442": "bell cote, bell cot",
|
| 596 |
+
"443": "bib",
|
| 597 |
+
"444": "bicycle-built-for-two, tandem bicycle, tandem",
|
| 598 |
+
"445": "bikini, two-piece",
|
| 599 |
+
"446": "binder, ring-binder",
|
| 600 |
+
"447": "binoculars, field glasses, opera glasses",
|
| 601 |
+
"448": "birdhouse",
|
| 602 |
+
"449": "boathouse",
|
| 603 |
+
"450": "bobsled, bobsleigh, bob",
|
| 604 |
+
"451": "bolo tie, bolo, bola tie, bola",
|
| 605 |
+
"452": "bonnet, poke bonnet",
|
| 606 |
+
"453": "bookcase",
|
| 607 |
+
"454": "bookshop, bookstore, bookstall",
|
| 608 |
+
"455": "bottlecap",
|
| 609 |
+
"456": "bow",
|
| 610 |
+
"457": "bow tie, bow-tie, bowtie",
|
| 611 |
+
"458": "brass, memorial tablet, plaque",
|
| 612 |
+
"459": "brassiere, bra, bandeau",
|
| 613 |
+
"460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty",
|
| 614 |
+
"461": "breastplate, aegis, egis",
|
| 615 |
+
"462": "broom",
|
| 616 |
+
"463": "bucket, pail",
|
| 617 |
+
"464": "buckle",
|
| 618 |
+
"465": "bulletproof vest",
|
| 619 |
+
"466": "bullet train, bullet",
|
| 620 |
+
"467": "butcher shop, meat market",
|
| 621 |
+
"468": "cab, hack, taxi, taxicab",
|
| 622 |
+
"469": "caldron, cauldron",
|
| 623 |
+
"470": "candle, taper, wax light",
|
| 624 |
+
"471": "cannon",
|
| 625 |
+
"472": "canoe",
|
| 626 |
+
"473": "can opener, tin opener",
|
| 627 |
+
"474": "cardigan",
|
| 628 |
+
"475": "car mirror",
|
| 629 |
+
"476": "carousel, carrousel, merry-go-round, roundabout, whirligig",
|
| 630 |
+
"477": "carpenter's kit, tool kit",
|
| 631 |
+
"478": "carton",
|
| 632 |
+
"479": "car wheel",
|
| 633 |
+
"480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
|
| 634 |
+
"481": "cassette",
|
| 635 |
+
"482": "cassette player",
|
| 636 |
+
"483": "castle",
|
| 637 |
+
"484": "catamaran",
|
| 638 |
+
"485": "CD player",
|
| 639 |
+
"486": "cello, violoncello",
|
| 640 |
+
"487": "cellular telephone, cellular phone, cellphone, cell, mobile phone",
|
| 641 |
+
"488": "chain",
|
| 642 |
+
"489": "chainlink fence",
|
| 643 |
+
"490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
|
| 644 |
+
"491": "chain saw, chainsaw",
|
| 645 |
+
"492": "chest",
|
| 646 |
+
"493": "chiffonier, commode",
|
| 647 |
+
"494": "chime, bell, gong",
|
| 648 |
+
"495": "china cabinet, china closet",
|
| 649 |
+
"496": "Christmas stocking",
|
| 650 |
+
"497": "church, church building",
|
| 651 |
+
"498": "cinema, movie theater, movie theatre, movie house, picture palace",
|
| 652 |
+
"499": "cleaver, meat cleaver, chopper",
|
| 653 |
+
"500": "cliff dwelling",
|
| 654 |
+
"501": "cloak",
|
| 655 |
+
"502": "clog, geta, patten, sabot",
|
| 656 |
+
"503": "cocktail shaker",
|
| 657 |
+
"504": "coffee mug",
|
| 658 |
+
"505": "coffeepot",
|
| 659 |
+
"506": "coil, spiral, volute, whorl, helix",
|
| 660 |
+
"507": "combination lock",
|
| 661 |
+
"508": "computer keyboard, keypad",
|
| 662 |
+
"509": "confectionery, confectionary, candy store",
|
| 663 |
+
"510": "container ship, containership, container vessel",
|
| 664 |
+
"511": "convertible",
|
| 665 |
+
"512": "corkscrew, bottle screw",
|
| 666 |
+
"513": "cornet, horn, trumpet, trump",
|
| 667 |
+
"514": "cowboy boot",
|
| 668 |
+
"515": "cowboy hat, ten-gallon hat",
|
| 669 |
+
"516": "cradle",
|
| 670 |
+
"517": "crane",
|
| 671 |
+
"518": "crash helmet",
|
| 672 |
+
"519": "crate",
|
| 673 |
+
"520": "crib, cot",
|
| 674 |
+
"521": "Crock Pot",
|
| 675 |
+
"522": "croquet ball",
|
| 676 |
+
"523": "crutch",
|
| 677 |
+
"524": "cuirass",
|
| 678 |
+
"525": "dam, dike, dyke",
|
| 679 |
+
"526": "desk",
|
| 680 |
+
"527": "desktop computer",
|
| 681 |
+
"528": "dial telephone, dial phone",
|
| 682 |
+
"529": "diaper, nappy, napkin",
|
| 683 |
+
"530": "digital clock",
|
| 684 |
+
"531": "digital watch",
|
| 685 |
+
"532": "dining table, board",
|
| 686 |
+
"533": "dishrag, dishcloth",
|
| 687 |
+
"534": "dishwasher, dish washer, dishwashing machine",
|
| 688 |
+
"535": "disk brake, disc brake",
|
| 689 |
+
"536": "dock, dockage, docking facility",
|
| 690 |
+
"537": "dogsled, dog sled, dog sleigh",
|
| 691 |
+
"538": "dome",
|
| 692 |
+
"539": "doormat, welcome mat",
|
| 693 |
+
"540": "drilling platform, offshore rig",
|
| 694 |
+
"541": "drum, membranophone, tympan",
|
| 695 |
+
"542": "drumstick",
|
| 696 |
+
"543": "dumbbell",
|
| 697 |
+
"544": "Dutch oven",
|
| 698 |
+
"545": "electric fan, blower",
|
| 699 |
+
"546": "electric guitar",
|
| 700 |
+
"547": "electric locomotive",
|
| 701 |
+
"548": "entertainment center",
|
| 702 |
+
"549": "envelope",
|
| 703 |
+
"550": "espresso maker",
|
| 704 |
+
"551": "face powder",
|
| 705 |
+
"552": "feather boa, boa",
|
| 706 |
+
"553": "file, file cabinet, filing cabinet",
|
| 707 |
+
"554": "fireboat",
|
| 708 |
+
"555": "fire engine, fire truck",
|
| 709 |
+
"556": "fire screen, fireguard",
|
| 710 |
+
"557": "flagpole, flagstaff",
|
| 711 |
+
"558": "flute, transverse flute",
|
| 712 |
+
"559": "folding chair",
|
| 713 |
+
"560": "football helmet",
|
| 714 |
+
"561": "forklift",
|
| 715 |
+
"562": "fountain",
|
| 716 |
+
"563": "fountain pen",
|
| 717 |
+
"564": "four-poster",
|
| 718 |
+
"565": "freight car",
|
| 719 |
+
"566": "French horn, horn",
|
| 720 |
+
"567": "frying pan, frypan, skillet",
|
| 721 |
+
"568": "fur coat",
|
| 722 |
+
"569": "garbage truck, dustcart",
|
| 723 |
+
"570": "gasmask, respirator, gas helmet",
|
| 724 |
+
"571": "gas pump, gasoline pump, petrol pump, island dispenser",
|
| 725 |
+
"572": "goblet",
|
| 726 |
+
"573": "go-kart",
|
| 727 |
+
"574": "golf ball",
|
| 728 |
+
"575": "golfcart, golf cart",
|
| 729 |
+
"576": "gondola",
|
| 730 |
+
"577": "gong, tam-tam",
|
| 731 |
+
"578": "gown",
|
| 732 |
+
"579": "grand piano, grand",
|
| 733 |
+
"580": "greenhouse, nursery, glasshouse",
|
| 734 |
+
"581": "grille, radiator grille",
|
| 735 |
+
"582": "grocery store, grocery, food market, market",
|
| 736 |
+
"583": "guillotine",
|
| 737 |
+
"584": "hair slide",
|
| 738 |
+
"585": "hair spray",
|
| 739 |
+
"586": "half track",
|
| 740 |
+
"587": "hammer",
|
| 741 |
+
"588": "hamper",
|
| 742 |
+
"589": "hand blower, blow dryer, blow drier, hair dryer, hair drier",
|
| 743 |
+
"590": "hand-held computer, hand-held microcomputer",
|
| 744 |
+
"591": "handkerchief, hankie, hanky, hankey",
|
| 745 |
+
"592": "hard disc, hard disk, fixed disk",
|
| 746 |
+
"593": "harmonica, mouth organ, harp, mouth harp",
|
| 747 |
+
"594": "harp",
|
| 748 |
+
"595": "harvester, reaper",
|
| 749 |
+
"596": "hatchet",
|
| 750 |
+
"597": "holster",
|
| 751 |
+
"598": "home theater, home theatre",
|
| 752 |
+
"599": "honeycomb",
|
| 753 |
+
"600": "hook, claw",
|
| 754 |
+
"601": "hoopskirt, crinoline",
|
| 755 |
+
"602": "horizontal bar, high bar",
|
| 756 |
+
"603": "horse cart, horse-cart",
|
| 757 |
+
"604": "hourglass",
|
| 758 |
+
"605": "iPod",
|
| 759 |
+
"606": "iron, smoothing iron",
|
| 760 |
+
"607": "jack-o'-lantern",
|
| 761 |
+
"608": "jean, blue jean, denim",
|
| 762 |
+
"609": "jeep, landrover",
|
| 763 |
+
"610": "jersey, T-shirt, tee shirt",
|
| 764 |
+
"611": "jigsaw puzzle",
|
| 765 |
+
"612": "jinrikisha, ricksha, rickshaw",
|
| 766 |
+
"613": "joystick",
|
| 767 |
+
"614": "kimono",
|
| 768 |
+
"615": "knee pad",
|
| 769 |
+
"616": "knot",
|
| 770 |
+
"617": "lab coat, laboratory coat",
|
| 771 |
+
"618": "ladle",
|
| 772 |
+
"619": "lampshade, lamp shade",
|
| 773 |
+
"620": "laptop, laptop computer",
|
| 774 |
+
"621": "lawn mower, mower",
|
| 775 |
+
"622": "lens cap, lens cover",
|
| 776 |
+
"623": "letter opener, paper knife, paperknife",
|
| 777 |
+
"624": "library",
|
| 778 |
+
"625": "lifeboat",
|
| 779 |
+
"626": "lighter, light, igniter, ignitor",
|
| 780 |
+
"627": "limousine, limo",
|
| 781 |
+
"628": "liner, ocean liner",
|
| 782 |
+
"629": "lipstick, lip rouge",
|
| 783 |
+
"630": "Loafer",
|
| 784 |
+
"631": "lotion",
|
| 785 |
+
"632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
|
| 786 |
+
"633": "loupe, jeweler's loupe",
|
| 787 |
+
"634": "lumbermill, sawmill",
|
| 788 |
+
"635": "magnetic compass",
|
| 789 |
+
"636": "mailbag, postbag",
|
| 790 |
+
"637": "mailbox, letter box",
|
| 791 |
+
"638": "maillot",
|
| 792 |
+
"639": "maillot, tank suit",
|
| 793 |
+
"640": "manhole cover",
|
| 794 |
+
"641": "maraca",
|
| 795 |
+
"642": "marimba, xylophone",
|
| 796 |
+
"643": "mask",
|
| 797 |
+
"644": "matchstick",
|
| 798 |
+
"645": "maypole",
|
| 799 |
+
"646": "maze, labyrinth",
|
| 800 |
+
"647": "measuring cup",
|
| 801 |
+
"648": "medicine chest, medicine cabinet",
|
| 802 |
+
"649": "megalith, megalithic structure",
|
| 803 |
+
"650": "microphone, mike",
|
| 804 |
+
"651": "microwave, microwave oven",
|
| 805 |
+
"652": "military uniform",
|
| 806 |
+
"653": "milk can",
|
| 807 |
+
"654": "minibus",
|
| 808 |
+
"655": "miniskirt, mini",
|
| 809 |
+
"656": "minivan",
|
| 810 |
+
"657": "missile",
|
| 811 |
+
"658": "mitten",
|
| 812 |
+
"659": "mixing bowl",
|
| 813 |
+
"660": "mobile home, manufactured home",
|
| 814 |
+
"661": "Model T",
|
| 815 |
+
"662": "modem",
|
| 816 |
+
"663": "monastery",
|
| 817 |
+
"664": "monitor",
|
| 818 |
+
"665": "moped",
|
| 819 |
+
"666": "mortar",
|
| 820 |
+
"667": "mortarboard",
|
| 821 |
+
"668": "mosque",
|
| 822 |
+
"669": "mosquito net",
|
| 823 |
+
"670": "motor scooter, scooter",
|
| 824 |
+
"671": "mountain bike, all-terrain bike, off-roader",
|
| 825 |
+
"672": "mountain tent",
|
| 826 |
+
"673": "mouse, computer mouse",
|
| 827 |
+
"674": "mousetrap",
|
| 828 |
+
"675": "moving van",
|
| 829 |
+
"676": "muzzle",
|
| 830 |
+
"677": "nail",
|
| 831 |
+
"678": "neck brace",
|
| 832 |
+
"679": "necklace",
|
| 833 |
+
"680": "nipple",
|
| 834 |
+
"681": "notebook, notebook computer",
|
| 835 |
+
"682": "obelisk",
|
| 836 |
+
"683": "oboe, hautboy, hautbois",
|
| 837 |
+
"684": "ocarina, sweet potato",
|
| 838 |
+
"685": "odometer, hodometer, mileometer, milometer",
|
| 839 |
+
"686": "oil filter",
|
| 840 |
+
"687": "organ, pipe organ",
|
| 841 |
+
"688": "oscilloscope, scope, cathode-ray oscilloscope, CRO",
|
| 842 |
+
"689": "overskirt",
|
| 843 |
+
"690": "oxcart",
|
| 844 |
+
"691": "oxygen mask",
|
| 845 |
+
"692": "packet",
|
| 846 |
+
"693": "paddle, boat paddle",
|
| 847 |
+
"694": "paddlewheel, paddle wheel",
|
| 848 |
+
"695": "padlock",
|
| 849 |
+
"696": "paintbrush",
|
| 850 |
+
"697": "pajama, pyjama, pj's, jammies",
|
| 851 |
+
"698": "palace",
|
| 852 |
+
"699": "panpipe, pandean pipe, syrinx",
|
| 853 |
+
"700": "paper towel",
|
| 854 |
+
"701": "parachute, chute",
|
| 855 |
+
"702": "parallel bars, bars",
|
| 856 |
+
"703": "park bench",
|
| 857 |
+
"704": "parking meter",
|
| 858 |
+
"705": "passenger car, coach, carriage",
|
| 859 |
+
"706": "patio, terrace",
|
| 860 |
+
"707": "pay-phone, pay-station",
|
| 861 |
+
"708": "pedestal, plinth, footstall",
|
| 862 |
+
"709": "pencil box, pencil case",
|
| 863 |
+
"710": "pencil sharpener",
|
| 864 |
+
"711": "perfume, essence",
|
| 865 |
+
"712": "Petri dish",
|
| 866 |
+
"713": "photocopier",
|
| 867 |
+
"714": "pick, plectrum, plectron",
|
| 868 |
+
"715": "pickelhaube",
|
| 869 |
+
"716": "picket fence, paling",
|
| 870 |
+
"717": "pickup, pickup truck",
|
| 871 |
+
"718": "pier",
|
| 872 |
+
"719": "piggy bank, penny bank",
|
| 873 |
+
"720": "pill bottle",
|
| 874 |
+
"721": "pillow",
|
| 875 |
+
"722": "ping-pong ball",
|
| 876 |
+
"723": "pinwheel",
|
| 877 |
+
"724": "pirate, pirate ship",
|
| 878 |
+
"725": "pitcher, ewer",
|
| 879 |
+
"726": "plane, carpenter's plane, woodworking plane",
|
| 880 |
+
"727": "planetarium",
|
| 881 |
+
"728": "plastic bag",
|
| 882 |
+
"729": "plate rack",
|
| 883 |
+
"730": "plow, plough",
|
| 884 |
+
"731": "plunger, plumber's helper",
|
| 885 |
+
"732": "Polaroid camera, Polaroid Land camera",
|
| 886 |
+
"733": "pole",
|
| 887 |
+
"734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
|
| 888 |
+
"735": "poncho",
|
| 889 |
+
"736": "pool table, billiard table, snooker table",
|
| 890 |
+
"737": "pop bottle, soda bottle",
|
| 891 |
+
"738": "pot, flowerpot",
|
| 892 |
+
"739": "potter's wheel",
|
| 893 |
+
"740": "power drill",
|
| 894 |
+
"741": "prayer rug, prayer mat",
|
| 895 |
+
"742": "printer",
|
| 896 |
+
"743": "prison, prison house",
|
| 897 |
+
"744": "projectile, missile",
|
| 898 |
+
"745": "projector",
|
| 899 |
+
"746": "puck, hockey puck",
|
| 900 |
+
"747": "punching bag, punch bag, punching ball, punchball",
|
| 901 |
+
"748": "purse",
|
| 902 |
+
"749": "quill, quill pen",
|
| 903 |
+
"750": "quilt, comforter, comfort, puff",
|
| 904 |
+
"751": "racer, race car, racing car",
|
| 905 |
+
"752": "racket, racquet",
|
| 906 |
+
"753": "radiator",
|
| 907 |
+
"754": "radio, wireless",
|
| 908 |
+
"755": "radio telescope, radio reflector",
|
| 909 |
+
"756": "rain barrel",
|
| 910 |
+
"757": "recreational vehicle, RV, R.V.",
|
| 911 |
+
"758": "reel",
|
| 912 |
+
"759": "reflex camera",
|
| 913 |
+
"760": "refrigerator, icebox",
|
| 914 |
+
"761": "remote control, remote",
|
| 915 |
+
"762": "restaurant, eating house, eating place, eatery",
|
| 916 |
+
"763": "revolver, six-gun, six-shooter",
|
| 917 |
+
"764": "rifle",
|
| 918 |
+
"765": "rocking chair, rocker",
|
| 919 |
+
"766": "rotisserie",
|
| 920 |
+
"767": "rubber eraser, rubber, pencil eraser",
|
| 921 |
+
"768": "rugby ball",
|
| 922 |
+
"769": "rule, ruler",
|
| 923 |
+
"770": "running shoe",
|
| 924 |
+
"771": "safe",
|
| 925 |
+
"772": "safety pin",
|
| 926 |
+
"773": "saltshaker, salt shaker",
|
| 927 |
+
"774": "sandal",
|
| 928 |
+
"775": "sarong",
|
| 929 |
+
"776": "sax, saxophone",
|
| 930 |
+
"777": "scabbard",
|
| 931 |
+
"778": "scale, weighing machine",
|
| 932 |
+
"779": "school bus",
|
| 933 |
+
"780": "schooner",
|
| 934 |
+
"781": "scoreboard",
|
| 935 |
+
"782": "screen, CRT screen",
|
| 936 |
+
"783": "screw",
|
| 937 |
+
"784": "screwdriver",
|
| 938 |
+
"785": "seat belt, seatbelt",
|
| 939 |
+
"786": "sewing machine",
|
| 940 |
+
"787": "shield, buckler",
|
| 941 |
+
"788": "shoe shop, shoe-shop, shoe store",
|
| 942 |
+
"789": "shoji",
|
| 943 |
+
"790": "shopping basket",
|
| 944 |
+
"791": "shopping cart",
|
| 945 |
+
"792": "shovel",
|
| 946 |
+
"793": "shower cap",
|
| 947 |
+
"794": "shower curtain",
|
| 948 |
+
"795": "ski",
|
| 949 |
+
"796": "ski mask",
|
| 950 |
+
"797": "sleeping bag",
|
| 951 |
+
"798": "slide rule, slipstick",
|
| 952 |
+
"799": "sliding door",
|
| 953 |
+
"800": "slot, one-armed bandit",
|
| 954 |
+
"801": "snorkel",
|
| 955 |
+
"802": "snowmobile",
|
| 956 |
+
"803": "snowplow, snowplough",
|
| 957 |
+
"804": "soap dispenser",
|
| 958 |
+
"805": "soccer ball",
|
| 959 |
+
"806": "sock",
|
| 960 |
+
"807": "solar dish, solar collector, solar furnace",
|
| 961 |
+
"808": "sombrero",
|
| 962 |
+
"809": "soup bowl",
|
| 963 |
+
"810": "space bar",
|
| 964 |
+
"811": "space heater",
|
| 965 |
+
"812": "space shuttle",
|
| 966 |
+
"813": "spatula",
|
| 967 |
+
"814": "speedboat",
|
| 968 |
+
"815": "spider web, spider's web",
|
| 969 |
+
"816": "spindle",
|
| 970 |
+
"817": "sports car, sport car",
|
| 971 |
+
"818": "spotlight, spot",
|
| 972 |
+
"819": "stage",
|
| 973 |
+
"820": "steam locomotive",
|
| 974 |
+
"821": "steel arch bridge",
|
| 975 |
+
"822": "steel drum",
|
| 976 |
+
"823": "stethoscope",
|
| 977 |
+
"824": "stole",
|
| 978 |
+
"825": "stone wall",
|
| 979 |
+
"826": "stopwatch, stop watch",
|
| 980 |
+
"827": "stove",
|
| 981 |
+
"828": "strainer",
|
| 982 |
+
"829": "streetcar, tram, tramcar, trolley, trolley car",
|
| 983 |
+
"830": "stretcher",
|
| 984 |
+
"831": "studio couch, day bed",
|
| 985 |
+
"832": "stupa, tope",
|
| 986 |
+
"833": "submarine, pigboat, sub, U-boat",
|
| 987 |
+
"834": "suit, suit of clothes",
|
| 988 |
+
"835": "sundial",
|
| 989 |
+
"836": "sunglass",
|
| 990 |
+
"837": "sunglasses, dark glasses, shades",
|
| 991 |
+
"838": "sunscreen, sunblock, sun blocker",
|
| 992 |
+
"839": "suspension bridge",
|
| 993 |
+
"840": "swab, swob, mop",
|
| 994 |
+
"841": "sweatshirt",
|
| 995 |
+
"842": "swimming trunks, bathing trunks",
|
| 996 |
+
"843": "swing",
|
| 997 |
+
"844": "switch, electric switch, electrical switch",
|
| 998 |
+
"845": "syringe",
|
| 999 |
+
"846": "table lamp",
|
| 1000 |
+
"847": "tank, army tank, armored combat vehicle, armoured combat vehicle",
|
| 1001 |
+
"848": "tape player",
|
| 1002 |
+
"849": "teapot",
|
| 1003 |
+
"850": "teddy, teddy bear",
|
| 1004 |
+
"851": "television, television system",
|
| 1005 |
+
"852": "tennis ball",
|
| 1006 |
+
"853": "thatch, thatched roof",
|
| 1007 |
+
"854": "theater curtain, theatre curtain",
|
| 1008 |
+
"855": "thimble",
|
| 1009 |
+
"856": "thresher, thrasher, threshing machine",
|
| 1010 |
+
"857": "throne",
|
| 1011 |
+
"858": "tile roof",
|
| 1012 |
+
"859": "toaster",
|
| 1013 |
+
"860": "tobacco shop, tobacconist shop, tobacconist",
|
| 1014 |
+
"861": "toilet seat",
|
| 1015 |
+
"862": "torch",
|
| 1016 |
+
"863": "totem pole",
|
| 1017 |
+
"864": "tow truck, tow car, wrecker",
|
| 1018 |
+
"865": "toyshop",
|
| 1019 |
+
"866": "tractor",
|
| 1020 |
+
"867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
|
| 1021 |
+
"868": "tray",
|
| 1022 |
+
"869": "trench coat",
|
| 1023 |
+
"870": "tricycle, trike, velocipede",
|
| 1024 |
+
"871": "trimaran",
|
| 1025 |
+
"872": "tripod",
|
| 1026 |
+
"873": "triumphal arch",
|
| 1027 |
+
"874": "trolleybus, trolley coach, trackless trolley",
|
| 1028 |
+
"875": "trombone",
|
| 1029 |
+
"876": "tub, vat",
|
| 1030 |
+
"877": "turnstile",
|
| 1031 |
+
"878": "typewriter keyboard",
|
| 1032 |
+
"879": "umbrella",
|
| 1033 |
+
"880": "unicycle, monocycle",
|
| 1034 |
+
"881": "upright, upright piano",
|
| 1035 |
+
"882": "vacuum, vacuum cleaner",
|
| 1036 |
+
"883": "vase",
|
| 1037 |
+
"884": "vault",
|
| 1038 |
+
"885": "velvet",
|
| 1039 |
+
"886": "vending machine",
|
| 1040 |
+
"887": "vestment",
|
| 1041 |
+
"888": "viaduct",
|
| 1042 |
+
"889": "violin, fiddle",
|
| 1043 |
+
"890": "volleyball",
|
| 1044 |
+
"891": "waffle iron",
|
| 1045 |
+
"892": "wall clock",
|
| 1046 |
+
"893": "wallet, billfold, notecase, pocketbook",
|
| 1047 |
+
"894": "wardrobe, closet, press",
|
| 1048 |
+
"895": "warplane, military plane",
|
| 1049 |
+
"896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin",
|
| 1050 |
+
"897": "washer, automatic washer, washing machine",
|
| 1051 |
+
"898": "water bottle",
|
| 1052 |
+
"899": "water jug",
|
| 1053 |
+
"900": "water tower",
|
| 1054 |
+
"901": "whiskey jug",
|
| 1055 |
+
"902": "whistle",
|
| 1056 |
+
"903": "wig",
|
| 1057 |
+
"904": "window screen",
|
| 1058 |
+
"905": "window shade",
|
| 1059 |
+
"906": "Windsor tie",
|
| 1060 |
+
"907": "wine bottle",
|
| 1061 |
+
"908": "wing",
|
| 1062 |
+
"909": "wok",
|
| 1063 |
+
"910": "wooden spoon",
|
| 1064 |
+
"911": "wool, woolen, woollen",
|
| 1065 |
+
"912": "worm fence, snake fence, snake-rail fence, Virginia fence",
|
| 1066 |
+
"913": "wreck",
|
| 1067 |
+
"914": "yawl",
|
| 1068 |
+
"915": "yurt",
|
| 1069 |
+
"916": "web site, website, internet site, site",
|
| 1070 |
+
"917": "comic book",
|
| 1071 |
+
"918": "crossword puzzle, crossword",
|
| 1072 |
+
"919": "street sign",
|
| 1073 |
+
"920": "traffic light, traffic signal, stoplight",
|
| 1074 |
+
"921": "book jacket, dust cover, dust jacket, dust wrapper",
|
| 1075 |
+
"922": "menu",
|
| 1076 |
+
"923": "plate",
|
| 1077 |
+
"924": "guacamole",
|
| 1078 |
+
"925": "consomme",
|
| 1079 |
+
"926": "hot pot, hotpot",
|
| 1080 |
+
"927": "trifle",
|
| 1081 |
+
"928": "ice cream, icecream",
|
| 1082 |
+
"929": "ice lolly, lolly, lollipop, popsicle",
|
| 1083 |
+
"930": "French loaf",
|
| 1084 |
+
"931": "bagel, beigel",
|
| 1085 |
+
"932": "pretzel",
|
| 1086 |
+
"933": "cheeseburger",
|
| 1087 |
+
"934": "hotdog, hot dog, red hot",
|
| 1088 |
+
"935": "mashed potato",
|
| 1089 |
+
"936": "head cabbage",
|
| 1090 |
+
"937": "broccoli",
|
| 1091 |
+
"938": "cauliflower",
|
| 1092 |
+
"939": "zucchini, courgette",
|
| 1093 |
+
"940": "spaghetti squash",
|
| 1094 |
+
"941": "acorn squash",
|
| 1095 |
+
"942": "butternut squash",
|
| 1096 |
+
"943": "cucumber, cuke",
|
| 1097 |
+
"944": "artichoke, globe artichoke",
|
| 1098 |
+
"945": "bell pepper",
|
| 1099 |
+
"946": "cardoon",
|
| 1100 |
+
"947": "mushroom",
|
| 1101 |
+
"948": "Granny Smith",
|
| 1102 |
+
"949": "strawberry",
|
| 1103 |
+
"950": "orange",
|
| 1104 |
+
"951": "lemon",
|
| 1105 |
+
"952": "fig",
|
| 1106 |
+
"953": "pineapple, ananas",
|
| 1107 |
+
"954": "banana",
|
| 1108 |
+
"955": "jackfruit, jak, jack",
|
| 1109 |
+
"956": "custard apple",
|
| 1110 |
+
"957": "pomegranate",
|
| 1111 |
+
"958": "hay",
|
| 1112 |
+
"959": "carbonara",
|
| 1113 |
+
"960": "chocolate sauce, chocolate syrup",
|
| 1114 |
+
"961": "dough",
|
| 1115 |
+
"962": "meat loaf, meatloaf",
|
| 1116 |
+
"963": "pizza, pizza pie",
|
| 1117 |
+
"964": "potpie",
|
| 1118 |
+
"965": "burrito",
|
| 1119 |
+
"966": "red wine",
|
| 1120 |
+
"967": "espresso",
|
| 1121 |
+
"968": "cup",
|
| 1122 |
+
"969": "eggnog",
|
| 1123 |
+
"970": "alp",
|
| 1124 |
+
"971": "bubble",
|
| 1125 |
+
"972": "cliff, drop, drop-off",
|
| 1126 |
+
"973": "coral reef",
|
| 1127 |
+
"974": "geyser",
|
| 1128 |
+
"975": "lakeside, lakeshore",
|
| 1129 |
+
"976": "promontory, headland, head, foreland",
|
| 1130 |
+
"977": "sandbar, sand bar",
|
| 1131 |
+
"978": "seashore, coast, seacoast, sea-coast",
|
| 1132 |
+
"979": "valley, vale",
|
| 1133 |
+
"980": "volcano",
|
| 1134 |
+
"981": "ballplayer, baseball player",
|
| 1135 |
+
"982": "groom, bridegroom",
|
| 1136 |
+
"983": "scuba diver",
|
| 1137 |
+
"984": "rapeseed",
|
| 1138 |
+
"985": "daisy",
|
| 1139 |
+
"986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
|
| 1140 |
+
"987": "corn",
|
| 1141 |
+
"988": "acorn",
|
| 1142 |
+
"989": "hip, rose hip, rosehip",
|
| 1143 |
+
"990": "buckeye, horse chestnut, conker",
|
| 1144 |
+
"991": "coral fungus",
|
| 1145 |
+
"992": "agaric",
|
| 1146 |
+
"993": "gyromitra",
|
| 1147 |
+
"994": "stinkhorn, carrion fungus",
|
| 1148 |
+
"995": "earthstar",
|
| 1149 |
+
"996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
|
| 1150 |
+
"997": "bolete",
|
| 1151 |
+
"998": "ear, spike, capitulum",
|
| 1152 |
+
"999": "toilet tissue, toilet paper, bathroom tissue"
|
| 1153 |
+
},
|
| 1154 |
+
"image_size": 384,
|
| 1155 |
+
"initializer_range": 0.02,
|
| 1156 |
+
"is_decoder": false,
|
| 1157 |
+
"is_encoder_decoder": false,
|
| 1158 |
+
"kernel_qkv": [
|
| 1159 |
+
3,
|
| 1160 |
+
3,
|
| 1161 |
+
3
|
| 1162 |
+
],
|
| 1163 |
+
"label2id": {
|
| 1164 |
+
"Afghan hound, Afghan": 160,
|
| 1165 |
+
"African chameleon, Chamaeleo chamaeleon": 47,
|
| 1166 |
+
"African crocodile, Nile crocodile, Crocodylus niloticus": 49,
|
| 1167 |
+
"African elephant, Loxodonta africana": 386,
|
| 1168 |
+
"African grey, African gray, Psittacus erithacus": 87,
|
| 1169 |
+
"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus": 275,
|
| 1170 |
+
"Airedale, Airedale terrier": 191,
|
| 1171 |
+
"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier": 180,
|
| 1172 |
+
"American alligator, Alligator mississipiensis": 50,
|
| 1173 |
+
"American black bear, black bear, Ursus americanus, Euarctos americanus": 295,
|
| 1174 |
+
"American chameleon, anole, Anolis carolinensis": 40,
|
| 1175 |
+
"American coot, marsh hen, mud hen, water hen, Fulica americana": 137,
|
| 1176 |
+
"American egret, great white heron, Egretta albus": 132,
|
| 1177 |
+
"American lobster, Northern lobster, Maine lobster, Homarus americanus": 122,
|
| 1178 |
+
"Angora, Angora rabbit": 332,
|
| 1179 |
+
"Appenzeller": 240,
|
| 1180 |
+
"Arabian camel, dromedary, Camelus dromedarius": 354,
|
| 1181 |
+
"Arctic fox, white fox, Alopex lagopus": 279,
|
| 1182 |
+
"Australian terrier": 193,
|
| 1183 |
+
"Band Aid": 419,
|
| 1184 |
+
"Bedlington terrier": 181,
|
| 1185 |
+
"Bernese mountain dog": 239,
|
| 1186 |
+
"Blenheim spaniel": 156,
|
| 1187 |
+
"Border collie": 232,
|
| 1188 |
+
"Border terrier": 182,
|
| 1189 |
+
"Boston bull, Boston terrier": 195,
|
| 1190 |
+
"Bouvier des Flandres, Bouviers des Flandres": 233,
|
| 1191 |
+
"Brabancon griffon": 262,
|
| 1192 |
+
"Brittany spaniel": 215,
|
| 1193 |
+
"CD player": 485,
|
| 1194 |
+
"Cardigan, Cardigan Welsh corgi": 264,
|
| 1195 |
+
"Chesapeake Bay retriever": 209,
|
| 1196 |
+
"Chihuahua": 151,
|
| 1197 |
+
"Christmas stocking": 496,
|
| 1198 |
+
"Crock Pot": 521,
|
| 1199 |
+
"Dandie Dinmont, Dandie Dinmont terrier": 194,
|
| 1200 |
+
"Doberman, Doberman pinscher": 236,
|
| 1201 |
+
"Dungeness crab, Cancer magister": 118,
|
| 1202 |
+
"Dutch oven": 544,
|
| 1203 |
+
"Egyptian cat": 285,
|
| 1204 |
+
"English foxhound": 167,
|
| 1205 |
+
"English setter": 212,
|
| 1206 |
+
"English springer, English springer spaniel": 217,
|
| 1207 |
+
"EntleBucher": 241,
|
| 1208 |
+
"Eskimo dog, husky": 248,
|
| 1209 |
+
"European fire salamander, Salamandra salamandra": 25,
|
| 1210 |
+
"European gallinule, Porphyrio porphyrio": 136,
|
| 1211 |
+
"French bulldog": 245,
|
| 1212 |
+
"French horn, horn": 566,
|
| 1213 |
+
"French loaf": 930,
|
| 1214 |
+
"German shepherd, German shepherd dog, German police dog, alsatian": 235,
|
| 1215 |
+
"German short-haired pointer": 210,
|
| 1216 |
+
"Gila monster, Heloderma suspectum": 45,
|
| 1217 |
+
"Gordon setter": 214,
|
| 1218 |
+
"Granny Smith": 948,
|
| 1219 |
+
"Great Dane": 246,
|
| 1220 |
+
"Great Pyrenees": 257,
|
| 1221 |
+
"Greater Swiss Mountain dog": 238,
|
| 1222 |
+
"Ibizan hound, Ibizan Podenco": 173,
|
| 1223 |
+
"Indian cobra, Naja naja": 63,
|
| 1224 |
+
"Indian elephant, Elephas maximus": 385,
|
| 1225 |
+
"Irish setter, red setter": 213,
|
| 1226 |
+
"Irish terrier": 184,
|
| 1227 |
+
"Irish water spaniel": 221,
|
| 1228 |
+
"Irish wolfhound": 170,
|
| 1229 |
+
"Italian greyhound": 171,
|
| 1230 |
+
"Japanese spaniel": 152,
|
| 1231 |
+
"Kerry blue terrier": 183,
|
| 1232 |
+
"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis": 48,
|
| 1233 |
+
"Labrador retriever": 208,
|
| 1234 |
+
"Lakeland terrier": 189,
|
| 1235 |
+
"Leonberg": 255,
|
| 1236 |
+
"Lhasa, Lhasa apso": 204,
|
| 1237 |
+
"Loafer": 630,
|
| 1238 |
+
"Madagascar cat, ring-tailed lemur, Lemur catta": 383,
|
| 1239 |
+
"Maltese dog, Maltese terrier, Maltese": 153,
|
| 1240 |
+
"Mexican hairless": 268,
|
| 1241 |
+
"Model T": 661,
|
| 1242 |
+
"Newfoundland, Newfoundland dog": 256,
|
| 1243 |
+
"Norfolk terrier": 185,
|
| 1244 |
+
"Norwegian elkhound, elkhound": 174,
|
| 1245 |
+
"Norwich terrier": 186,
|
| 1246 |
+
"Old English sheepdog, bobtail": 229,
|
| 1247 |
+
"Pekinese, Pekingese, Peke": 154,
|
| 1248 |
+
"Pembroke, Pembroke Welsh corgi": 263,
|
| 1249 |
+
"Persian cat": 283,
|
| 1250 |
+
"Petri dish": 712,
|
| 1251 |
+
"Polaroid camera, Polaroid Land camera": 732,
|
| 1252 |
+
"Pomeranian": 259,
|
| 1253 |
+
"Rhodesian ridgeback": 159,
|
| 1254 |
+
"Rottweiler": 234,
|
| 1255 |
+
"Saint Bernard, St Bernard": 247,
|
| 1256 |
+
"Saluki, gazelle hound": 176,
|
| 1257 |
+
"Samoyed, Samoyede": 258,
|
| 1258 |
+
"Scotch terrier, Scottish terrier, Scottie": 199,
|
| 1259 |
+
"Scottish deerhound, deerhound": 177,
|
| 1260 |
+
"Sealyham terrier, Sealyham": 190,
|
| 1261 |
+
"Shetland sheepdog, Shetland sheep dog, Shetland": 230,
|
| 1262 |
+
"Shih-Tzu": 155,
|
| 1263 |
+
"Siamese cat, Siamese": 284,
|
| 1264 |
+
"Siberian husky": 250,
|
| 1265 |
+
"Staffordshire bullterrier, Staffordshire bull terrier": 179,
|
| 1266 |
+
"Sussex spaniel": 220,
|
| 1267 |
+
"Tibetan mastiff": 244,
|
| 1268 |
+
"Tibetan terrier, chrysanthemum dog": 200,
|
| 1269 |
+
"Walker hound, Walker foxhound": 166,
|
| 1270 |
+
"Weimaraner": 178,
|
| 1271 |
+
"Welsh springer spaniel": 218,
|
| 1272 |
+
"West Highland white terrier": 203,
|
| 1273 |
+
"Windsor tie": 906,
|
| 1274 |
+
"Yorkshire terrier": 187,
|
| 1275 |
+
"abacus": 398,
|
| 1276 |
+
"abaya": 399,
|
| 1277 |
+
"academic gown, academic robe, judge's robe": 400,
|
| 1278 |
+
"accordion, piano accordion, squeeze box": 401,
|
| 1279 |
+
"acorn": 988,
|
| 1280 |
+
"acorn squash": 941,
|
| 1281 |
+
"acoustic guitar": 402,
|
| 1282 |
+
"admiral": 321,
|
| 1283 |
+
"affenpinscher, monkey pinscher, monkey dog": 252,
|
| 1284 |
+
"agama": 42,
|
| 1285 |
+
"agaric": 992,
|
| 1286 |
+
"aircraft carrier, carrier, flattop, attack aircraft carrier": 403,
|
| 1287 |
+
"airliner": 404,
|
| 1288 |
+
"airship, dirigible": 405,
|
| 1289 |
+
"albatross, mollymawk": 146,
|
| 1290 |
+
"alligator lizard": 44,
|
| 1291 |
+
"alp": 970,
|
| 1292 |
+
"altar": 406,
|
| 1293 |
+
"ambulance": 407,
|
| 1294 |
+
"amphibian, amphibious vehicle": 408,
|
| 1295 |
+
"analog clock": 409,
|
| 1296 |
+
"anemone fish": 393,
|
| 1297 |
+
"ant, emmet, pismire": 310,
|
| 1298 |
+
"apiary, bee house": 410,
|
| 1299 |
+
"apron": 411,
|
| 1300 |
+
"armadillo": 363,
|
| 1301 |
+
"artichoke, globe artichoke": 944,
|
| 1302 |
+
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin": 412,
|
| 1303 |
+
"assault rifle, assault gun": 413,
|
| 1304 |
+
"axolotl, mud puppy, Ambystoma mexicanum": 29,
|
| 1305 |
+
"baboon": 372,
|
| 1306 |
+
"backpack, back pack, knapsack, packsack, rucksack, haversack": 414,
|
| 1307 |
+
"badger": 362,
|
| 1308 |
+
"bagel, beigel": 931,
|
| 1309 |
+
"bakery, bakeshop, bakehouse": 415,
|
| 1310 |
+
"balance beam, beam": 416,
|
| 1311 |
+
"bald eagle, American eagle, Haliaeetus leucocephalus": 22,
|
| 1312 |
+
"balloon": 417,
|
| 1313 |
+
"ballplayer, baseball player": 981,
|
| 1314 |
+
"ballpoint, ballpoint pen, ballpen, Biro": 418,
|
| 1315 |
+
"banana": 954,
|
| 1316 |
+
"banded gecko": 38,
|
| 1317 |
+
"banjo": 420,
|
| 1318 |
+
"bannister, banister, balustrade, balusters, handrail": 421,
|
| 1319 |
+
"barbell": 422,
|
| 1320 |
+
"barber chair": 423,
|
| 1321 |
+
"barbershop": 424,
|
| 1322 |
+
"barn": 425,
|
| 1323 |
+
"barn spider, Araneus cavaticus": 73,
|
| 1324 |
+
"barometer": 426,
|
| 1325 |
+
"barracouta, snoek": 389,
|
| 1326 |
+
"barrel, cask": 427,
|
| 1327 |
+
"barrow, garden cart, lawn cart, wheelbarrow": 428,
|
| 1328 |
+
"baseball": 429,
|
| 1329 |
+
"basenji": 253,
|
| 1330 |
+
"basketball": 430,
|
| 1331 |
+
"basset, basset hound": 161,
|
| 1332 |
+
"bassinet": 431,
|
| 1333 |
+
"bassoon": 432,
|
| 1334 |
+
"bath towel": 434,
|
| 1335 |
+
"bathing cap, swimming cap": 433,
|
| 1336 |
+
"bathtub, bathing tub, bath, tub": 435,
|
| 1337 |
+
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon": 436,
|
| 1338 |
+
"beacon, lighthouse, beacon light, pharos": 437,
|
| 1339 |
+
"beagle": 162,
|
| 1340 |
+
"beaker": 438,
|
| 1341 |
+
"bearskin, busby, shako": 439,
|
| 1342 |
+
"beaver": 337,
|
| 1343 |
+
"bee": 309,
|
| 1344 |
+
"bee eater": 92,
|
| 1345 |
+
"beer bottle": 440,
|
| 1346 |
+
"beer glass": 441,
|
| 1347 |
+
"bell cote, bell cot": 442,
|
| 1348 |
+
"bell pepper": 945,
|
| 1349 |
+
"bib": 443,
|
| 1350 |
+
"bicycle-built-for-two, tandem bicycle, tandem": 444,
|
| 1351 |
+
"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis": 349,
|
| 1352 |
+
"bikini, two-piece": 445,
|
| 1353 |
+
"binder, ring-binder": 446,
|
| 1354 |
+
"binoculars, field glasses, opera glasses": 447,
|
| 1355 |
+
"birdhouse": 448,
|
| 1356 |
+
"bison": 347,
|
| 1357 |
+
"bittern": 133,
|
| 1358 |
+
"black and gold garden spider, Argiope aurantia": 72,
|
| 1359 |
+
"black grouse": 80,
|
| 1360 |
+
"black stork, Ciconia nigra": 128,
|
| 1361 |
+
"black swan, Cygnus atratus": 100,
|
| 1362 |
+
"black widow, Latrodectus mactans": 75,
|
| 1363 |
+
"black-and-tan coonhound": 165,
|
| 1364 |
+
"black-footed ferret, ferret, Mustela nigripes": 359,
|
| 1365 |
+
"bloodhound, sleuthhound": 163,
|
| 1366 |
+
"bluetick": 164,
|
| 1367 |
+
"boa constrictor, Constrictor constrictor": 61,
|
| 1368 |
+
"boathouse": 449,
|
| 1369 |
+
"bobsled, bobsleigh, bob": 450,
|
| 1370 |
+
"bolete": 997,
|
| 1371 |
+
"bolo tie, bolo, bola tie, bola": 451,
|
| 1372 |
+
"bonnet, poke bonnet": 452,
|
| 1373 |
+
"book jacket, dust cover, dust jacket, dust wrapper": 921,
|
| 1374 |
+
"bookcase": 453,
|
| 1375 |
+
"bookshop, bookstore, bookstall": 454,
|
| 1376 |
+
"borzoi, Russian wolfhound": 169,
|
| 1377 |
+
"bottlecap": 455,
|
| 1378 |
+
"bow": 456,
|
| 1379 |
+
"bow tie, bow-tie, bowtie": 457,
|
| 1380 |
+
"box turtle, box tortoise": 37,
|
| 1381 |
+
"boxer": 242,
|
| 1382 |
+
"brain coral": 109,
|
| 1383 |
+
"brambling, Fringilla montifringilla": 10,
|
| 1384 |
+
"brass, memorial tablet, plaque": 458,
|
| 1385 |
+
"brassiere, bra, bandeau": 459,
|
| 1386 |
+
"breakwater, groin, groyne, mole, bulwark, seawall, jetty": 460,
|
| 1387 |
+
"breastplate, aegis, egis": 461,
|
| 1388 |
+
"briard": 226,
|
| 1389 |
+
"broccoli": 937,
|
| 1390 |
+
"broom": 462,
|
| 1391 |
+
"brown bear, bruin, Ursus arctos": 294,
|
| 1392 |
+
"bubble": 971,
|
| 1393 |
+
"bucket, pail": 463,
|
| 1394 |
+
"buckeye, horse chestnut, conker": 990,
|
| 1395 |
+
"buckle": 464,
|
| 1396 |
+
"bulbul": 16,
|
| 1397 |
+
"bull mastiff": 243,
|
| 1398 |
+
"bullet train, bullet": 466,
|
| 1399 |
+
"bulletproof vest": 465,
|
| 1400 |
+
"bullfrog, Rana catesbeiana": 30,
|
| 1401 |
+
"burrito": 965,
|
| 1402 |
+
"bustard": 138,
|
| 1403 |
+
"butcher shop, meat market": 467,
|
| 1404 |
+
"butternut squash": 942,
|
| 1405 |
+
"cab, hack, taxi, taxicab": 468,
|
| 1406 |
+
"cabbage butterfly": 324,
|
| 1407 |
+
"cairn, cairn terrier": 192,
|
| 1408 |
+
"caldron, cauldron": 469,
|
| 1409 |
+
"can opener, tin opener": 473,
|
| 1410 |
+
"candle, taper, wax light": 470,
|
| 1411 |
+
"cannon": 471,
|
| 1412 |
+
"canoe": 472,
|
| 1413 |
+
"capuchin, ringtail, Cebus capucinus": 378,
|
| 1414 |
+
"car mirror": 475,
|
| 1415 |
+
"car wheel": 479,
|
| 1416 |
+
"carbonara": 959,
|
| 1417 |
+
"cardigan": 474,
|
| 1418 |
+
"cardoon": 946,
|
| 1419 |
+
"carousel, carrousel, merry-go-round, roundabout, whirligig": 476,
|
| 1420 |
+
"carpenter's kit, tool kit": 477,
|
| 1421 |
+
"carton": 478,
|
| 1422 |
+
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480,
|
| 1423 |
+
"cassette": 481,
|
| 1424 |
+
"cassette player": 482,
|
| 1425 |
+
"castle": 483,
|
| 1426 |
+
"catamaran": 484,
|
| 1427 |
+
"cauliflower": 938,
|
| 1428 |
+
"cello, violoncello": 486,
|
| 1429 |
+
"cellular telephone, cellular phone, cellphone, cell, mobile phone": 487,
|
| 1430 |
+
"centipede": 79,
|
| 1431 |
+
"chain": 488,
|
| 1432 |
+
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490,
|
| 1433 |
+
"chain saw, chainsaw": 491,
|
| 1434 |
+
"chainlink fence": 489,
|
| 1435 |
+
"chambered nautilus, pearly nautilus, nautilus": 117,
|
| 1436 |
+
"cheeseburger": 933,
|
| 1437 |
+
"cheetah, chetah, Acinonyx jubatus": 293,
|
| 1438 |
+
"chest": 492,
|
| 1439 |
+
"chickadee": 19,
|
| 1440 |
+
"chiffonier, commode": 493,
|
| 1441 |
+
"chime, bell, gong": 494,
|
| 1442 |
+
"chimpanzee, chimp, Pan troglodytes": 367,
|
| 1443 |
+
"china cabinet, china closet": 495,
|
| 1444 |
+
"chiton, coat-of-mail shell, sea cradle, polyplacophore": 116,
|
| 1445 |
+
"chocolate sauce, chocolate syrup": 960,
|
| 1446 |
+
"chow, chow chow": 260,
|
| 1447 |
+
"church, church building": 497,
|
| 1448 |
+
"cicada, cicala": 316,
|
| 1449 |
+
"cinema, movie theater, movie theatre, movie house, picture palace": 498,
|
| 1450 |
+
"cleaver, meat cleaver, chopper": 499,
|
| 1451 |
+
"cliff dwelling": 500,
|
| 1452 |
+
"cliff, drop, drop-off": 972,
|
| 1453 |
+
"cloak": 501,
|
| 1454 |
+
"clog, geta, patten, sabot": 502,
|
| 1455 |
+
"clumber, clumber spaniel": 216,
|
| 1456 |
+
"cock": 7,
|
| 1457 |
+
"cocker spaniel, English cocker spaniel, cocker": 219,
|
| 1458 |
+
"cockroach, roach": 314,
|
| 1459 |
+
"cocktail shaker": 503,
|
| 1460 |
+
"coffee mug": 504,
|
| 1461 |
+
"coffeepot": 505,
|
| 1462 |
+
"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391,
|
| 1463 |
+
"coil, spiral, volute, whorl, helix": 506,
|
| 1464 |
+
"collie": 231,
|
| 1465 |
+
"colobus, colobus monkey": 375,
|
| 1466 |
+
"combination lock": 507,
|
| 1467 |
+
"comic book": 917,
|
| 1468 |
+
"common iguana, iguana, Iguana iguana": 39,
|
| 1469 |
+
"common newt, Triturus vulgaris": 26,
|
| 1470 |
+
"computer keyboard, keypad": 508,
|
| 1471 |
+
"conch": 112,
|
| 1472 |
+
"confectionery, confectionary, candy store": 509,
|
| 1473 |
+
"consomme": 925,
|
| 1474 |
+
"container ship, containership, container vessel": 510,
|
| 1475 |
+
"convertible": 511,
|
| 1476 |
+
"coral fungus": 991,
|
| 1477 |
+
"coral reef": 973,
|
| 1478 |
+
"corkscrew, bottle screw": 512,
|
| 1479 |
+
"corn": 987,
|
| 1480 |
+
"cornet, horn, trumpet, trump": 513,
|
| 1481 |
+
"coucal": 91,
|
| 1482 |
+
"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286,
|
| 1483 |
+
"cowboy boot": 514,
|
| 1484 |
+
"cowboy hat, ten-gallon hat": 515,
|
| 1485 |
+
"coyote, prairie wolf, brush wolf, Canis latrans": 272,
|
| 1486 |
+
"cradle": 516,
|
| 1487 |
+
"crane": 517,
|
| 1488 |
+
"crash helmet": 518,
|
| 1489 |
+
"crate": 519,
|
| 1490 |
+
"crayfish, crawfish, crawdad, crawdaddy": 124,
|
| 1491 |
+
"crib, cot": 520,
|
| 1492 |
+
"cricket": 312,
|
| 1493 |
+
"croquet ball": 522,
|
| 1494 |
+
"crossword puzzle, crossword": 918,
|
| 1495 |
+
"crutch": 523,
|
| 1496 |
+
"cucumber, cuke": 943,
|
| 1497 |
+
"cuirass": 524,
|
| 1498 |
+
"cup": 968,
|
| 1499 |
+
"curly-coated retriever": 206,
|
| 1500 |
+
"custard apple": 956,
|
| 1501 |
+
"daisy": 985,
|
| 1502 |
+
"dalmatian, coach dog, carriage dog": 251,
|
| 1503 |
+
"dam, dike, dyke": 525,
|
| 1504 |
+
"damselfly": 320,
|
| 1505 |
+
"desk": 526,
|
| 1506 |
+
"desktop computer": 527,
|
| 1507 |
+
"dhole, Cuon alpinus": 274,
|
| 1508 |
+
"dial telephone, dial phone": 528,
|
| 1509 |
+
"diamondback, diamondback rattlesnake, Crotalus adamanteus": 67,
|
| 1510 |
+
"diaper, nappy, napkin": 529,
|
| 1511 |
+
"digital clock": 530,
|
| 1512 |
+
"digital watch": 531,
|
| 1513 |
+
"dingo, warrigal, warragal, Canis dingo": 273,
|
| 1514 |
+
"dining table, board": 532,
|
| 1515 |
+
"dishrag, dishcloth": 533,
|
| 1516 |
+
"dishwasher, dish washer, dishwashing machine": 534,
|
| 1517 |
+
"disk brake, disc brake": 535,
|
| 1518 |
+
"dock, dockage, docking facility": 536,
|
| 1519 |
+
"dogsled, dog sled, dog sleigh": 537,
|
| 1520 |
+
"dome": 538,
|
| 1521 |
+
"doormat, welcome mat": 539,
|
| 1522 |
+
"dough": 961,
|
| 1523 |
+
"dowitcher": 142,
|
| 1524 |
+
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319,
|
| 1525 |
+
"drake": 97,
|
| 1526 |
+
"drilling platform, offshore rig": 540,
|
| 1527 |
+
"drum, membranophone, tympan": 541,
|
| 1528 |
+
"drumstick": 542,
|
| 1529 |
+
"dugong, Dugong dugon": 149,
|
| 1530 |
+
"dumbbell": 543,
|
| 1531 |
+
"dung beetle": 305,
|
| 1532 |
+
"ear, spike, capitulum": 998,
|
| 1533 |
+
"earthstar": 995,
|
| 1534 |
+
"echidna, spiny anteater, anteater": 102,
|
| 1535 |
+
"eel": 390,
|
| 1536 |
+
"eft": 27,
|
| 1537 |
+
"eggnog": 969,
|
| 1538 |
+
"electric fan, blower": 545,
|
| 1539 |
+
"electric guitar": 546,
|
| 1540 |
+
"electric locomotive": 547,
|
| 1541 |
+
"electric ray, crampfish, numbfish, torpedo": 5,
|
| 1542 |
+
"entertainment center": 548,
|
| 1543 |
+
"envelope": 549,
|
| 1544 |
+
"espresso": 967,
|
| 1545 |
+
"espresso maker": 550,
|
| 1546 |
+
"face powder": 551,
|
| 1547 |
+
"feather boa, boa": 552,
|
| 1548 |
+
"fiddler crab": 120,
|
| 1549 |
+
"fig": 952,
|
| 1550 |
+
"file, file cabinet, filing cabinet": 553,
|
| 1551 |
+
"fire engine, fire truck": 555,
|
| 1552 |
+
"fire screen, fireguard": 556,
|
| 1553 |
+
"fireboat": 554,
|
| 1554 |
+
"flagpole, flagstaff": 557,
|
| 1555 |
+
"flamingo": 130,
|
| 1556 |
+
"flat-coated retriever": 205,
|
| 1557 |
+
"flatworm, platyhelminth": 110,
|
| 1558 |
+
"flute, transverse flute": 558,
|
| 1559 |
+
"fly": 308,
|
| 1560 |
+
"folding chair": 559,
|
| 1561 |
+
"football helmet": 560,
|
| 1562 |
+
"forklift": 561,
|
| 1563 |
+
"fountain": 562,
|
| 1564 |
+
"fountain pen": 563,
|
| 1565 |
+
"four-poster": 564,
|
| 1566 |
+
"fox squirrel, eastern fox squirrel, Sciurus niger": 335,
|
| 1567 |
+
"freight car": 565,
|
| 1568 |
+
"frilled lizard, Chlamydosaurus kingi": 43,
|
| 1569 |
+
"frying pan, frypan, skillet": 567,
|
| 1570 |
+
"fur coat": 568,
|
| 1571 |
+
"gar, garfish, garpike, billfish, Lepisosteus osseus": 395,
|
| 1572 |
+
"garbage truck, dustcart": 569,
|
| 1573 |
+
"garden spider, Aranea diademata": 74,
|
| 1574 |
+
"garter snake, grass snake": 57,
|
| 1575 |
+
"gas pump, gasoline pump, petrol pump, island dispenser": 571,
|
| 1576 |
+
"gasmask, respirator, gas helmet": 570,
|
| 1577 |
+
"gazelle": 353,
|
| 1578 |
+
"geyser": 974,
|
| 1579 |
+
"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388,
|
| 1580 |
+
"giant schnauzer": 197,
|
| 1581 |
+
"gibbon, Hylobates lar": 368,
|
| 1582 |
+
"go-kart": 573,
|
| 1583 |
+
"goblet": 572,
|
| 1584 |
+
"golden retriever": 207,
|
| 1585 |
+
"goldfinch, Carduelis carduelis": 11,
|
| 1586 |
+
"goldfish, Carassius auratus": 1,
|
| 1587 |
+
"golf ball": 574,
|
| 1588 |
+
"golfcart, golf cart": 575,
|
| 1589 |
+
"gondola": 576,
|
| 1590 |
+
"gong, tam-tam": 577,
|
| 1591 |
+
"goose": 99,
|
| 1592 |
+
"gorilla, Gorilla gorilla": 366,
|
| 1593 |
+
"gown": 578,
|
| 1594 |
+
"grand piano, grand": 579,
|
| 1595 |
+
"grasshopper, hopper": 311,
|
| 1596 |
+
"great grey owl, great gray owl, Strix nebulosa": 24,
|
| 1597 |
+
"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2,
|
| 1598 |
+
"green lizard, Lacerta viridis": 46,
|
| 1599 |
+
"green mamba": 64,
|
| 1600 |
+
"green snake, grass snake": 55,
|
| 1601 |
+
"greenhouse, nursery, glasshouse": 580,
|
| 1602 |
+
"grey fox, gray fox, Urocyon cinereoargenteus": 280,
|
| 1603 |
+
"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147,
|
| 1604 |
+
"grille, radiator grille": 581,
|
| 1605 |
+
"grocery store, grocery, food market, market": 582,
|
| 1606 |
+
"groenendael": 224,
|
| 1607 |
+
"groom, bridegroom": 982,
|
| 1608 |
+
"ground beetle, carabid beetle": 302,
|
| 1609 |
+
"guacamole": 924,
|
| 1610 |
+
"guenon, guenon monkey": 370,
|
| 1611 |
+
"guillotine": 583,
|
| 1612 |
+
"guinea pig, Cavia cobaya": 338,
|
| 1613 |
+
"gyromitra": 993,
|
| 1614 |
+
"hair slide": 584,
|
| 1615 |
+
"hair spray": 585,
|
| 1616 |
+
"half track": 586,
|
| 1617 |
+
"hammer": 587,
|
| 1618 |
+
"hammerhead, hammerhead shark": 4,
|
| 1619 |
+
"hamper": 588,
|
| 1620 |
+
"hamster": 333,
|
| 1621 |
+
"hand blower, blow dryer, blow drier, hair dryer, hair drier": 589,
|
| 1622 |
+
"hand-held computer, hand-held microcomputer": 590,
|
| 1623 |
+
"handkerchief, hankie, hanky, hankey": 591,
|
| 1624 |
+
"hard disc, hard disk, fixed disk": 592,
|
| 1625 |
+
"hare": 331,
|
| 1626 |
+
"harmonica, mouth organ, harp, mouth harp": 593,
|
| 1627 |
+
"harp": 594,
|
| 1628 |
+
"hartebeest": 351,
|
| 1629 |
+
"harvester, reaper": 595,
|
| 1630 |
+
"harvestman, daddy longlegs, Phalangium opilio": 70,
|
| 1631 |
+
"hatchet": 596,
|
| 1632 |
+
"hay": 958,
|
| 1633 |
+
"head cabbage": 936,
|
| 1634 |
+
"hen": 8,
|
| 1635 |
+
"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996,
|
| 1636 |
+
"hermit crab": 125,
|
| 1637 |
+
"hip, rose hip, rosehip": 989,
|
| 1638 |
+
"hippopotamus, hippo, river horse, Hippopotamus amphibius": 344,
|
| 1639 |
+
"hog, pig, grunter, squealer, Sus scrofa": 341,
|
| 1640 |
+
"hognose snake, puff adder, sand viper": 54,
|
| 1641 |
+
"holster": 597,
|
| 1642 |
+
"home theater, home theatre": 598,
|
| 1643 |
+
"honeycomb": 599,
|
| 1644 |
+
"hook, claw": 600,
|
| 1645 |
+
"hoopskirt, crinoline": 601,
|
| 1646 |
+
"horizontal bar, high bar": 602,
|
| 1647 |
+
"hornbill": 93,
|
| 1648 |
+
"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus": 66,
|
| 1649 |
+
"horse cart, horse-cart": 603,
|
| 1650 |
+
"hot pot, hotpot": 926,
|
| 1651 |
+
"hotdog, hot dog, red hot": 934,
|
| 1652 |
+
"hourglass": 604,
|
| 1653 |
+
"house finch, linnet, Carpodacus mexicanus": 12,
|
| 1654 |
+
"howler monkey, howler": 379,
|
| 1655 |
+
"hummingbird": 94,
|
| 1656 |
+
"hyena, hyaena": 276,
|
| 1657 |
+
"iPod": 605,
|
| 1658 |
+
"ibex, Capra ibex": 350,
|
| 1659 |
+
"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus": 296,
|
| 1660 |
+
"ice cream, icecream": 928,
|
| 1661 |
+
"ice lolly, lolly, lollipop, popsicle": 929,
|
| 1662 |
+
"impala, Aepyceros melampus": 352,
|
| 1663 |
+
"indigo bunting, indigo finch, indigo bird, Passerina cyanea": 14,
|
| 1664 |
+
"indri, indris, Indri indri, Indri brevicaudatus": 384,
|
| 1665 |
+
"iron, smoothing iron": 606,
|
| 1666 |
+
"isopod": 126,
|
| 1667 |
+
"jacamar": 95,
|
| 1668 |
+
"jack-o'-lantern": 607,
|
| 1669 |
+
"jackfruit, jak, jack": 955,
|
| 1670 |
+
"jaguar, panther, Panthera onca, Felis onca": 290,
|
| 1671 |
+
"jay": 17,
|
| 1672 |
+
"jean, blue jean, denim": 608,
|
| 1673 |
+
"jeep, landrover": 609,
|
| 1674 |
+
"jellyfish": 107,
|
| 1675 |
+
"jersey, T-shirt, tee shirt": 610,
|
| 1676 |
+
"jigsaw puzzle": 611,
|
| 1677 |
+
"jinrikisha, ricksha, rickshaw": 612,
|
| 1678 |
+
"joystick": 613,
|
| 1679 |
+
"junco, snowbird": 13,
|
| 1680 |
+
"keeshond": 261,
|
| 1681 |
+
"kelpie": 227,
|
| 1682 |
+
"killer whale, killer, orca, grampus, sea wolf, Orcinus orca": 148,
|
| 1683 |
+
"kimono": 614,
|
| 1684 |
+
"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica": 121,
|
| 1685 |
+
"king penguin, Aptenodytes patagonica": 145,
|
| 1686 |
+
"king snake, kingsnake": 56,
|
| 1687 |
+
"kit fox, Vulpes macrotis": 278,
|
| 1688 |
+
"kite": 21,
|
| 1689 |
+
"knee pad": 615,
|
| 1690 |
+
"knot": 616,
|
| 1691 |
+
"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus": 105,
|
| 1692 |
+
"komondor": 228,
|
| 1693 |
+
"kuvasz": 222,
|
| 1694 |
+
"lab coat, laboratory coat": 617,
|
| 1695 |
+
"lacewing, lacewing fly": 318,
|
| 1696 |
+
"ladle": 618,
|
| 1697 |
+
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle": 301,
|
| 1698 |
+
"lakeside, lakeshore": 975,
|
| 1699 |
+
"lampshade, lamp shade": 619,
|
| 1700 |
+
"langur": 374,
|
| 1701 |
+
"laptop, laptop computer": 620,
|
| 1702 |
+
"lawn mower, mower": 621,
|
| 1703 |
+
"leaf beetle, chrysomelid": 304,
|
| 1704 |
+
"leafhopper": 317,
|
| 1705 |
+
"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea": 34,
|
| 1706 |
+
"lemon": 951,
|
| 1707 |
+
"lens cap, lens cover": 622,
|
| 1708 |
+
"leopard, Panthera pardus": 288,
|
| 1709 |
+
"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens": 387,
|
| 1710 |
+
"letter opener, paper knife, paperknife": 623,
|
| 1711 |
+
"library": 624,
|
| 1712 |
+
"lifeboat": 625,
|
| 1713 |
+
"lighter, light, igniter, ignitor": 626,
|
| 1714 |
+
"limousine, limo": 627,
|
| 1715 |
+
"limpkin, Aramus pictus": 135,
|
| 1716 |
+
"liner, ocean liner": 628,
|
| 1717 |
+
"lion, king of beasts, Panthera leo": 291,
|
| 1718 |
+
"lionfish": 396,
|
| 1719 |
+
"lipstick, lip rouge": 629,
|
| 1720 |
+
"little blue heron, Egretta caerulea": 131,
|
| 1721 |
+
"llama": 355,
|
| 1722 |
+
"loggerhead, loggerhead turtle, Caretta caretta": 33,
|
| 1723 |
+
"long-horned beetle, longicorn, longicorn beetle": 303,
|
| 1724 |
+
"lorikeet": 90,
|
| 1725 |
+
"lotion": 631,
|
| 1726 |
+
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system": 632,
|
| 1727 |
+
"loupe, jeweler's loupe": 633,
|
| 1728 |
+
"lumbermill, sawmill": 634,
|
| 1729 |
+
"lycaenid, lycaenid butterfly": 326,
|
| 1730 |
+
"lynx, catamount": 287,
|
| 1731 |
+
"macaque": 373,
|
| 1732 |
+
"macaw": 88,
|
| 1733 |
+
"magnetic compass": 635,
|
| 1734 |
+
"magpie": 18,
|
| 1735 |
+
"mailbag, postbag": 636,
|
| 1736 |
+
"mailbox, letter box": 637,
|
| 1737 |
+
"maillot": 638,
|
| 1738 |
+
"maillot, tank suit": 639,
|
| 1739 |
+
"malamute, malemute, Alaskan malamute": 249,
|
| 1740 |
+
"malinois": 225,
|
| 1741 |
+
"manhole cover": 640,
|
| 1742 |
+
"mantis, mantid": 315,
|
| 1743 |
+
"maraca": 641,
|
| 1744 |
+
"marimba, xylophone": 642,
|
| 1745 |
+
"marmoset": 377,
|
| 1746 |
+
"marmot": 336,
|
| 1747 |
+
"mashed potato": 935,
|
| 1748 |
+
"mask": 643,
|
| 1749 |
+
"matchstick": 644,
|
| 1750 |
+
"maypole": 645,
|
| 1751 |
+
"maze, labyrinth": 646,
|
| 1752 |
+
"measuring cup": 647,
|
| 1753 |
+
"meat loaf, meatloaf": 962,
|
| 1754 |
+
"medicine chest, medicine cabinet": 648,
|
| 1755 |
+
"meerkat, mierkat": 299,
|
| 1756 |
+
"megalith, megalithic structure": 649,
|
| 1757 |
+
"menu": 922,
|
| 1758 |
+
"microphone, mike": 650,
|
| 1759 |
+
"microwave, microwave oven": 651,
|
| 1760 |
+
"military uniform": 652,
|
| 1761 |
+
"milk can": 653,
|
| 1762 |
+
"miniature pinscher": 237,
|
| 1763 |
+
"miniature poodle": 266,
|
| 1764 |
+
"miniature schnauzer": 196,
|
| 1765 |
+
"minibus": 654,
|
| 1766 |
+
"miniskirt, mini": 655,
|
| 1767 |
+
"minivan": 656,
|
| 1768 |
+
"mink": 357,
|
| 1769 |
+
"missile": 657,
|
| 1770 |
+
"mitten": 658,
|
| 1771 |
+
"mixing bowl": 659,
|
| 1772 |
+
"mobile home, manufactured home": 660,
|
| 1773 |
+
"modem": 662,
|
| 1774 |
+
"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus": 323,
|
| 1775 |
+
"monastery": 663,
|
| 1776 |
+
"mongoose": 298,
|
| 1777 |
+
"monitor": 664,
|
| 1778 |
+
"moped": 665,
|
| 1779 |
+
"mortar": 666,
|
| 1780 |
+
"mortarboard": 667,
|
| 1781 |
+
"mosque": 668,
|
| 1782 |
+
"mosquito net": 669,
|
| 1783 |
+
"motor scooter, scooter": 670,
|
| 1784 |
+
"mountain bike, all-terrain bike, off-roader": 671,
|
| 1785 |
+
"mountain tent": 672,
|
| 1786 |
+
"mouse, computer mouse": 673,
|
| 1787 |
+
"mousetrap": 674,
|
| 1788 |
+
"moving van": 675,
|
| 1789 |
+
"mud turtle": 35,
|
| 1790 |
+
"mushroom": 947,
|
| 1791 |
+
"muzzle": 676,
|
| 1792 |
+
"nail": 677,
|
| 1793 |
+
"neck brace": 678,
|
| 1794 |
+
"necklace": 679,
|
| 1795 |
+
"nematode, nematode worm, roundworm": 111,
|
| 1796 |
+
"night snake, Hypsiglena torquata": 60,
|
| 1797 |
+
"nipple": 680,
|
| 1798 |
+
"notebook, notebook computer": 681,
|
| 1799 |
+
"obelisk": 682,
|
| 1800 |
+
"oboe, hautboy, hautbois": 683,
|
| 1801 |
+
"ocarina, sweet potato": 684,
|
| 1802 |
+
"odometer, hodometer, mileometer, milometer": 685,
|
| 1803 |
+
"oil filter": 686,
|
| 1804 |
+
"orange": 950,
|
| 1805 |
+
"orangutan, orang, orangutang, Pongo pygmaeus": 365,
|
| 1806 |
+
"organ, pipe organ": 687,
|
| 1807 |
+
"oscilloscope, scope, cathode-ray oscilloscope, CRO": 688,
|
| 1808 |
+
"ostrich, Struthio camelus": 9,
|
| 1809 |
+
"otter": 360,
|
| 1810 |
+
"otterhound, otter hound": 175,
|
| 1811 |
+
"overskirt": 689,
|
| 1812 |
+
"ox": 345,
|
| 1813 |
+
"oxcart": 690,
|
| 1814 |
+
"oxygen mask": 691,
|
| 1815 |
+
"oystercatcher, oyster catcher": 143,
|
| 1816 |
+
"packet": 692,
|
| 1817 |
+
"paddle, boat paddle": 693,
|
| 1818 |
+
"paddlewheel, paddle wheel": 694,
|
| 1819 |
+
"padlock": 695,
|
| 1820 |
+
"paintbrush": 696,
|
| 1821 |
+
"pajama, pyjama, pj's, jammies": 697,
|
| 1822 |
+
"palace": 698,
|
| 1823 |
+
"panpipe, pandean pipe, syrinx": 699,
|
| 1824 |
+
"paper towel": 700,
|
| 1825 |
+
"papillon": 157,
|
| 1826 |
+
"parachute, chute": 701,
|
| 1827 |
+
"parallel bars, bars": 702,
|
| 1828 |
+
"park bench": 703,
|
| 1829 |
+
"parking meter": 704,
|
| 1830 |
+
"partridge": 86,
|
| 1831 |
+
"passenger car, coach, carriage": 705,
|
| 1832 |
+
"patas, hussar monkey, Erythrocebus patas": 371,
|
| 1833 |
+
"patio, terrace": 706,
|
| 1834 |
+
"pay-phone, pay-station": 707,
|
| 1835 |
+
"peacock": 84,
|
| 1836 |
+
"pedestal, plinth, footstall": 708,
|
| 1837 |
+
"pelican": 144,
|
| 1838 |
+
"pencil box, pencil case": 709,
|
| 1839 |
+
"pencil sharpener": 710,
|
| 1840 |
+
"perfume, essence": 711,
|
| 1841 |
+
"photocopier": 713,
|
| 1842 |
+
"pick, plectrum, plectron": 714,
|
| 1843 |
+
"pickelhaube": 715,
|
| 1844 |
+
"picket fence, paling": 716,
|
| 1845 |
+
"pickup, pickup truck": 717,
|
| 1846 |
+
"pier": 718,
|
| 1847 |
+
"piggy bank, penny bank": 719,
|
| 1848 |
+
"pill bottle": 720,
|
| 1849 |
+
"pillow": 721,
|
| 1850 |
+
"pineapple, ananas": 953,
|
| 1851 |
+
"ping-pong ball": 722,
|
| 1852 |
+
"pinwheel": 723,
|
| 1853 |
+
"pirate, pirate ship": 724,
|
| 1854 |
+
"pitcher, ewer": 725,
|
| 1855 |
+
"pizza, pizza pie": 963,
|
| 1856 |
+
"plane, carpenter's plane, woodworking plane": 726,
|
| 1857 |
+
"planetarium": 727,
|
| 1858 |
+
"plastic bag": 728,
|
| 1859 |
+
"plate": 923,
|
| 1860 |
+
"plate rack": 729,
|
| 1861 |
+
"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus": 103,
|
| 1862 |
+
"plow, plough": 730,
|
| 1863 |
+
"plunger, plumber's helper": 731,
|
| 1864 |
+
"pole": 733,
|
| 1865 |
+
"polecat, fitch, foulmart, foumart, Mustela putorius": 358,
|
| 1866 |
+
"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria": 734,
|
| 1867 |
+
"pomegranate": 957,
|
| 1868 |
+
"poncho": 735,
|
| 1869 |
+
"pool table, billiard table, snooker table": 736,
|
| 1870 |
+
"pop bottle, soda bottle": 737,
|
| 1871 |
+
"porcupine, hedgehog": 334,
|
| 1872 |
+
"pot, flowerpot": 738,
|
| 1873 |
+
"potpie": 964,
|
| 1874 |
+
"potter's wheel": 739,
|
| 1875 |
+
"power drill": 740,
|
| 1876 |
+
"prairie chicken, prairie grouse, prairie fowl": 83,
|
| 1877 |
+
"prayer rug, prayer mat": 741,
|
| 1878 |
+
"pretzel": 932,
|
| 1879 |
+
"printer": 742,
|
| 1880 |
+
"prison, prison house": 743,
|
| 1881 |
+
"proboscis monkey, Nasalis larvatus": 376,
|
| 1882 |
+
"projectile, missile": 744,
|
| 1883 |
+
"projector": 745,
|
| 1884 |
+
"promontory, headland, head, foreland": 976,
|
| 1885 |
+
"ptarmigan": 81,
|
| 1886 |
+
"puck, hockey puck": 746,
|
| 1887 |
+
"puffer, pufferfish, blowfish, globefish": 397,
|
| 1888 |
+
"pug, pug-dog": 254,
|
| 1889 |
+
"punching bag, punch bag, punching ball, punchball": 747,
|
| 1890 |
+
"purse": 748,
|
| 1891 |
+
"quail": 85,
|
| 1892 |
+
"quill, quill pen": 749,
|
| 1893 |
+
"quilt, comforter, comfort, puff": 750,
|
| 1894 |
+
"racer, race car, racing car": 751,
|
| 1895 |
+
"racket, racquet": 752,
|
| 1896 |
+
"radiator": 753,
|
| 1897 |
+
"radio telescope, radio reflector": 755,
|
| 1898 |
+
"radio, wireless": 754,
|
| 1899 |
+
"rain barrel": 756,
|
| 1900 |
+
"ram, tup": 348,
|
| 1901 |
+
"rapeseed": 984,
|
| 1902 |
+
"recreational vehicle, RV, R.V.": 757,
|
| 1903 |
+
"red fox, Vulpes vulpes": 277,
|
| 1904 |
+
"red wine": 966,
|
| 1905 |
+
"red wolf, maned wolf, Canis rufus, Canis niger": 271,
|
| 1906 |
+
"red-backed sandpiper, dunlin, Erolia alpina": 140,
|
| 1907 |
+
"red-breasted merganser, Mergus serrator": 98,
|
| 1908 |
+
"redbone": 168,
|
| 1909 |
+
"redshank, Tringa totanus": 141,
|
| 1910 |
+
"reel": 758,
|
| 1911 |
+
"reflex camera": 759,
|
| 1912 |
+
"refrigerator, icebox": 760,
|
| 1913 |
+
"remote control, remote": 761,
|
| 1914 |
+
"restaurant, eating house, eating place, eatery": 762,
|
| 1915 |
+
"revolver, six-gun, six-shooter": 763,
|
| 1916 |
+
"rhinoceros beetle": 306,
|
| 1917 |
+
"rifle": 764,
|
| 1918 |
+
"ringlet, ringlet butterfly": 322,
|
| 1919 |
+
"ringneck snake, ring-necked snake, ring snake": 53,
|
| 1920 |
+
"robin, American robin, Turdus migratorius": 15,
|
| 1921 |
+
"rock beauty, Holocanthus tricolor": 392,
|
| 1922 |
+
"rock crab, Cancer irroratus": 119,
|
| 1923 |
+
"rock python, rock snake, Python sebae": 62,
|
| 1924 |
+
"rocking chair, rocker": 765,
|
| 1925 |
+
"rotisserie": 766,
|
| 1926 |
+
"rubber eraser, rubber, pencil eraser": 767,
|
| 1927 |
+
"ruddy turnstone, Arenaria interpres": 139,
|
| 1928 |
+
"ruffed grouse, partridge, Bonasa umbellus": 82,
|
| 1929 |
+
"rugby ball": 768,
|
| 1930 |
+
"rule, ruler": 769,
|
| 1931 |
+
"running shoe": 770,
|
| 1932 |
+
"safe": 771,
|
| 1933 |
+
"safety pin": 772,
|
| 1934 |
+
"saltshaker, salt shaker": 773,
|
| 1935 |
+
"sandal": 774,
|
| 1936 |
+
"sandbar, sand bar": 977,
|
| 1937 |
+
"sarong": 775,
|
| 1938 |
+
"sax, saxophone": 776,
|
| 1939 |
+
"scabbard": 777,
|
| 1940 |
+
"scale, weighing machine": 778,
|
| 1941 |
+
"schipperke": 223,
|
| 1942 |
+
"school bus": 779,
|
| 1943 |
+
"schooner": 780,
|
| 1944 |
+
"scoreboard": 781,
|
| 1945 |
+
"scorpion": 71,
|
| 1946 |
+
"screen, CRT screen": 782,
|
| 1947 |
+
"screw": 783,
|
| 1948 |
+
"screwdriver": 784,
|
| 1949 |
+
"scuba diver": 983,
|
| 1950 |
+
"sea anemone, anemone": 108,
|
| 1951 |
+
"sea cucumber, holothurian": 329,
|
| 1952 |
+
"sea lion": 150,
|
| 1953 |
+
"sea slug, nudibranch": 115,
|
| 1954 |
+
"sea snake": 65,
|
| 1955 |
+
"sea urchin": 328,
|
| 1956 |
+
"seashore, coast, seacoast, sea-coast": 978,
|
| 1957 |
+
"seat belt, seatbelt": 785,
|
| 1958 |
+
"sewing machine": 786,
|
| 1959 |
+
"shield, buckler": 787,
|
| 1960 |
+
"shoe shop, shoe-shop, shoe store": 788,
|
| 1961 |
+
"shoji": 789,
|
| 1962 |
+
"shopping basket": 790,
|
| 1963 |
+
"shopping cart": 791,
|
| 1964 |
+
"shovel": 792,
|
| 1965 |
+
"shower cap": 793,
|
| 1966 |
+
"shower curtain": 794,
|
| 1967 |
+
"siamang, Hylobates syndactylus, Symphalangus syndactylus": 369,
|
| 1968 |
+
"sidewinder, horned rattlesnake, Crotalus cerastes": 68,
|
| 1969 |
+
"silky terrier, Sydney silky": 201,
|
| 1970 |
+
"ski": 795,
|
| 1971 |
+
"ski mask": 796,
|
| 1972 |
+
"skunk, polecat, wood pussy": 361,
|
| 1973 |
+
"sleeping bag": 797,
|
| 1974 |
+
"slide rule, slipstick": 798,
|
| 1975 |
+
"sliding door": 799,
|
| 1976 |
+
"slot, one-armed bandit": 800,
|
| 1977 |
+
"sloth bear, Melursus ursinus, Ursus ursinus": 297,
|
| 1978 |
+
"slug": 114,
|
| 1979 |
+
"snail": 113,
|
| 1980 |
+
"snorkel": 801,
|
| 1981 |
+
"snow leopard, ounce, Panthera uncia": 289,
|
| 1982 |
+
"snowmobile": 802,
|
| 1983 |
+
"snowplow, snowplough": 803,
|
| 1984 |
+
"soap dispenser": 804,
|
| 1985 |
+
"soccer ball": 805,
|
| 1986 |
+
"sock": 806,
|
| 1987 |
+
"soft-coated wheaten terrier": 202,
|
| 1988 |
+
"solar dish, solar collector, solar furnace": 807,
|
| 1989 |
+
"sombrero": 808,
|
| 1990 |
+
"sorrel": 339,
|
| 1991 |
+
"soup bowl": 809,
|
| 1992 |
+
"space bar": 810,
|
| 1993 |
+
"space heater": 811,
|
| 1994 |
+
"space shuttle": 812,
|
| 1995 |
+
"spaghetti squash": 940,
|
| 1996 |
+
"spatula": 813,
|
| 1997 |
+
"speedboat": 814,
|
| 1998 |
+
"spider monkey, Ateles geoffroyi": 381,
|
| 1999 |
+
"spider web, spider's web": 815,
|
| 2000 |
+
"spindle": 816,
|
| 2001 |
+
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish": 123,
|
| 2002 |
+
"spoonbill": 129,
|
| 2003 |
+
"sports car, sport car": 817,
|
| 2004 |
+
"spotlight, spot": 818,
|
| 2005 |
+
"spotted salamander, Ambystoma maculatum": 28,
|
| 2006 |
+
"squirrel monkey, Saimiri sciureus": 382,
|
| 2007 |
+
"stage": 819,
|
| 2008 |
+
"standard poodle": 267,
|
| 2009 |
+
"standard schnauzer": 198,
|
| 2010 |
+
"starfish, sea star": 327,
|
| 2011 |
+
"steam locomotive": 820,
|
| 2012 |
+
"steel arch bridge": 821,
|
| 2013 |
+
"steel drum": 822,
|
| 2014 |
+
"stethoscope": 823,
|
| 2015 |
+
"stingray": 6,
|
| 2016 |
+
"stinkhorn, carrion fungus": 994,
|
| 2017 |
+
"stole": 824,
|
| 2018 |
+
"stone wall": 825,
|
| 2019 |
+
"stopwatch, stop watch": 826,
|
| 2020 |
+
"stove": 827,
|
| 2021 |
+
"strainer": 828,
|
| 2022 |
+
"strawberry": 949,
|
| 2023 |
+
"street sign": 919,
|
| 2024 |
+
"streetcar, tram, tramcar, trolley, trolley car": 829,
|
| 2025 |
+
"stretcher": 830,
|
| 2026 |
+
"studio couch, day bed": 831,
|
| 2027 |
+
"stupa, tope": 832,
|
| 2028 |
+
"sturgeon": 394,
|
| 2029 |
+
"submarine, pigboat, sub, U-boat": 833,
|
| 2030 |
+
"suit, suit of clothes": 834,
|
| 2031 |
+
"sulphur butterfly, sulfur butterfly": 325,
|
| 2032 |
+
"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita": 89,
|
| 2033 |
+
"sundial": 835,
|
| 2034 |
+
"sunglass": 836,
|
| 2035 |
+
"sunglasses, dark glasses, shades": 837,
|
| 2036 |
+
"sunscreen, sunblock, sun blocker": 838,
|
| 2037 |
+
"suspension bridge": 839,
|
| 2038 |
+
"swab, swob, mop": 840,
|
| 2039 |
+
"sweatshirt": 841,
|
| 2040 |
+
"swimming trunks, bathing trunks": 842,
|
| 2041 |
+
"swing": 843,
|
| 2042 |
+
"switch, electric switch, electrical switch": 844,
|
| 2043 |
+
"syringe": 845,
|
| 2044 |
+
"tabby, tabby cat": 281,
|
| 2045 |
+
"table lamp": 846,
|
| 2046 |
+
"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui": 32,
|
| 2047 |
+
"tank, army tank, armored combat vehicle, armoured combat vehicle": 847,
|
| 2048 |
+
"tape player": 848,
|
| 2049 |
+
"tarantula": 76,
|
| 2050 |
+
"teapot": 849,
|
| 2051 |
+
"teddy, teddy bear": 850,
|
| 2052 |
+
"television, television system": 851,
|
| 2053 |
+
"tench, Tinca tinca": 0,
|
| 2054 |
+
"tennis ball": 852,
|
| 2055 |
+
"terrapin": 36,
|
| 2056 |
+
"thatch, thatched roof": 853,
|
| 2057 |
+
"theater curtain, theatre curtain": 854,
|
| 2058 |
+
"thimble": 855,
|
| 2059 |
+
"three-toed sloth, ai, Bradypus tridactylus": 364,
|
| 2060 |
+
"thresher, thrasher, threshing machine": 856,
|
| 2061 |
+
"throne": 857,
|
| 2062 |
+
"thunder snake, worm snake, Carphophis amoenus": 52,
|
| 2063 |
+
"tick": 78,
|
| 2064 |
+
"tiger beetle": 300,
|
| 2065 |
+
"tiger cat": 282,
|
| 2066 |
+
"tiger shark, Galeocerdo cuvieri": 3,
|
| 2067 |
+
"tiger, Panthera tigris": 292,
|
| 2068 |
+
"tile roof": 858,
|
| 2069 |
+
"timber wolf, grey wolf, gray wolf, Canis lupus": 269,
|
| 2070 |
+
"titi, titi monkey": 380,
|
| 2071 |
+
"toaster": 859,
|
| 2072 |
+
"tobacco shop, tobacconist shop, tobacconist": 860,
|
| 2073 |
+
"toilet seat": 861,
|
| 2074 |
+
"toilet tissue, toilet paper, bathroom tissue": 999,
|
| 2075 |
+
"torch": 862,
|
| 2076 |
+
"totem pole": 863,
|
| 2077 |
+
"toucan": 96,
|
| 2078 |
+
"tow truck, tow car, wrecker": 864,
|
| 2079 |
+
"toy poodle": 265,
|
| 2080 |
+
"toy terrier": 158,
|
| 2081 |
+
"toyshop": 865,
|
| 2082 |
+
"tractor": 866,
|
| 2083 |
+
"traffic light, traffic signal, stoplight": 920,
|
| 2084 |
+
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi": 867,
|
| 2085 |
+
"tray": 868,
|
| 2086 |
+
"tree frog, tree-frog": 31,
|
| 2087 |
+
"trench coat": 869,
|
| 2088 |
+
"triceratops": 51,
|
| 2089 |
+
"tricycle, trike, velocipede": 870,
|
| 2090 |
+
"trifle": 927,
|
| 2091 |
+
"trilobite": 69,
|
| 2092 |
+
"trimaran": 871,
|
| 2093 |
+
"tripod": 872,
|
| 2094 |
+
"triumphal arch": 873,
|
| 2095 |
+
"trolleybus, trolley coach, trackless trolley": 874,
|
| 2096 |
+
"trombone": 875,
|
| 2097 |
+
"tub, vat": 876,
|
| 2098 |
+
"turnstile": 877,
|
| 2099 |
+
"tusker": 101,
|
| 2100 |
+
"typewriter keyboard": 878,
|
| 2101 |
+
"umbrella": 879,
|
| 2102 |
+
"unicycle, monocycle": 880,
|
| 2103 |
+
"upright, upright piano": 881,
|
| 2104 |
+
"vacuum, vacuum cleaner": 882,
|
| 2105 |
+
"valley, vale": 979,
|
| 2106 |
+
"vase": 883,
|
| 2107 |
+
"vault": 884,
|
| 2108 |
+
"velvet": 885,
|
| 2109 |
+
"vending machine": 886,
|
| 2110 |
+
"vestment": 887,
|
| 2111 |
+
"viaduct": 888,
|
| 2112 |
+
"vine snake": 59,
|
| 2113 |
+
"violin, fiddle": 889,
|
| 2114 |
+
"vizsla, Hungarian pointer": 211,
|
| 2115 |
+
"volcano": 980,
|
| 2116 |
+
"volleyball": 890,
|
| 2117 |
+
"vulture": 23,
|
| 2118 |
+
"waffle iron": 891,
|
| 2119 |
+
"walking stick, walkingstick, stick insect": 313,
|
| 2120 |
+
"wall clock": 892,
|
| 2121 |
+
"wallaby, brush kangaroo": 104,
|
| 2122 |
+
"wallet, billfold, notecase, pocketbook": 893,
|
| 2123 |
+
"wardrobe, closet, press": 894,
|
| 2124 |
+
"warplane, military plane": 895,
|
| 2125 |
+
"warthog": 343,
|
| 2126 |
+
"washbasin, handbasin, washbowl, lavabo, wash-hand basin": 896,
|
| 2127 |
+
"washer, automatic washer, washing machine": 897,
|
| 2128 |
+
"water bottle": 898,
|
| 2129 |
+
"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis": 346,
|
| 2130 |
+
"water jug": 899,
|
| 2131 |
+
"water ouzel, dipper": 20,
|
| 2132 |
+
"water snake": 58,
|
| 2133 |
+
"water tower": 900,
|
| 2134 |
+
"weasel": 356,
|
| 2135 |
+
"web site, website, internet site, site": 916,
|
| 2136 |
+
"weevil": 307,
|
| 2137 |
+
"whippet": 172,
|
| 2138 |
+
"whiptail, whiptail lizard": 41,
|
| 2139 |
+
"whiskey jug": 901,
|
| 2140 |
+
"whistle": 902,
|
| 2141 |
+
"white stork, Ciconia ciconia": 127,
|
| 2142 |
+
"white wolf, Arctic wolf, Canis lupus tundrarum": 270,
|
| 2143 |
+
"wig": 903,
|
| 2144 |
+
"wild boar, boar, Sus scrofa": 342,
|
| 2145 |
+
"window screen": 904,
|
| 2146 |
+
"window shade": 905,
|
| 2147 |
+
"wine bottle": 907,
|
| 2148 |
+
"wing": 908,
|
| 2149 |
+
"wire-haired fox terrier": 188,
|
| 2150 |
+
"wok": 909,
|
| 2151 |
+
"wolf spider, hunting spider": 77,
|
| 2152 |
+
"wombat": 106,
|
| 2153 |
+
"wood rabbit, cottontail, cottontail rabbit": 330,
|
| 2154 |
+
"wooden spoon": 910,
|
| 2155 |
+
"wool, woolen, woollen": 911,
|
| 2156 |
+
"worm fence, snake fence, snake-rail fence, Virginia fence": 912,
|
| 2157 |
+
"wreck": 913,
|
| 2158 |
+
"yawl": 914,
|
| 2159 |
+
"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum": 986,
|
| 2160 |
+
"yurt": 915,
|
| 2161 |
+
"zebra": 340,
|
| 2162 |
+
"zucchini, courgette": 939
|
| 2163 |
+
},
|
| 2164 |
+
"layer_norm_eps": 1e-12,
|
| 2165 |
+
"length_penalty": 1.0,
|
| 2166 |
+
"max_length": 20,
|
| 2167 |
+
"min_length": 0,
|
| 2168 |
+
"mlp_ratio": [
|
| 2169 |
+
4.0,
|
| 2170 |
+
4.0,
|
| 2171 |
+
4.0
|
| 2172 |
+
],
|
| 2173 |
+
"model_type": "cvt",
|
| 2174 |
+
"no_repeat_ngram_size": 0,
|
| 2175 |
+
"num_beam_groups": 1,
|
| 2176 |
+
"num_beams": 1,
|
| 2177 |
+
"num_channels": 3,
|
| 2178 |
+
"num_heads": [
|
| 2179 |
+
1,
|
| 2180 |
+
3,
|
| 2181 |
+
6
|
| 2182 |
+
],
|
| 2183 |
+
"num_return_sequences": 1,
|
| 2184 |
+
"num_stages": 3,
|
| 2185 |
+
"output_attentions": false,
|
| 2186 |
+
"output_hidden_states": false,
|
| 2187 |
+
"output_scores": false,
|
| 2188 |
+
"pad_token_id": null,
|
| 2189 |
+
"padding_kv": [
|
| 2190 |
+
1,
|
| 2191 |
+
1,
|
| 2192 |
+
1
|
| 2193 |
+
],
|
| 2194 |
+
"padding_q": [
|
| 2195 |
+
1,
|
| 2196 |
+
1,
|
| 2197 |
+
1
|
| 2198 |
+
],
|
| 2199 |
+
"patch_padding": [
|
| 2200 |
+
2,
|
| 2201 |
+
1,
|
| 2202 |
+
1
|
| 2203 |
+
],
|
| 2204 |
+
"patch_sizes": [
|
| 2205 |
+
7,
|
| 2206 |
+
3,
|
| 2207 |
+
3
|
| 2208 |
+
],
|
| 2209 |
+
"patch_stride": [
|
| 2210 |
+
4,
|
| 2211 |
+
2,
|
| 2212 |
+
2
|
| 2213 |
+
],
|
| 2214 |
+
"pos_embed": [
|
| 2215 |
+
false,
|
| 2216 |
+
false,
|
| 2217 |
+
false
|
| 2218 |
+
],
|
| 2219 |
+
"prefix": null,
|
| 2220 |
+
"problem_type": null,
|
| 2221 |
+
"projection_size": 768,
|
| 2222 |
+
"pruned_heads": {},
|
| 2223 |
+
"qkv_bias": [
|
| 2224 |
+
true,
|
| 2225 |
+
true,
|
| 2226 |
+
true
|
| 2227 |
+
],
|
| 2228 |
+
"qkv_projection_method": [
|
| 2229 |
+
"dw_bn",
|
| 2230 |
+
"dw_bn",
|
| 2231 |
+
"dw_bn"
|
| 2232 |
+
],
|
| 2233 |
+
"remove_invalid_values": false,
|
| 2234 |
+
"repetition_penalty": 1.0,
|
| 2235 |
+
"return_dict": true,
|
| 2236 |
+
"return_dict_in_generate": false,
|
| 2237 |
+
"sep_token_id": null,
|
| 2238 |
+
"stride_kv": [
|
| 2239 |
+
2,
|
| 2240 |
+
2,
|
| 2241 |
+
2
|
| 2242 |
+
],
|
| 2243 |
+
"stride_q": [
|
| 2244 |
+
1,
|
| 2245 |
+
1,
|
| 2246 |
+
1
|
| 2247 |
+
],
|
| 2248 |
+
"suppress_tokens": null,
|
| 2249 |
+
"task_specific_params": null,
|
| 2250 |
+
"temperature": 1.0,
|
| 2251 |
+
"tf_legacy_loss": false,
|
| 2252 |
+
"tie_encoder_decoder": false,
|
| 2253 |
+
"tie_word_embeddings": true,
|
| 2254 |
+
"tokenizer_class": null,
|
| 2255 |
+
"top_k": 50,
|
| 2256 |
+
"top_p": 1.0,
|
| 2257 |
+
"torch_dtype": "float32",
|
| 2258 |
+
"torchscript": false,
|
| 2259 |
+
"transformers_version": "4.31.0",
|
| 2260 |
+
"typical_p": 1.0,
|
| 2261 |
+
"use_bfloat16": false
|
| 2262 |
+
},
|
| 2263 |
+
"is_encoder_decoder": true,
|
| 2264 |
+
"model_type": "vision-encoder-decoder",
|
| 2265 |
+
"tie_word_embeddings": false,
|
| 2266 |
+
"torch_dtype": "float32",
|
| 2267 |
+
"transformers_version": null
|
| 2268 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 50256,
|
| 4 |
+
"eos_token_id": 50256,
|
| 5 |
+
"transformers_version": "4.31.0"
|
| 6 |
+
}
|
modelling_medicap.py
ADDED
|
@@ -0,0 +1,518 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from typing import Any, Optional, Tuple, Union
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
import transformers
|
| 6 |
+
from torch.nn import CrossEntropyLoss
|
| 7 |
+
from transformers import PreTrainedTokenizerFast, VisionEncoderDecoderModel
|
| 8 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 9 |
+
from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
|
| 10 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 11 |
+
from transformers.models.vision_encoder_decoder.configuration_vision_encoder_decoder import \
|
| 12 |
+
VisionEncoderDecoderConfig
|
| 13 |
+
from transformers.utils import logging
|
| 14 |
+
|
| 15 |
+
logger = logging.get_logger(__name__)
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
class CvtWithProjectionHeadConfig(transformers.CvtConfig):
|
| 19 |
+
def __init__(self, projection_size: int = None, **kwargs: Any) -> None:
|
| 20 |
+
super().__init__(**kwargs)
|
| 21 |
+
self.projection_size = projection_size
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
class ModelOutputWithProjectionEmbedding(transformers.modeling_outputs.ModelOutput):
|
| 25 |
+
last_hidden_state: torch.FloatTensor
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
class CvtProjectionHead(torch.nn.Module):
|
| 29 |
+
|
| 30 |
+
def __init__(self, config) -> None:
|
| 31 |
+
super().__init__()
|
| 32 |
+
|
| 33 |
+
# https://github.com/huggingface/transformers/blob/68287689f2f0d8b7063c400230b3766987abf18d/src/transformers/models/cvt/modeling_cvt.py#L657
|
| 34 |
+
self.layer_norm = torch.nn.LayerNorm(config.embed_dim[-1], eps=config.layer_norm_eps)
|
| 35 |
+
|
| 36 |
+
# No bias as following layer normalisation with bias:
|
| 37 |
+
self.projection = torch.nn.Linear(config.embed_dim[-1], config.projection_size, bias=False)
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 41 |
+
x = self.layer_norm(x)
|
| 42 |
+
x = self.projection(x)
|
| 43 |
+
return x
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
class CvtWithProjectionHead(transformers.CvtPreTrainedModel):
|
| 47 |
+
def __init__(self, config):
|
| 48 |
+
super().__init__(config)
|
| 49 |
+
|
| 50 |
+
self.cvt = transformers.CvtModel(config, add_pooling_layer=False)
|
| 51 |
+
self.projection_head = CvtProjectionHead(config)
|
| 52 |
+
|
| 53 |
+
# Initialize weights and apply final processing:
|
| 54 |
+
self.post_init()
|
| 55 |
+
|
| 56 |
+
def forward(
|
| 57 |
+
self,
|
| 58 |
+
pixel_values: Optional[torch.Tensor] = None,
|
| 59 |
+
output_hidden_states: Optional[bool] = None,
|
| 60 |
+
return_dict: Optional[bool] = None,
|
| 61 |
+
) -> Union[Tuple, ModelOutputWithProjectionEmbedding]:
|
| 62 |
+
|
| 63 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 64 |
+
|
| 65 |
+
outputs = self.cvt(
|
| 66 |
+
pixel_values,
|
| 67 |
+
output_hidden_states=output_hidden_states,
|
| 68 |
+
return_dict=return_dict,
|
| 69 |
+
)
|
| 70 |
+
|
| 71 |
+
projection = self.projection_head(
|
| 72 |
+
torch.permute(torch.flatten(outputs.last_hidden_state, 2), [0, 2, 1]),
|
| 73 |
+
)
|
| 74 |
+
|
| 75 |
+
if not return_dict:
|
| 76 |
+
return projection
|
| 77 |
+
|
| 78 |
+
return ModelOutputWithProjectionEmbedding(
|
| 79 |
+
last_hidden_state=projection,
|
| 80 |
+
)
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
class MedICapEncoderDecoderModel(VisionEncoderDecoderModel):
|
| 84 |
+
|
| 85 |
+
config_class = VisionEncoderDecoderConfig
|
| 86 |
+
base_model_prefix = "vision_encoder_decoder"
|
| 87 |
+
main_input_name = "pixel_values"
|
| 88 |
+
supports_gradient_checkpointing = True
|
| 89 |
+
|
| 90 |
+
def __init__(
|
| 91 |
+
self,
|
| 92 |
+
config: Optional[PretrainedConfig] = None,
|
| 93 |
+
encoder: Optional[PreTrainedModel] = None,
|
| 94 |
+
decoder: Optional[PreTrainedModel] = None,
|
| 95 |
+
):
|
| 96 |
+
|
| 97 |
+
if decoder:
|
| 98 |
+
assert not decoder.config.add_cross_attention, '"add_cross_attention" must be False for the given decoder'
|
| 99 |
+
assert decoder.config.is_decoder, '"is_decoder" must be True for the given decoder'
|
| 100 |
+
|
| 101 |
+
if config is None and (encoder is None or decoder is None):
|
| 102 |
+
raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
|
| 103 |
+
if config is None:
|
| 104 |
+
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
|
| 105 |
+
else:
|
| 106 |
+
if not isinstance(config, self.config_class):
|
| 107 |
+
raise ValueError(f"Config: {config} has to be of type {self.config_class}")
|
| 108 |
+
|
| 109 |
+
config.tie_word_embeddings = False
|
| 110 |
+
|
| 111 |
+
# initialize with config
|
| 112 |
+
PreTrainedModel.__init__(self, config)
|
| 113 |
+
|
| 114 |
+
# Encoder:
|
| 115 |
+
if encoder is None:
|
| 116 |
+
encoder = CvtWithProjectionHead(config=config.encoder)
|
| 117 |
+
|
| 118 |
+
# Decoder:
|
| 119 |
+
if decoder is None:
|
| 120 |
+
decoder = transformers.GPT2LMHeadModel(config=config.decoder)
|
| 121 |
+
|
| 122 |
+
# Resize GPT2 token embedding to include the padding and beginning of sentence tokens:
|
| 123 |
+
decoder.resize_token_embeddings(config.decoder.vocab_size + 2)
|
| 124 |
+
|
| 125 |
+
self.encoder = encoder
|
| 126 |
+
self.decoder = decoder
|
| 127 |
+
|
| 128 |
+
if self.encoder.config.to_dict() != self.config.encoder.to_dict():
|
| 129 |
+
logger.warning(
|
| 130 |
+
f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:"
|
| 131 |
+
f" {self.config.encoder}"
|
| 132 |
+
)
|
| 133 |
+
if self.decoder.config.to_dict() != self.config.decoder.to_dict():
|
| 134 |
+
logger.warning(
|
| 135 |
+
f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
|
| 136 |
+
f" {self.config.decoder}"
|
| 137 |
+
)
|
| 138 |
+
|
| 139 |
+
self.encoder.config = self.config.encoder
|
| 140 |
+
self.decoder.config = self.config.decoder
|
| 141 |
+
|
| 142 |
+
@classmethod
|
| 143 |
+
def from_encoder_decoder_pretrained(
|
| 144 |
+
cls,
|
| 145 |
+
encoder_pretrained_model_name_or_path: str = None,
|
| 146 |
+
decoder_pretrained_model_name_or_path: str = None,
|
| 147 |
+
*model_args,
|
| 148 |
+
**kwargs,
|
| 149 |
+
) -> PreTrainedModel:
|
| 150 |
+
kwargs_encoder = {
|
| 151 |
+
argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
|
| 152 |
+
}
|
| 153 |
+
|
| 154 |
+
kwargs_decoder = {
|
| 155 |
+
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
|
| 156 |
+
}
|
| 157 |
+
|
| 158 |
+
# remove encoder, decoder kwargs from kwargs
|
| 159 |
+
for key in kwargs_encoder.keys():
|
| 160 |
+
del kwargs["encoder_" + key]
|
| 161 |
+
for key in kwargs_decoder.keys():
|
| 162 |
+
del kwargs["decoder_" + key]
|
| 163 |
+
|
| 164 |
+
# Load and initialize the encoder and decoder
|
| 165 |
+
# The distinction between encoder and decoder at the model level is made
|
| 166 |
+
# by the value of the flag `is_decoder` that we need to set correctly.
|
| 167 |
+
encoder = kwargs_encoder.pop("model", None)
|
| 168 |
+
if encoder is None:
|
| 169 |
+
if encoder_pretrained_model_name_or_path is None:
|
| 170 |
+
raise ValueError(
|
| 171 |
+
"If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
|
| 172 |
+
"to be defined."
|
| 173 |
+
)
|
| 174 |
+
|
| 175 |
+
if "config" not in kwargs_encoder:
|
| 176 |
+
encoder_config, kwargs_encoder = AutoConfig.from_pretrained(
|
| 177 |
+
encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
|
| 178 |
+
)
|
| 179 |
+
|
| 180 |
+
if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
|
| 181 |
+
logger.info(
|
| 182 |
+
f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
|
| 183 |
+
"from a decoder model. Cross-attention and casual mask are disabled."
|
| 184 |
+
)
|
| 185 |
+
encoder_config.is_decoder = False
|
| 186 |
+
encoder_config.add_cross_attention = False
|
| 187 |
+
|
| 188 |
+
kwargs_encoder["config"] = encoder_config
|
| 189 |
+
|
| 190 |
+
encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
|
| 191 |
+
|
| 192 |
+
decoder = kwargs_decoder.pop("model", None)
|
| 193 |
+
if decoder is None:
|
| 194 |
+
if decoder_pretrained_model_name_or_path is None:
|
| 195 |
+
raise ValueError(
|
| 196 |
+
"If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
|
| 197 |
+
"to be defined."
|
| 198 |
+
)
|
| 199 |
+
|
| 200 |
+
if "config" not in kwargs_decoder:
|
| 201 |
+
decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
|
| 202 |
+
decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
|
| 203 |
+
)
|
| 204 |
+
|
| 205 |
+
if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
|
| 206 |
+
logger.info(
|
| 207 |
+
f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
|
| 208 |
+
f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
|
| 209 |
+
f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
|
| 210 |
+
)
|
| 211 |
+
decoder_config.is_decoder = True
|
| 212 |
+
decoder_config.add_cross_attention = True
|
| 213 |
+
|
| 214 |
+
kwargs_decoder["config"] = decoder_config
|
| 215 |
+
|
| 216 |
+
if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
|
| 217 |
+
logger.warning(
|
| 218 |
+
f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
|
| 219 |
+
f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
|
| 220 |
+
"make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
|
| 221 |
+
"passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
|
| 222 |
+
"`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
|
| 223 |
+
)
|
| 224 |
+
|
| 225 |
+
decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
|
| 226 |
+
|
| 227 |
+
# instantiate config with corresponding kwargs
|
| 228 |
+
config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
|
| 229 |
+
|
| 230 |
+
# make sure input & output embeddings is not tied
|
| 231 |
+
config.tie_word_embeddings = False
|
| 232 |
+
return cls(encoder=encoder, decoder=decoder, config=config)
|
| 233 |
+
|
| 234 |
+
def forward(
|
| 235 |
+
self,
|
| 236 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
| 237 |
+
decoder_input_ids: Optional[torch.LongTensor] = None,
|
| 238 |
+
decoder_attention_mask: Optional[torch.BoolTensor] = None,
|
| 239 |
+
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
|
| 240 |
+
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
| 241 |
+
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 242 |
+
labels: Optional[torch.LongTensor] = None,
|
| 243 |
+
use_cache: Optional[bool] = None,
|
| 244 |
+
output_attentions: Optional[bool] = None,
|
| 245 |
+
output_hidden_states: Optional[bool] = None,
|
| 246 |
+
return_dict: Optional[bool] = None,
|
| 247 |
+
**kwargs,
|
| 248 |
+
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
|
| 249 |
+
|
| 250 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 251 |
+
|
| 252 |
+
kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
|
| 253 |
+
|
| 254 |
+
kwargs_decoder = {
|
| 255 |
+
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
|
| 256 |
+
}
|
| 257 |
+
|
| 258 |
+
if encoder_outputs is None:
|
| 259 |
+
if pixel_values is None:
|
| 260 |
+
raise ValueError("You have to specify pixel_values")
|
| 261 |
+
|
| 262 |
+
encoder_outputs = self.encoder(
|
| 263 |
+
pixel_values,
|
| 264 |
+
output_hidden_states=output_hidden_states,
|
| 265 |
+
return_dict=return_dict,
|
| 266 |
+
**kwargs_encoder,
|
| 267 |
+
) # CvT does not support output_attentions.
|
| 268 |
+
elif isinstance(encoder_outputs, tuple):
|
| 269 |
+
encoder_outputs = BaseModelOutput(*encoder_outputs)
|
| 270 |
+
|
| 271 |
+
# encoder_hidden_states = encoder_outputs[0]
|
| 272 |
+
# encoder_attention_mask = None
|
| 273 |
+
|
| 274 |
+
# image_features = self.encoder(images).projected_last_hidden_state
|
| 275 |
+
|
| 276 |
+
embeddings = self.decoder.transformer.wte(decoder_input_ids)
|
| 277 |
+
embeddings = torch.cat([encoder_outputs[0], embeddings], dim=1)
|
| 278 |
+
|
| 279 |
+
decoder_attention_mask = torch.cat(
|
| 280 |
+
[
|
| 281 |
+
torch.ones(encoder_outputs[0].shape[:-1], dtype=decoder_attention_mask.dtype, device=self.device),
|
| 282 |
+
decoder_attention_mask
|
| 283 |
+
],
|
| 284 |
+
dim=1,
|
| 285 |
+
)
|
| 286 |
+
|
| 287 |
+
decoder_outputs = self.decoder(
|
| 288 |
+
input_ids=decoder_input_ids,
|
| 289 |
+
attention_mask=decoder_attention_mask,
|
| 290 |
+
inputs_embeds=decoder_inputs_embeds,
|
| 291 |
+
output_attentions=output_attentions,
|
| 292 |
+
output_hidden_states=output_hidden_states,
|
| 293 |
+
use_cache=use_cache,
|
| 294 |
+
past_key_values=past_key_values,
|
| 295 |
+
return_dict=return_dict,
|
| 296 |
+
**kwargs_decoder,
|
| 297 |
+
)
|
| 298 |
+
|
| 299 |
+
# Loss:
|
| 300 |
+
loss = None
|
| 301 |
+
if labels is not None:
|
| 302 |
+
logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
|
| 303 |
+
loss_fct = CrossEntropyLoss()
|
| 304 |
+
loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1))
|
| 305 |
+
|
| 306 |
+
if not return_dict:
|
| 307 |
+
if loss is not None:
|
| 308 |
+
return (loss,) + decoder_outputs + encoder_outputs
|
| 309 |
+
else:
|
| 310 |
+
return decoder_outputs + encoder_outputs
|
| 311 |
+
|
| 312 |
+
return Seq2SeqLMOutput(
|
| 313 |
+
loss=loss,
|
| 314 |
+
logits=decoder_outputs.logits,
|
| 315 |
+
past_key_values=decoder_outputs.past_key_values,
|
| 316 |
+
decoder_hidden_states=decoder_outputs.hidden_states,
|
| 317 |
+
decoder_attentions=decoder_outputs.attentions,
|
| 318 |
+
cross_attentions=decoder_outputs.cross_attentions,
|
| 319 |
+
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
|
| 320 |
+
# encoder_hidden_states=encoder_outputs.hidden_states,
|
| 321 |
+
# encoder_attentions=encoder_outputs.attentions,
|
| 322 |
+
)
|
| 323 |
+
|
| 324 |
+
def prepare_inputs_for_generation(
|
| 325 |
+
self,
|
| 326 |
+
input_ids,
|
| 327 |
+
special_token_ids,
|
| 328 |
+
past_key_values=None,
|
| 329 |
+
attention_mask=None,
|
| 330 |
+
use_cache=None,
|
| 331 |
+
encoder_outputs=None,
|
| 332 |
+
**kwargs,
|
| 333 |
+
):
|
| 334 |
+
"""
|
| 335 |
+
Modification of:
|
| 336 |
+
https://github.com/huggingface/transformers/blob/main/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py#L660
|
| 337 |
+
"""
|
| 338 |
+
|
| 339 |
+
decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
|
| 340 |
+
decoder_attention_mask = decoder_inputs['attention_mask'] if 'attention_mask' in decoder_inputs else None
|
| 341 |
+
|
| 342 |
+
if not past_key_values:
|
| 343 |
+
token_type_ids = self.token_ids_to_token_type_ids(input_ids, special_token_ids)
|
| 344 |
+
else:
|
| 345 |
+
token_type_ids = self.token_ids_to_token_type_ids_past(input_ids, special_token_ids)
|
| 346 |
+
|
| 347 |
+
input_dict = {
|
| 348 |
+
'attention_mask': attention_mask,
|
| 349 |
+
'decoder_attention_mask': decoder_attention_mask,
|
| 350 |
+
'decoder_input_ids': decoder_inputs['input_ids'],
|
| 351 |
+
'decoder_token_type_ids': token_type_ids,
|
| 352 |
+
'encoder_outputs': encoder_outputs,
|
| 353 |
+
'past_key_values': decoder_inputs['past_key_values'],
|
| 354 |
+
'use_cache': use_cache,
|
| 355 |
+
}
|
| 356 |
+
return input_dict
|
| 357 |
+
|
| 358 |
+
def token_ids_to_token_type_ids(self, token_ids, special_token_ids, token_type_id_sections=None):
|
| 359 |
+
"""
|
| 360 |
+
Extract token type identifiers from the token identifiers.
|
| 361 |
+
|
| 362 |
+
Argument/s:
|
| 363 |
+
token_ids - token identifiers.
|
| 364 |
+
special_token_ids - special token identifiers that indicate the separation between sections.
|
| 365 |
+
token_type_id_section - token type identifier for each section.
|
| 366 |
+
|
| 367 |
+
Returns:
|
| 368 |
+
token_type_ids - token type identifiers.
|
| 369 |
+
"""
|
| 370 |
+
|
| 371 |
+
token_type_id_sections = token_type_id_sections if token_type_id_sections is not None else list(range(len(special_token_ids) + 1))
|
| 372 |
+
|
| 373 |
+
mbatch_size, seq_len = token_ids.shape
|
| 374 |
+
token_type_ids = torch.full_like(token_ids, token_type_id_sections[0], dtype=torch.long, device=token_ids.device)
|
| 375 |
+
|
| 376 |
+
for i, j in enumerate(special_token_ids):
|
| 377 |
+
# Find first occurrence of special tokens that indicate the boundary between sections:
|
| 378 |
+
cols = (token_ids == j).int().argmax(dim=1)
|
| 379 |
+
rows = torch.arange(mbatch_size, device=token_ids.device)
|
| 380 |
+
|
| 381 |
+
# https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer.create_token_type_ids_from_sequences.example
|
| 382 |
+
cols += 1
|
| 383 |
+
|
| 384 |
+
# Ensure that the column index is not out of bounds. If 0, then token_id not present.
|
| 385 |
+
# This is safe as index 0 is always a special token (now equal to 1 due to +1):
|
| 386 |
+
rows = rows[torch.logical_and(cols != 1, cols < seq_len)]
|
| 387 |
+
cols = cols[torch.logical_and(cols != 1, cols < seq_len)]
|
| 388 |
+
|
| 389 |
+
# Indices to that correspond to the second sequence:
|
| 390 |
+
if rows.nelement() != 0:
|
| 391 |
+
ids = torch.stack([
|
| 392 |
+
torch.stack([x, z]) for (x, y) in zip(rows, cols) for z in torch.arange(
|
| 393 |
+
y, seq_len, device=token_ids.device,
|
| 394 |
+
)
|
| 395 |
+
])
|
| 396 |
+
|
| 397 |
+
token_type_ids[ids[:, 0], ids[:, 1]] = token_type_id_sections[i + 1]
|
| 398 |
+
|
| 399 |
+
return token_type_ids
|
| 400 |
+
|
| 401 |
+
def token_ids_to_token_type_ids_past(self, token_ids, special_token_ids, token_type_id_sections=None):
|
| 402 |
+
"""
|
| 403 |
+
Extract token type identifiers from the token identifiers if past != None.
|
| 404 |
+
|
| 405 |
+
Argument/s:
|
| 406 |
+
token_ids - token identifiers.
|
| 407 |
+
special_token_ids - special token identifiers that indicate the separation between sections.
|
| 408 |
+
|
| 409 |
+
Returns:
|
| 410 |
+
token_type_ids - token type identifiers.
|
| 411 |
+
"""
|
| 412 |
+
|
| 413 |
+
token_type_id_sections = token_type_id_sections if token_type_id_sections is not None else list(range(len(special_token_ids) + 1))
|
| 414 |
+
token_type_ids = torch.full([token_ids.shape[0], 1], token_type_id_sections[0], dtype=torch.long, device=token_ids.device)
|
| 415 |
+
|
| 416 |
+
# https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer.create_token_type_ids_from_sequences.example
|
| 417 |
+
token_ids = token_ids[:, :-1]
|
| 418 |
+
|
| 419 |
+
for i, j in enumerate(special_token_ids):
|
| 420 |
+
|
| 421 |
+
# Find first occurrence of special token, which indicates the boundary between sections:
|
| 422 |
+
exists = torch.any(token_ids == j, dim=1, keepdim=True)
|
| 423 |
+
token_type_ids[exists] = token_type_id_sections[i + 1]
|
| 424 |
+
|
| 425 |
+
return token_type_ids
|
| 426 |
+
|
| 427 |
+
def tokenize_report_teacher_forcing(self, findings: str, impression: str, tokenizer: PreTrainedTokenizerFast, max_len: int):
|
| 428 |
+
"""
|
| 429 |
+
Tokenize the reports and creates the inputs and targets for teacher forcing.
|
| 430 |
+
|
| 431 |
+
Argument/s:
|
| 432 |
+
findings - findings section.
|
| 433 |
+
impression - impression section.
|
| 434 |
+
return_token_type_ids - return the token type identifiers.
|
| 435 |
+
tokenizer - Hugging Face tokenizer.
|
| 436 |
+
max_len - maximum number of tokens.
|
| 437 |
+
|
| 438 |
+
Returns:
|
| 439 |
+
decoder_input_ids - the token identifiers for the input of the decoder.
|
| 440 |
+
decoder_attention_mask - the attention mask for the decoder_input_ids.
|
| 441 |
+
label_ids - the label token identifiers for the decoder.
|
| 442 |
+
"""
|
| 443 |
+
|
| 444 |
+
# Prepare the sections for the tokenizer by placing special tokens between each section:
|
| 445 |
+
report = [f'{tokenizer.bos_token}{i}{tokenizer.sep_token}{j}{tokenizer.eos_token}' for i, j in
|
| 446 |
+
zip(findings, impression)]
|
| 447 |
+
|
| 448 |
+
# Tokenize the report:
|
| 449 |
+
tokenized = tokenizer(
|
| 450 |
+
report,
|
| 451 |
+
padding='longest',
|
| 452 |
+
truncation=True,
|
| 453 |
+
max_length=max_len + 1, # +1 to account for the bias between input and target.
|
| 454 |
+
return_tensors='pt',
|
| 455 |
+
return_token_type_ids=False,
|
| 456 |
+
add_special_tokens=False,
|
| 457 |
+
).to(self.device)
|
| 458 |
+
|
| 459 |
+
# Modify for language modelling:
|
| 460 |
+
batch_dict = {
|
| 461 |
+
|
| 462 |
+
# Labels for the decoder (shifted right by one for autoregression):
|
| 463 |
+
'label_ids': tokenized['input_ids'][:, 1:].detach().clone(),
|
| 464 |
+
|
| 465 |
+
# Remove last token identifier to match the sequence length of the labels:
|
| 466 |
+
'decoder_input_ids': tokenized['input_ids'][:, :-1],
|
| 467 |
+
|
| 468 |
+
# Attention mask for the decoder_input_ids (remove first token so that the eos_token_id is not considered):
|
| 469 |
+
'decoder_attention_mask': tokenized['attention_mask'][:, 1:],
|
| 470 |
+
}
|
| 471 |
+
|
| 472 |
+
return batch_dict
|
| 473 |
+
|
| 474 |
+
def split_and_decode_sections(self, token_ids, special_token_ids, tokenizer: PreTrainedTokenizerFast):
|
| 475 |
+
"""
|
| 476 |
+
Split the token identifiers into sections, then convert the token identifiers into strings.
|
| 477 |
+
|
| 478 |
+
Argument/s:
|
| 479 |
+
token_ids - token identifiers.
|
| 480 |
+
special_token_ids - special token identifiers that indicate the end of each section.
|
| 481 |
+
tokenizer - Hugging Face tokenizer.
|
| 482 |
+
|
| 483 |
+
Returns:
|
| 484 |
+
token_type_ids - token type identifiers.
|
| 485 |
+
"""
|
| 486 |
+
|
| 487 |
+
_, seq_len = token_ids.shape
|
| 488 |
+
|
| 489 |
+
# The number of sections is the same as the number of special_token_ids:
|
| 490 |
+
num_sections = len(special_token_ids)
|
| 491 |
+
|
| 492 |
+
sections = {k: [] for k in range(num_sections)}
|
| 493 |
+
|
| 494 |
+
for i in token_ids:
|
| 495 |
+
prev_col = 0
|
| 496 |
+
for j, k in enumerate(special_token_ids):
|
| 497 |
+
|
| 498 |
+
# The maximum sequence length was exceeded, thus no more tokens:
|
| 499 |
+
if prev_col >= seq_len:
|
| 500 |
+
sections[j].append('')
|
| 501 |
+
continue
|
| 502 |
+
|
| 503 |
+
# Find first occurrence of special tokens that indicate the boundary between sections:
|
| 504 |
+
col = (i == k).int().argmax().item()
|
| 505 |
+
|
| 506 |
+
# If equal to 0, token was not found, set the column to the sequence length (as the decoder exceeded
|
| 507 |
+
# the maximum sequence length):
|
| 508 |
+
if col == 0:
|
| 509 |
+
col = seq_len
|
| 510 |
+
|
| 511 |
+
# Extract section token identifiers:
|
| 512 |
+
section_token_ids = i[prev_col:col]
|
| 513 |
+
prev_col = col
|
| 514 |
+
section_string = tokenizer.decode(section_token_ids, skip_special_tokens=True)
|
| 515 |
+
|
| 516 |
+
sections[j].append(section_string)
|
| 517 |
+
|
| 518 |
+
return tuple(sections.values())
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:24d381da8c400df77a3c0584d68395b1f3c35c83162bea3f0221facbbbc2cb55
|
| 3 |
+
size 454257509
|