diff --git a/lit_nlp/examples/blank_slate_demo.py b/lit_nlp/examples/blank_slate_demo.py
index 9d235dcb..6fbefd40 100644
--- a/lit_nlp/examples/blank_slate_demo.py
+++ b/lit_nlp/examples/blank_slate_demo.py
@@ -12,7 +12,6 @@
   left-to-right language model, with the Stanford Sentiment Treebank dataset,
   the IMDB reviews dataset, Billion Word Benchmark (lm1b) dataset and the option
   to load sentences from a flat text file.
-- MobileNet model, with the Imagenette TFDS dataset.
 
 To run:
   python -m lit_nlp.examples.blank_slate_demo --port=5432
@@ -32,12 +31,10 @@
 from lit_nlp import server_flags
 from lit_nlp.examples.datasets import classification
 from lit_nlp.examples.datasets import glue
-from lit_nlp.examples.datasets import imagenette
 from lit_nlp.examples.datasets import lm
 from lit_nlp.examples.datasets import mt
 from lit_nlp.examples.datasets import summarization
 from lit_nlp.examples.models import glue_models
-from lit_nlp.examples.models import mobilenet
 from lit_nlp.examples.models import pretrained_lms
 from lit_nlp.examples.models import t5
 from lit_nlp.examples.penguin import data as penguin_data
@@ -111,12 +108,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       pretrained_lms.GPT2LanguageModel.init_spec(),
   )
 
-  # image model loaders.
-  model_loaders["image"] = (
-      mobilenet.MobileNet,
-      mobilenet.MobileNet.init_spec(),
-  )
-
   datasets = {}
   dataset_loaders: lit_app.DatasetLoadersMap = {}
 
@@ -156,12 +147,6 @@ def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
       lm.BillionWordBenchmark.init_spec(),
   )
 
-  # image demo dataset loaders.
-  dataset_loaders["image"] = (
-      imagenette.ImagenetteDataset,
-      imagenette.ImagenetteDataset.init_spec(),
-  )
-
   # Start the LIT server. See server_flags.py for server options.
   lit_demo = dev_server.Server(
       models,
diff --git a/lit_nlp/examples/datasets/imagenette.py b/lit_nlp/examples/datasets/imagenette.py
deleted file mode 100644
index 7630709f..00000000
--- a/lit_nlp/examples/datasets/imagenette.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""Imagenette dataset from tfds."""
-
-from lit_nlp.api import dataset
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import image_utils
-from PIL import Image as PILImage
-import tensorflow_datasets as tfds
-
-
-class ImagenetteDataset(dataset.Dataset):
-  """Imagenette TFDS dataset.
-
-  See https://www.tensorflow.org/datasets/catalog/imagenette for details.
-  Images are at low quality and by default load 100 examples from the validation
-  set though this can be changed through the `split` parameter, as per TFDS
-  documentation.
-  """
-
-  def __init__(self, split: str = 'validation[:100]'):
-    tfds_examples = tfds.as_numpy(
-        tfds.load('imagenette/160px', split=split, download=True,
-                  try_gcs=True))
-    def convert_input(inp):
-      pil_image = PILImage.fromarray(inp['image'])
-      image_str = image_utils.convert_pil_to_image_str(pil_image)
-      return {'image': image_str}
-    self._examples = [convert_input(inp) for inp in tfds_examples]
-
-  def spec(self) -> lit_types.Spec:
-    return {
-        'image': lit_types.ImageBytes(),
-    }
-
-
diff --git a/lit_nlp/examples/datasets/open_images.py b/lit_nlp/examples/datasets/open_images.py
deleted file mode 100644
index 71e0779a..00000000
--- a/lit_nlp/examples/datasets/open_images.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""Open Images dataset from tfds."""
-
-from lit_nlp.api import dataset
-from lit_nlp.api import types as lit_types
-from lit_nlp.lib import image_utils
-from PIL import Image as PILImage
-import tensorflow_datasets as tfds
-
-
-class OpenImagesDataset(dataset.Dataset):
-  """OpenImages TFDS dataset.
-
-  See https://www.tensorflow.org/datasets/catalog/open_images_v4 for details.
-  Images are at 72 JPEG quality and by default load 100 examples from the test
-  set though this can be changed through the `split` parameter, as per TFDS
-  documentation.
-  """
-
-  def __init__(self, split: str = 'test[:100]'):
-    tfds_examples = tfds.as_numpy(
-        tfds.load('open_images_v4/200k', split=split, download=True,
-                  try_gcs=True))
-    def convert_input(inp):
-      pil_image = PILImage.fromarray(inp['image'])
-      image_str = image_utils.convert_pil_to_image_str(pil_image)
-      return {'image': image_str}
-    self._examples = [convert_input(inp) for inp in tfds_examples]
-
-  def spec(self) -> lit_types.Spec:
-    return {
-        'image': lit_types.ImageBytes(),
-    }
diff --git a/lit_nlp/examples/image_demo.py b/lit_nlp/examples/image_demo.py
deleted file mode 100644
index 5d9d25a4..00000000
--- a/lit_nlp/examples/image_demo.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""LIT demo for image model.
-
-To run:
-  python -m lit_nlp.examples.image_demo --port=5432
-
-Then navigate to localhost:5432 to access the demo UI.
-"""
-
-from collections.abc import Sequence
-import sys
-from typing import Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-from lit_nlp import dev_server
-from lit_nlp import server_flags
-from lit_nlp.api import layout
-from lit_nlp.components import classification_results
-from lit_nlp.components import image_gradient_maps
-from lit_nlp.examples.datasets import imagenette
-from lit_nlp.examples.models import mobilenet
-
-
-FLAGS = flags.FLAGS
-
-FLAGS.set_default('development_demo', True)
-FLAGS.set_default('warm_start', 1)
-FLAGS.set_default('page_title', 'LIT Image Demo')
-
-_MAX_EXAMPLES = flags.DEFINE_integer(
-    'max_examples',
-    None,
-    (
-        'Maximum number of examples to load into LIT. '
-        'Set --max_examples=200 for a quick start.'
-    ),
-)
-
-
-def get_wsgi_app():
-  """Returns a LitApp instance for consumption by gunicorn."""
-  FLAGS.set_default('server_type', 'external')
-  FLAGS.set_default('demo_mode', True)
-  # Parse flags without calling app.run(main), to avoid conflict with
-  # gunicorn command line flags.
-  unused = flags.FLAGS(sys.argv, known_only=True)
-  if unused:
-    logging.info('image_demo:get_wsgi_app() called with unused args: %s',
-                 unused)
-  return main([])
-
-# Custom frontend layout; see api/layout.py
-modules = layout.LitModuleName
-DEMO_LAYOUT = layout.LitCanonicalLayout(
-    upper={
-        'Main': [modules.DataTableModule, modules.DatapointEditorModule],
-    },
-    lower={
-        'Predictions': [modules.ClassificationModule, modules.ScalarModule],
-        'Explanations': [
-            modules.ClassificationModule,
-            modules.SalienceMapModule,
-        ],
-    },
-    description='Basic layout for image demo',
-)
-
-CUSTOM_LAYOUTS = layout.DEFAULT_LAYOUTS | {'default': DEMO_LAYOUT}
-
-
-def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
-  if len(argv) > 1:
-    raise app.UsageError('Too many command-line arguments.')
-
-  datasets = {'imagenette': imagenette.ImagenetteDataset()}
-  # Truncate datasets if --max_examples is set.
-  if _MAX_EXAMPLES.value is not None:
-    for name in datasets:
-      logging.info("Dataset: '%s' with %d examples", name, len(datasets[name]))
-      datasets[name] = datasets[name].slice[: _MAX_EXAMPLES.value]
-      logging.info('  truncated to %d examples', len(datasets[name]))
-
-  models = {'mobilenet': mobilenet.MobileNet()}
-  interpreters = {
-      'classification': classification_results.ClassificationInterpreter(),
-  } | image_gradient_maps.all_interpreters()
-
-  lit_demo = dev_server.Server(
-      models,
-      datasets,
-      interpreters=interpreters,
-      generators={},
-      layouts={'default': DEMO_LAYOUT},
-      **server_flags.get_flags())
-  return lit_demo.serve()
-
-
-if __name__ == '__main__':
-  app.run(main)
diff --git a/lit_nlp/examples/models/imagenet_labels.py b/lit_nlp/examples/models/imagenet_labels.py
deleted file mode 100644
index 14db6708..00000000
--- a/lit_nlp/examples/models/imagenet_labels.py
+++ /dev/null
@@ -1,2019 +0,0 @@
-"""Imagenet labels."""
-# pylint: disable=line-too-long
-IMAGENET_2012_LABELS = {
-    0:
-        'tench, Tinca tinca',
-    1:
-        'goldfish, Carassius auratus',
-    2:
-        'great white shark, white shark, man-eater, man-eating shark, '
-        'Carcharodon carcharias',
-    3:
-        'tiger shark, Galeocerdo cuvieri',
-    4:
-        'hammerhead, hammerhead shark',
-    5:
-        'electric ray, crampfish, numbfish, torpedo',
-    6:
-        'stingray',
-    7:
-        'cock',
-    8:
-        'hen',
-    9:
-        'ostrich, Struthio camelus',
-    10:
-        'brambling, Fringilla montifringilla',
-    11:
-        'goldfinch, Carduelis carduelis',
-    12:
-        'house finch, linnet, Carpodacus mexicanus',
-    13:
-        'junco, snowbird',
-    14:
-        'indigo bunting, indigo finch, indigo bird, Passerina cyanea',
-    15:
-        'robin, American robin, Turdus migratorius',
-    16:
-        'bulbul',
-    17:
-        'jay',
-    18:
-        'magpie',
-    19:
-        'chickadee',
-    20:
-        'water ouzel, dipper',
-    21:
-        'kite',
-    22:
-        'bald eagle, American eagle, Haliaeetus leucocephalus',
-    23:
-        'vulture',
-    24:
-        'great grey owl, great gray owl, Strix nebulosa',
-    25:
-        'European fire salamander, Salamandra salamandra',
-    26:
-        'common newt, Triturus vulgaris',
-    27:
-        'eft',
-    28:
-        'spotted salamander, Ambystoma maculatum',
-    29:
-        'axolotl, mud puppy, Ambystoma mexicanum',
-    30:
-        'bullfrog, Rana catesbeiana',
-    31:
-        'tree frog, tree-frog',
-    32:
-        'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',
-    33:
-        'loggerhead, loggerhead turtle, Caretta caretta',
-    34:
-        'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',
-    35:
-        'mud turtle',
-    36:
-        'terrapin',
-    37:
-        'box turtle, box tortoise',
-    38:
-        'banded gecko',
-    39:
-        'common iguana, iguana, Iguana iguana',
-    40:
-        'American chameleon, anole, Anolis carolinensis',
-    41:
-        'whiptail, whiptail lizard',
-    42:
-        'agama',
-    43:
-        'frilled lizard, Chlamydosaurus kingi',
-    44:
-        'alligator lizard',
-    45:
-        'Gila monster, Heloderma suspectum',
-    46:
-        'green lizard, Lacerta viridis',
-    47:
-        'African chameleon, Chamaeleo chamaeleon',
-    48:
-        'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus '
-        'komodoensis',
-    49:
-        'African crocodile, Nile crocodile, Crocodylus niloticus',
-    50:
-        'American alligator, Alligator mississipiensis',
-    51:
-        'triceratops',
-    52:
-        'thunder snake, worm snake, Carphophis amoenus',
-    53:
-        'ringneck snake, ring-necked snake, ring snake',
-    54:
-        'hognose snake, puff adder, sand viper',
-    55:
-        'green snake, grass snake',
-    56:
-        'king snake, kingsnake',
-    57:
-        'garter snake, grass snake',
-    58:
-        'water snake',
-    59:
-        'vine snake',
-    60:
-        'night snake, Hypsiglena torquata',
-    61:
-        'boa constrictor, Constrictor constrictor',
-    62:
-        'rock python, rock snake, Python sebae',
-    63:
-        'Indian cobra, Naja naja',
-    64:
-        'green mamba',
-    65:
-        'sea snake',
-    66:
-        'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',
-    67:
-        'diamondback, diamondback rattlesnake, Crotalus adamanteus',
-    68:
-        'sidewinder, horned rattlesnake, Crotalus cerastes',
-    69:
-        'trilobite',
-    70:
-        'harvestman, daddy longlegs, Phalangium opilio',
-    71:
-        'scorpion',
-    72:
-        'black and gold garden spider, Argiope aurantia',
-    73:
-        'barn spider, Araneus cavaticus',
-    74:
-        'garden spider, Aranea diademata',
-    75:
-        'black widow, Latrodectus mactans',
-    76:
-        'tarantula',
-    77:
-        'wolf spider, hunting spider',
-    78:
-        'tick',
-    79:
-        'centipede',
-    80:
-        'black grouse',
-    81:
-        'ptarmigan',
-    82:
-        'ruffed grouse, partridge, Bonasa umbellus',
-    83:
-        'prairie chicken, prairie grouse, prairie fowl',
-    84:
-        'peacock',
-    85:
-        'quail',
-    86:
-        'partridge',
-    87:
-        'African grey, African gray, Psittacus erithacus',
-    88:
-        'macaw',
-    89:
-        'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
-    90:
-        'lorikeet',
-    91:
-        'coucal',
-    92:
-        'bee eater',
-    93:
-        'hornbill',
-    94:
-        'hummingbird',
-    95:
-        'jacamar',
-    96:
-        'toucan',
-    97:
-        'drake',
-    98:
-        'red-breasted merganser, Mergus serrator',  # NOTYPO
-    99:
-        'goose',
-    100:
-        'black swan, Cygnus atratus',
-    101:
-        'tusker',
-    102:
-        'echidna, spiny anteater, anteater',
-    103:
-        'platypus, duckbill, duckbilled platypus, duck-billed platypus, '
-        'Ornithorhynchus anatinus',
-    104:
-        'wallaby, brush kangaroo',
-    105:
-        'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',
-    106:
-        'wombat',
-    107:
-        'jellyfish',
-    108:
-        'sea anemone, anemone',
-    109:
-        'brain coral',
-    110:
-        'flatworm, platyhelminth',
-    111:
-        'nematode, nematode worm, roundworm',
-    112:
-        'conch',
-    113:
-        'snail',
-    114:
-        'slug',
-    115:
-        'sea slug, nudibranch',
-    116:
-        'chiton, coat-of-mail shell, sea cradle, polyplacophore',
-    117:
-        'chambered nautilus, pearly nautilus, nautilus',
-    118:
-        'Dungeness crab, Cancer magister',
-    119:
-        'rock crab, Cancer irroratus',
-    120:
-        'fiddler crab',
-    121:
-        'king crab, Alaska crab, Alaskan king crab, Alaska king crab, '
-        'Paralithodes camtschatica',
-    122:
-        'American lobster, Northern lobster, Maine lobster, Homarus americanus',
-    123:
-        'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',
-    124:
-        'crayfish, crawfish, crawdad, crawdaddy',
-    125:
-        'hermit crab',
-    126:
-        'isopod',
-    127:
-        'white stork, Ciconia ciconia',
-    128:
-        'black stork, Ciconia nigra',
-    129:
-        'spoonbill',
-    130:
-        'flamingo',
-    131:
-        'little blue heron, Egretta caerulea',
-    132:
-        'American egret, great white heron, Egretta albus',
-    133:
-        'bittern',
-    134:
-        'crane',
-    135:
-        'limpkin, Aramus pictus',
-    136:
-        'European gallinule, Porphyrio porphyrio',
-    137:
-        'American coot, marsh hen, mud hen, water hen, Fulica americana',
-    138:
-        'bustard',
-    139:
-        'ruddy turnstone, Arenaria interpres',
-    140:
-        'red-backed sandpiper, dunlin, Erolia alpina',
-    141:
-        'redshank, Tringa totanus',
-    142:
-        'dowitcher',
-    143:
-        'oystercatcher, oyster catcher',
-    144:
-        'pelican',
-    145:
-        'king penguin, Aptenodytes patagonica',
-    146:
-        'albatross, mollymawk',
-    147:
-        'grey whale, gray whale, devilfish, Eschrichtius gibbosus, '
-        'Eschrichtius robustus',
-    148:
-        'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',
-    149:
-        'dugong, Dugong dugon',
-    150:
-        'sea lion',
-    151:
-        'Chihuahua',
-    152:
-        'Japanese spaniel',
-    153:
-        'Maltese dog, Maltese terrier, Maltese',
-    154:
-        'Pekinese, Pekingese, Peke',
-    155:
-        'Shih-Tzu',
-    156:
-        'Blenheim spaniel',
-    157:
-        'papillon',
-    158:
-        'toy terrier',
-    159:
-        'Rhodesian ridgeback',
-    160:
-        'Afghan hound, Afghan',
-    161:
-        'basset, basset hound',
-    162:
-        'beagle',
-    163:
-        'bloodhound, sleuthhound',
-    164:
-        'bluetick',
-    165:
-        'black-and-tan coonhound',
-    166:
-        'Walker hound, Walker foxhound',
-    167:
-        'English foxhound',
-    168:
-        'redbone',
-    169:
-        'borzoi, Russian wolfhound',
-    170:
-        'Irish wolfhound',
-    171:
-        'Italian greyhound',
-    172:
-        'whippet',
-    173:
-        'Ibizan hound, Ibizan Podenco',
-    174:
-        'Norwegian elkhound, elkhound',
-    175:
-        'otterhound, otter hound',
-    176:
-        'Saluki, gazelle hound',
-    177:
-        'Scottish deerhound, deerhound',
-    178:
-        'Weimaraner',
-    179:
-        'Staffordshire bullterrier, Staffordshire bull terrier',
-    180:
-        'American Staffordshire terrier, Staffordshire terrier, American pit '
-        'bull terrier, pit bull terrier',
-    181:
-        'Bedlington terrier',
-    182:
-        'Border terrier',
-    183:
-        'Kerry blue terrier',
-    184:
-        'Irish terrier',
-    185:
-        'Norfolk terrier',
-    186:
-        'Norwich terrier',
-    187:
-        'Yorkshire terrier',
-    188:
-        'wire-haired fox terrier',
-    189:
-        'Lakeland terrier',
-    190:
-        'Sealyham terrier, Sealyham',
-    191:
-        'Airedale, Airedale terrier',
-    192:
-        'cairn, cairn terrier',
-    193:
-        'Australian terrier',
-    194:
-        'Dandie Dinmont, Dandie Dinmont terrier',
-    195:
-        'Boston bull, Boston terrier',
-    196:
-        'miniature schnauzer',
-    197:
-        'giant schnauzer',
-    198:
-        'standard schnauzer',
-    199:
-        'Scotch terrier, Scottish terrier, Scottie',
-    200:
-        'Tibetan terrier, chrysanthemum dog',
-    201:
-        'silky terrier, Sydney silky',
-    202:
-        'soft-coated wheaten terrier',
-    203:
-        'West Highland white terrier',
-    204:
-        'Lhasa, Lhasa apso',
-    205:
-        'flat-coated retriever',
-    206:
-        'curly-coated retriever',
-    207:
-        'golden retriever',
-    208:
-        'Labrador retriever',
-    209:
-        'Chesapeake Bay retriever',
-    210:
-        'German short-haired pointer',
-    211:
-        'vizsla, Hungarian pointer',
-    212:
-        'English setter',
-    213:
-        'Irish setter, red setter',
-    214:
-        'Gordon setter',
-    215:
-        'Brittany spaniel',
-    216:
-        'clumber, clumber spaniel',
-    217:
-        'English springer, English springer spaniel',
-    218:
-        'Welsh springer spaniel',
-    219:
-        'cocker spaniel, English cocker spaniel, cocker',
-    220:
-        'Sussex spaniel',
-    221:
-        'Irish water spaniel',
-    222:
-        'kuvasz',
-    223:
-        'schipperke',
-    224:
-        'groenendael',
-    225:
-        'malinois',
-    226:
-        'briard',
-    227:
-        'kelpie',
-    228:
-        'komondor',
-    229:
-        'Old English sheepdog, bobtail',
-    230:
-        'Shetland sheepdog, Shetland sheep dog, Shetland',
-    231:
-        'collie',
-    232:
-        'Border collie',
-    233:
-        'Bouvier des Flandres, Bouviers des Flandres',
-    234:
-        'Rottweiler',
-    235:
-        'German shepherd, German shepherd dog, German police dog, alsatian',
-    236:
-        'Doberman, Doberman pinscher',
-    237:
-        'miniature pinscher',
-    238:
-        'Greater Swiss Mountain dog',
-    239:
-        'Bernese mountain dog',
-    240:
-        'Appenzeller',
-    241:
-        'EntleBucher',
-    242:
-        'boxer',
-    243:
-        'bull mastiff',
-    244:
-        'Tibetan mastiff',
-    245:
-        'French bulldog',
-    246:
-        'Great Dane',
-    247:
-        'Saint Bernard, St Bernard',
-    248:
-        'Eskimo dog, husky',
-    249:
-        'malamute, malemute, Alaskan malamute',
-    250:
-        'Siberian husky',
-    251:
-        'dalmatian, coach dog, carriage dog',
-    252:
-        'affenpinscher, monkey pinscher, monkey dog',
-    253:
-        'basenji',
-    254:
-        'pug, pug-dog',
-    255:
-        'Leonberg',
-    256:
-        'Newfoundland, Newfoundland dog',
-    257:
-        'Great Pyrenees',
-    258:
-        'Samoyed, Samoyede',
-    259:
-        'Pomeranian',
-    260:
-        'chow, chow chow',
-    261:
-        'keeshond',
-    262:
-        'Brabancon griffon',
-    263:
-        'Pembroke, Pembroke Welsh corgi',
-    264:
-        'Cardigan, Cardigan Welsh corgi',
-    265:
-        'toy poodle',
-    266:
-        'miniature poodle',
-    267:
-        'standard poodle',
-    268:
-        'Mexican hairless',
-    269:
-        'timber wolf, grey wolf, gray wolf, Canis lupus',
-    270:
-        'white wolf, Arctic wolf, Canis lupus tundrarum',
-    271:
-        'red wolf, maned wolf, Canis rufus, Canis niger',
-    272:
-        'coyote, prairie wolf, brush wolf, Canis latrans',
-    273:
-        'dingo, warrigal, warragal, Canis dingo',
-    274:
-        'dhole, Cuon alpinus',
-    275:
-        'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',
-    276:
-        'hyena, hyaena',
-    277:
-        'red fox, Vulpes vulpes',
-    278:
-        'kit fox, Vulpes macrotis',
-    279:
-        'Arctic fox, white fox, Alopex lagopus',
-    280:
-        'grey fox, gray fox, Urocyon cinereoargenteus',
-    281:
-        'tabby, tabby cat',
-    282:
-        'tiger cat',
-    283:
-        'Persian cat',
-    284:
-        'Siamese cat, Siamese',
-    285:
-        'Egyptian cat',
-    286:
-        'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
-    287:
-        'lynx, catamount',
-    288:
-        'leopard, Panthera pardus',
-    289:
-        'snow leopard, ounce, Panthera uncia',
-    290:
-        'jaguar, panther, Panthera onca, Felis onca',
-    291:
-        'lion, king of beasts, Panthera leo',
-    292:
-        'tiger, Panthera tigris',
-    293:
-        'cheetah, chetah, Acinonyx jubatus',
-    294:
-        'brown bear, bruin, Ursus arctos',
-    295:
-        'American black bear, black bear, Ursus americanus, Euarctos americanus',
-    296:
-        'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',
-    297:
-        'sloth bear, Melursus ursinus, Ursus ursinus',
-    298:
-        'mongoose',
-    299:
-        'meerkat, mierkat',
-    300:
-        'tiger beetle',
-    301:
-        'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',
-    302:
-        'ground beetle, carabid beetle',
-    303:
-        'long-horned beetle, longicorn, longicorn beetle',
-    304:
-        'leaf beetle, chrysomelid',
-    305:
-        'dung beetle',
-    306:
-        'rhinoceros beetle',
-    307:
-        'weevil',
-    308:
-        'fly',
-    309:
-        'bee',
-    310:
-        'ant, emmet, pismire',
-    311:
-        'grasshopper, hopper',
-    312:
-        'cricket',
-    313:
-        'walking stick, walkingstick, stick insect',
-    314:
-        'cockroach, roach',
-    315:
-        'mantis, mantid',
-    316:
-        'cicada, cicala',
-    317:
-        'leafhopper',
-    318:
-        'lacewing, lacewing fly',
-    319:
-        "dragonfly, darning needle, devil's darning needle, sewing needle, "
-        'snake feeder, snake doctor, mosquito hawk, skeeter hawk',
-    320:
-        'damselfly',
-    321:
-        'admiral',
-    322:
-        'ringlet, ringlet butterfly',
-    323:
-        'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
-    324:
-        'cabbage butterfly',
-    325:
-        'sulphur butterfly, sulfur butterfly',
-    326:
-        'lycaenid, lycaenid butterfly',
-    327:
-        'starfish, sea star',
-    328:
-        'sea urchin',
-    329:
-        'sea cucumber, holothurian',
-    330:
-        'wood rabbit, cottontail, cottontail rabbit',
-    331:
-        'hare',
-    332:
-        'Angora, Angora rabbit',
-    333:
-        'hamster',
-    334:
-        'porcupine, hedgehog',
-    335:
-        'fox squirrel, eastern fox squirrel, Sciurus niger',
-    336:
-        'marmot',
-    337:
-        'beaver',
-    338:
-        'guinea pig, Cavia cobaya',
-    339:
-        'sorrel',
-    340:
-        'zebra',
-    341:
-        'hog, pig, grunter, squealer, Sus scrofa',
-    342:
-        'wild boar, boar, Sus scrofa',
-    343:
-        'warthog',
-    344:
-        'hippopotamus, hippo, river horse, Hippopotamus amphibius',
-    345:
-        'ox',
-    346:
-        'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',
-    347:
-        'bison',
-    348:
-        'ram, tup',
-    349:
-        'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky '
-        'Mountain sheep, Ovis canadensis',
-    350:
-        'ibex, Capra ibex',
-    351:
-        'hartebeest',
-    352:
-        'impala, Aepyceros melampus',
-    353:
-        'gazelle',
-    354:
-        'Arabian camel, dromedary, Camelus dromedarius',
-    355:
-        'llama',
-    356:
-        'weasel',
-    357:
-        'mink',
-    358:
-        'polecat, fitch, foulmart, foumart, Mustela putorius',
-    359:
-        'black-footed ferret, ferret, Mustela nigripes',
-    360:
-        'otter',
-    361:
-        'skunk, polecat, wood pussy',
-    362:
-        'badger',
-    363:
-        'armadillo',
-    364:
-        'three-toed sloth, ai, Bradypus tridactylus',
-    365:
-        'orangutan, orang, orangutang, Pongo pygmaeus',
-    366:
-        'gorilla, Gorilla gorilla',
-    367:
-        'chimpanzee, chimp, Pan troglodytes',
-    368:
-        'gibbon, Hylobates lar',
-    369:
-        'siamang, Hylobates syndactylus, Symphalangus syndactylus',
-    370:
-        'guenon, guenon monkey',
-    371:
-        'patas, hussar monkey, Erythrocebus patas',
-    372:
-        'baboon',
-    373:
-        'macaque',
-    374:
-        'langur',
-    375:
-        'colobus, colobus monkey',
-    376:
-        'proboscis monkey, Nasalis larvatus',
-    377:
-        'marmoset',
-    378:
-        'capuchin, ringtail, Cebus capucinus',
-    379:
-        'howler monkey, howler',
-    380:
-        'titi, titi monkey',
-    381:
-        'spider monkey, Ateles geoffroyi',
-    382:
-        'squirrel monkey, Saimiri sciureus',
-    383:
-        'Madagascar cat, ring-tailed lemur, Lemur catta',
-    384:
-        'indri, indris, Indri indri, Indri brevicaudatus',
-    385:
-        'Indian elephant, Elephas maximus',
-    386:
-        'African elephant, Loxodonta africana',
-    387:
-        'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',
-    388:
-        'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',
-    389:
-        'barracouta, snoek',
-    390:
-        'eel',
-    391:
-        'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',
-    392:
-        'rock beauty, Holocanthus tricolor',
-    393:
-        'anemone fish',
-    394:
-        'sturgeon',
-    395:
-        'gar, garfish, garpike, billfish, Lepisosteus osseus',
-    396:
-        'lionfish',
-    397:
-        'puffer, pufferfish, blowfish, globefish',
-    398:
-        'abacus',
-    399:
-        'abaya',
-    400:
-        "academic gown, academic robe, judge's robe",
-    401:
-        'accordion, piano accordion, squeeze box',
-    402:
-        'acoustic guitar',
-    403:
-        'aircraft carrier, carrier, flattop, attack aircraft carrier',
-    404:
-        'airliner',
-    405:
-        'airship, dirigible',
-    406:
-        'altar',
-    407:
-        'ambulance',
-    408:
-        'amphibian, amphibious vehicle',
-    409:
-        'analog clock',
-    410:
-        'apiary, bee house',
-    411:
-        'apron',
-    412:
-        'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, '
-        'dustbin, trash barrel, trash bin',
-    413:
-        'assault rifle, assault gun',
-    414:
-        'backpack, back pack, knapsack, packsack, rucksack, haversack',
-    415:
-        'bakery, bakeshop, bakehouse',
-    416:
-        'balance beam, beam',
-    417:
-        'balloon',
-    418:
-        'ballpoint, ballpoint pen, ballpen, Biro',
-    419:
-        'Band Aid',
-    420:
-        'banjo',
-    421:
-        'bannister, banister, balustrade, balusters, handrail',
-    422:
-        'barbell',
-    423:
-        'barber chair',
-    424:
-        'barbershop',
-    425:
-        'barn',
-    426:
-        'barometer',
-    427:
-        'barrel, cask',
-    428:
-        'barrow, garden cart, lawn cart, wheelbarrow',
-    429:
-        'baseball',
-    430:
-        'basketball',
-    431:
-        'bassinet',
-    432:
-        'bassoon',
-    433:
-        'bathing cap, swimming cap',
-    434:
-        'bath towel',
-    435:
-        'bathtub, bathing tub, bath, tub',
-    436:
-        'beach wagon, station wagon, wagon, estate car, beach waggon, station '
-        'waggon, waggon',
-    437:
-        'beacon, lighthouse, beacon light, pharos',
-    438:
-        'beaker',
-    439:
-        'bearskin, busby, shako',
-    440:
-        'beer bottle',
-    441:
-        'beer glass',
-    442:
-        'bell cote, bell cot',
-    443:
-        'bib',
-    444:
-        'bicycle-built-for-two, tandem bicycle, tandem',
-    445:
-        'bikini, two-piece',
-    446:
-        'binder, ring-binder',
-    447:
-        'binoculars, field glasses, opera glasses',
-    448:
-        'birdhouse',
-    449:
-        'boathouse',
-    450:
-        'bobsled, bobsleigh, bob',
-    451:
-        'bolo tie, bolo, bola tie, bola',
-    452:
-        'bonnet, poke bonnet',
-    453:
-        'bookcase',
-    454:
-        'bookshop, bookstore, bookstall',
-    455:
-        'bottlecap',
-    456:
-        'bow',
-    457:
-        'bow tie, bow-tie, bowtie',
-    458:
-        'brass, memorial tablet, plaque',
-    459:
-        'brassiere, bra, bandeau',
-    460:
-        'breakwater, groin, groyne, mole, bulwark, seawall, jetty',
-    461:
-        'breastplate, aegis, egis',
-    462:
-        'broom',
-    463:
-        'bucket, pail',
-    464:
-        'buckle',
-    465:
-        'bulletproof vest',
-    466:
-        'bullet train, bullet',
-    467:
-        'butcher shop, meat market',
-    468:
-        'cab, hack, taxi, taxicab',
-    469:
-        'caldron, cauldron',
-    470:
-        'candle, taper, wax light',
-    471:
-        'cannon',
-    472:
-        'canoe',
-    473:
-        'can opener, tin opener',
-    474:
-        'cardigan',
-    475:
-        'car mirror',
-    476:
-        'carousel, carrousel, merry-go-round, roundabout, whirligig',
-    477:
-        "carpenter's kit, tool kit",
-    478:
-        'carton',
-    479:
-        'car wheel',
-    480:
-        'cash machine, cash dispenser, automated teller machine, automatic '
-        'teller machine, automated teller, automatic teller, ATM',
-    481:
-        'cassette',
-    482:
-        'cassette player',
-    483:
-        'castle',
-    484:
-        'catamaran',
-    485:
-        'CD player',
-    486:
-        'cello, violoncello',
-    487:
-        'cellular telephone, cellular phone, cellphone, cell, mobile phone',
-    488:
-        'chain',
-    489:
-        'chainlink fence',
-    490:
-        'chain mail, ring mail, mail, chain armor, chain armour, ring armor, '
-        'ring armour',
-    491:
-        'chain saw, chainsaw',
-    492:
-        'chest',
-    493:
-        'chiffonier, commode',
-    494:
-        'chime, bell, gong',
-    495:
-        'china cabinet, china closet',
-    496:
-        'Christmas stocking',
-    497:
-        'church, church building',
-    498:
-        'cinema, movie theater, movie theatre, movie house, picture palace',
-    499:
-        'cleaver, meat cleaver, chopper',
-    500:
-        'cliff dwelling',
-    501:
-        'cloak',
-    502:
-        'clog, geta, patten, sabot',
-    503:
-        'cocktail shaker',
-    504:
-        'coffee mug',
-    505:
-        'coffeepot',
-    506:
-        'coil, spiral, volute, whorl, helix',
-    507:
-        'combination lock',
-    508:
-        'computer keyboard, keypad',
-    509:
-        'confectionery, confectionary, candy store',
-    510:
-        'container ship, containership, container vessel',
-    511:
-        'convertible',
-    512:
-        'corkscrew, bottle screw',
-    513:
-        'cornet, horn, trumpet, trump',
-    514:
-        'cowboy boot',
-    515:
-        'cowboy hat, ten-gallon hat',
-    516:
-        'cradle',
-    517:
-        'crane',
-    518:
-        'crash helmet',
-    519:
-        'crate',
-    520:
-        'crib, cot',
-    521:
-        'Crock Pot',
-    522:
-        'croquet ball',
-    523:
-        'crutch',
-    524:
-        'cuirass',
-    525:
-        'dam, dike, dyke',
-    526:
-        'desk',
-    527:
-        'desktop computer',
-    528:
-        'dial telephone, dial phone',
-    529:
-        'diaper, nappy, napkin',
-    530:
-        'digital clock',
-    531:
-        'digital watch',
-    532:
-        'dining table, board',
-    533:
-        'dishrag, dishcloth',
-    534:
-        'dishwasher, dish washer, dishwashing machine',
-    535:
-        'disk brake, disc brake',
-    536:
-        'dock, dockage, docking facility',
-    537:
-        'dogsled, dog sled, dog sleigh',
-    538:
-        'dome',
-    539:
-        'doormat, welcome mat',
-    540:
-        'drilling platform, offshore rig',
-    541:
-        'drum, membranophone, tympan',
-    542:
-        'drumstick',
-    543:
-        'dumbbell',
-    544:
-        'Dutch oven',
-    545:
-        'electric fan, blower',
-    546:
-        'electric guitar',
-    547:
-        'electric locomotive',
-    548:
-        'entertainment center',
-    549:
-        'envelope',
-    550:
-        'espresso maker',
-    551:
-        'face powder',
-    552:
-        'feather boa, boa',
-    553:
-        'file, file cabinet, filing cabinet',
-    554:
-        'fireboat',
-    555:
-        'fire engine, fire truck',
-    556:
-        'fire screen, fireguard',
-    557:
-        'flagpole, flagstaff',
-    558:
-        'flute, transverse flute',
-    559:
-        'folding chair',
-    560:
-        'football helmet',
-    561:
-        'forklift',
-    562:
-        'fountain',
-    563:
-        'fountain pen',
-    564:
-        'four-poster',
-    565:
-        'freight car',
-    566:
-        'French horn, horn',
-    567:
-        'frying pan, frypan, skillet',
-    568:
-        'fur coat',
-    569:
-        'garbage truck, dustcart',
-    570:
-        'gasmask, respirator, gas helmet',
-    571:
-        'gas pump, gasoline pump, petrol pump, island dispenser',
-    572:
-        'goblet',
-    573:
-        'go-kart',
-    574:
-        'golf ball',
-    575:
-        'golfcart, golf cart',
-    576:
-        'gondola',
-    577:
-        'gong, tam-tam',
-    578:
-        'gown',
-    579:
-        'grand piano, grand',
-    580:
-        'greenhouse, nursery, glasshouse',
-    581:
-        'grille, radiator grille',
-    582:
-        'grocery store, grocery, food market, market',
-    583:
-        'guillotine',
-    584:
-        'hair slide',
-    585:
-        'hair spray',
-    586:
-        'half track',
-    587:
-        'hammer',
-    588:
-        'hamper',
-    589:
-        'hand blower, blow dryer, blow drier, hair dryer, hair drier',
-    590:
-        'hand-held computer, hand-held microcomputer',
-    591:
-        'handkerchief, hankie, hanky, hankey',
-    592:
-        'hard disc, hard disk, fixed disk',
-    593:
-        'harmonica, mouth organ, harp, mouth harp',
-    594:
-        'harp',
-    595:
-        'harvester, reaper',
-    596:
-        'hatchet',
-    597:
-        'holster',
-    598:
-        'home theater, home theatre',
-    599:
-        'honeycomb',
-    600:
-        'hook, claw',
-    601:
-        'hoopskirt, crinoline',
-    602:
-        'horizontal bar, high bar',
-    603:
-        'horse cart, horse-cart',
-    604:
-        'hourglass',
-    605:
-        'iPod',
-    606:
-        'iron, smoothing iron',
-    607:
-        "jack-o'-lantern",
-    608:
-        'jean, blue jean, denim',
-    609:
-        'jeep, landrover',
-    610:
-        'jersey, T-shirt, tee shirt',
-    611:
-        'jigsaw puzzle',
-    612:
-        'jinrikisha, ricksha, rickshaw',
-    613:
-        'joystick',
-    614:
-        'kimono',
-    615:
-        'knee pad',
-    616:
-        'knot',
-    617:
-        'lab coat, laboratory coat',
-    618:
-        'ladle',
-    619:
-        'lampshade, lamp shade',
-    620:
-        'laptop, laptop computer',
-    621:
-        'lawn mower, mower',
-    622:
-        'lens cap, lens cover',
-    623:
-        'letter opener, paper knife, paperknife',
-    624:
-        'library',
-    625:
-        'lifeboat',
-    626:
-        'lighter, light, igniter, ignitor',
-    627:
-        'limousine, limo',
-    628:
-        'liner, ocean liner',
-    629:
-        'lipstick, lip rouge',
-    630:
-        'Loafer',
-    631:
-        'lotion',
-    632:
-        'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',
-    633:
-        "loupe, jeweler's loupe",
-    634:
-        'lumbermill, sawmill',
-    635:
-        'magnetic compass',
-    636:
-        'mailbag, postbag',
-    637:
-        'mailbox, letter box',
-    638:
-        'maillot',
-    639:
-        'maillot, tank suit',
-    640:
-        'manhole cover',
-    641:
-        'maraca',
-    642:
-        'marimba, xylophone',
-    643:
-        'mask',
-    644:
-        'matchstick',
-    645:
-        'maypole',
-    646:
-        'maze, labyrinth',
-    647:
-        'measuring cup',
-    648:
-        'medicine chest, medicine cabinet',
-    649:
-        'megalith, megalithic structure',
-    650:
-        'microphone, mike',
-    651:
-        'microwave, microwave oven',
-    652:
-        'military uniform',
-    653:
-        'milk can',
-    654:
-        'minibus',
-    655:
-        'miniskirt, mini',
-    656:
-        'minivan',
-    657:
-        'missile',
-    658:
-        'mitten',
-    659:
-        'mixing bowl',
-    660:
-        'mobile home, manufactured home',
-    661:
-        'Model T',
-    662:
-        'modem',
-    663:
-        'monastery',
-    664:
-        'monitor',
-    665:
-        'moped',
-    666:
-        'mortar',
-    667:
-        'mortarboard',
-    668:
-        'mosque',
-    669:
-        'mosquito net',
-    670:
-        'motor scooter, scooter',
-    671:
-        'mountain bike, all-terrain bike, off-roader',
-    672:
-        'mountain tent',
-    673:
-        'mouse, computer mouse',
-    674:
-        'mousetrap',
-    675:
-        'moving van',
-    676:
-        'muzzle',
-    677:
-        'nail',
-    678:
-        'neck brace',
-    679:
-        'necklace',
-    680:
-        'nipple',
-    681:
-        'notebook, notebook computer',
-    682:
-        'obelisk',
-    683:
-        'oboe, hautboy, hautbois',
-    684:
-        'ocarina, sweet potato',
-    685:
-        'odometer, hodometer, mileometer, milometer',
-    686:
-        'oil filter',
-    687:
-        'organ, pipe organ',
-    688:
-        'oscilloscope, scope, cathode-ray oscilloscope, CRO',
-    689:
-        'overskirt',
-    690:
-        'oxcart',
-    691:
-        'oxygen mask',
-    692:
-        'packet',
-    693:
-        'paddle, boat paddle',
-    694:
-        'paddlewheel, paddle wheel',
-    695:
-        'padlock',
-    696:
-        'paintbrush',
-    697:
-        "pajama, pyjama, pj's, jammies",
-    698:
-        'palace',
-    699:
-        'panpipe, pandean pipe, syrinx',
-    700:
-        'paper towel',
-    701:
-        'parachute, chute',
-    702:
-        'parallel bars, bars',
-    703:
-        'park bench',
-    704:
-        'parking meter',
-    705:
-        'passenger car, coach, carriage',
-    706:
-        'patio, terrace',
-    707:
-        'pay-phone, pay-station',
-    708:
-        'pedestal, plinth, footstall',
-    709:
-        'pencil box, pencil case',
-    710:
-        'pencil sharpener',
-    711:
-        'perfume, essence',
-    712:
-        'Petri dish',
-    713:
-        'photocopier',
-    714:
-        'pick, plectrum, plectron',
-    715:
-        'pickelhaube',
-    716:
-        'picket fence, paling',
-    717:
-        'pickup, pickup truck',
-    718:
-        'pier',
-    719:
-        'piggy bank, penny bank',
-    720:
-        'pill bottle',
-    721:
-        'pillow',
-    722:
-        'ping-pong ball',
-    723:
-        'pinwheel',
-    724:
-        'pirate, pirate ship',
-    725:
-        'pitcher, ewer',
-    726:
-        "plane, carpenter's plane, woodworking plane",
-    727:
-        'planetarium',
-    728:
-        'plastic bag',
-    729:
-        'plate rack',
-    730:
-        'plow, plough',
-    731:
-        "plunger, plumber's helper",
-    732:
-        'Polaroid camera, Polaroid Land camera',
-    733:
-        'pole',
-    734:
-        'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',
-    735:
-        'poncho',
-    736:
-        'pool table, billiard table, snooker table',
-    737:
-        'pop bottle, soda bottle',
-    738:
-        'pot, flowerpot',
-    739:
-        "potter's wheel",
-    740:
-        'power drill',
-    741:
-        'prayer rug, prayer mat',
-    742:
-        'printer',
-    743:
-        'prison, prison house',
-    744:
-        'projectile, missile',
-    745:
-        'projector',
-    746:
-        'puck, hockey puck',
-    747:
-        'punching bag, punch bag, punching ball, punchball',
-    748:
-        'purse',
-    749:
-        'quill, quill pen',
-    750:
-        'quilt, comforter, comfort, puff',
-    751:
-        'racer, race car, racing car',
-    752:
-        'racket, racquet',
-    753:
-        'radiator',
-    754:
-        'radio, wireless',
-    755:
-        'radio telescope, radio reflector',
-    756:
-        'rain barrel',
-    757:
-        'recreational vehicle, RV, R.V.',
-    758:
-        'reel',
-    759:
-        'reflex camera',
-    760:
-        'refrigerator, icebox',
-    761:
-        'remote control, remote',
-    762:
-        'restaurant, eating house, eating place, eatery',
-    763:
-        'revolver, six-gun, six-shooter',
-    764:
-        'rifle',
-    765:
-        'rocking chair, rocker',
-    766:
-        'rotisserie',
-    767:
-        'rubber eraser, rubber, pencil eraser',
-    768:
-        'rugby ball',
-    769:
-        'rule, ruler',
-    770:
-        'running shoe',
-    771:
-        'safe',
-    772:
-        'safety pin',
-    773:
-        'saltshaker, salt shaker',
-    774:
-        'sandal',
-    775:
-        'sarong',
-    776:
-        'sax, saxophone',
-    777:
-        'scabbard',
-    778:
-        'scale, weighing machine',
-    779:
-        'school bus',
-    780:
-        'schooner',
-    781:
-        'scoreboard',
-    782:
-        'screen, CRT screen',
-    783:
-        'screw',
-    784:
-        'screwdriver',
-    785:
-        'seat belt, seatbelt',
-    786:
-        'sewing machine',
-    787:
-        'shield, buckler',
-    788:
-        'shoe shop, shoe-shop, shoe store',
-    789:
-        'shoji',
-    790:
-        'shopping basket',
-    791:
-        'shopping cart',
-    792:
-        'shovel',
-    793:
-        'shower cap',
-    794:
-        'shower curtain',
-    795:
-        'ski',
-    796:
-        'ski mask',
-    797:
-        'sleeping bag',
-    798:
-        'slide rule, slipstick',
-    799:
-        'sliding door',
-    800:
-        'slot, one-armed bandit',
-    801:
-        'snorkel',
-    802:
-        'snowmobile',
-    803:
-        'snowplow, snowplough',
-    804:
-        'soap dispenser',
-    805:
-        'soccer ball',
-    806:
-        'sock',
-    807:
-        'solar dish, solar collector, solar furnace',
-    808:
-        'sombrero',
-    809:
-        'soup bowl',
-    810:
-        'space bar',
-    811:
-        'space heater',
-    812:
-        'space shuttle',
-    813:
-        'spatula',
-    814:
-        'speedboat',
-    815:
-        "spider web, spider's web",
-    816:
-        'spindle',
-    817:
-        'sports car, sport car',
-    818:
-        'spotlight, spot',
-    819:
-        'stage',
-    820:
-        'steam locomotive',
-    821:
-        'steel arch bridge',
-    822:
-        'steel drum',
-    823:
-        'stethoscope',
-    824:
-        'stole',
-    825:
-        'stone wall',
-    826:
-        'stopwatch, stop watch',
-    827:
-        'stove',
-    828:
-        'strainer',
-    829:
-        'streetcar, tram, tramcar, trolley, trolley car',
-    830:
-        'stretcher',
-    831:
-        'studio couch, day bed',
-    832:
-        'stupa, tope',
-    833:
-        'submarine, pigboat, sub, U-boat',
-    834:
-        'suit, suit of clothes',
-    835:
-        'sundial',
-    836:
-        'sunglass',
-    837:
-        'sunglasses, dark glasses, shades',
-    838:
-        'sunscreen, sunblock, sun blocker',
-    839:
-        'suspension bridge',
-    840:
-        'swab, swob, mop',
-    841:
-        'sweatshirt',
-    842:
-        'swimming trunks, bathing trunks',
-    843:
-        'swing',
-    844:
-        'switch, electric switch, electrical switch',
-    845:
-        'syringe',
-    846:
-        'table lamp',
-    847:
-        'tank, army tank, armored combat vehicle, armoured combat vehicle',
-    848:
-        'tape player',
-    849:
-        'teapot',
-    850:
-        'teddy, teddy bear',
-    851:
-        'television, television system',
-    852:
-        'tennis ball',
-    853:
-        'thatch, thatched roof',
-    854:
-        'theater curtain, theatre curtain',
-    855:
-        'thimble',
-    856:
-        'thresher, thrasher, threshing machine',
-    857:
-        'throne',
-    858:
-        'tile roof',
-    859:
-        'toaster',
-    860:
-        'tobacco shop, tobacconist shop, tobacconist',
-    861:
-        'toilet seat',
-    862:
-        'torch',
-    863:
-        'totem pole',
-    864:
-        'tow truck, tow car, wrecker',
-    865:
-        'toyshop',
-    866:
-        'tractor',
-    867:
-        'trailer truck, tractor trailer, trucking rig, rig, articulated lorry,'
-        ' semi',
-    868:
-        'tray',
-    869:
-        'trench coat',
-    870:
-        'tricycle, trike, velocipede',
-    871:
-        'trimaran',
-    872:
-        'tripod',
-    873:
-        'triumphal arch',
-    874:
-        'trolleybus, trolley coach, trackless trolley',
-    875:
-        'trombone',
-    876:
-        'tub, vat',
-    877:
-        'turnstile',
-    878:
-        'typewriter keyboard',
-    879:
-        'umbrella',
-    880:
-        'unicycle, monocycle',
-    881:
-        'upright, upright piano',
-    882:
-        'vacuum, vacuum cleaner',
-    883:
-        'vase',
-    884:
-        'vault',
-    885:
-        'velvet',
-    886:
-        'vending machine',
-    887:
-        'vestment',
-    888:
-        'viaduct',
-    889:
-        'violin, fiddle',
-    890:
-        'volleyball',
-    891:
-        'waffle iron',
-    892:
-        'wall clock',
-    893:
-        'wallet, billfold, notecase, pocketbook',
-    894:
-        'wardrobe, closet, press',
-    895:
-        'warplane, military plane',
-    896:
-        'washbasin, handbasin, washbowl, lavabo, wash-hand basin',
-    897:
-        'washer, automatic washer, washing machine',
-    898:
-        'water bottle',
-    899:
-        'water jug',
-    900:
-        'water tower',
-    901:
-        'whiskey jug',
-    902:
-        'whistle',
-    903:
-        'wig',
-    904:
-        'window screen',
-    905:
-        'window shade',
-    906:
-        'Windsor tie',
-    907:
-        'wine bottle',
-    908:
-        'wing',
-    909:
-        'wok',
-    910:
-        'wooden spoon',
-    911:
-        'wool, woolen, woollen',
-    912:
-        'worm fence, snake fence, snake-rail fence, Virginia fence',
-    913:
-        'wreck',
-    914:
-        'yawl',
-    915:
-        'yurt',
-    916:
-        'web site, website, internet site, site',
-    917:
-        'comic book',
-    918:
-        'crossword puzzle, crossword',
-    919:
-        'street sign',
-    920:
-        'traffic light, traffic signal, stoplight',
-    921:
-        'book jacket, dust cover, dust jacket, dust wrapper',
-    922:
-        'menu',
-    923:
-        'plate',
-    924:
-        'guacamole',
-    925:
-        'consomme',
-    926:
-        'hot pot, hotpot',
-    927:
-        'trifle',
-    928:
-        'ice cream, icecream',
-    929:
-        'ice lolly, lolly, lollipop, popsicle',
-    930:
-        'French loaf',
-    931:
-        'bagel, beigel',
-    932:
-        'pretzel',
-    933:
-        'cheeseburger',
-    934:
-        'hotdog, hot dog, red hot',
-    935:
-        'mashed potato',
-    936:
-        'head cabbage',
-    937:
-        'broccoli',
-    938:
-        'cauliflower',
-    939:
-        'zucchini, courgette',
-    940:
-        'spaghetti squash',
-    941:
-        'acorn squash',
-    942:
-        'butternut squash',
-    943:
-        'cucumber, cuke',
-    944:
-        'artichoke, globe artichoke',
-    945:
-        'bell pepper',
-    946:
-        'cardoon',
-    947:
-        'mushroom',
-    948:
-        'Granny Smith',
-    949:
-        'strawberry',
-    950:
-        'orange',
-    951:
-        'lemon',
-    952:
-        'fig',
-    953:
-        'pineapple, ananas',
-    954:
-        'banana',
-    955:
-        'jackfruit, jak, jack',
-    956:
-        'custard apple',
-    957:
-        'pomegranate',
-    958:
-        'hay',
-    959:
-        'carbonara',
-    960:
-        'chocolate sauce, chocolate syrup',
-    961:
-        'dough',
-    962:
-        'meat loaf, meatloaf',
-    963:
-        'pizza, pizza pie',
-    964:
-        'potpie',
-    965:
-        'burrito',
-    966:
-        'red wine',
-    967:
-        'espresso',
-    968:
-        'cup',
-    969:
-        'eggnog',
-    970:
-        'alp',
-    971:
-        'bubble',
-    972:
-        'cliff, drop, drop-off',
-    973:
-        'coral reef',
-    974:
-        'geyser',
-    975:
-        'lakeside, lakeshore',
-    976:
-        'promontory, headland, head, foreland',
-    977:
-        'sandbar, sand bar',
-    978:
-        'seashore, coast, seacoast, sea-coast',
-    979:
-        'valley, vale',
-    980:
-        'volcano',
-    981:
-        'ballplayer, baseball player',
-    982:
-        'groom, bridegroom',
-    983:
-        'scuba diver',
-    984:
-        'rapeseed',
-    985:
-        'daisy',
-    986:
-        "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, "
-        'Cypripedium parviflorum',
-    987:
-        'corn',
-    988:
-        'acorn',
-    989:
-        'hip, rose hip, rosehip',
-    990:
-        'buckeye, horse chestnut, conker',
-    991:
-        'coral fungus',
-    992:
-        'agaric',
-    993:
-        'gyromitra',
-    994:
-        'stinkhorn, carrion fungus',
-    995:
-        'earthstar',
-    996:
-        'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola '
-        'frondosa',
-    997:
-        'bolete',
-    998:
-        'ear, spike, capitulum',
-    999:
-        'toilet tissue, toilet paper, bathroom tissue'
-}
diff --git a/lit_nlp/examples/models/mobilenet.py b/lit_nlp/examples/models/mobilenet.py
deleted file mode 100644
index a9fc7105..00000000
--- a/lit_nlp/examples/models/mobilenet.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""MobileNet model trained on ImageNet dataset."""
-
-from lit_nlp.api import model
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.models import imagenet_labels
-from lit_nlp.lib import image_utils
-import numpy as np
-import tensorflow as tf
-
-# Internal shape of the model input (h, w, c).
-IMAGE_SHAPE = (224, 224, 3)
-
-
-class MobileNet(model.BatchedModel):
-  """MobileNet model trained on ImageNet dataset."""
-
-  def __init__(self, name='mobilenet_v2') -> None:
-    # Initialize imagenet labels.
-    self.labels = [''] * len(imagenet_labels.IMAGENET_2012_LABELS)
-    self.label_to_idx = {}
-    for i, l in imagenet_labels.IMAGENET_2012_LABELS.items():
-      l = l.split(',', 1)[0]
-      self.labels[i] = l
-      self.label_to_idx[l] = i
-
-    if name == 'mobilenet_v2':
-      self.model = tf.keras.applications.mobilenet_v2.MobileNetV2()
-    elif name == 'mobilenet':
-      self.model = tf.keras.applications.mobilenet.MobileNet()
-
-  def predict_minibatch(
-      self, input_batch: list[lit_types.JsonDict]
-  ) -> list[lit_types.JsonDict]:
-    output = []
-    for example in input_batch:
-      # Convert input to the model acceptable format.
-      img_data = example['image']
-      if isinstance(img_data, str):
-        img_data = image_utils.convert_image_str_to_array(img_data, IMAGE_SHAPE)
-      # Get predictions.
-      x = img_data[np.newaxis, ...]
-      x = tf.convert_to_tensor(x)
-      preds = self.model(x).numpy()[0]
-      # Determine the gradient target.
-      if (grad_target := example.get('label')) is None:
-        grad_target_idx = np.argmax(preds)
-      else:
-        grad_target_idx = self.label_to_idx[grad_target]
-      # Calculate gradients.
-      with tf.GradientTape() as tape:
-        tape.watch(x)
-        y = self.model(x)[0, grad_target_idx]
-        grads = tape.gradient(y, x).numpy()[0]
-      # Add results to the output.
-      output.append({
-          'preds': preds,
-          'grads': grads,
-      })
-
-    return output
-
-  def input_spec(self):
-    return {
-        'image': lit_types.ImageBytes(),
-        # If `grad_target` is not specified then the label with the highest
-        # predicted score is used as the gradient target.
-        'label': lit_types.CategoryLabel(vocab=self.labels, required=False),
-    }
-
-  def output_spec(self):
-    return {
-        'preds': lit_types.MulticlassPreds(
-            vocab=self.labels, autosort=True, parent='label'
-        ),
-        'grads': lit_types.ImageGradients(
-            align='image', grad_target_field_key='label'
-        ),
-    }
diff --git a/lit_nlp/examples/models/mobilenet_test.py b/lit_nlp/examples/models/mobilenet_test.py
deleted file mode 100644
index 6df157cc..00000000
--- a/lit_nlp/examples/models/mobilenet_test.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from absl.testing import absltest
-from absl.testing import parameterized
-from lit_nlp.api import dataset as lit_dataset
-from lit_nlp.api import types
-from lit_nlp.examples.models import mobilenet
-from lit_nlp.lib import image_utils
-import numpy as np
-from PIL import Image as PILImage
-
-
-class MobileNetTest(parameterized.TestCase):
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='compatible_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={'image': types.ImageBytes()},
-          expected=True,
-      ),
-      dict(
-          testcase_name='empty_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={},
-          expected=False,
-      ),
-      dict(
-          testcase_name='no_images_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={'text': types.TextSegment()},
-          expected=False,
-      ),
-      dict(
-          testcase_name='wrong_keys_spec_model_v2',
-          model_name='mobilenet_v2',
-          dataset_spec={'wrong_image_key': types.ImageBytes()},
-          expected=False,
-      ),
-      dict(
-          testcase_name='compatible_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={'image': types.ImageBytes()},
-          expected=True,
-      ),
-      dict(
-          testcase_name='empty_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={},
-          expected=False,
-      ),
-      dict(
-          testcase_name='no_images_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={'text': types.TextSegment()},
-          expected=False,
-      ),
-      dict(
-          testcase_name='wrong_keys_spec_model_v1',
-          model_name='mobilenet',
-          dataset_spec={'wrong_image_key': types.ImageBytes()},
-          expected=False,
-      ),
-  )
-  def test_compatibility(
-      self, model_name: str, dataset_spec: types.Spec, expected: bool
-  ):
-    dataset = lit_dataset.Dataset(spec=dataset_spec)
-    model = mobilenet.MobileNet(model_name)
-    self.assertEqual(model.is_compatible_with_dataset(dataset), expected)
-
-  @parameterized.named_parameters(
-      dict(
-          testcase_name='model_v1',
-          model_name='mobilenet',
-      ),
-      dict(
-          testcase_name='model_v2',
-          model_name='mobilenet_v2',
-      ),
-  )
-  def test_model(self, model_name: str):
-    # Create an input with base64 encoded image.
-    input_1 = {
-        'image': np.zeros(shape=(mobilenet.IMAGE_SHAPE), dtype=np.float32)
-    }
-    # Create an input with image data in Numpy array.
-    pil_image = PILImage.new(mode='RGB', size=(300, 200))
-    input_2 = {'image': image_utils.convert_pil_to_image_str(pil_image)}
-    model = mobilenet.MobileNet(model_name)
-    model_out = model.predict([input_1, input_2])
-    model_out = list(model_out)
-    # Check first output.
-    self.assertIn('preds', model_out[0])
-    self.assertIn('grads', model_out[0])
-    # Check second output.
-    self.assertIn('preds', model_out[1])
-    self.assertIn('grads', model_out[1])
-
-
-if __name__ == '__main__':
-  absltest.main()
diff --git a/website/sphinx_src/components.md b/website/sphinx_src/components.md
index 19c4dde0..3af3a0eb 100644
--- a/website/sphinx_src/components.md
+++ b/website/sphinx_src/components.md
@@ -147,8 +147,6 @@ LIT supports multi-label tasks, when a model can label a single example with
 more than one label. Multi-label classification is implemented with the
 `SparseMultilabelPreds` and `SparseMultilabel` types.
 
-*   For a basic example on an image labeling task, see
-    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
 *   Models should define a `SparseMultilabelPreds` field in their output spec
     with the`vocab=` attribute as the set of class labels, and for each example
     should return a list of class score tuples. Each tuple contains two
@@ -246,11 +244,8 @@ NOTE: We may transition images away from encoded strings, moving to individual
 pixel color values. We will ensure we don't break existing checked-in code with
 such a change.
 
-*   See [examples/datasets/open_images.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py) for a
-    dataset containing images, including converting images to base64 encoded
-    strings.
-*   For a demo of an image classifier, see
-    [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py).
+*   See [examples/dalle/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/dalle/demo.py) for a demo on
+    image generation and how LIT handles image bytes.
 
 ## Token-based Salience
 
@@ -488,8 +483,6 @@ using either KerasNLP or Transformers.
 *   Transformers model wrappers:
     [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py)
 
-
-
 ## Salience Clustering
 
 LIT includes a basic implementation of the salience clustering method from
diff --git a/website/sphinx_src/demos.md b/website/sphinx_src/demos.md
index dfc77b50..c919790d 100644
--- a/website/sphinx_src/demos.md
+++ b/website/sphinx_src/demos.md
@@ -169,13 +169,3 @@ https://pair-code.github.io/lit/tutorials/coref
 *   Use binary classifier threshold setters to find best thresholds for slices
     of examples to achieve specific fairness constraints, such as demographic
     parity.
-
-### Image Classification with MobileNet <!-- DO NOT REMOVE {#mobilenet .demo-header} -->
-
-**Hosted instance:** https://pair-code.github.io/lit/demos/images.html \
-**Code:** [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py)
-
-*   Classification on ImageNet labels using a MobileNet model.
-*   Showing using of LIT on image data.
-*   Explore results of multiple gradient-based image saliency techniques in the
-    Salience Maps module.
diff --git a/website/sphinx_src/faq.md b/website/sphinx_src/faq.md
index de6a5b57..9ff5b3d5 100644
--- a/website/sphinx_src/faq.md
+++ b/website/sphinx_src/faq.md
@@ -17,8 +17,8 @@ works with any modern ML framework. For more information, see
 In addition to text, LIT has good support for different modalities, including
 images and tabular data. For examples, see:
 
-*   [Image demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) -
-    image classification, using a Mobilenet model.
+*   [DALL·E demo](google3/third_party/py/lit_nlp/examples/dalle/) - image
+    generation, using DALL·E Mini model.
 *   [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) -
     multi-class classification on tabular (numeric and categorical string) data,
     using the
@@ -99,8 +99,8 @@ manually entered in the web UI) are stored in server memory, and if `--data_dir`
 is specified, may be cached to disk.
 
 LIT has the ability to create or edit datapoints in the UI and then save them to
-disk. If you do not want the tool to be able to write edited datapoints to
-disk, then pass the `--demo_mode` runtime flag to the LIT server.
+disk. If you do not want the tool to be able to write edited datapoints to disk,
+then pass the `--demo_mode` runtime flag to the LIT server.
 
 ### I have proprietary data. Is LIT secure for my team to use?