
# Dataset Card for ImageNet

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://image-net.org/index.php
- **Repository:**
- **Paper:** https://arxiv.org/abs/1409.0575
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171
- **Point of Contact:** mailto: imagenet.help.desk@gmail.com  

### Dataset Summary

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.

💡 This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used **subset** of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the [patch](https://drive.google.com/file/d/16RYnHpVOW0XKCsn3G3S9GTHUyoV2-4WX/view) which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](https://ieeexplore.ieee.org/abstract/document/5206848), please check the download section of the [main website](https://image-net.org/download-images.php).

### Supported Tasks and Leaderboards

- `image-classification`: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171).

To evaluate the `imagenet-classification` accuracy on the test split, one must first create an account at https://image-net.org. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at https://image-net.org/challenges/LSVRC/eval_server.php The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:

```
670 778 794 387 650
217 691 564 909 364
737 369 430 531 124
755 930 755 512 152
```

The export format is described in full in "readme.txt" within the 2013 development kit available here: https://image-net.org/data/ILSVRC/2013/ILSVRC2013_devkit.tgz. Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See `imagenet2012_labels.txt`.

### Languages

The class labels in the dataset are in English.

## Dataset Structure

### Data Instances

An example looks like below: 

```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
  'label': 23
}
```

### Data Fields

The data instances have the following fields:

- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: an `int` classification label. -1 for `test` set as the labels are missing.

The labels are indexed based on a sorted list of synset ids such as `n07565083` which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file [LOC_synset_mapping.txt](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt) available on Kaggle challenge page. You can also use `dataset_instance.features["labels"].int2str` function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.

<details>
  <summary>
  Click here to see the full list of ImageNet class labels mapping:
  </summary>

  |id|Class|
  |--|-----|
  |0 | tench, Tinca tinca|
  |1 | goldfish, Carassius auratus|
  |2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias|
  |3 | tiger shark, Galeocerdo cuvieri|
  |4 | hammerhead, hammerhead shark|
  |5 | electric ray, crampfish, numbfish, torpedo|
  |6 | stingray|
  |7 | cock|
  |8 | hen|
  |9 | ostrich, Struthio camelus|
  |10 | brambling, Fringilla montifringilla|
  |11 | goldfinch, Carduelis carduelis|
  |12 | house finch, linnet, Carpodacus mexicanus|
  |13 | junco, snowbird|
  |14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea|
  |15 | robin, American robin, Turdus migratorius|
  |16 | bulbul|
  |17 | jay|
  |18 | magpie|
  |19 | chickadee|
  |20 | water ouzel, dipper|
  |21 | kite|
  |22 | bald eagle, American eagle, Haliaeetus leucocephalus|
  |23 | vulture|
  |24 | great grey owl, great gray owl, Strix nebulosa|
  |25 | European fire salamander, Salamandra salamandra|
  |26 | common newt, Triturus vulgaris|
  |27 | eft|
  |28 | spotted salamander, Ambystoma maculatum|
  |29 | axolotl, mud puppy, Ambystoma mexicanum|
  |30 | bullfrog, Rana catesbeiana|
  |31 | tree frog, tree-frog|
  |32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui|
  |33 | loggerhead, loggerhead turtle, Caretta caretta|
  |34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea|
  |35 | mud turtle|
  |36 | terrapin|
  |37 | box turtle, box tortoise|
  |38 | banded gecko|
  |39 | common iguana, iguana, Iguana iguana|
  |40 | American chameleon, anole, Anolis carolinensis|
  |41 | whiptail, whiptail lizard|
  |42 | agama|
  |43 | frilled lizard, Chlamydosaurus kingi|
  |44 | alligator lizard|
  |45 | Gila monster, Heloderma suspectum|
  |46 | green lizard, Lacerta viridis|
  |47 | African chameleon, Chamaeleo chamaeleon|
  |48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis|
  |49 | African crocodile, Nile crocodile, Crocodylus niloticus|
  |50 | American alligator, Alligator mississipiensis|
  |51 | triceratops|
  |52 | thunder snake, worm snake, Carphophis amoenus|
  |53 | ringneck snake, ring-necked snake, ring snake|
  |54 | hognose snake, puff adder, sand viper|
  |55 | green snake, grass snake|
  |56 | king snake, kingsnake|
  |57 | garter snake, grass snake|
  |58 | water snake|
  |59 | vine snake|
  |60 | night snake, Hypsiglena torquata|
  |61 | boa constrictor, Constrictor constrictor|
  |62 | rock python, rock snake, Python sebae|
  |63 | Indian cobra, Naja naja|
  |64 | green mamba|
  |65 | sea snake|
  |66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus|
  |67 | diamondback, diamondback rattlesnake, Crotalus adamanteus|
  |68 | sidewinder, horned rattlesnake, Crotalus cerastes|
  |69 | trilobite|
  |70 | harvestman, daddy longlegs, Phalangium opilio|
  |71 | scorpion|
  |72 | black and gold garden spider, Argiope aurantia|
  |73 | barn spider, Araneus cavaticus|
  |74 | garden spider, Aranea diademata|
  |75 | black widow, Latrodectus mactans|
  |76 | tarantula|
  |77 | wolf spider, hunting spider|
  |78 | tick|
  |79 | centipede|
  |80 | black grouse|
  |81 | ptarmigan|
  |82 | ruffed grouse, partridge, Bonasa umbellus|
  |83 | prairie chicken, prairie grouse, prairie fowl|
  |84 | peacock|
  |85 | quail|
  |86 | partridge|
  |87 | African grey, African gray, Psittacus erithacus|
  |88 | macaw|
  |89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita|
  |90 | lorikeet|
  |91 | coucal|
  |92 | bee eater|
  |93 | hornbill|
  |94 | hummingbird|
  |95 | jacamar|
  |96 | toucan|
  |97 | drake|
  |98 | red-breasted merganser, Mergus serrator|
  |99 | goose|
  |100 | black swan, Cygnus atratus|
  |101 | tusker|
  |102 | echidna, spiny anteater, anteater|
  |103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus|
  |104 | wallaby, brush kangaroo|
  |105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus|
  |106 | wombat|
  |107 | jellyfish|
  |108 | sea anemone, anemone|
  |109 | brain coral|
  |110 | flatworm, platyhelminth|
  |111 | nematode, nematode worm, roundworm|
  |112 | conch|
  |113 | snail|
  |114 | slug|
  |115 | sea slug, nudibranch|
  |116 | chiton, coat-of-mail shell, sea cradle, polyplacophore|
  |117 | chambered nautilus, pearly nautilus, nautilus|
  |118 | Dungeness crab, Cancer magister|
  |119 | rock crab, Cancer irroratus|
  |120 | fiddler crab|
  |121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica|
  |122 | American lobster, Northern lobster, Maine lobster, Homarus americanus|
  |123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish|
  |124 | crayfish, crawfish, crawdad, crawdaddy|
  |125 | hermit crab|
  |126 | isopod|
  |127 | white stork, Ciconia ciconia|
  |128 | black stork, Ciconia nigra|
  |129 | spoonbill|
  |130 | flamingo|
  |131 | little blue heron, Egretta caerulea|
  |132 | American egret, great white heron, Egretta albus|
  |133 | bittern|
  |134 | crane|
  |135 | limpkin, Aramus pictus|
  |136 | European gallinule, Porphyrio porphyrio|
  |137 | American coot, marsh hen, mud hen, water hen, Fulica americana|
  |138 | bustard|
  |139 | ruddy turnstone, Arenaria interpres|
  |140 | red-backed sandpiper, dunlin, Erolia alpina|
  |141 | redshank, Tringa totanus|
  |142 | dowitcher|
  |143 | oystercatcher, oyster catcher|
  |144 | pelican|
  |145 | king penguin, Aptenodytes patagonica|
  |146 | albatross, mollymawk|
  |147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus|
  |148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca|
  |149 | dugong, Dugong dugon|
  |150 | sea lion|
  |151 | Chihuahua|
  |152 | Japanese spaniel|
  |153 | Maltese dog, Maltese terrier, Maltese|
  |154 | Pekinese, Pekingese, Peke|
  |155 | Shih-Tzu|
  |156 | Blenheim spaniel|
  |157 | papillon|
  |158 | toy terrier|
  |159 | Rhodesian ridgeback|
  |160 | Afghan hound, Afghan|
  |161 | basset, basset hound|
  |162 | beagle|
  |163 | bloodhound, sleuthhound|
  |164 | bluetick|
  |165 | black-and-tan coonhound|
  |166 | Walker hound, Walker foxhound|
  |167 | English foxhound|
  |168 | redbone|
  |169 | borzoi, Russian wolfhound|
  |170 | Irish wolfhound|
  |171 | Italian greyhound|
  |172 | whippet|
  |173 | Ibizan hound, Ibizan Podenco|
  |174 | Norwegian elkhound, elkhound|
  |175 | otterhound, otter hound|
  |176 | Saluki, gazelle hound|
  |177 | Scottish deerhound, deerhound|
  |178 | Weimaraner|
  |179 | Staffordshire bullterrier, Staffordshire bull terrier|
  |180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier|
  |181 | Bedlington terrier|
  |182 | Border terrier|
  |183 | Kerry blue terrier|
  |184 | Irish terrier|
  |185 | Norfolk terrier|
  |186 | Norwich terrier|
  |187 | Yorkshire terrier|
  |188 | wire-haired fox terrier|
  |189 | Lakeland terrier|
  |190 | Sealyham terrier, Sealyham|
  |191 | Airedale, Airedale terrier|
  |192 | cairn, cairn terrier|
  |193 | Australian terrier|
  |194 | Dandie Dinmont, Dandie Dinmont terrier|
  |195 | Boston bull, Boston terrier|
  |196 | miniature schnauzer|
  |197 | giant schnauzer|
  |198 | standard schnauzer|
  |199 | Scotch terrier, Scottish terrier, Scottie|
  |200 | Tibetan terrier, chrysanthemum dog|
  |201 | silky terrier, Sydney silky|
  |202 | soft-coated wheaten terrier|
  |203 | West Highland white terrier|
  |204 | Lhasa, Lhasa apso|
  |205 | flat-coated retriever|
  |206 | curly-coated retriever|
  |207 | golden retriever|
  |208 | Labrador retriever|
  |209 | Chesapeake Bay retriever|
  |210 | German short-haired pointer|
  |211 | vizsla, Hungarian pointer|
  |212 | English setter|
  |213 | Irish setter, red setter|
  |214 | Gordon setter|
  |215 | Brittany spaniel|
  |216 | clumber, clumber spaniel|
  |217 | English springer, English springer spaniel|
  |218 | Welsh springer spaniel|
  |219 | cocker spaniel, English cocker spaniel, cocker|
  |220 | Sussex spaniel|
  |221 | Irish water spaniel|
  |222 | kuvasz|
  |223 | schipperke|
  |224 | groenendael|
  |225 | malinois|
  |226 | briard|
  |227 | kelpie|
  |228 | komondor|
  |229 | Old English sheepdog, bobtail|
  |230 | Shetland sheepdog, Shetland sheep dog, Shetland|
  |231 | collie|
  |232 | Border collie|
  |233 | Bouvier des Flandres, Bouviers des Flandres|
  |234 | Rottweiler|
  |235 | German shepherd, German shepherd dog, German police dog, alsatian|
  |236 | Doberman, Doberman pinscher|
  |237 | miniature pinscher|
  |238 | Greater Swiss Mountain dog|
  |239 | Bernese mountain dog|
  |240 | Appenzeller|
  |241 | EntleBucher|
  |242 | boxer|
  |243 | bull mastiff|
  |244 | Tibetan mastiff|
  |245 | French bulldog|
  |246 | Great Dane|
  |247 | Saint Bernard, St Bernard|
  |248 | Eskimo dog, husky|
  |249 | malamute, malemute, Alaskan malamute|
  |250 | Siberian husky|
  |251 | dalmatian, coach dog, carriage dog|
  |252 | affenpinscher, monkey pinscher, monkey dog|
  |253 | basenji|
  |254 | pug, pug-dog|
  |255 | Leonberg|
  |256 | Newfoundland, Newfoundland dog|
  |257 | Great Pyrenees|
  |258 | Samoyed, Samoyede|
  |259 | Pomeranian|
  |260 | chow, chow chow|
  |261 | keeshond|
  |262 | Brabancon griffon|
  |263 | Pembroke, Pembroke Welsh corgi|
  |264 | Cardigan, Cardigan Welsh corgi|
  |265 | toy poodle|
  |266 | miniature poodle|
  |267 | standard poodle|
  |268 | Mexican hairless|
  |269 | timber wolf, grey wolf, gray wolf, Canis lupus|
  |270 | white wolf, Arctic wolf, Canis lupus tundrarum|
  |271 | red wolf, maned wolf, Canis rufus, Canis niger|
  |272 | coyote, prairie wolf, brush wolf, Canis latrans|
  |273 | dingo, warrigal, warragal, Canis dingo|
  |274 | dhole, Cuon alpinus|
  |275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus|
  |276 | hyena, hyaena|
  |277 | red fox, Vulpes vulpes|
  |278 | kit fox, Vulpes macrotis|
  |279 | Arctic fox, white fox, Alopex lagopus|
  |280 | grey fox, gray fox, Urocyon cinereoargenteus|
  |281 | tabby, tabby cat|
  |282 | tiger cat|
  |283 | Persian cat|
  |284 | Siamese cat, Siamese|
  |285 | Egyptian cat|
  |286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor|
  |287 | lynx, catamount|
  |288 | leopard, Panthera pardus|
  |289 | snow leopard, ounce, Panthera uncia|
  |290 | jaguar, panther, Panthera onca, Felis onca|
  |291 | lion, king of beasts, Panthera leo|
  |292 | tiger, Panthera tigris|
  |293 | cheetah, chetah, Acinonyx jubatus|
  |294 | brown bear, bruin, Ursus arctos|
  |295 | American black bear, black bear, Ursus americanus, Euarctos americanus|
  |296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus|
  |297 | sloth bear, Melursus ursinus, Ursus ursinus|
  |298 | mongoose|
  |299 | meerkat, mierkat|
  |300 | tiger beetle|
  |301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle|
  |302 | ground beetle, carabid beetle|
  |303 | long-horned beetle, longicorn, longicorn beetle|
  |304 | leaf beetle, chrysomelid|
  |305 | dung beetle|
  |306 | rhinoceros beetle|
  |307 | weevil|
  |308 | fly|
  |309 | bee|
  |310 | ant, emmet, pismire|
  |311 | grasshopper, hopper|
  |312 | cricket|
  |313 | walking stick, walkingstick, stick insect|
  |314 | cockroach, roach|
  |315 | mantis, mantid|
  |316 | cicada, cicala|
  |317 | leafhopper|
  |318 | lacewing, lacewing fly|
  |319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk|
  |320 | damselfly|
  |321 | admiral|
  |322 | ringlet, ringlet butterfly|
  |323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus|
  |324 | cabbage butterfly|
  |325 | sulphur butterfly, sulfur butterfly|
  |326 | lycaenid, lycaenid butterfly|
  |327 | starfish, sea star|
  |328 | sea urchin|
  |329 | sea cucumber, holothurian|
  |330 | wood rabbit, cottontail, cottontail rabbit|
  |331 | hare|
  |332 | Angora, Angora rabbit|
  |333 | hamster|
  |334 | porcupine, hedgehog|
  |335 | fox squirrel, eastern fox squirrel, Sciurus niger|
  |336 | marmot|
  |337 | beaver|
  |338 | guinea pig, Cavia cobaya|
  |339 | sorrel|
  |340 | zebra|
  |341 | hog, pig, grunter, squealer, Sus scrofa|
  |342 | wild boar, boar, Sus scrofa|
  |343 | warthog|
  |344 | hippopotamus, hippo, river horse, Hippopotamus amphibius|
  |345 | ox|
  |346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis|
  |347 | bison|
  |348 | ram, tup|
  |349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis|
  |350 | ibex, Capra ibex|
  |351 | hartebeest|
  |352 | impala, Aepyceros melampus|
  |353 | gazelle|
  |354 | Arabian camel, dromedary, Camelus dromedarius|
  |355 | llama|
  |356 | weasel|
  |357 | mink|
  |358 | polecat, fitch, foulmart, foumart, Mustela putorius|
  |359 | black-footed ferret, ferret, Mustela nigripes|
  |360 | otter|
  |361 | skunk, polecat, wood pussy|
  |362 | badger|
  |363 | armadillo|
  |364 | three-toed sloth, ai, Bradypus tridactylus|
  |365 | orangutan, orang, orangutang, Pongo pygmaeus|
  |366 | gorilla, Gorilla gorilla|
  |367 | chimpanzee, chimp, Pan troglodytes|
  |368 | gibbon, Hylobates lar|
  |369 | siamang, Hylobates syndactylus, Symphalangus syndactylus|
  |370 | guenon, guenon monkey|
  |371 | patas, hussar monkey, Erythrocebus patas|
  |372 | baboon|
  |373 | macaque|
  |374 | langur|
  |375 | colobus, colobus monkey|
  |376 | proboscis monkey, Nasalis larvatus|
  |377 | marmoset|
  |378 | capuchin, ringtail, Cebus capucinus|
  |379 | howler monkey, howler|
  |380 | titi, titi monkey|
  |381 | spider monkey, Ateles geoffroyi|
  |382 | squirrel monkey, Saimiri sciureus|
  |383 | Madagascar cat, ring-tailed lemur, Lemur catta|
  |384 | indri, indris, Indri indri, Indri brevicaudatus|
  |385 | Indian elephant, Elephas maximus|
  |386 | African elephant, Loxodonta africana|
  |387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens|
  |388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca|
  |389 | barracouta, snoek|
  |390 | eel|
  |391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch|
  |392 | rock beauty, Holocanthus tricolor|
  |393 | anemone fish|
  |394 | sturgeon|
  |395 | gar, garfish, garpike, billfish, Lepisosteus osseus|
  |396 | lionfish|
  |397 | puffer, pufferfish, blowfish, globefish|
  |398 | abacus|
  |399 | abaya|
  |400 | academic gown, academic robe, judge's robe|
  |401 | accordion, piano accordion, squeeze box|
  |402 | acoustic guitar|
  |403 | aircraft carrier, carrier, flattop, attack aircraft carrier|
  |404 | airliner|
  |405 | airship, dirigible|
  |406 | altar|
  |407 | ambulance|
  |408 | amphibian, amphibious vehicle|
  |409 | analog clock|
  |410 | apiary, bee house|
  |411 | apron|
  |412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin|
  |413 | assault rifle, assault gun|
  |414 | backpack, back pack, knapsack, packsack, rucksack, haversack|
  |415 | bakery, bakeshop, bakehouse|
  |416 | balance beam, beam|
  |417 | balloon|
  |418 | ballpoint, ballpoint pen, ballpen, Biro|
  |419 | Band Aid|
  |420 | banjo|
  |421 | bannister, banister, balustrade, balusters, handrail|
  |422 | barbell|
  |423 | barber chair|
  |424 | barbershop|
  |425 | barn|
  |426 | barometer|
  |427 | barrel, cask|
  |428 | barrow, garden cart, lawn cart, wheelbarrow|
  |429 | baseball|
  |430 | basketball|
  |431 | bassinet|
  |432 | bassoon|
  |433 | bathing cap, swimming cap|
  |434 | bath towel|
  |435 | bathtub, bathing tub, bath, tub|
  |436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon|
  |437 | beacon, lighthouse, beacon light, pharos|
  |438 | beaker|
  |439 | bearskin, busby, shako|
  |440 | beer bottle|
  |441 | beer glass|
  |442 | bell cote, bell cot|
  |443 | bib|
  |444 | bicycle-built-for-two, tandem bicycle, tandem|
  |445 | bikini, two-piece|
  |446 | binder, ring-binder|
  |447 | binoculars, field glasses, opera glasses|
  |448 | birdhouse|
  |449 | boathouse|
  |450 | bobsled, bobsleigh, bob|
  |451 | bolo tie, bolo, bola tie, bola|
  |452 | bonnet, poke bonnet|
  |453 | bookcase|
  |454 | bookshop, bookstore, bookstall|
  |455 | bottlecap|
  |456 | bow|
  |457 | bow tie, bow-tie, bowtie|
  |458 | brass, memorial tablet, plaque|
  |459 | brassiere, bra, bandeau|
  |460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty|
  |461 | breastplate, aegis, egis|
  |462 | broom|
  |463 | bucket, pail|
  |464 | buckle|
  |465 | bulletproof vest|
  |466 | bullet train, bullet|
  |467 | butcher shop, meat market|
  |468 | cab, hack, taxi, taxicab|
  |469 | caldron, cauldron|
  |470 | candle, taper, wax light|
  |471 | cannon|
  |472 | canoe|
  |473 | can opener, tin opener|
  |474 | cardigan|
  |475 | car mirror|
  |476 | carousel, carrousel, merry-go-round, roundabout, whirligig|
  |477 | carpenter's kit, tool kit|
  |478 | carton|
  |479 | car wheel|
  |480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM|
  |481 | cassette|
  |482 | cassette player|
  |483 | castle|
  |484 | catamaran|
  |485 | CD player|
  |486 | cello, violoncello|
  |487 | cellular telephone, cellular phone, cellphone, cell, mobile phone|
  |488 | chain|
  |489 | chainlink fence|
  |490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour|
  |491 | chain saw, chainsaw|
  |492 | chest|
  |493 | chiffonier, commode|
  |494 | chime, bell, gong|
  |495 | china cabinet, china closet|
  |496 | Christmas stocking|
  |497 | church, church building|
  |498 | cinema, movie theater, movie theatre, movie house, picture palace|
  |499 | cleaver, meat cleaver, chopper|
  |500 | cliff dwelling|
  |501 | cloak|
  |502 | clog, geta, patten, sabot|
  |503 | cocktail shaker|
  |504 | coffee mug|
  |505 | coffeepot|
  |506 | coil, spiral, volute, whorl, helix|
  |507 | combination lock|
  |508 | computer keyboard, keypad|
  |509 | confectionery, confectionary, candy store|
  |510 | container ship, containership, container vessel|
  |511 | convertible|
  |512 | corkscrew, bottle screw|
  |513 | cornet, horn, trumpet, trump|
  |514 | cowboy boot|
  |515 | cowboy hat, ten-gallon hat|
  |516 | cradle|
  |517 | crane_1|
  |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|
</details>

### Data Splits

|             |train  |validation| test  |
|-------------|------:|---------:|------:|
|# of examples|1281167|50000     |100000 |

## Dataset Creation

### Curation Rationale

The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.

### Source Data

#### Initial Data Collection and Normalization

Initial data for ImageNet image classification task consists of photographs collected from [Flickr](https://www.flickr.com) and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs [1](https://ieeexplore.ieee.org/abstract/document/5206848). The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.

#### Who are the source language producers?

WordNet synsets further quality controlled by human annotators. The images are from Flickr.

### Annotations

#### Annotation process

The annotation process of collecting ImageNet for image classification task is a three step process.

1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
1. Collecting the candidate image for these object categories using a search engine.
1. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.

See the section 3.1 in [1](https://arxiv.org/abs/1409.0575) for more details on data collection procedure and [2](https://ieeexplore.ieee.org/abstract/document/5206848) for general information on ImageNet.

#### Who are the annotators?

Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See [1](https://arxiv.org/abs/1409.0575) for more details.

### Personal and Sensitive Information

The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](https://image-net.org/face-obfuscation/) on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been [an attempt](https://arxiv.org/abs/1912.07726) at filtering and balancing the people subtree in the larger ImageNet.

## Considerations for Using the Data

### Social Impact of Dataset

The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in [1](https://arxiv.org/abs/1409.0575) for a discussion on social impact of the dataset.

### Discussion of Biases

1. A [study](https://image-net.org/update-sep-17-2019.php) of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
1. A [study](https://arxiv.org/abs/1811.12231) has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
1. Another [study](https://arxiv.org/abs/2109.13228) more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
1. ImageNet data with face obfuscation is also provided at [this link](https://image-net.org/face-obfuscation/)
1. A study on genealogy of ImageNet is can be found at [this link](https://journals.sagepub.com/doi/full/10.1177/20539517211035955) about the "norms, values, and assumptions" in ImageNet.
1. See [this study](https://arxiv.org/abs/1912.07726) on filtering and balancing the distribution of people subtree in the larger complete ImageNet.

### Other Known Limitations

1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](https://arxiv.org/abs/2109.13228) [[2]](https://arxiv.org/abs/1409.0575) [[3]](https://ieeexplore.ieee.org/abstract/document/5206848).

## Additional Information

### Dataset Curators

Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):

- Olga Russakovsky
- Jia Deng
- Hao Su
- Jonathan Krause
- Sanjeev Satheesh
- Wei Dong
- Richard Socher
- Li-Jia Li
- Kai Li
- Sean Ma
- Zhiheng Huang
- Andrej Karpathy
- Aditya Khosla
- Michael Bernstein
- Alexander C Berg
- Li Fei-Fei

### Licensing Information

In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:

1. Researcher shall use the Database only for non-commercial research and educational purposes.
1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
1. The law of the State of New Jersey shall apply to all disputes under this agreement.

### Citation Information

```bibtex
@article{imagenet15russakovsky,
    Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
    Title = { {ImageNet Large Scale Visual Recognition Challenge} },
    Year = {2015},
    journal   = {International Journal of Computer Vision (IJCV)},
    doi = {10.1007/s11263-015-0816-y},
    volume={115},
    number={3},
    pages={211-252}
}
```

### Contributions

Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.
task_categories:image-classification task_ids:multi-class-image-classification annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:en license:other arxiv:1409.0575 arxiv:1912.07726 arxiv:1811.12231 arxiv:2109.13228 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "license_details": "imagenet-agreement", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "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"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}}
TAGS
#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-other #arxiv-1409.0575 #arxiv-1912.07726 #arxiv-1811.12231 #arxiv-2109.13228 #region-us 

Dataset Card for ImageNet
=========================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper: URL
* Leaderboard: URL
* Point of Contact: mailto: URL@URL


### Dataset Summary


ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.


This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website.


### Supported Tasks and Leaderboards


* 'image-classification': The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available here.


To evaluate the 'imagenet-classification' accuracy on the test split, one must first create an account at URL. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at URL The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:


The export format is described in full in "URL" within the 2013 development kit available here: URL Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See 'imagenet2012\_labels.txt'.


### Languages


The class labels in the dataset are in English.


Dataset Structure
-----------------


### Data Instances


An example looks like below:


### Data Fields


The data instances have the following fields:


* 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'.
* 'label': an 'int' classification label. -1 for 'test' set as the labels are missing.


The labels are indexed based on a sorted list of synset ids such as 'n07565083' which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file LOC\_synset\_mapping.txt available on Kaggle challenge page. You can also use 'dataset\_instance.features["labels"].int2str' function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.




 Click here to see the full list of ImageNet class labels mapping:
 


### Data Splits



Dataset Creation
----------------


### Curation Rationale


The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.


### Source Data


#### Initial Data Collection and Normalization


Initial data for ImageNet image classification task consists of photographs collected from Flickr and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs 1. The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.


#### Who are the source language producers?


WordNet synsets further quality controlled by human annotators. The images are from Flickr.


### Annotations


#### Annotation process


The annotation process of collecting ImageNet for image classification task is a three step process.


1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
2. Collecting the candidate image for these object categories using a search engine.
3. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.


See the section 3.1 in 1 for more details on data collection procedure and 2 for general information on ImageNet.


#### Who are the annotators?


Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See 1 for more details.


### Personal and Sensitive Information


The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](URL on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been an attempt at filtering and balancing the people subtree in the larger ImageNet.


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in 1 for a discussion on social impact of the dataset.


### Discussion of Biases


1. A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
2. A study has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
3. Another study more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
4. ImageNet data with face obfuscation is also provided at this link
5. A study on genealogy of ImageNet is can be found at this link about the "norms, values, and assumptions" in ImageNet.
6. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet.


### Other Known Limitations


1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](URL [[2]](URL [[3]](URL


Additional Information
----------------------


### Dataset Curators


Authors of [[1]](URL and [[2]](URL


* Olga Russakovsky
* Jia Deng
* Hao Su
* Jonathan Krause
* Sanjeev Satheesh
* Wei Dong
* Richard Socher
* Li-Jia Li
* Kai Li
* Sean Ma
* Zhiheng Huang
* Andrej Karpathy
* Aditya Khosla
* Michael Bernstein
* Alexander C Berg
* Li Fei-Fei


### Licensing Information


In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:


1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
7. The law of the State of New Jersey shall apply to all disputes under this agreement.


### Contributions


Thanks to @apsdehal for adding this dataset.



### Dataset Summary


ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.


This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website. ### Supported Tasks and Leaderboards


* 'image-classification': The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available here.


To evaluate the 'imagenet-classification' accuracy on the test split, one must first create an account at URL. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at URL The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:


The export format is described in full in "URL" within the 2013 development kit available here: URL Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See 'imagenet2012\_labels.txt'. ### Languages


The class labels in the dataset are in English.


Dataset Structure
----------------- ### Data Instances


An example looks like below: ### Data Fields


The data instances have the following fields:


* 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'.
* 'label': an 'int' classification label. -1 for 'test' set as the labels are missing.


The labels are indexed based on a sorted list of synset ids such as 'n07565083' which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file LOC\_synset\_mapping.txt available on Kaggle challenge page. You can also use 'dataset\_instance.features["labels"].int2str' function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.




 Click here to see the full list of ImageNet class labels mapping: ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet. ### Source Data #### Initial Data Collection and Normalization


Initial data for ImageNet image classification task consists of photographs collected from Flickr and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs 1. The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow. #### Who are the source language producers?


WordNet synsets further quality controlled by human annotators. The images are from Flickr. ### Annotations #### Annotation process


The annotation process of collecting ImageNet for image classification task is a three step process.


1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
2. Collecting the candidate image for these object categories using a search engine.
3. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.


See the section 3.1 in 1 for more details on data collection procedure and 2 for general information on ImageNet. #### Who are the annotators?


Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See 1 for more details. ### Personal and Sensitive Information


The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](URL on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been an attempt at filtering and balancing the people subtree in the larger ImageNet.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in 1 for a discussion on social impact of the dataset. ### Discussion of Biases


1. A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
2. A study has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
3. Another study more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
4. ImageNet data with face obfuscation is also provided at this link
5. A study on genealogy of ImageNet is can be found at this link about the "norms, values, and assumptions" in ImageNet.
6. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet. ### Other Known Limitations


1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](URL [[2]](URL [[3]](URL


Additional Information
---------------------- ### Dataset Curators


Authors of [[1]](URL and [[2]](URL


* Olga Russakovsky
* Jia Deng
* Hao Su
* Jonathan Krause
* Sanjeev Satheesh
* Wei Dong
* Richard Socher
* Li-Jia Li
* Kai Li
* Sean Ma
* Zhiheng Huang
* Andrej Karpathy
* Aditya Khosla
* Michael Bernstein
* Alexander C Berg
* Li Fei-Fei ### Licensing Information


In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:


1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
7. The law of the State of New Jersey shall apply to all disputes under this agreement. ### Contributions


Thanks to @apsdehal for adding this dataset.
TAGS
#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-other #arxiv-1409.0575 #arxiv-1912.07726 #arxiv-1811.12231 #arxiv-2109.13228 #region-us 
 ### Dataset Summary


ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.


This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website. ### Supported Tasks and Leaderboards


* 'image-classification': The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available here.


To evaluate the 'imagenet-classification' accuracy on the test split, one must first create an account at URL. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at URL The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:


The export format is described in full in "URL" within the 2013 development kit available here: URL Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See 'imagenet2012\_labels.txt'. ### Languages


The class labels in the dataset are in English.


Dataset Structure
----------------- ### Data Instances


An example looks like below: ### Data Fields


The data instances have the following fields:


* 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'.
* 'label': an 'int' classification label. -1 for 'test' set as the labels are missing.


The labels are indexed based on a sorted list of synset ids such as 'n07565083' which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file LOC\_synset\_mapping.txt available on Kaggle challenge page. You can also use 'dataset\_instance.features["labels"].int2str' function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.




 Click here to see the full list of ImageNet class labels mapping: ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet. ### Source Data #### Initial Data Collection and Normalization


Initial data for ImageNet image classification task consists of photographs collected from Flickr and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs 1. The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow. #### Who are the source language producers?


WordNet synsets further quality controlled by human annotators. The images are from Flickr. ### Annotations #### Annotation process


The annotation process of collecting ImageNet for image classification task is a three step process.


1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
2. Collecting the candidate image for these object categories using a search engine.
3. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.


See the section 3.1 in 1 for more details on data collection procedure and 2 for general information on ImageNet. #### Who are the annotators?


Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See 1 for more details. ### Personal and Sensitive Information


The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](URL on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been an attempt at filtering and balancing the people subtree in the larger ImageNet.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in 1 for a discussion on social impact of the dataset. ### Discussion of Biases


1. A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
2. A study has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
3. Another study more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
4. ImageNet data with face obfuscation is also provided at this link
5. A study on genealogy of ImageNet is can be found at this link about the "norms, values, and assumptions" in ImageNet.
6. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet. ### Other Known Limitations


1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](URL [[2]](URL [[3]](URL


Additional Information
---------------------- ### Dataset Curators


Authors of [[1]](URL and [[2]](URL


* Olga Russakovsky
* Jia Deng
* Hao Su
* Jonathan Krause
* Sanjeev Satheesh
* Wei Dong
* Richard Socher
* Li-Jia Li
* Kai Li
* Sean Ma
* Zhiheng Huang
* Andrej Karpathy
* Aditya Khosla
* Michael Bernstein
* Alexander C Berg
* Li Fei-Fei ### Licensing Information


In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:


1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
7. The law of the State of New Jersey shall apply to all disputes under this agreement. ### Contributions


Thanks to @apsdehal for adding this dataset.
passage: TAGS
#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-other #arxiv-1409.0575 #arxiv-1912.07726 #arxiv-1811.12231 #arxiv-2109.13228 #region-us 
### Dataset Summary


ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.


This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website. passage: ### Supported Tasks and Leaderboards


* 'image-classification': The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available here.


To evaluate the 'imagenet-classification' accuracy on the test split, one must first create an account at URL. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at URL The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:


The export format is described in full in "URL" within the 2013 development kit available here: URL Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See 'imagenet2012\_labels.txt'.### Languages


The class labels in the dataset are in English.


Dataset Structure
-----------------### Data Instances


An example looks like below:### Data Fields


The data instances have the following fields:


* 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'.
* 'label': an 'int' classification label. -1 for 'test' set as the labels are missing.


The labels are indexed based on a sorted list of synset ids such as 'n07565083' which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file LOC\_synset\_mapping.txt available on Kaggle challenge page. You can also use 'dataset\_instance.features["labels"].int2str' function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.




 Click here to see the full list of ImageNet class labels mapping:### Data Splits



Dataset Creation
---------------- passage: ### Curation Rationale


The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.### Source Data#### Initial Data Collection and Normalization


Initial data for ImageNet image classification task consists of photographs collected from Flickr and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs 1. The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.#### Who are the source language producers?


WordNet synsets further quality controlled by human annotators. The images are from Flickr.### Annotations#### Annotation process


The annotation process of collecting ImageNet for image classification task is a three step process.


1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.
2. Collecting the candidate image for these object categories using a search engine.
3. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.


See the section 3.1 in 1 for more details on data collection procedure and 2 for general information on ImageNet.#### Who are the annotators?


Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See 1 for more details. passage: ### Personal and Sensitive Information


The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](URL on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been an attempt at filtering and balancing the people subtree in the larger ImageNet.


Considerations for Using the Data
---------------------------------### Social Impact of Dataset


The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in 1 for a discussion on social impact of the dataset.### Discussion of Biases


1. A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.
2. A study has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.
3. Another study more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.
4. ImageNet data with face obfuscation is also provided at this link
5. A study on genealogy of ImageNet is can be found at this link about the "norms, values, and assumptions" in ImageNet.
6. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet.### Other Known Limitations


1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](URL [[2]](URL [[3]](URL


Additional Information
----------------------### Dataset Curators


Authors of [[1]](URL and [[2]](URL


* Olga Russakovsky
* Jia Deng
* Hao Su
* Jonathan Krause
* Sanjeev Satheesh
* Wei Dong
* Richard Socher
* Li-Jia Li
* Kai Li
* Sean Ma
* Zhiheng Huang
* Andrej Karpathy
* Aditya Khosla
* Michael Bernstein
* Alexander C Berg
* Li Fei-Fei