# Dataset Card for "ImageNet-Hard"


[Project Page](https://taesiri.github.io/ZoomIsAllYouNeed/) - [ArXiv](https://arxiv.org/abs/2304.05538) - [Paper](https://huggingface.co/papers/2304.05538) - [Github](https://github.com/taesiri/ZoomIsAllYouNeed) - [Image Browser](https://huggingface.co/spaces/taesiri/ImageNet-Hard-Browser)

## Dataset Summary


**ImageNet-Hard** is a new benchmark that comprises 10,980 images collected from various existing ImageNet-scale benchmarks (ImageNet, ImageNet-V2, ImageNet-Sketch, ImageNet-C, ImageNet-R, ImageNet-ReaL, ImageNet-A, and ObjectNet). This dataset poses a significant challenge to state-of-the-art vision models as merely zooming in often fails to improve their ability to classify images correctly. As a result, even the most advanced models, such as `CLIP-ViT-L/14@336px`, struggle to perform well on this dataset, achieving a mere `2.02%` accuracy.


*ImageNet-Hard-4K*: For  the 4K version please refere to [this dataset](https://huggingface.co/datasets/taesiri/imagenet-hard-4K).

### Dataset Distribution

![Dataset Distribution](https://taesiri.github.io/ZoomIsAllYouNeed/static/svg/imagenet_hard_distribution.svg)


### Classifiers Performance


| Model               | Accuracy |
| ------------------- | -------- |
| AlexNet             | 7.34     |
| VGG-16              | 12.00    |
| ResNet-18           | 10.86    |
| ResNet-50           | 14.74    |
| ViT-B/32            | 18.52    |
| EfficientNet-B0     | 16.57    |
| EfficientNet-B7     | 23.20    |
| EfficientNet-L2-Ns  | 39.00    |
| CLIP-ViT-L/14@224px | 1.86     |
| CLIP-ViT-L/14@336px | 2.02     |
| OpenCLIP-ViT-bigG-14| 15.93    |
| OpenCLIP-ViT-L-14   | 15.60    |



**Evaluation Code**

* CLIP <a target="_blank" href="https://colab.research.google.com/github/taesiri/ZoomIsAllYouNeed/blob/main/src/ImageNet_Hard/Prompt_Engineering_for_ImageNet_Hard.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
* [OpenCLIP](https://github.com/taesiri/ZoomIsAllYouNeed/blob/main/src/ImageNet_Hard/benchmark_openclip.py)
* Other models <a target="_blank" href="https://colab.research.google.com/github/taesiri/ZoomIsAllYouNeed/blob/main/src/ImageNet_Hard/Benchmark_ImageNet_Hard.ipynb">  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>

## Supported Tasks

- `image-classification`: The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).

## Languages
The `english_label` field in the dataset are in English.

## Dataset Structure

Data Instances

An example looks like this:

```python
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=575x409 at 0x7F09456B53A0>,
 'label': [0],
 'origin': 'imagenet_sketch',
 'english_label': ['tench']
}
```

### 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: A List[int] collection containing the ground-truth ids.
- origin: A string containing source dataset.
- english_label: A List[str] collection containg the english labels for the ground-truth classes.


<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

This dataset is a validation-only set.

## Dataset Creation


### Source Data

This dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet.

## Citation Information

```
@article{taesiri2023zoom,
  title={ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification},
  author={Taesiri, Mohammad Reza and Nguyen, Giang and Habchi, Sarra and Bezemer, Cor-Paul and Nguyen, Anh},
  journal={arXiv preprint arXiv:2304.05538},
  year={2023}
}
```
task_categories:image-classification size_categories:10K<n<100K language:en license:mit OOD ImageNet Out Of Distribution arxiv:2304.05538 region:us
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "pretty_name": "ImageNet-Hard", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "sequence": "int64"}, {"name": "origin", "dtype": "string"}, {"name": "english_label", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1771418938.94, "num_examples": 10980}], "download_size": 6380094503, "dataset_size": 1771418938.94}, "tags": ["OOD", "ImageNet", "Out Of Distribution"]}
TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #OOD #ImageNet #Out Of Distribution #arxiv-2304.05538 #region-us 

Dataset Card for "ImageNet-Hard"
================================


Project Page - ArXiv - Paper - Github - Image Browser


Dataset Summary
---------------


ImageNet-Hard is a new benchmark that comprises 10,980 images collected from various existing ImageNet-scale benchmarks (ImageNet, ImageNet-V2, ImageNet-Sketch, ImageNet-C, ImageNet-R, ImageNet-ReaL, ImageNet-A, and ObjectNet). This dataset poses a significant challenge to state-of-the-art vision models as merely zooming in often fails to improve their ability to classify images correctly. As a result, even the most advanced models, such as 'CLIP-ViT-L/14@336px', struggle to perform well on this dataset, achieving a mere '2.02%' accuracy.


*ImageNet-Hard-4K*: For the 4K version please refere to this dataset.


### Dataset Distribution


!Dataset Distribution


### Classifiers Performance



Evaluation Code


* CLIP <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>
* OpenCLIP
* Other models <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>


Supported Tasks
---------------


* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).


Languages
---------


The 'english\_label' field in the dataset are in English.


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


Data Instances


An example looks like this:


### 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: A List[int] collection containing the ground-truth ids.
* origin: A string containing source dataset.
* english\_label: A List[str] collection containg the english labels for the ground-truth classes.




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


### Data Splits


This dataset is a validation-only set.


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


### Source Data


This dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet.



### Dataset Distribution


!Dataset Distribution ### Classifiers Performance



Evaluation Code


* CLIP <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>
* OpenCLIP
* Other models <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>


Supported Tasks
---------------


* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).


Languages
---------


The 'english\_label' field in the dataset are in English.


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


Data Instances


An example looks like this: ### 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: A List[int] collection containing the ground-truth ids.
* origin: A string containing source dataset.
* english\_label: A List[str] collection containg the english labels for the ground-truth classes.




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


This dataset is a validation-only set.


Dataset Creation
---------------- ### Source Data


This dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet.
TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #OOD #ImageNet #Out Of Distribution #arxiv-2304.05538 #region-us 
 ### Dataset Distribution


!Dataset Distribution ### Classifiers Performance



Evaluation Code


* CLIP <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>
* OpenCLIP
* Other models <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>


Supported Tasks
---------------


* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).


Languages
---------


The 'english\_label' field in the dataset are in English.


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


Data Instances


An example looks like this: ### 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: A List[int] collection containing the ground-truth ids.
* origin: A string containing source dataset.
* english\_label: A List[str] collection containg the english labels for the ground-truth classes.




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


This dataset is a validation-only set.


Dataset Creation
---------------- ### Source Data


This dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet.
passage: TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #OOD #ImageNet #Out Of Distribution #arxiv-2304.05538 #region-us 
### Dataset Distribution


!Dataset Distribution### Classifiers Performance



Evaluation Code


* CLIP <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>
* OpenCLIP
* Other models <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>


Supported Tasks
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* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).


Languages
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The 'english\_label' field in the dataset are in English.


Dataset Structure
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Data Instances


An example looks like this:### 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: A List[int] collection containing the ground-truth ids.
* origin: A string containing source dataset.
* english\_label: A List[str] collection containg the english labels for the ground-truth classes.




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


This dataset is a validation-only set.


Dataset Creation
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