imagenet-1k-random-20.0-frac-1over2

Hugging Face2024-12-21 更新2024-12-22 收录

下载链接：

https://huggingface.co/datasets/datacomp/imagenet-1k-random-20.0-frac-1over2

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资源简介：

该数据集是一个图像分类数据集，包含图像和对应的分类标签。标签涵盖了多种生物类别（如鱼类、鸟类、哺乳动物等）和非生物类别（如工具、服装等），总计461个类别。

创建时间：

2024-12-07

原始信息汇总

数据集概述

数据集信息

特征:
- image: 图像数据，数据类型为 image。
- label: 标签数据，数据类型为 class_label，包含以下类别名称：
  - 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

搜集汇总

数据集介绍

构建方式

该数据集基于ImageNet-1K数据集构建，通过随机抽样方法选取了20%的样本，形成了imagenet-1k-random-20.0-frac-1over2数据集。这一构建方式确保了数据集的多样性和代表性，同时通过随机抽样减少了数据集的规模，便于在资源有限的环境下进行实验和研究。

特点

该数据集的特点在于其精简的规模和丰富的类别标签。尽管样本数量仅为原始ImageNet-1K的20%，但涵盖了1000个类别，每个类别均具有详细的标签描述，便于进行图像分类和识别任务。此外，数据集的随机抽样特性使得其在保持类别多样性的同时，具有较高的实用性和可操作性。

使用方法

该数据集适用于多种计算机视觉任务，尤其是图像分类和识别。用户可以通过加载数据集中的图像和标签，进行模型训练、验证和测试。由于数据集规模适中，适合在资源有限的环境下进行快速实验和模型迭代。此外，数据集的标签信息丰富，可用于多标签分类和细粒度识别任务。

背景与挑战

背景概述

ImageNet-1k-random-20.0-frac-1over2数据集是基于ImageNet-1k数据集的一个子集，由主要研究人员或机构在创建时进行了随机采样，旨在为图像分类任务提供一个更小规模的基准数据集。该数据集包含了20%的ImageNet-1k数据，涵盖了1000个类别，每个类别包含若干图像。其核心研究问题在于如何在小规模数据集上验证和优化图像分类模型的性能，尤其是在资源受限的环境中。该数据集的创建对图像分类领域具有重要意义，因为它为研究人员提供了一个更高效、更快速的实验平台，尤其是在深度学习模型的训练和验证过程中。

当前挑战

ImageNet-1k-random-20.0-frac-1over2数据集的主要挑战在于其小规模特性带来的数据稀缺性问题。首先，由于数据量仅为ImageNet-1k的20%，模型在训练过程中可能会面临过拟合的风险，尤其是在复杂模型如深度神经网络中。其次，构建过程中需要确保随机采样的代表性，以避免类别不平衡或数据分布偏差，这要求在数据采样和预处理阶段进行精细的控制。此外，该数据集的应用场景通常涉及资源受限的环境，如何在有限的计算资源下高效地训练和验证模型也是一个重要的挑战。

常用场景

经典使用场景

imagenet-1k-random-20.0-frac-1over2数据集在计算机视觉领域中被广泛用于图像分类任务的基准测试。其丰富的图像类别和高质量的标注使其成为评估深度学习模型性能的理想选择。研究人员常利用该数据集训练和验证卷积神经网络（CNN）等模型，以提升图像识别的准确性和鲁棒性。

衍生相关工作

基于imagenet-1k-random-20.0-frac-1over2数据集，许多经典工作得以展开，如AlexNet、VGG、ResNet等深度学习模型的提出和优化。这些模型在图像分类、目标检测等任务中取得了显著成果，并进一步推动了计算机视觉领域的发展。此外，该数据集还激发了大量关于数据增强、模型压缩和迁移学习的研究。

数据集最近研究