imagenet-1k-random-20.0-frac-1over32

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-1over32

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资源简介：

该数据集包含图像和对应的分类标签，标签涵盖了101个不同的类别，包括各种动物和物体。

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

该数据集名为imagenet-1k-random-20.0-frac-1over32，是从ImageNet-1K数据集中随机抽取的子集，抽取比例为20%，且进一步将数据集大小缩减至原大小的1/32。数据集包含了图像及其对应的标签，标签涵盖了多种动物和物体类别，共计超过1000个类别。通过这种方式，数据集在保留ImageNet多样性的同时，显著减少了数据量，便于在资源有限的环境下进行实验和模型训练。

特点

此数据集的主要特点在于其精简性和多样性。尽管数据量大幅减少，但仍保留了ImageNet-1K的核心类别，涵盖了从动物到日常物品的广泛领域。这种设计使得数据集在保持高质量图像数据的同时，适合用于快速验证模型性能或进行小规模实验。此外，数据集的随机抽样方式确保了样本的均匀分布，避免了类别偏差。

使用方法

该数据集适用于图像分类任务的模型训练和验证。用户可以通过加载数据集中的图像和标签，进行模型的训练、测试和评估。由于数据集的精简性，特别适合在计算资源有限的情况下进行快速实验或初步模型验证。此外，数据集的多样性也使其适用于多类别分类任务的研究。使用时，建议结合适当的图像预处理技术，以确保模型能够充分利用数据集的特征。

背景与挑战

背景概述

imagenet-1k-random-20.0-frac-1over32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近年创建。该数据集的核心研究问题在于通过随机抽样和特定比例的子集构建，探索在大规模图像分类任务中，数据集规模与模型性能之间的关系。ImageNet-1K作为图像分类领域的基准数据集，其子集的构建为研究人员提供了一个更为灵活的实验平台，尤其是在资源受限的情况下，能够有效减少计算和存储开销。该数据集的创建对图像分类领域的研究具有重要影响，尤其是在模型训练效率和泛化能力方面提供了新的研究视角。

当前挑战

该数据集在构建过程中面临的主要挑战包括：首先，如何在保持数据多样性的同时，确保随机抽样的代表性，以避免数据偏差对模型性能的影响。其次，由于ImageNet-1K本身已经是一个大规模数据集，如何在子集构建过程中平衡数据量与类别分布，确保每个类别的样本数量足够以支持有效的模型训练，是一个重要的技术难题。此外，该数据集的应用场景主要集中在图像分类任务中，如何在有限的样本数量下提升模型的分类精度，尤其是在面对类别不平衡和数据噪声时，仍然是一个亟待解决的挑战。

常用场景

经典使用场景

imagenet-1k-random-20.0-frac-1over32数据集在计算机视觉领域中广泛应用于图像分类任务。该数据集包含了从ImageNet-1K中随机抽取的20%的图像，且每类图像数量为原数据集的1/32。这一设计使得数据集在保持多样性的同时，大幅减少了数据量，特别适合用于快速验证模型在小样本情况下的性能。

衍生相关工作

基于imagenet-1k-random-20.0-frac-1over32数据集，研究者们开发了多种小样本学习算法和数据增强技术。例如，一些研究工作探讨了如何在数据量有限的情况下，通过数据增强和迁移学习提升模型的泛化能力。此外，该数据集还激发了对轻量级神经网络架构的研究，旨在设计更高效的模型以适应资源受限的设备。

数据集最近研究