imagenet-1k-64x64|图像分类数据集|多类分类数据集

ImageNet是一个大规模的图像分类数据集，由大量贡献者众包创建。该数据集是单语种的，仅包含英语内容。它采用'其他'许可证，具体许可证详情为'imagenet-agreement'。数据集大小在100万到1000万项之间。它属于'图像分类'任务类别，特别是'多类图像分类'。数据集包括图像和相应的标签，标签范围从'丁鲷，丁鲷'到'飞艇，飞艇'。

原始信息汇总

ImageNet-1k-64x64 数据集概述

基本信息

• 数据集名称: ImageNet-1k-64x64
• 数据集类型: 图像分类
• 语言: 英语
• 许可证: 其他（imagenet-agreement）
• 多语言性: 单语种
• 数据集大小: 1M < n < 10M
• 来源数据集: 原始数据集
• 任务类别: 图像分类
• 任务ID: 多类图像分类
• Papers with Code ID: imagenet-1k-1
• 别名: ImageNet

数据集特征

• 图像: 包含图像数据
• 标签: 包含类别标签

标签类别

• 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

AI搜集汇总

数据集介绍

构建方式

ImageNet-1k-64x64数据集是基于ImageNet大规模视觉识别挑战赛（ILSVRC）构建的，旨在为图像分类任务提供高质量的基准数据。该数据集通过对原始ImageNet图像进行下采样，生成64x64像素的低分辨率图像，保留了图像的主要视觉特征。数据集的构建过程包括图像采集、标注和预处理，标注信息由众包平台完成，确保了数据的多样性和准确性。

特点

ImageNet-1k-64x64数据集包含1000个类别的图像，每个类别涵盖广泛的视觉对象，从动物、植物到日常物品。其低分辨率特性使其特别适用于计算资源受限的场景，同时保留了足够的视觉信息以支持有效的分类任务。数据集的规模介于100万到1000万张图像之间，提供了丰富的训练样本，适合深度学习模型的训练与验证。

使用方法

该数据集主要用于图像分类任务，特别适用于低分辨率图像处理的研究。研究人员可以通过加载数据集，使用深度学习框架（如PyTorch或TensorFlow）进行模型训练与评估。数据集的标注信息可直接用于监督学习，同时也可用于迁移学习或数据增强技术的实验。使用前需同意ImageNet的访问条款，确保仅用于非商业研究目的。

背景与挑战

背景概述

ImageNet-1k-64x64数据集是ImageNet数据集的一个子集，专注于图像分类任务。ImageNet项目由斯坦福大学和普林斯顿大学的研究团队于2009年发起，旨在为计算机视觉领域提供一个大规模、多样化的图像数据集。该数据集的核心研究问题是通过大规模图像分类任务推动深度学习模型的发展，尤其是卷积神经网络（CNN）的进步。ImageNet的出现极大地推动了计算机视觉领域的研究，尤其是在图像分类、目标检测和图像分割等任务上，成为该领域的基准数据集之一。

当前挑战

ImageNet-1k-64x64数据集在构建和应用过程中面临多重挑战。首先，图像分类任务本身具有较高的复杂性，尤其是当图像分辨率较低（如64x64像素）时，模型难以捕捉到足够的细节信息，导致分类精度下降。其次，数据集的构建依赖于众包标注，虽然能够覆盖广泛的类别，但标注质量的不一致性可能影响模型的训练效果。此外，数据集的规模庞大，处理和管理这些数据需要高效的存储和计算资源，尤其是在深度学习模型的训练过程中，计算成本和时间消耗较高。最后，尽管ImageNet在非商业研究领域广泛应用，但其使用受到严格的许可限制，限制了其在商业应用中的推广。

常用场景

经典使用场景

ImageNet-1k-64x64数据集在计算机视觉领域中被广泛用于图像分类任务的研究与开发。该数据集包含了1000个类别的图像，每个类别的图像被统一缩放到64x64像素大小，便于在计算资源有限的环境下进行模型训练和测试。研究人员通常利用该数据集来验证新的图像分类算法，尤其是在处理大规模图像数据时的性能表现。

解决学术问题

ImageNet-1k-64x64数据集解决了图像分类任务中的多个关键学术问题。首先，它为研究者提供了一个标准化的基准数据集，便于不同算法之间的公平比较。其次，该数据集的高质量标注和丰富的类别多样性，使得研究者能够深入探讨模型在复杂场景下的泛化能力。此外，64x64像素的图像尺寸为研究如何在低分辨率图像上保持分类精度提供了独特的研究视角。

衍生相关工作

ImageNet-1k-64x64数据集催生了大量经典研究工作。例如，基于该数据集的深度卷积神经网络（CNN）模型在图像分类任务中取得了突破性进展，推动了深度学习在计算机视觉领域的广泛应用。此外，该数据集还启发了许多关于数据增强、迁移学习和模型压缩的研究，这些工作进一步提升了图像分类模型的效率和精度。