imagenet-1k-32x32
收藏Hugging Face2024-09-14 更新2024-12-12 收录
下载链接：
https://huggingface.co/datasets/benjamin-paine/imagenet-1k-32x32
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资源简介：
ImageNet是一个大规模的图像分类数据集，由众包方式创建。它包含大量标注了特定类别的图像，所有标签均为英文。该数据集的规模在1百万到1千万之间，源数据为原创，任务类别为图像分类，具体任务ID为多类图像分类。数据集详细描述了特征，包括图像和标签，并提供了类名的列表。
ImageNet is a large-scale image classification dataset created via crowdsourcing. It contains a large number of images annotated with specific categories, and all labels are in English. The total number of images in the dataset ranges from 1 million to 10 million, and the source data is original. Its task category is image classification, with the specific task ID being multi-class image classification. The dataset provides detailed descriptions of its features including images and labels, as well as a list of class names.
创建时间：
2024-09-13
原始信息汇总
ImageNet-1k-32x32 数据集概述
基本信息
- 数据集名称: ImageNet-1k-32x32
- 数据集类型: 图像分类
- 数据集大小: 1M < n < 10M
- 语言: 英语 (en)
- 多语言性: 单语种 (monolingual)
- 许可证: 其他 (other)
- 许可证详情: ImageNet 访问协议 (imagenet-agreement)
数据集来源
- 来源: 原始数据 (original)
- 标注创建者: 众包 (crowdsourced)
- 语言创建者: 众包 (crowdsourced)
任务分类
- 任务类别: 图像分类 (image-classification)
- 任务ID: 多类图像分类 (multi-class-image-classification)
数据集特征
- 特征:
- image: 图像数据
- label: 类别标签
- 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
- 类别名称:
搜集汇总
数据集介绍
构建方式
ImageNet-1k-32x32数据集是基于ImageNet大规模视觉识别挑战赛（ILSVRC）的原始数据集构建而成。该数据集通过将高分辨率图像下采样至32x32像素，生成了低分辨率的图像版本。数据集的标注由众包方式完成，确保了类别标签的准确性和多样性。构建过程中，研究人员对图像进行了标准化处理，以确保数据的一致性和可用性。
特点
ImageNet-1k-32x32数据集包含了1000个类别的图像，每个类别均有丰富的样本，涵盖了广泛的视觉对象类别，如动物、植物、交通工具等。数据集的特点在于其低分辨率图像格式，这使得它特别适用于计算资源有限的环境下的图像分类任务。此外，数据集的标注信息详细且准确，为模型训练提供了高质量的监督信号。
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算法研究。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）进行模型训练和评估。数据集的使用需遵守ImageNet的访问协议，仅限于非商业研究和教育用途。通过Hugging Face平台，用户可以便捷地访问和下载数据集，并利用其提供的API进行数据预处理和模型训练。
背景与挑战
背景概述
ImageNet-1k-32x32数据集是基于ImageNet大规模视觉识别挑战赛（ILSVRC）的经典数据集之一，旨在推动图像分类领域的研究。该数据集由普林斯顿大学和斯坦福大学的研究团队于2009年创建，包含了1000个类别的图像，每张图像的分辨率为32x32像素。ImageNet的推出极大地推动了深度学习在计算机视觉领域的应用，尤其是卷积神经网络（CNN）的发展。其广泛的应用场景和高质量的数据标注使其成为图像分类任务中的基准数据集，对学术界和工业界产生了深远影响。
当前挑战
ImageNet-1k-32x32数据集在图像分类任务中面临的主要挑战包括类别多样性带来的分类难度，尤其是对于视觉特征相似的类别（如不同品种的鸟类或犬类）。此外，低分辨率（32x32像素）的图像限制了模型对细节特征的提取能力，增加了分类的复杂性。在数据构建过程中，如何确保大规模数据的标注准确性也是一个重要挑战，尽管采用了众包标注方式，但仍可能存在标注不一致或错误的情况。这些挑战促使研究者开发更强大的模型和算法，以提升分类精度和鲁棒性。
常用场景
经典使用场景
ImageNet-1k-32x32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，每个类别的图像被缩放到32x32像素，适合用于训练和评估深度学习模型，尤其是卷积神经网络（CNN）。由于其规模适中且类别丰富，该数据集常被用于验证新算法的性能，尤其是在图像分类、特征提取和模型泛化能力的研究中。
解决学术问题
ImageNet-1k-32x32数据集为图像分类领域的研究提供了重要的基准数据。通过该数据集，研究者能够解决图像分类中的多类分类问题，探索模型在不同类别间的泛化能力。此外，该数据集还帮助研究者验证新算法的鲁棒性和效率，尤其是在处理低分辨率图像时的表现。其广泛的应用推动了深度学习模型在图像识别领域的进步，并为后续的研究提供了坚实的基础。
衍生相关工作
ImageNet-1k-32x32数据集衍生了许多经典的计算机视觉研究工作。例如，基于该数据集的AlexNet、VGGNet和ResNet等深度学习模型在图像分类任务中取得了突破性进展。这些模型不仅在学术研究中被广泛引用，还在工业界得到了实际应用。此外，该数据集还催生了大量关于模型压缩、迁移学习和数据增强的研究，进一步推动了计算机视觉领域的发展。
以上内容由遇见数据集搜集并总结生成
