imagenet-1k-256x256|图像分类数据集|大规模数据集数据集

ImageNet是一个大规模的图像分类数据集，包含了超过1000个类别的图像数据。每个图像都有一个对应的标签，标签是图像中物体的类别。数据集的语言为英语，许可证为'other'，并且是单语种的。数据集的大小在1M到10M之间，源数据为原始数据。任务类别为图像分类，具体任务为多类图像分类。

原始信息汇总

ImageNet-1k-256x256 数据集概述

基本信息

• 数据集名称: ImageNet-1k-256x256
• 数据集别名: ImageNet
• 数据集ID: imagenet-1k-1
• 数据集标签: imagenet, imagenet-1k, ilsvrc-2012

数据集描述

• 任务类别: 图像分类
• 任务ID: 多类图像分类
• 语言: 英语
• 多语言性: 单语种
• 数据集大小: 1M < n < 10M
• 数据来源: 原始数据
• 注释创建者: 众包
• 语言创建者: 众包
• 许可证: 其他（imagenet-agreement）

数据集特征

• 图像: 包含256x256分辨率的图像
• 标签: 包含1000个类别的标签，每个类别对应一个具体的物体或动物名称

标签列表（部分）

• 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, U

AI搜集汇总

数据集介绍

构建方式

ImageNet-1k-256x256数据集是通过众包方式构建的，涵盖了1000个类别的高分辨率图像。该数据集的构建依赖于大规模的标注工作，每个图像都与特定的类别标签相关联，确保了数据集的多样性和广泛性。通过这种方式，ImageNet-1k-256x256为图像分类任务提供了丰富的训练和测试资源。

特点

ImageNet-1k-256x256数据集的主要特点在于其庞大的规模和多样性。该数据集包含超过100万张图像，涵盖了从自然界到人工制品的广泛类别，适合用于多类图像分类任务。此外，图像的分辨率统一为256x256像素，确保了数据处理的一致性，便于模型训练和评估。

使用方法

ImageNet-1k-256x256数据集主要用于图像分类任务，研究人员可以通过加载该数据集进行模型训练和验证。使用时，用户需遵守ImageNet的访问条款，确保数据仅用于非商业研究和教育目的。数据集的图像和标签可以直接用于深度学习模型的输入，支持多种图像处理和分类算法的研究与开发。

背景与挑战

背景概述

ImageNet-1k-256x256数据集是计算机视觉领域中具有里程碑意义的数据集之一，由普林斯顿大学和斯坦福大学的研究人员主导开发，最早于2012年作为ImageNet大规模视觉识别挑战赛（ILSVRC）的一部分发布。该数据集包含了超过100万张标注图像，涵盖1000个类别，主要用于多类别图像分类任务。ImageNet-1k的推出极大地推动了深度学习在图像识别领域的应用，尤其是卷积神经网络（CNN）的发展，成为许多现代计算机视觉模型的基准数据集。

当前挑战

ImageNet-1k-256x256数据集在构建过程中面临了诸多挑战。首先，图像分类任务的核心挑战在于如何准确识别图像中的复杂对象，尤其是在不同光照、背景和视角下的识别。其次，数据集的构建涉及大规模的图像采集与标注工作，依赖于众包平台，确保标注的准确性和一致性是一个巨大的挑战。此外，数据集的版权和使用限制也增加了其使用的复杂性，用户需遵守严格的非商业研究用途协议，这限制了其在商业领域的应用。

常用场景

经典使用场景

ImageNet-1k-256x256 数据集在计算机视觉领域中被广泛应用于图像分类任务。其经典使用场景包括训练深度卷积神经网络（CNN）模型，如AlexNet、VGG、ResNet等，这些模型在ImageNet大规模视觉识别挑战赛（ILSVRC）中取得了显著的性能提升。通过使用该数据集，研究人员能够验证和优化模型的泛化能力，从而推动图像分类技术的发展。

解决学术问题

ImageNet-1k-256x256 数据集解决了计算机视觉领域中图像分类的基准问题。它为研究人员提供了一个标准化的数据集，用于评估和比较不同模型的性能。通过该数据集，学术界能够深入研究图像特征提取、模型架构设计以及优化算法，从而推动了深度学习在图像分类任务中的应用和发展。

衍生相关工作

ImageNet-1k-256x256 数据集的广泛应用催生了许多相关的经典工作。例如，AlexNet的提出标志着深度学习在图像分类中的突破，而VGG和ResNet则进一步优化了网络架构。此外，基于ImageNet的预训练模型也被广泛应用于迁移学习，使得在其他数据集上的任务表现得到了显著提升。这些工作不仅推动了计算机视觉领域的发展，也为其他领域的深度学习应用提供了重要参考。