imagenet-1k-128x128
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-128x128
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资源简介:
ImageNet是一个大规模的图像分类数据集,由大量贡献者共同创建。该数据集是单语种的,仅包含英语内容。它采用'其他'许可证,具体为'imagenet-agreement',该许可证规定了仅用于非商业研究和教育目的的条款。数据集大小在100万到1000万张图像之间。任务类别包括图像分类,特别是多类图像分类。数据集包含各种动物、物体和其他实体的图像,并带有相应的标签。
创建时间:
2024-09-13
原始信息汇总
ImageNet-1k-128x128 数据集概述
基本信息
- 名称: ImageNet-1k-128x128
- 别名: ImageNet
- 语言: 英语 (en)
- 许可证: 其他 (other)
- 许可证详情: imagenet-agreement
- 多语言性: 单语种 (monolingual)
- 数据集大小: 1M < n < 10M
- 来源数据集: 原始数据集 (original)
- 任务类别: 图像分类 (image-classification)
- 任务ID: 多类图像分类 (multi-class-image-classification)
- PapersWithCode ID: imagenet-1k-1
数据集特征
- 特征:
- image: 图像数据类型 (dtype: image)
- label: 类别标签数据类型 (dtype: class_label)
- 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
- 类别名称:
搜集汇总
数据集介绍

构建方式
ImageNet-1k-128x128数据集是基于ImageNet大规模视觉识别挑战赛(ILSVRC)构建的,包含了1000个类别的图像数据。数据集的构建过程依赖于众包标注,确保了图像标签的准确性和多样性。原始图像经过预处理,统一调整为128x128像素的分辨率,以便于在计算资源有限的环境下进行高效的模型训练和评估。
使用方法
ImageNet-1k-128x128数据集主要用于图像分类任务,特别适用于多类别图像分类模型的训练和评估。研究人员可以通过HuggingFace平台访问该数据集,并按照ImageNet的使用协议进行非商业性研究。数据集的使用方法包括加载图像和标签数据,进行数据预处理,并将其输入到深度学习模型中进行训练和测试。通过这种方式,研究人员可以验证和改进图像分类算法的性能。
背景与挑战
背景概述
ImageNet-1k-128x128数据集是ImageNet项目的一个子集,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的图像数据库。该数据集包含了1000个类别的图像,每个类别包含约1000张图像,总计约128万张图像。ImageNet的出现极大地推动了深度学习在图像分类领域的发展,尤其是卷积神经网络(CNN)的广泛应用。该数据集不仅在学术界产生了深远影响,还为工业界的图像识别技术提供了坚实的基础。
当前挑战
ImageNet-1k-128x128数据集在解决图像分类问题时面临的主要挑战包括类别间的相似性较高、图像背景复杂以及光照条件多变等问题。这些因素使得模型在区分某些类别时容易产生混淆。此外,构建该数据集的过程中,研究人员需要处理海量的图像数据,确保每张图像的标注准确无误,这需要大量的人力和时间投入。同时,由于数据集的规模庞大,存储和计算资源的消耗也是一个不容忽视的挑战。
常用场景
经典使用场景
ImageNet-1k-128x128数据集作为计算机视觉领域的经典基准数据集,广泛用于图像分类任务的研究与开发。其包含的128x128分辨率的图像数据,为深度学习模型的训练和评估提供了丰富的视觉信息。研究人员通常利用该数据集来验证和优化卷积神经网络(CNN)等模型的性能,尤其是在多类别图像分类任务中,ImageNet-1k-128x128成为了衡量模型泛化能力的重要工具。
解决学术问题
ImageNet-1k-128x128数据集解决了图像分类领域中的多个关键学术问题。首先,它提供了大规模的标注数据,使得深度学习模型能够在复杂的视觉场景中进行有效学习。其次,该数据集的多类别特性为研究多分类问题提供了基础,帮助学术界探索如何在高维度特征空间中实现精确分类。此外,其标准化的图像尺寸和格式为不同研究之间的结果对比提供了便利,推动了图像分类算法的公平评估与持续改进。
实际应用
在实际应用中,ImageNet-1k-128x128数据集被广泛用于开发智能视觉系统,例如自动驾驶中的物体识别、医疗影像分析以及安防监控中的异常检测。其高分辨率的图像数据为这些系统提供了丰富的视觉信息,使得模型能够在复杂环境中实现高精度的分类与识别。此外,该数据集还被用于教育领域,作为教学工具帮助学生理解深度学习模型在图像处理中的应用。
数据集最近研究
最新研究方向
近年来,ImageNet-1k-128x128数据集在计算机视觉领域的研究方向主要集中在高效图像分类模型的开发与优化。随着深度学习技术的不断进步,研究者们致力于在保持高分类精度的同时,降低模型的计算复杂度和内存占用。特别是在轻量级神经网络架构的设计中,ImageNet-1k-128x128数据集被广泛用于验证模型的性能。此外,该数据集还在自监督学习和对比学习等新兴领域发挥了重要作用,推动了无监督或弱监督学习方法的创新。这些研究不仅提升了图像分类任务的效率,还为其他视觉任务如目标检测和语义分割提供了有力的支持。
以上内容由遇见数据集搜集并总结生成



