imagenet-1k-vl-enriched

该数据集用于图像分类和目标检测任务，包含多种动物的图像及其详细标签，标签从0到393编号，涵盖了广泛的动物种类。

原始信息汇总

数据集概述

语言

• 英语 (en)

许可证

• Apache 2.0

任务类别

• 目标检测 (object-detection)
• 图像分类 (image-classification)
• 文本到图像 (text-to-image)
• 图像到文本 (image-to-text)
• 视觉问答 (visual-question-answering)

数据集特征

• image_id: 字符串类型 (string)
• image: 图像类型 (image)
• label: 分类标签类型 (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

搜集汇总

数据集介绍

构建方式

imagenet-1k-vl-enriched数据集是基于ImageNet-1K数据集扩展而来，旨在为视觉语言任务提供更丰富的标注信息。该数据集在原有图像分类任务的基础上，增加了文本描述、视觉问答等多模态任务的支持。数据集的构建过程包括从ImageNet-1K中精选图像，并通过人工标注和自动化工具结合的方式，为每张图像生成详细的文本描述和问答对。这一过程确保了数据的高质量和多样性，适用于多种视觉语言任务的研究与开发。

特点

imagenet-1k-vl-enriched数据集的特点在于其多模态特性，不仅包含高质量的图像数据，还提供了丰富的文本标注信息。数据集涵盖了1000个类别的图像，每个类别都有详细的标签和描述，支持图像分类、目标检测、文本生成、视觉问答等多种任务。此外，数据集的图像分辨率高，标注信息准确且多样化，能够为模型训练提供丰富的上下文信息，特别适合用于多模态深度学习模型的训练与评估。

使用方法

imagenet-1k-vl-enriched数据集的使用方法灵活多样，适用于多种视觉语言任务的研究。用户可以通过加载数据集中的图像和对应的文本标注，进行图像分类、目标检测、文本生成等任务的训练与测试。对于视觉问答任务，数据集提供了问答对，用户可以直接使用这些数据进行模型的训练与评估。此外，数据集还支持多模态模型的联合训练，用户可以将图像和文本信息结合，构建更复杂的深度学习模型。通过HuggingFace平台，用户可以方便地访问和下载该数据集，快速开展相关研究工作。

背景与挑战

背景概述

ImageNet-1k-VL-Enriched数据集是基于ImageNet-1k的扩展版本，旨在为视觉与语言（Vision-Language, VL）任务提供丰富的多模态数据支持。该数据集由多个研究机构联合开发，最早可追溯至2010年ImageNet项目的发布。ImageNet-1k-VL-Enriched不仅继承了ImageNet在图像分类领域的权威性，还通过引入文本描述、视觉问答等任务，进一步推动了计算机视觉与自然语言处理的交叉研究。其核心研究问题在于如何通过多模态数据的融合，提升模型在复杂场景下的理解与推理能力。该数据集对视觉-语言模型的研究具有重要影响，为图像生成、文本到图像检索等任务提供了坚实的基础。

当前挑战

ImageNet-1k-VL-Enriched数据集在解决视觉-语言任务时面临多重挑战。首先，多模态数据的对齐问题尤为突出，图像与文本之间的语义关联需要精确标注，这对数据构建提出了极高的要求。其次，数据集的规模与多样性虽然较原始ImageNet有所扩展，但在处理复杂场景时，仍可能面临数据不足的问题。此外，视觉问答任务中的推理能力要求模型具备跨模态的理解能力，这对模型的架构设计提出了更高的挑战。在数据构建过程中，如何确保标注的一致性与准确性，以及如何处理多语言、多文化背景下的语义差异，也是构建团队需要克服的关键问题。

常用场景

经典使用场景

ImageNet-1K-VL-Enriched数据集在计算机视觉领域中被广泛用于图像分类、目标检测、视觉问答等任务。其丰富的图像和标签信息为研究人员提供了多样化的视觉数据，能够有效支持深度学习模型的训练和评估。特别是在图像分类任务中，该数据集通过提供1000个类别的精细标注，帮助模型学习到更细致的视觉特征，从而提升分类精度。

解决学术问题

ImageNet-1K-VL-Enriched数据集解决了计算机视觉领域中的多个关键问题，如大规模图像分类的挑战、跨模态学习中的视觉与语言对齐问题等。通过提供高质量的图像和详细的标签信息，该数据集为研究人员提供了可靠的基准，推动了深度学习模型在视觉任务中的性能提升。此外，其多任务支持特性也为视觉与语言结合的跨模态研究提供了重要数据基础。

衍生相关工作

基于ImageNet-1K-VL-Enriched数据集，研究人员开发了多项经典工作，如ResNet、EfficientNet等深度学习模型。这些模型在ImageNet挑战赛中取得了显著成绩，并成为计算机视觉领域的基准模型。此外，该数据集还催生了大量跨模态研究，如视觉问答系统和图像生成模型，进一步拓展了其在多模态学习中的应用价值。