imagenet-1k-vl-enriched
收藏Hugging Face2024-07-11 更新2024-12-12 收录
下载链接:
https://huggingface.co/datasets/dnth/imagenet-1k-vl-enriched
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该数据集是一个用于图像分类和目标检测的集合,包含了399种不同动物的图像。每个图像都关联一个详细的标签,标签中不仅包括动物的常见名称,还提供了其学名。数据集的特征包括图像路径、图像数据和分类标签,标签编号从0到399,每个编号对应一个特定的动物种类。
This dataset is a collection tailored for image classification and object detection tasks, encompassing images of 399 distinct animal species. Each image is paired with a detailed label that includes both the common name of the animal and its scientific binomial name. The dataset features image paths, image data, and classification labels, with label IDs ranging from 0 to 399, where each ID corresponds to a specific animal species.
创建时间:
2024-07-09
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
- 图像分类
- 文本到图像
- 图像到文本
- 视觉问答
数据集信息
- 特征
image_id: 字符串类型image: 图像类型label: 类别标签- 类别名称及其对应编号:
- 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, Can
- 类别名称及其对应编号:
AI搜集汇总
数据集介绍

构建方式
imagenet-1k-vl-enriched数据集的构建基于ImageNet-1K数据集,通过扩展其视觉和语言信息,增强了图像与文本之间的关联性。该数据集在原有图像分类任务的基础上,引入了文本描述和视觉问答等任务,使得数据集在视觉与语言的多模态任务中更具应用价值。数据集的构建过程包括对图像进行标注、生成文本描述以及设计多模态任务,确保了数据的多样性和丰富性。
使用方法
imagenet-1k-vl-enriched数据集的使用方法多样,适用于多种多模态任务的研究。用户可以通过加载数据集中的图像和文本信息,进行图像分类、目标检测、文本生成图像等任务的训练和评估。数据集还支持视觉问答任务,用户可以通过结合图像和文本信息,生成或回答相关问题。此外,数据集的结构清晰,便于用户快速加载和处理数据,适用于深度学习模型的训练和测试。
背景与挑战
背景概述
ImageNet-1k-VL-Enriched数据集是基于ImageNet的扩展版本,旨在为视觉与语言任务提供更丰富的标注信息。该数据集由多个研究机构共同开发,涵盖了1000个类别的图像,每张图像均附有详细的类别标签和视觉描述。ImageNet自2009年发布以来,已成为计算机视觉领域的基石,推动了深度学习模型在图像分类、目标检测等任务中的突破性进展。ImageNet-1k-VL-Enriched的推出进一步扩展了其应用范围,特别是在视觉问答、图像生成等跨模态任务中展现了重要价值。
当前挑战
ImageNet-1k-VL-Enriched数据集面临的挑战主要体现在两个方面。首先,在领域问题方面,尽管数据集提供了丰富的视觉与语言信息,但如何有效利用这些多模态数据进行模型训练仍是一个难题,尤其是在跨模态对齐和语义理解方面。其次,在构建过程中,数据标注的准确性和一致性是主要挑战。由于涉及大量图像和复杂的类别描述,确保每张图像的标签和描述准确无误需要耗费大量人力物力,且容易引入主观偏差。此外,数据集的多样性和覆盖范围仍需进一步提升,以应对实际应用中的复杂场景。
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、目标检测和视觉问答等任务。其丰富的图像标注和多样化的类别标签为研究人员提供了一个强大的基准测试平台,尤其是在深度学习模型的训练和评估中,该数据集能够有效验证模型在复杂场景下的泛化能力。
解决学术问题
该数据集解决了计算机视觉领域中的多个核心问题,如大规模图像分类的准确性提升、目标检测的精确度优化以及视觉问答系统的语义理解能力增强。通过提供高质量的图像和详细的类别标签,imagenet-1k-vl-enriched为研究人员提供了可靠的实验数据,推动了深度学习模型在视觉任务中的性能突破。
实际应用
在实际应用中,imagenet-1k-vl-enriched数据集被广泛用于自动驾驶、医疗影像分析、智能安防等领域。例如,在自动驾驶系统中,该数据集可用于训练车辆识别道路上的各类物体;在医疗影像分析中,其丰富的图像标注有助于提升疾病检测的准确性;在智能安防中,目标检测模型能够更精确地识别潜在威胁。
数据集最近研究
最新研究方向
近年来,imagenet-1k-vl-enriched数据集在计算机视觉与自然语言处理的交叉领域引起了广泛关注。该数据集不仅包含了丰富的图像分类标签,还融合了文本描述,为多模态学习提供了坚实的基础。研究者们正致力于利用该数据集进行视觉问答(VQA)、图像生成文本(Image-to-Text)以及文本生成图像(Text-to-Image)等前沿任务的研究。特别是在生成式模型如GPT和CLIP的推动下,imagenet-1k-vl-enriched数据集在跨模态理解与生成任务中展现了巨大的潜力。此外,随着深度学习模型的不断优化,该数据集在细粒度图像分类和零样本学习中的应用也日益增多,推动了多模态人工智能技术的快速发展。
以上内容由AI搜集并总结生成



