dnth/imagenet-1k-vl-enriched

Name: dnth/imagenet-1k-vl-enriched
Creator: dnth
Published: 2024-07-12 12:29:36
License: 暂无描述

Hugging Face2024-07-12 更新2024-07-13 收录

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https://hf-mirror.com/datasets/dnth/imagenet-1k-vl-enriched

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资源简介：

该数据集包含图像路径、图像和标签等特征。标签分类详细，涵盖了从动物到物体的多种类别。数据集主要用于目标检测和图像分类任务，采用Apache 2.0许可证，语言为英语。

This dataset is a multi-class animal image dataset designed for image classification and object detection tasks. It includes images of animals ranging from 0 to 399, each with a detailed label containing the animals name and scientific name. The dataset features include the image path, the image itself, and the label.

提供机构：

dnth

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）

数据集信息

特征

image_path: 图像路径，数据类型为字符串（string）
image: 图像数据，数据类型为图像（image）
label: 标签，数据类型为类别标签（class_label）

标签类别

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, Urocyon cinereoargenteus
281: tabby, tabby cat
282: tiger cat
283: Persian cat
284: Siamese cat, Siamese
285: Egyptian cat
286: cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
287: lynx, catamount
288: leopard, Panthera pardus
289: snow leopard

搜集汇总

数据集介绍

构建方式

在计算机视觉领域，大规模图像分类数据集的构建是模型性能提升的关键。该数据集基于经典的ImageNet-1K数据集进行扩展，通过整合多模态信息，丰富了原始数据的语义层次。构建过程中，每张图像不仅保留了原始的类别标签，还关联了详细的文本描述，涵盖了物种学名、常见别名等专业信息。这种构建方式确保了数据在视觉识别任务中的基础性，同时为跨模态学习提供了结构化支持。

使用方法

在视觉与语言交叉研究中，该数据集可作为多任务学习的基准工具。用户可通过HuggingFace平台直接加载数据，利用其图像与文本对进行模型训练或评估。对于图像分类任务，可直接调用类别标签；在跨模态应用中，可结合文本描述进行视觉问答或图像生成实验。数据集的标准化接口支持批量处理，便于集成到现有研究流程中，加速模型迭代与验证过程。

背景与挑战

背景概述

在计算机视觉领域，大规模图像数据集是推动模型性能突破的关键基石。ImageNet-1K数据集由斯坦福大学李飞飞教授团队于2009年创建，其核心研究问题在于为图像分类任务提供标准化、多样化的基准测试平台。该数据集包含约140万张图像，涵盖1000个物体类别，通过年度竞赛极大地促进了深度卷积神经网络的发展，成为衡量模型泛化能力的重要标尺，对人工智能视觉研究产生了深远影响。

当前挑战

ImageNet-1K数据集所解决的图像分类任务面临诸多挑战，包括类别间视觉相似性高导致的细粒度识别困难、图像背景复杂干扰特征提取，以及类别不平衡可能引发的模型偏差。在构建过程中，研究人员需应对海量图像的人工标注成本高昂、标签一致性维护艰巨，以及确保数据来源版权合规等实际问题，这些因素共同构成了数据集构建与应用的核心难点。

常用场景

经典使用场景

在计算机视觉领域，ImageNet-1K数据集作为大规模图像分类任务的基准，其经典使用场景在于为深度神经网络模型提供训练与评估的基础。该数据集包含1000个类别的图像，涵盖了从动物、植物到人造物品的广泛类别，为模型学习丰富的视觉特征提供了坚实基础。研究者通常利用该数据集进行图像分类模型的预训练，随后通过微调适应特定下游任务，这一流程已成为视觉模型开发的标准化范式。

解决学术问题

ImageNet-1K数据集解决了计算机视觉研究中模型泛化能力不足的核心问题，为大规模监督学习提供了关键支撑。通过提供海量标注数据，该数据集使得深度学习模型能够学习到更具判别性的特征表示，从而显著提升了图像分类的准确率。其意义在于推动了卷积神经网络等架构的突破性进展，为视觉识别任务的性能提升奠定了数据基础，并促进了迁移学习、领域自适应等研究方向的发展。

实际应用

在实际应用中，基于ImageNet-1K预训练的模型已广泛部署于智能安防、自动驾驶、医疗影像分析等领域。例如，在自动驾驶系统中，模型利用从该数据集学到的特征进行车辆、行人及交通标志的实时识别；在医疗领域，预训练模型可作为基础网络，辅助医生进行病理图像的初步筛查。这些应用体现了该数据集在推动视觉技术落地中的桥梁作用，将学术成果转化为实际生产力。

数据集最近研究