metadata
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: boxes
list:
list: float32
length: 4
- name: labels
list:
class_label:
names:
'0': __background__
'1': tortoise
'2': container
'3': magpie
'4': sea turtle
'5': football
'6': ambulance
'7': ladder
'8': toothbrush
'9': syringe
'10': sink
'11': toy
'12': organ (musical instrument)
'13': cassette deck
'14': apple
'15': human eye
'16': cosmetics
'17': paddle
'18': snowman
'19': beer
'20': chopsticks
'21': human beard
'22': bird
'23': parking meter
'24': traffic light
'25': croissant
'26': cucumber
'27': radish
'28': towel
'29': doll
'30': skull
'31': washing machine
'32': glove
'33': tick
'34': belt
'35': sunglasses
'36': banjo
'37': cart
'38': ball
'39': backpack
'40': bicycle
'41': home appliance
'42': centipede
'43': boat
'44': surfboard
'45': boot
'46': headphones
'47': hot dog
'48': shorts
'49': fast food
'50': bus
'51': boy
'52': screwdriver
'53': bicycle wheel
'54': barge
'55': laptop
'56': miniskirt
'57': drill (tool)
'58': dress
'59': bear
'60': waffle
'61': pancake
'62': brown bear
'63': woodpecker
'64': blue jay
'65': pretzel
'66': bagel
'67': tower
'68': teapot
'69': person
'70': bow and arrow
'71': swimwear
'72': beehive
'73': brassiere
'74': bee
'75': bat (animal)
'76': starfish
'77': popcorn
'78': burrito
'79': chainsaw
'80': balloon
'81': wrench
'82': tent
'83': vehicle registration plate
'84': lantern
'85': toaster
'86': flashlight
'87': billboard
'88': tiara
'89': limousine
'90': necklace
'91': carnivore
'92': scissors
'93': stairs
'94': computer keyboard
'95': printer
'96': traffic sign
'97': chair
'98': shirt
'99': poster
'100': cheese
'101': sock
'102': fire hydrant
'103': land vehicle
'104': earrings
'105': tie
'106': watercraft
'107': cabinetry
'108': suitcase
'109': muffin
'110': bidet
'111': snack
'112': snowmobile
'113': clock
'114': medical equipment
'115': cattle
'116': cello
'117': jet ski
'118': camel
'119': coat
'120': suit
'121': desk
'122': cat
'123': bronze sculpture
'124': juice
'125': gondola
'126': beetle
'127': cannon
'128': computer mouse
'129': cookie
'130': office building
'131': fountain
'132': coin
'133': calculator
'134': cocktail
'135': computer monitor
'136': box
'137': stapler
'138': christmas tree
'139': cowboy hat
'140': hiking equipment
'141': studio couch
'142': drum
'143': dessert
'144': wine rack
'145': drink
'146': zucchini
'147': ladle
'148': human mouth
'149': dairy product
'150': dice
'151': oven
'152': dinosaur
'153': ratchet (device)
'154': couch
'155': cricket ball
'156': winter melon
'157': spatula
'158': whiteboard
'159': pencil sharpener
'160': door
'161': hat
'162': shower
'163': eraser
'164': fedora
'165': guacamole
'166': dagger
'167': scarf
'168': dolphin
'169': sombrero
'170': tin can
'171': mug
'172': tap
'173': harbor seal
'174': stretcher
'175': can opener
'176': goggles
'177': human body
'178': roller skates
'179': coffee cup
'180': cutting board
'181': blender
'182': plumbing fixture
'183': stop sign
'184': office supplies
'185': volleyball (ball)
'186': vase
'187': slow cooker
'188': wardrobe
'189': coffee
'190': whisk
'191': paper towel
'192': personal care
'193': food
'194': sun hat
'195': tree house
'196': flying disc
'197': skirt
'198': gas stove
'199': salt and pepper shakers
'200': mechanical fan
'201': face powder
'202': fax
'203': fruit
'204': french fries
'205': nightstand
'206': barrel
'207': kite
'208': tart
'209': treadmill
'210': fox
'211': flag
'212': french horn
'213': window blind
'214': human foot
'215': golf cart
'216': jacket
'217': egg (food)
'218': street light
'219': guitar
'220': pillow
'221': human leg
'222': isopod
'223': grape
'224': human ear
'225': power plugs and sockets
'226': panda
'227': giraffe
'228': woman
'229': door handle
'230': rhinoceros
'231': bathtub
'232': goldfish
'233': houseplant
'234': goat
'235': baseball bat
'236': baseball glove
'237': mixing bowl
'238': marine invertebrates
'239': kitchen utensil
'240': light switch
'241': house
'242': horse
'243': stationary bicycle
'244': hammer
'245': ceiling fan
'246': sofa bed
'247': adhesive tape
'248': harp
'249': sandal
'250': bicycle helmet
'251': saucer
'252': harpsichord
'253': human hair
'254': heater
'255': harmonica
'256': hamster
'257': curtain
'258': bed
'259': kettle
'260': fireplace
'261': scale
'262': drinking straw
'263': insect
'264': hair dryer
'265': kitchenware
'266': indoor rower
'267': invertebrate
'268': food processor
'269': bookcase
'270': refrigerator
'271': wood-burning stove
'272': punching bag
'273': common fig
'274': cocktail shaker
'275': jaguar (animal)
'276': golf ball
'277': fashion accessory
'278': alarm clock
'279': filing cabinet
'280': artichoke
'281': table
'282': tableware
'283': kangaroo
'284': koala
'285': knife
'286': bottle
'287': bottle opener
'288': lynx
'289': lavender (plant)
'290': lighthouse
'291': dumbbell
'292': human head
'293': bowl
'294': humidifier
'295': porch
'296': lizard
'297': billiard table
'298': mammal
'299': mouse
'300': motorcycle
'301': musical instrument
'302': swim cap
'303': frying pan
'304': snowplow
'305': bathroom cabinet
'306': missile
'307': bust
'308': man
'309': waffle iron
'310': milk
'311': ring binder
'312': plate
'313': mobile phone
'314': baked goods
'315': mushroom
'316': crutch
'317': pitcher (container)
'318': mirror
'319': personal flotation device
'320': table tennis racket
'321': pencil case
'322': musical keyboard
'323': scoreboard
'324': briefcase
'325': kitchen knife
'326': nail (construction)
'327': tennis ball
'328': plastic bag
'329': oboe
'330': chest of drawers
'331': ostrich
'332': piano
'333': girl
'334': plant
'335': potato
'336': hair spray
'337': sports equipment
'338': pasta
'339': penguin
'340': pumpkin
'341': pear
'342': infant bed
'343': polar bear
'344': mixer
'345': cupboard
'346': jacuzzi
'347': pizza
'348': digital clock
'349': pig
'350': reptile
'351': rifle
'352': lipstick
'353': skateboard
'354': raven
'355': high heels
'356': red panda
'357': rose
'358': rabbit
'359': sculpture
'360': saxophone
'361': shotgun
'362': seafood
'363': submarine sandwich
'364': snowboard
'365': sword
'366': picture frame
'367': sushi
'368': loveseat
'369': ski
'370': squirrel
'371': tripod
'372': stethoscope
'373': submarine
'374': scorpion
'375': segway
'376': training bench
'377': snake
'378': coffee table
'379': skyscraper
'380': sheep
'381': television
'382': trombone
'383': tea
'384': tank
'385': taco
'386': telephone
'387': torch
'388': tiger
'389': strawberry
'390': trumpet
'391': tree
'392': tomato
'393': train
'394': tool
'395': picnic basket
'396': cooking spray
'397': trousers
'398': bowling equipment
'399': football helmet
'400': truck
'401': measuring cup
'402': coffeemaker
'403': violin
'404': vehicle
'405': handbag
'406': paper cutter
'407': wine
'408': weapon
'409': wheel
'410': worm
'411': wok
'412': whale
'413': zebra
'414': auto part
'415': jug
'416': pizza cutter
'417': cream
'418': monkey
'419': lion
'420': bread
'421': platter
'422': chicken
'423': eagle
'424': helicopter
'425': owl
'426': duck
'427': turtle
'428': hippopotamus
'429': crocodile
'430': toilet
'431': toilet paper
'432': squid
'433': clothing
'434': footwear
'435': lemon
'436': spider
'437': deer
'438': frog
'439': banana
'440': rocket
'441': wine glass
'442': countertop
'443': tablet computer
'444': waste container
'445': swimming pool
'446': dog
'447': book
'448': elephant
'449': shark
'450': candle
'451': leopard
'452': axe
'453': hand dryer
'454': soap dispenser
'455': porcupine
'456': flower
'457': canary
'458': cheetah
'459': palm tree
'460': hamburger
'461': maple
'462': building
'463': fish
'464': lobster
'465': garden asparagus
'466': furniture
'467': hedgehog
'468': airplane
'469': spoon
'470': otter
'471': bull
'472': oyster
'473': horizontal bar
'474': convenience store
'475': bomb
'476': bench
'477': ice cream
'478': caterpillar
'479': butterfly
'480': parachute
'481': orange
'482': antelope
'483': beaker
'484': moths and butterflies
'485': window
'486': closet
'487': castle
'488': jellyfish
'489': goose
'490': mule
'491': swan
'492': peach
'493': coconut
'494': seat belt
'495': raccoon
'496': chisel
'497': fork
'498': lamp
'499': camera
'500': squash (plant)
'501': racket
'502': human face
'503': human arm
'504': vegetable
'505': diaper
'506': unicycle
'507': falcon
'508': chime
'509': snail
'510': shellfish
'511': cabbage
'512': carrot
'513': mango
'514': jeans
'515': flowerpot
'516': pineapple
'517': drawer
'518': stool
'519': envelope
'520': cake
'521': dragonfly
'522': common sunflower
'523': microwave oven
'524': honeycomb
'525': marine mammal
'526': sea lion
'527': ladybug
'528': shelf
'529': watch
'530': candy
'531': salad
'532': parrot
'533': handgun
'534': sparrow
'535': van
'536': grinder
'537': spice rack
'538': light bulb
'539': corded phone
'540': sports uniform
'541': tennis racket
'542': wall clock
'543': serving tray
'544': kitchen & dining room table
'545': dog bed
'546': cake stand
'547': cat furniture
'548': bathroom accessory
'549': facial tissue holder
'550': pressure cooker
'551': kitchen appliance
'552': tire
'553': ruler
'554': luggage and bags
'555': microphone
'556': broccoli
'557': umbrella
'558': pastry
'559': grapefruit
'560': band-aid
'561': animal
'562': bell pepper
'563': turkey
'564': lily
'565': pomegranate
'566': doughnut
'567': glasses
'568': human nose
'569': pen
'570': ant
'571': car
'572': aircraft
'573': human hand
'574': skunk
'575': teddy bear
'576': watermelon
'577': cantaloupe
'578': dishwasher
'579': flute
'580': balance beam
'581': sandwich
'582': shrimp
'583': sewing machine
'584': binoculars
'585': rays and skates
'586': ipod
'587': accordion
'588': willow
'589': crab
'590': crown
'591': seahorse
'592': perfume
'593': alpaca
'594': taxi
'595': canoe
'596': remote control
'597': wheelchair
'598': rugby ball
'599': armadillo
'600': maracas
'601': helmet
- name: relations
struct:
- name: subject_index
list: int64
- name: object_index
list: int64
- name: predicate
list:
class_label:
names:
'0': __background__
'1': at
'2': holds
'3': wears
'4': surf
'5': hang
'6': drink
'7': holding_hands
'8': 'on'
'9': ride
'10': dance
'11': skateboard
'12': catch
'13': highfive
'14': inside_of
'15': eat
'16': cut
'17': contain
'18': handshake
'19': kiss
'20': talk_on_phone
'21': interacts_with
'22': under
'23': hug
'24': throw
'25': hits
'26': snowboard
'27': kick
'28': ski
'29': plays
'30': read
splits:
- name: train
num_bytes: 37969157094
num_examples: 126368
- name: validation
num_bytes: 536519603
num_examples: 1813
- name: test
num_bytes: 1596173138
num_examples: 5322
download_size: 40106623995
dataset_size: 40101849835
Open Images V6 Relationships
OIV6 是基于 Open Images V6 的视觉关系检测数据集，包含 133,503 张图片、
601 个对象前景类别和 30 个关系谓词前景类别。图片字节直接嵌入 Parquet，
可通过 Hugging Face Image feature 解码。
数据集规模
| Split | 图片 | 对象 | 关系 |
|---|---|---|---|
train |
126,368 | 512,259 | 348,560 |
validation |
1,813 | 6,386 | 4,951 |
test |
5,322 | 19,284 | 14,403 |
| 总计 | 133,503 | 537,929 | 367,914 |
三个 split 互不重叠。每个样本都包含至少一个对象和一条关系。
加载
from datasets import load_dataset
dataset = load_dataset("wliafe/OIV6")
sample = dataset["train"][0]
image = sample["image"] # PIL.Image.Image
print(sample["id"], image.size)
图片已嵌入 Parquet，不需要额外下载或拼接图片目录。
数据字段
| 字段 | 类型 | 说明 |
|---|---|---|
id |
string |
Open Images 图片 ID |
image |
Image |
可直接解码的嵌入式 JPEG |
width |
int32 |
JPEG 实际宽度，单位为像素 |
height |
int32 |
JPEG 实际高度，单位为像素 |
boxes |
List[[float32; 4]] |
与对象平行的 [x1, y1, x2, y2] 边界框 |
labels |
List[ClassLabel] |
与 boxes 平行的对象类别 |
relations.subject_index |
List[int64] |
关系主语在当前对象数组中的索引 |
relations.object_index |
List[int64] |
关系宾语在当前对象数组中的索引 |
relations.predicate |
List[ClassLabel] |
关系谓词类别 |
boxes 和 labels 长度相同。三个关系数组也具有相同长度；相同位置的主语索引、
宾语索引和谓词共同表示一条有向关系。
类别与关系名称
对象和谓词 taxonomy 均在索引 0 保留 __background__：
- 对象前景类别编号为
1–601。 - 谓词前景类别编号为
1–30。 subject_index和object_index是当前样本对象数组的零基位置，不是类别 ID。
features = dataset["train"].features
object_names = features["labels"].feature.names
predicate_names = features["relations"]["predicate"].feature.names
sample = dataset["train"][0]
for subject, object_, predicate in zip(
sample["relations"]["subject_index"],
sample["relations"]["object_index"],
sample["relations"]["predicate"],
):
print(
object_names[sample["labels"][subject]],
predicate_names[predicate],
object_names[sample["labels"][object_]],
)
坐标约定
boxes 使用实际图片像素坐标下的 [x1, y1, x2, y2] 格式，坐标位于图片
边界内。width 和 height 与解码后 image 的尺寸一致。边界框表示对象检测
区域，不是实例分割轮廓。
使用限制
- 对象和关系类别呈长尾分布，模型结果可能被高频类别主导。
- 标注可能包含遗漏、歧义或类别噪声。
- 本仓库不重新授予原始图片版权；使用者应遵守 Open Images 的许可与使用要求。
- 比较模型结果时，应确认使用相同的 taxonomy、background 编号和 split。
引用
使用本数据集时，请引用 Open Images：
@article{kuznetsova2020open,
title={The Open Images Dataset V4: Unified Image Classification,
Object Detection, and Visual Relationship Detection at Scale},
author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and
Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and
Kamali, Shahab and Popov, Stefan and Malloci, Matteo and
Kolesnikov, Alexander and Duerig, Tom and Ferrari, Vittorio},
journal={International Journal of Computer Vision},
volume={128},
pages={1956--1981},
year={2020}
}