| --- |
| 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 |
|
|
| ```python |
| 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。 |
|
|
| ```python |
| 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： |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
