| --- |
| 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': airplane |
| '2': animal |
| '3': arm |
| '4': bag |
| '5': banana |
| '6': basket |
| '7': beach |
| '8': bear |
| '9': bed |
| '10': bench |
| '11': bike |
| '12': bird |
| '13': board |
| '14': boat |
| '15': book |
| '16': boot |
| '17': bottle |
| '18': bowl |
| '19': box |
| '20': boy |
| '21': branch |
| '22': building |
| '23': bus |
| '24': cabinet |
| '25': cap |
| '26': car |
| '27': cat |
| '28': chair |
| '29': child |
| '30': clock |
| '31': coat |
| '32': counter |
| '33': cow |
| '34': cup |
| '35': curtain |
| '36': desk |
| '37': dog |
| '38': door |
| '39': drawer |
| '40': ear |
| '41': elephant |
| '42': engine |
| '43': eye |
| '44': face |
| '45': fence |
| '46': finger |
| '47': flag |
| '48': flower |
| '49': food |
| '50': fork |
| '51': fruit |
| '52': giraffe |
| '53': girl |
| '54': glass |
| '55': glove |
| '56': guy |
| '57': hair |
| '58': hand |
| '59': handle |
| '60': hat |
| '61': head |
| '62': helmet |
| '63': hill |
| '64': horse |
| '65': house |
| '66': jacket |
| '67': jean |
| '68': kid |
| '69': kite |
| '70': lady |
| '71': lamp |
| '72': laptop |
| '73': leaf |
| '74': leg |
| '75': letter |
| '76': light |
| '77': logo |
| '78': man |
| '79': men |
| '80': motorcycle |
| '81': mountain |
| '82': mouth |
| '83': neck |
| '84': nose |
| '85': number |
| '86': orange |
| '87': pant |
| '88': paper |
| '89': paw |
| '90': people |
| '91': person |
| '92': phone |
| '93': pillow |
| '94': pizza |
| '95': plane |
| '96': plant |
| '97': plate |
| '98': player |
| '99': pole |
| '100': post |
| '101': pot |
| '102': racket |
| '103': railing |
| '104': rock |
| '105': roof |
| '106': room |
| '107': screen |
| '108': seat |
| '109': sheep |
| '110': shelf |
| '111': shirt |
| '112': shoe |
| '113': short |
| '114': sidewalk |
| '115': sign |
| '116': sink |
| '117': skateboard |
| '118': ski |
| '119': skier |
| '120': sneaker |
| '121': snow |
| '122': sock |
| '123': stand |
| '124': street |
| '125': surfboard |
| '126': table |
| '127': tail |
| '128': tie |
| '129': tile |
| '130': tire |
| '131': toilet |
| '132': towel |
| '133': tower |
| '134': track |
| '135': train |
| '136': tree |
| '137': truck |
| '138': trunk |
| '139': umbrella |
| '140': vase |
| '141': vegetable |
| '142': vehicle |
| '143': wave |
| '144': wheel |
| '145': window |
| '146': windshield |
| '147': wing |
| '148': wire |
| '149': woman |
| '150': zebra |
| - name: attributes |
| list: |
| list: |
| class_label: |
| names: |
| '0': __background__ |
| '1': white |
| '2': black |
| '3': blue |
| '4': green |
| '5': red |
| '6': brown |
| '7': yellow |
| '8': small |
| '9': large |
| '10': wooden |
| '11': silver |
| '12': orange |
| '13': grey |
| '14': tall |
| '15': long |
| '16': dark |
| '17': pink |
| '18': standing |
| '19': round |
| '20': tan |
| '21': glass |
| '22': here |
| '23': wood |
| '24': open |
| '25': purple |
| '26': short |
| '27': plastic |
| '28': parked |
| '29': sitting |
| '30': walking |
| '31': striped |
| '32': brick |
| '33': young |
| '34': gold |
| '35': old |
| '36': hanging |
| '37': empty |
| '38': 'on' |
| '39': bright |
| '40': concrete |
| '41': cloudy |
| '42': colorful |
| '43': one |
| '44': beige |
| '45': bare |
| '46': wet |
| '47': light |
| '48': square |
| '49': closed |
| '50': stone |
| '51': shiny |
| '52': thin |
| '53': dirty |
| '54': flying |
| '55': smiling |
| '56': painted |
| '57': thick |
| '58': part |
| '59': sliced |
| '60': playing |
| '61': tennis |
| '62': calm |
| '63': leather |
| '64': distant |
| '65': rectangular |
| '66': looking |
| '67': grassy |
| '68': dry |
| '69': cement |
| '70': leafy |
| '71': wearing |
| '72': tiled |
| '73': man's |
| '74': baseball |
| '75': cooked |
| '76': pictured |
| '77': curved |
| '78': decorative |
| '79': dead |
| '80': eating |
| '81': paper |
| '82': paved |
| '83': fluffy |
| '84': lit |
| '85': back |
| '86': framed |
| '87': plaid |
| '88': dirt |
| '89': watching |
| '90': colored |
| '91': stuffed |
| '92': clean |
| '93': in the picture |
| '94': steel |
| '95': stacked |
| '96': covered |
| '97': full |
| '98': three |
| '99': street |
| '100': flat |
| '101': baby |
| '102': black and white |
| '103': beautiful |
| '104': ceramic |
| '105': present |
| '106': grazing |
| '107': sandy |
| '108': golden |
| '109': blurry |
| '110': side |
| '111': chocolate |
| '112': wide |
| '113': growing |
| '114': chrome |
| '115': cut |
| '116': bent |
| '117': train |
| '118': holding |
| '119': water |
| '120': up |
| '121': arched |
| '122': metallic |
| '123': spotted |
| '124': folded |
| '125': electrical |
| '126': pointy |
| '127': running |
| '128': leafless |
| '129': electric |
| '130': in background |
| '131': rusty |
| '132': furry |
| '133': traffic |
| '134': ripe |
| '135': behind |
| '136': laying |
| '137': rocky |
| '138': tiny |
| '139': down |
| '140': fresh |
| '141': floral |
| '142': stainless steel |
| '143': high |
| '144': surfing |
| '145': close |
| '146': 'off' |
| '147': leaning |
| '148': moving |
| '149': multicolored |
| '150': woman's |
| '151': pair |
| '152': huge |
| '153': some |
| '154': background |
| '155': chain link |
| '156': checkered |
| '157': top |
| '158': tree |
| '159': broken |
| '160': maroon |
| '161': iron |
| '162': worn |
| '163': patterned |
| '164': ski |
| '165': overcast |
| '166': waiting |
| '167': rubber |
| '168': riding |
| '169': skinny |
| '170': grass |
| '171': porcelain |
| '172': adult |
| '173': wire |
| '174': cloudless |
| '175': curly |
| '176': cardboard |
| '177': jumping |
| '178': tile |
| '179': pointed |
| '180': blond |
| '181': cream |
| '182': four |
| '183': male |
| '184': smooth |
| '185': hazy |
| '186': computer |
| '187': older |
| '188': pine |
| '189': raised |
| '190': many |
| '191': bald |
| '192': snow covered |
| '193': skateboarding |
| '194': narrow |
| '195': reflective |
| '196': rear |
| '197': khaki |
| '198': extended |
| '199': roman |
| '200': american |
| - name: relations |
| struct: |
| - name: subject_index |
| list: int64 |
| - name: object_index |
| list: int64 |
| - name: predicate |
| list: |
| class_label: |
| names: |
| '0': __background__ |
| '1': above |
| '2': across |
| '3': against |
| '4': along |
| '5': and |
| '6': at |
| '7': attached to |
| '8': behind |
| '9': belonging to |
| '10': between |
| '11': carrying |
| '12': covered in |
| '13': covering |
| '14': eating |
| '15': flying in |
| '16': for |
| '17': from |
| '18': growing on |
| '19': hanging from |
| '20': has |
| '21': holding |
| '22': in |
| '23': in front of |
| '24': laying on |
| '25': looking at |
| '26': lying on |
| '27': made of |
| '28': mounted on |
| '29': near |
| '30': of |
| '31': 'on' |
| '32': on back of |
| '33': over |
| '34': painted on |
| '35': parked on |
| '36': part of |
| '37': playing |
| '38': riding |
| '39': says |
| '40': sitting on |
| '41': standing on |
| '42': to |
| '43': under |
| '44': using |
| '45': walking in |
| '46': walking on |
| '47': watching |
| '48': wearing |
| '49': wears |
| '50': with |
| splits: |
| - name: train |
| num_bytes: 8372499067 |
| num_examples: 57723 |
| - name: validation |
| num_bytes: 782338184 |
| num_examples: 5000 |
| - name: test |
| num_bytes: 3501331914 |
| num_examples: 26446 |
| download_size: 12653741212 |
| dataset_size: 12656169165 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| --- |
| |
| # VG150 |
|
|
| VG150 是从 [Visual Genome](https://visualgenome.org/) 派生的场景图生成 |
| （Scene Graph Generation, SGG）基准数据集，包含 150 个对象类别、50 个 |
| 关系谓词类别和 200 个属性类别。图片直接嵌入 Parquet。 |
|
|
| 该数据集适用于场景图生成、场景图检测、视觉关系检测和相关多模态研究。 |
|
|
| ## 数据集规模 |
|
|
| | Split | 图片数量 | 用途 | |
| | --- | ---: | --- | |
| | `train` | 57,723 | 模型训练 | |
| | `validation` | 5,000 | 验证与模型选择 | |
| | `test` | 26,446 | 最终评估 | |
| | **总计** | **89,169** | 仅包含同时具有对象和关系标注的图片 | |
|
|
| 所有 split 均包含完整的对象、属性和关系标注。 |
|
|
| ## 加载 |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("wliafe/VG150") |
| print(dataset) |
| |
| sample = dataset["train"][0] |
| image = sample["image"] # PIL.Image.Image |
| image.show() |
| ``` |
|
|
| 图片字节已经嵌入 Parquet，因此不需要额外下载或拼接 Visual Genome 图片目录。 |
| `image` 使用 Hugging Face `Image` feature，默认读取时会解码为 Pillow 图像。 |
|
|
| 如需取得类别名称，可以读取 `ClassLabel` feature： |
|
|
| ```python |
| features = dataset["train"].features |
| |
| object_names = features["labels"].feature.names |
| predicate_names = features["relations"]["predicate"].feature.names |
| attribute_names = features["attributes"].feature.feature.names |
| |
| sample = dataset["train"][0] |
| object_labels = [object_names[index] for index in sample["labels"]] |
| predicate_labels = [ |
| predicate_names[index] |
| for index in sample["relations"]["predicate"] |
| ] |
| attribute_labels = [ |
| [attribute_names[index] for index in object_attributes] |
| for object_attributes in sample["attributes"] |
| ] |
| ``` |
|
|
| ## 数据字段 |
|
|
| | 字段 | 类型 | 说明 | |
| | --- | --- | --- | |
| | `id` | `string` | Visual Genome 图片 ID | |
| | `image` | `Image` | 嵌入 Parquet 并可直接解码的图片 | |
| | `width` | `int32` | 实际 JPEG 宽度，单位为像素 | |
| | `height` | `int32` | 实际 JPEG 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 | |
| | `attributes` | `List[List[ClassLabel]]` | 每个对象对应的零个或多个属性 | |
| | `relations.subject_index` | `List[int64]` | 每条关系的主语对象索引 | |
| | `relations.object_index` | `List[int64]` | 每条关系的宾语对象索引 | |
| | `relations.predicate` | `List[ClassLabel]` | 每条关系的谓词类别 | |
|
|
| `boxes`、`labels` 和 `attributes` 的外层长度相同。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构成一条有向关系： |
|
|
| ```python |
| 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_]], |
| ) |
| ``` |
|
|
| ## 类别编号 |
|
|
| 对象、谓词和属性 taxonomy 均在索引 `0` 保留 `__background__`： |
|
|
| - 对象前景类别编号为 `1`–`150`。 |
| - 谓词前景类别编号为 `1`–`50`。 |
| - 属性前景类别编号为 `1`–`200`。 |
|
|
| 没有属性的对象对应空列表，而不是包含 `__background__` 的列表。 |
|
|
| ## 边界框与图片尺寸 |
|
|
| `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 `image` 的实际尺寸一致。关系端点使用当前样本 |
| 对象数组中的零基索引。 |
|
|
| 本仓库提供可直接加载的 Parquet 数据，不包含原始 H5 或 JSON 文件。对象、关系和 |
| 属性标注可能继承 Visual Genome 中的类别偏差、长尾分布、歧义和错误。 |
|
|
| ## 使用限制 |
|
|
| - VG150 只保留高频类别，不能代表 Visual Genome 的完整开放词汇分布。 |
| - 关系类别分布高度不均衡，模型结果可能被少数高频谓词主导。 |
| - `boxes` 是对象边界框，不是实例分割轮廓。 |
| - 本仓库不重新授予原始图片版权。使用者应同时遵守 Visual Genome 的使用条款， |
| 并确认原始图片适用于自己的研究或发布场景。 |
| - 比较论文结果时，应确认对方使用相同的 VG150 taxonomy、过滤规则和 split。 |
|
|
| ## 引用 |
|
|
| 使用本数据集时，请引用 Visual Genome： |
|
|
| ```bibtex |
| @article{krishna2017visual, |
| title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations}, |
| author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and |
| Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and |
| Kalantidis, Yannis and Li, Li-Jia and Shamma, David A. and |
| Bernstein, Michael S. and Fei-Fei, Li}, |
| journal={International Journal of Computer Vision}, |
| volume={123}, |
| number={1}, |
| pages={32--73}, |
| year={2017} |
| } |
| ``` |
|
|
| 标准 VG150 场景图划分也常用于 Neural Motifs： |
|
|
| ```bibtex |
| @inproceedings{zellers2018neural, |
| title={Neural Motifs: Scene Graph Parsing with Global Context}, |
| author={Zellers, Rowan and Yatskar, Mark and Thomson, Sam and Choi, Yejin}, |
| booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, |
| year={2018} |
| } |
| ``` |
|
|
