README.md

---
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}
}
```