---
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: 8889584511
    num_examples: 62407
  - name: validation
    num_bytes: 1260526902
    num_examples: 8915
  - name: test
    num_bytes: 2706740524
    num_examples: 17832
  download_size: 12730567170
  dataset_size: 12856851937
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---


# Visual Genome Scene Graph Dataset

该仓库提供经过结构化处理的 Visual Genome 场景图数据集，可直接通过 Hugging Face `datasets` 加载。数据采用常用的 VG150 类别体系，包含 150 个前景对象类别、50 个前景关系类别和 200 个前景属性类别；每组类别还包含索引为 0 的 `__background__`。

## 数据划分

| Split | 样本数 |
| --- | ---: |
| train | 62,407 |
| validation | 8,915 |
| test | 17,832 |
| **合计** | **89,154** |

仓库下载大小约为 12.7 GB。图片直接嵌入 Parquet，因此 `image` 字段由 Hugging Face 解码为 PIL 图片对象。

## 字段说明

每行表示一张图片及其场景图：

- `id`：样本 ID。
- `image`：Hugging Face `Image` 字段，读取样本时返回 PIL 图片。
- `width`、`height`：图片宽高。
- `boxes`：对象边界框，格式为 `[x_min, y_min, x_max, y_max]`。
- `labels`：与 `boxes` 一一对应的对象类别 ID。
- `attributes`：与对象一一对应的属性类别 ID 列表。
- `relations.subject_index`：关系主语在对象数组中的索引。
- `relations.object_index`：关系宾语在对象数组中的索引。
- `relations.predicate`：关系类别 ID。

对象、属性和关系均使用 `ClassLabel`，可以通过 Dataset 的 `features` 取得类别名称。

## 加载数据

```python
from datasets import load_dataset


dataset = load_dataset("wliafe/vg")
print(dataset)

sample = dataset["train"][0]
print(sample["id"])
print(sample["image"].size)
print(len(sample["boxes"]))
print(len(sample["relations"]["predicate"]))
```

如需避免立即解码图片，可以先把 `image` 字段转换为 `decode=False`：

```python
from datasets import Image, load_dataset


dataset = load_dataset("wliafe/vg")
dataset = dataset.cast_column("image", Image(decode=False))

sample = dataset["train"][0]
print(sample["image"])
```

## 读取类别名称

```python
from datasets import load_dataset


dataset = load_dataset("wliafe/vg")
features = dataset["train"].features

object_names = features["labels"].feature.names
attribute_names = features["attributes"].feature.feature.names
predicate_names = features["relations"]["predicate"].feature.names

sample = dataset["train"][0]
for subject_index, object_index, predicate_id in zip(
    sample["relations"]["subject_index"],
    sample["relations"]["object_index"],
    sample["relations"]["predicate"],
):
    subject_label = object_names[sample["labels"][subject_index]]
    object_label = object_names[sample["labels"][object_index]]
    predicate = predicate_names[predicate_id]
    print(f"{subject_label} --{predicate}--> {object_label}")
```

## 标注约定

- 边界框会裁剪到图片范围内。
- 裁剪后面积为零的对象不会保留。
- 引用已删除对象的关系不会保留。
- 关系端点保存为当前对象数组的索引，而不是 Visual Genome 的原始对象 ID。
- `boxes`、`labels` 和 `attributes` 使用相同的对象顺序。

## 引用

如果该数据集对你的研究有帮助，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},
  pages={32--73},
  year={2017}
}
```

数据的使用许可和适用范围请以 Visual Genome 官方发布条款为准。