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dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: panoptic_mask
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': person
'2': bicycle
'3': car
'4': motorcycle
'5': airplane
'6': bus
'7': train
'8': truck
'9': boat
'10': traffic light
'11': fire hydrant
'12': stop sign
'13': parking meter
'14': bench
'15': bird
'16': cat
'17': dog
'18': horse
'19': sheep
'20': cow
'21': elephant
'22': bear
'23': zebra
'24': giraffe
'25': backpack
'26': umbrella
'27': handbag
'28': tie
'29': suitcase
'30': frisbee
'31': skis
'32': snowboard
'33': sports ball
'34': kite
'35': baseball bat
'36': baseball glove
'37': skateboard
'38': surfboard
'39': tennis racket
'40': bottle
'41': wine glass
'42': cup
'43': fork
'44': knife
'45': spoon
'46': bowl
'47': banana
'48': apple
'49': sandwich
'50': orange
'51': broccoli
'52': carrot
'53': hot dog
'54': pizza
'55': donut
'56': cake
'57': chair
'58': couch
'59': potted plant
'60': bed
'61': dining table
'62': toilet
'63': tv
'64': laptop
'65': mouse
'66': remote
'67': keyboard
'68': cell phone
'69': microwave
'70': oven
'71': toaster
'72': sink
'73': refrigerator
'74': book
'75': clock
'76': vase
'77': scissors
'78': teddy bear
'79': hair drier
'80': toothbrush
'81': banner
'82': blanket
'83': bridge
'84': cardboard
'85': counter
'86': curtain
'87': door-stuff
'88': floor-wood
'89': flower
'90': fruit
'91': gravel
'92': house
'93': light
'94': mirror-stuff
'95': net
'96': pillow
'97': platform
'98': playingfield
'99': railroad
'100': river
'101': road
'102': roof
'103': sand
'104': sea
'105': shelf
'106': snow
'107': stairs
'108': tent
'109': towel
'110': wall-brick
'111': wall-stone
'112': wall-tile
'113': wall-wood
'114': water-other
'115': window-blind
'116': window-other
'117': tree-merged
'118': fence-merged
'119': ceiling-merged
'120': sky-other-merged
'121': cabinet-merged
'122': table-merged
'123': floor-other-merged
'124': pavement-merged
'125': mountain-merged
'126': grass-merged
'127': dirt-merged
'128': paper-merged
'129': food-other-merged
'130': building-other-merged
'131': rock-merged
'132': wall-other-merged
'133': rug-merged
- name: segments
struct:
- name: id
list: int64
- name: area
list: int64
- name: iscrowd
list: bool
- name: isthing
list: bool
- name: relations
struct:
- name: subject_index
list: int64
- name: object_index
list: int64
- name: predicate
list:
class_label:
names:
'0': __background__
'1': over
'2': in front of
'3': beside
'4': 'on'
'5': in
'6': attached to
'7': hanging from
'8': on back of
'9': falling off
'10': going down
'11': painted on
'12': walking on
'13': running on
'14': crossing
'15': standing on
'16': lying on
'17': sitting on
'18': flying over
'19': jumping over
'20': jumping from
'21': wearing
'22': holding
'23': carrying
'24': looking at
'25': guiding
'26': kissing
'27': eating
'28': drinking
'29': feeding
'30': biting
'31': catching
'32': picking
'33': playing with
'34': chasing
'35': climbing
'36': cleaning
'37': playing
'38': touching
'39': pushing
'40': pulling
'41': opening
'42': cooking
'43': talking to
'44': throwing
'45': slicing
'46': driving
'47': riding
'48': parked on
'49': driving on
'50': about to hit
'51': kicking
'52': swinging
'53': entering
'54': exiting
'55': enclosing
'56': leaning on
splits:
- name: train
num_bytes: 8178055254
num_examples: 46563
- name: validation
num_bytes: 380822615
num_examples: 2186
download_size: 8560720927
dataset_size: 8558877869
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Panoptic Scene Graph
PSG（Panoptic Scene Graph）是面向全景场景图生成的数据集。每个场景图节点不仅包含对象类别和边界框，还通过 panoptic segmentation mask 与像素级区域对应。数据集同时覆盖 thing 和 stuff 类别。
本仓库将 RGB 图片和 panoptic PNG 直接嵌入 Parquet。通过 Hugging Face `Image` feature 加载后，两者都会解码为 Pillow 图像，不需要额外下载图片目录。
## 数据集规模
| Split | 样本 | 对象 / segments | 关系 |
| --- | ---: | ---: | ---: |
| `train` | 46,563 | 513,243 | 261,666 |
| `validation` | 2,186 | 24,910 | 13,705 |
| **总计** | **48,749** | **538,153** | **275,371** |
`validation` 对应完整 PSG 发布版中的官方 test split。完整发布版带有 test ground truth，因此这里保留其对象、mask 和关系标注，并改名为 Hugging Face 常用的 `validation`。部分样本没有关系，其关系数组为空。
PSG 中有少量不同样本引用同一张 COCO 图片；`id` 是 PSG 样本 ID，不应使用图片文件名替代。
## 加载
```python
from datasets import load_dataset
dataset = load_dataset("wliafe/PSG")
sample = dataset["train"][0]
image = sample["image"] # PIL.Image.Image
panoptic_mask = sample["panoptic_mask"] # PIL.Image.Image
print(sample["id"], image.size, panoptic_mask.size)
```
## 字段
| 字段 | 类型 | 说明 |
| --- | --- | --- |
| `id` | `string` | PSG 样本 ID |
| `image` | `Image` | 嵌入式 RGB 图片；源文件主要是 JPEG，少量 `.jpg` 实际为 PNG |
| `panoptic_mask` | `Image` | 嵌入式 RGB 编码 panoptic PNG |
| `width` / `height` | `int32` | 解码后 RGB 图片的实际像素尺寸 |
| `boxes` | `List[[float32; 4]]` | 与 segments 平行的 `[x1, y1, x2, y2]` 边界框 |
| `labels` | `List[ClassLabel]` | 与 segments 平行的对象类别 |
| `segments.id` | `List[int64]` | panoptic mask 中的 segment ID |
| `segments.area` | `List[int64]` | segment 的标注面积 |
| `segments.iscrowd` | `List[bool]` | COCO crowd 标记 |
| `segments.isthing` | `List[bool]` | thing/stuff 标记 |
| `relations.subject_index` | `List[int64]` | 主语在当前 segment 数组中的零基索引 |
| `relations.object_index` | `List[int64]` | 宾语在当前 segment 数组中的零基索引 |
| `relations.predicate` | `List[ClassLabel]` | 有向关系的谓词类别 |
`boxes`、`labels` 和四个 `segments` 数组长度相同。同一位置表示同一个场景图节点。三个 `relations` 数组长度也相同，同一位置共同表示一条有向关系。
## Panoptic mask
panoptic PNG 使用 RGB 三通道编码整数 segment ID：
```python
import numpy as np
rgb = np.asarray(sample["panoptic_mask"].convert("RGB"), dtype=np.int64)
segment_id_map = (
rgb[..., 0]
+ 256 * rgb[..., 1]
+ 256 * 256 * rgb[..., 2]
)
first_segment_id = sample["segments"]["id"][0]
first_segment_mask = segment_id_map == first_segment_id
print(first_segment_mask.sum())
```
mask 中的非零 segment ID 与 `segments.id` 对应。背景像素的 ID 为 0，这与 taxonomy 中保留的 `__background__` 类别编号是两个不同概念。
## 类别与关系
对象和谓词 taxonomy 都在索引 `0` 保留 `__background__`：
- 对象前景类别编号为 `1`–`133`，依次对应 80 个 thing 类和 53 个 stuff 类。
- 谓词前景类别编号为 `1`–`56`。
- `subject_index` 和 `object_index` 是样本内部 segment 数组的位置，不是类别 ID。
```python
features = dataset["train"].features
object_names = features["labels"].feature.names
predicate_names = features["relations"]["predicate"].feature.names
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，并位于图片边界内。`width` 和 `height` 与解码后的 RGB 图片及 panoptic mask 尺寸一致。
## 使用限制
- 类别和谓词呈长尾分布，评估结果可能受高频类别主导。
- 场景图和分割标注可能包含遗漏、歧义或类别噪声。
- 本仓库不重新授予原始 COCO 图片版权；使用者应遵守 COCO 与 PSG 的许可和使用要求。
- 比较模型结果时，应确认采用相同 split、taxonomy 和 background 编号。
## 引用
```bibtex
@inproceedings{yang2022panoptic,
title={Panoptic Scene Graph Generation},
author={Yang, Jingkang and Ang, Yi Zhe and Guo, Zujin and Zhou, Kaiyang
and Zhang, Wayne and Liu, Ziwei},
booktitle={European Conference on Computer Vision},
year={2022}
}
```
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