README.md

---
license: mit
task_categories:
  - object-detection
tags:
  - scene-graph
  - visual-relationship-detection
  - panoptic-scene-graph
  - coco-format
language:
  - en
pretty_name: PSG — Panoptic Scene Graph (COCO format)
size_categories:
  - 10K<n<100K
---

# PSG — Panoptic Scene Graph (COCO format)

This dataset is a reformatted version of the **Panoptic Scene Graph (PSG)** benchmark
([Yang et al., NeurIPS 2022](https://arxiv.org/abs/2207.11247)) in standard COCO-JSON
format, ready for use with object detection and scene graph generation pipelines.

It was produced as part of the
[SGG-Benchmark](https://github.com/Maelic/SGG-Benchmark) framework and used to train
the models described in the **REACT** paper
([Neau et al., BMVC 2025](https://arxiv.org/abs/2405.16116)).

/!\ Disclaimer: this dataset does NOT contain original segmentation masks, but only 
bounding boxes and category labels. Thus this is NOT a panoptic dataset, but rather a 
scene graph dataset that can only be used to train bounding-box-based SGG models. 
The original PSG dataset can be downloaded from the [PSG project page](https://github.com/Jingkang50/OpenPSG).


---

## Annotation overview

Each image comes with:
- **Object bounding boxes** — 133 COCO object categories.
- **Scene-graph relations** — 56 predicate categories connecting pairs of objects as
  directed `(subject, predicate, object)` triplets.

![Annotation example — val split](psg_samples_val.png)

*Four random validation images with bounding boxes (coloured by category) and
relation arrows (yellow, labelled with the predicate name).*

---

## Dataset statistics

| Split | Images | Object annotations | Relations |
|-------|-------:|-------------------:|----------:|
| train | 45 564 | 494 213 | 254 214 |
| val   |  1 000 |  19 039 |   7 458 |
| test  |  2 186 |  24 910 |  13 705 |

---

## Object categories (133)

Standard 133-class COCO panoptic vocabulary: *person, bicycle, car, motorcycle,
airplane, bus, train, truck, boat, traffic light, …* (full list embedded in
`dataset_info.description`).

## Predicate categories (56)

> over · in front of · beside · on · in · attached to · hanging from · on back of ·
> falling off · going down · painted on · walking on · running on · crossing ·
> standing on · lying on · sitting on · flying over · jumping over · jumping from ·
> wearing · holding · carrying · looking at · guiding · kissing · eating · drinking ·
> feeding · biting · catching · picking · playing with · chasing · climbing ·
> cleaning · playing · touching · pushing · pulling · opening · cooking · talking to ·
> throwing · slicing · driving · riding · parked on · driving on · about to hit ·
> kicking · swinging · entering · exiting · enclosing · leaning on

---

## Dataset structure

```python
DatasetDict({
    train: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 45564
    }),
    val: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 1000
    }),
    test: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 2186
    }),
})
```

Each row contains:

| Field | Type | Description |
|-------|------|-------------|
| `image` | `Image` | PIL image |
| `image_id` | `int` | Original COCO image id |
| `width` / `height` | `int` | Image dimensions |
| `file_name` | `str` | Original filename |
| `objects` | `List[dict]` | `{id, category_id, bbox (xywh), area, iscrowd, segmentation}` |
| `relations` | `List[dict]` | `{id, subject_id, object_id, predicate_id}` — ids refer to `objects[*].id` |

---

## Usage

```python
from datasets import load_dataset
import json

ds = load_dataset("maelic/PSG-coco-format")

# Recover label maps from the embedded metadata
meta = json.loads(ds["train"].info.description)
cat_id2name  = {c["id"]: c["name"] for c in meta["categories"]}
pred_id2name = {c["id"]: c["name"] for c in meta["rel_categories"]}

sample = ds["train"][0]
image  = sample["image"]          # PIL Image
for obj in sample["objects"]:
    print(cat_id2name[obj["category_id"]], obj["bbox"])
for rel in sample["relations"]:
    print(rel["subject_id"], "--", pred_id2name[rel["predicate_id"]], "->", rel["object_id"])
```

---

## Citation

If you use this dataset, please cite the original PSG paper:

```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},
  pages        = {178--196},
  year         = {2022},
  organization = {Springer},
}
```

And the REACT paper if you use the SGG-Benchmark models:

```bibtex
@inproceedings{Neau_2025_BMVC,
  author    = {Ma\"elic Neau and Paulo Eduardo Santos and Anne-Gwenn Bosser
               and Akihiro Sugimoto and Cedric Buche},
  title     = {REACT: Real-time Efficiency and Accuracy Compromise for Tradeoffs
               in Scene Graph Generation},
  booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025,
               Sheffield, UK, November 24-27, 2025},
  publisher = {BMVA},
  year      = {2025},
  url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_239/paper.pdf},
}
```

---

## License

This dataset inherits the **MIT** license of the original PSG benchmark.
See the [MIT License](https://opensource.org/licenses/MIT) for details.