README.md

---
license: mit
task_categories:
  - object-detection
tags:
  - scene-graph-generation
  - visual-relationship-detection
  - visual-genome
  - vg150
  - coco-format
language:
  - en
pretty_name: VG150 — Visual Genome 150 (COCO format)
size_categories:
  - 100K<n<1M
---

# VG150 — Visual Genome 150 (COCO format)

This dataset is the standard **VG150** split of
[Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html)
(Krishna et al., 2017), the most widely used benchmark for Scene Graph Generation,
reformatted in standard COCO-JSON format. VG150 contains the top 150 object categories 
and 50 relations from the original Visual Genome dataset, selected by frequency in the 
[Scene Graph Generation by Iterative Message Passing paper](https://arxiv.org/abs/1701.02426).

This version in COCO format 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://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_239/paper.pdf)).

> ⚠️ **Bias warning**: VG150 has been heavily criticised for high class overlap and
> annotation biases (e.g. *person / man / men / people*). See
> [VrR-VG (ICCV'19)](https://openaccess.thecvf.com/content_ICCV_2019/html/Liang_VrR-VG_Refocusing_Visually-Relevant_Relationships_ICCV_2019_paper.html)
> and
> [Neau et al. (ICCVW'23)](https://openaccess.thecvf.com/content/ICCV2023W/SG2RL/html/Neau_Fine-Grained_is_Too_Coarse_A_Novel_Data-Centric_Approach_for_Efficient_ICCVW_2023_paper.html)
> for reference.

---

## Annotation overview

Each image comes with:
- **Object bounding boxes** — 150 Visual Genome object categories.
- **Scene-graph relations** — 50 predicate categories connecting pairs of objects as
  directed `(subject, predicate, object)` triplets.

![Annotation example — val split](vg150_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 |  73 538 |  793 061           |  439 063   |
| val   |   4 844 |   54 415           |   30 133   |
| test  |  27 032 |  297 922           |  153 509   |

---

## Object categories (150)

Top-150 Visual Genome object vocabulary used by the standard SGG split. Full list
embedded in `dataset_info.description`.

## Predicate categories (50)

> and · says · belonging to · over · parked on · growing on · standing on · made of ·
> attached to · at · in · hanging from · wears · in front of · from · for · watching ·
> lying on · to · behind · flying in · looking at · on back of · holding · between ·
> laying on · riding · has · across · wearing · walking on · eating · above · part of ·
> walking in · sitting on · under · covered in · carrying · using · along · with · on ·
> covering · of · against · playing · near · painted on · mounted on

---

## Dataset structure

```python
DatasetDict({
    train: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 73538
    }),
    val: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 4844
    }),
    test: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 27032
    }),
})
```

Each row contains:

| Field | Type | Description |
|-------|------|-------------|
| `image` | `Image` | PIL image |
| `image_id` | `int` | Original Visual Genome 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/VG150-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 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 others},
  journal={International journal of computer vision},
  volume={123},
  number={1},
  pages={32--73},
  year={2017},
  publisher={Springer}
}
```

And the original paper that established the VG150 split:

```bibtex
@inproceedings{xu2017scene,
  title={Scene graph generation by iterative message passing},
  author={Xu, Danfei and Zhu, Yuke and Choy, Christopher B and Fei-Fei, Li},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5410--5419},
  year={2017}
}
```

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

Visual Genome images and annotations are released under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
license.