---
license: cc-by-4.0
task_categories:
  - object-detection
tags:
  - scene-graph-generation
  - visual-relationship-detection
  - visual-genome
  - coco-format
language:
  - en
pretty_name: IndoorVG — Indoor Visual Genome (COCO format)
size_categories:
  - 10K<n<100K
---

# IndoorVG — Indoor Visual Genome (COCO format)

**IndoorVG** is a curated split of
[Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html)
targeting real-world **indoor** scenarios (kitchens, offices, living rooms, …).
It was proposed in
[Neau et al. (2024)](https://link.springer.com/chapter/10.1007/978-3-031-55015-7_25)
and reformatted here in standard COCO-JSON format.

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://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_239/paper.pdf)).

The 84 object classes and 37 predicate classes were **manually selected and
semi-automatically merged** to reduce label noise and ambiguity compared to VG150,
focusing on indoor-relevant concepts.

---

## Annotation overview

Each image comes with:
- **Object bounding boxes** — 84 indoor-focused object categories.
- **Scene-graph relations** — 37 predicate categories connecting pairs of objects as
  directed `(subject, predicate, object)` triplets.

![Annotation example — val split](indoorvg_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 |  9 538 |  125 411           |  72 291   |
| val   |    733 |   10 246           |   4 866   |
| test  |  4 403 |   61 278           |  29 367   |

---

## Object categories (84)

Manually curated indoor vocabulary: *bag, basket, bin, blind, book, bottle, bowl,
cabinet, ceiling, chair, …* Full list embedded in `dataset_info.description`.

## Predicate categories (37)

> above · against · at · attached to · behind · between · carrying · covering ·
> cutting · drinking · eating · filled with · for · hanging from · has · holding ·
> in · in front of · laying on · looking at · lying on · mounted on · near · of ·
> on · playing with · reading · sitting at · sitting on · standing on · taking ·
> talking on · under · using · watching · wearing · with

---

## Dataset structure

```python
DatasetDict({
    train: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 9538
    }),
    val: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 733
    }),
    test: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 4403
    }),
})
```

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/IndoorVG-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"])
```

This dataset can be used with the pycocotools API for scene graph generation:
```bash
pip install git+https://github.com/Maelic/pycocotools
```

```python
from pycocootools.coco import COCO

from datasets import load_dataset
ds = load_dataset("maelic/IndoorVG-coco-format")
# Convert Hugging Face dataset to COCO format
coco_ds = {
    "images": ds["train"]["image_id"],
    "annotations": ds["train"]["objects"],
    "rel_annotations": ds["train"]["relations"],
    "categories": json.loads(ds["train"].info.description)["categories"],
    "rel_categories": json.loads(ds["train"].info.description)["rel_categories"],
}
coco = COCO()
coco.dataset = coco_ds
coco.createIndex()

for img_id in coco.getImgIds():
    rel_ids = coco.getRelIds(imgIds=img_id)
    relations.extend(coco.loadRels(rel_ids))
```
---

## Citation

If you use this dataset, please cite the IndoorVG paper:

```bibtex
@incollection{neau2023defense,
  title={In defense of scene graph generation for human-robot open-ended interaction in service robotics},
  author={Neau, Ma{"e}lic and Santos, Paulo and Bosser, Anne-Gwenn and Buche, C{'e}dric},
  booktitle={Robot World Cup},
  pages={299--310},
  year={2023},
  publisher={Springer}
}
```

And 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 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.