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maelic/GQA200-coco-format

GQA — General Question Answering (COCO format) This dataset is the GQA200 split of the GQA dataset (Hudson et al., 2019), reformatted in standard COCO-JSON format. GQA200 contains the top 200 object categories and 100 relations from the original GQA dataset, selected by frequency in the Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation paper. This dataset has no official test split since it was used for question answering rather… See the full description on the dataset page: https://huggingface.co/datasets/maelic/GQA200-coco-format.

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GQA — General Question Answering (COCO format)

This dataset is the GQA200 split of the GQA dataset (Hudson et al., 2019), reformatted in standard COCO-JSON format. GQA200 contains the top 200 object categories and 100 relations from the original GQA dataset, selected by frequency in the Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation paper. This dataset has no official test split since it was used for question answering rather than scene graph generation (for test there is no scene graph annotations).

This version in COCO format was produced as part of the SGG-Benchmark framework and used to train the models described in the REACT++ paper (Neau et al., 2026).

Annotation overview

Each image comes with:

—Object bounding boxes — 200 GQA object categories.
—Scene-graph relations — 100 predicate categories connecting pairs of objects as directed (subject, predicate, object) triplets.

[image]

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	57 623	775 744	238 720
val	8 209	110 030	33 487

Object categories (200)

Top-200 GQA object vocabulary used by the standard SGG split. Full list embedded in dataset_info.description.

Predicate categories (100)

Top 100 GQA predicate vocabulary used by the standard SGG split. Full list embedded in dataset_info.description.

Dataset structure

python

DatasetDict({
    train: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 57623
    }),
    val: Dataset({
        features: ['image', 'image_id', 'width', 'height', 'file_name',
                   'objects', 'relations'],
        num_rows: 8209
    }),
})

Each row contains:

Field	Type	Description
`image`	`Image`	PIL image
`image_id`	`int`	Original GQA200 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/GQA200-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 GQA:

bibtex

@inproceedings{hudson2019gqa,
  title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
  author={Hudson, Drew A and Manning, Christopher D},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={6700--6709},
  year={2019}
}

And also the paper that established the GQA-200 split:

bibtex

@inproceedings{dong2022stacked,
  title={Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation},
  author={Dong, Xingning and Gan, Tian and Song, Xuemeng and Wu, Jianlong and Cheng, Yuan and Nie, Liqiang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19427--19436},
  year={2022}
}

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

The GQA images and annotations are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.