GQA200

GQA200 is the scene-graph-generation benchmark subset of GQA. This repository uses the standard GQA200 taxonomy with 200 foreground object classes and 100 foreground predicate classes.

Splits

Split	Images	Source
train	57,623	Standard GQA200 Train
validation	8,209	Standard GQA200 Test

The standard GQA200 Test annotations are intentionally exposed as validation. This repository does not define a separate test split.

Fields

• id: GQA image identifier.
• image: image embedded in Parquet and decoded by datasets as a PIL image.
• width, height: image dimensions.
• location, weather: nullable strings copied from the original GQA scene graph metadata.
• boxes: object boxes in [x1, y1, x2, y2] order.
• labels: GQA200 object ClassLabel IDs.
• relations: parallel subject_index, object_index, and predicate arrays.

Index 0 is __background__ in both ClassLabel vocabularies. Foreground object IDs are 1–200 and foreground predicate IDs are 1–100. Attributes and the full raw GQA taxonomy are not included.

Boxes are clipped to image bounds. Objects with zero area after clipping are removed, remaining object indices are compacted, and relations touching removed objects are discarded.

Loading

from datasets import load_dataset

dataset = load_dataset("wliafe/GQA200")
sample = dataset["train"][0]

image = sample["image"]  # PIL.Image.Image
object_name = dataset["train"].features["labels"].feature.int2str(
    sample["labels"][0]
)
predicate_name = (
    dataset["train"]
    .features["relations"]["predicate"]
    .feature.int2str(sample["relations"]["predicate"][0])
)

location and weather may be None when the original scene graph omits the field.

Sources and citation

The original format is documented on the GQA download page. The GQA200 benchmark split and taxonomy follow the SHA-GCL evaluation setup.

@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={CVPR},
  year={2019}
}

@inproceedings{dong2022stacked,
  title={Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation},
  author={Dong, Xingning and Gan, Tian and Song, Xianjing and Wu, Jinhui and Cheng, Yuan and Nie, Liqiang},
  booktitle={CVPR},
  year={2022}
}