Voxel51/GQA-Scene-Graph

Dataset Card for GQA-35k

The GQA (Visual Reasoning in the Real World) dataset is a large-scale visual question answering dataset that includes scene graph annotations for each image.

This is a FiftyOne dataset with 35000 samples.

Note: This is a 35,000 sample subset which does not contain questions, only the scene graph annotations as detection-level attributes.

You can find the recipe notebook for creating the dataset here

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/GQA-Scene-Graph")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

Scene Graph Annotations

• Each of the 113K images in GQA is associated with a detailed scene graph describing the objects, attributes and relations present.

• The scene graphs are based on a cleaner version of the Visual Genome scene graphs.

• For each image, the scene graph is provided as a dictionary (sceneGraph) containing:

  • Image metadata like width, height, location, weather
  • A dictionary (objects) mapping each object ID to its name, bounding box coordinates, attributes, and relations[6]
  • Relations are represented as triples specifying the predicate (e.g. "holding", "on", "left of") and the target object ID[6]

• Curated by: Drew Hudson & Christopher Manning

• Shared by: Harpreet Sahota, Hacker-in-Residence at Voxel51

• Language(s) (NLP): en

• License:

• GQA annotations (scene graphs, questions, programs) licensed under CC BY 4.0

• Images sourced from Visual Genome may have different licensing terms

Dataset Sources

• Repository: https://cs.stanford.edu/people/dorarad/gqa/
• Paper : https://arxiv.org/pdf/1902.09506
• Demo: https://cs.stanford.edu/people/dorarad/gqa/vis.html

Dataset Structure

Here's the information presented as a markdown table:

Field	Type	Description
location	str	Optional. The location of the image, e.g. kitchen, beach.
weather	str	Optional. The weather in the image, e.g. sunny, cloudy.
objects	dict	A dictionary from objectId to its object.
object	dict	A visual element in the image (node).
name	str	The name of the object, e.g. person, apple or sky.
x	int	Horizontal position of the object bounding box (top left).
y	int	Vertical position of the object bounding box (top left).
w	int	The object bounding box width in pixels.
h	int	The object bounding box height in pixels.
attributes	[str]	A list of all the attributes of the object, e.g. blue, small, running.
relations	[dict]	A list of all outgoing relations (edges) from the object (source).
relation	dict	A triple representing the relation between source and target objects.

Note: I've used non-breaking spaces ( ) to indent the nested fields in the 'Field' column to represent the hierarchy. This helps to visually distinguish the nested structure within the table.

Citation

BibTeX:

@article{Hudson_2019,
   title={GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering},
   ISBN={9781728132938},
   url={http://dx.doi.org/10.1109/CVPR.2019.00686},
   DOI={10.1109/cvpr.2019.00686},
   journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   publisher={IEEE},
   author={Hudson, Drew A. and Manning, Christopher D.},
   year={2019},
   month={Jun}
}