#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the Hugging Face integration docs.
# 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](https://github.com/voxel51/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](https://colab.research.google.com/drive/1IjyvUSFuRtW2c5ErzSnz1eB9syKm0vo4?usp=sharing)
## Installation
If you haven’t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
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](https://x.com/datascienceharp), 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:**
```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}
}
```