---
annotations_creators: []
language: en
size_categories:
- 10K<n<100K
task_categories:
- object-detection
task_ids: []
pretty_name: GQA-35k
tags:
- fiftyone
- image
- object-detection
dataset_summary: '

  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.


  ## 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 Card for GQA-35k

![image](gqa.png)

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. |
| &nbsp;&nbsp;&nbsp;&nbsp;object | dict | A visual element in the image (node). |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;name | str | The name of the object, e.g. person, apple or sky. |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x | int | Horizontal position of the object bounding box (top left). |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;y | int | Vertical position of the object bounding box (top left). |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;w | int | The object bounding box width in pixels. |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;h | int | The object bounding box height in pixels. |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;attributes | [str] | A list of all the attributes of the object, e.g. blue, small, running. |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;relations | [dict] | A list of all outgoing relations (edges) from the object (source). |
| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;relation | dict | A triple representing the relation between source and target objects. |

Note: I've used non-breaking spaces (`&nbsp;`) 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}
}
```