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--- |
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annotations_creators: [] |
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language: en |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- object-detection |
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task_ids: [] |
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pretty_name: GQA-35k |
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tags: |
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- fiftyone |
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- image |
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- object-detection |
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dataset_summary: ' |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples. |
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## Installation |
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If you haven''t already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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import fiftyone.utils.huggingface as fouh |
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# Load the dataset |
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# Note: other available arguments include ''max_samples'', etc |
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dataset = fouh.load_from_hub("Voxel51/GQA-Scene-Graph") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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' |
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--- |
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# Dataset Card for GQA-35k |
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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. |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples. |
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Note: This is a 35,000 sample subset which does not contain questions, only the scene graph annotations as detection-level attributes. |
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You can find the recipe notebook for creating the dataset [here](https://colab.research.google.com/drive/1IjyvUSFuRtW2c5ErzSnz1eB9syKm0vo4?usp=sharing) |
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## Installation |
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If you haven't already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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import fiftyone.utils.huggingface as fouh |
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# Load the dataset |
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# Note: other available arguments include 'max_samples', etc |
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dataset = fouh.load_from_hub("Voxel51/GQA-Scene-Graph") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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## Dataset Details |
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### Dataset Description |
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## Scene Graph Annotations |
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- Each of the 113K images in GQA is associated with a detailed scene graph describing the objects, attributes and relations present. |
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- The scene graphs are based on a cleaner version of the Visual Genome scene graphs. |
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- For each image, the scene graph is provided as a dictionary (sceneGraph) containing: |
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- Image metadata like width, height, location, weather |
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- A dictionary (objects) mapping each object ID to its name, bounding box coordinates, attributes, and relations[6] |
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- Relations are represented as triples specifying the predicate (e.g. "holding", "on", "left of") and the target object ID[6] |
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- **Curated by:** Drew Hudson & Christopher Manning |
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- **Shared by:** [Harpreet Sahota](https://x.com/datascienceharp), Hacker-in-Residence at Voxel51 |
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- **Language(s) (NLP):** en |
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- **License:** |
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- GQA annotations (scene graphs, questions, programs) licensed under CC BY 4.0 |
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- Images sourced from Visual Genome may have different licensing terms |
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### Dataset Sources |
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- **Repository:** https://cs.stanford.edu/people/dorarad/gqa/ |
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- **Paper :** https://arxiv.org/pdf/1902.09506 |
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- **Demo:** https://cs.stanford.edu/people/dorarad/gqa/vis.html |
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## Dataset Structure |
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Here's the information presented as a markdown table: |
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| Field | Type | Description | |
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|-------|------|-------------| |
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| location | str | Optional. The location of the image, e.g. kitchen, beach. | |
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| weather | str | Optional. The weather in the image, e.g. sunny, cloudy. | |
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| objects | dict | A dictionary from objectId to its object. | |
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| object | dict | A visual element in the image (node). | |
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| name | str | The name of the object, e.g. person, apple or sky. | |
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| x | int | Horizontal position of the object bounding box (top left). | |
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| y | int | Vertical position of the object bounding box (top left). | |
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| w | int | The object bounding box width in pixels. | |
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| h | int | The object bounding box height in pixels. | |
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| attributes | [str] | A list of all the attributes of the object, e.g. blue, small, running. | |
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| relations | [dict] | A list of all outgoing relations (edges) from the object (source). | |
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| relation | dict | A triple representing the relation between source and target objects. | |
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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. |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@article{Hudson_2019, |
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title={GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering}, |
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ISBN={9781728132938}, |
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url={http://dx.doi.org/10.1109/CVPR.2019.00686}, |
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DOI={10.1109/cvpr.2019.00686}, |
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journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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publisher={IEEE}, |
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author={Hudson, Drew A. and Manning, Christopher D.}, |
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year={2019}, |
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month={Jun} |
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} |
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``` |
