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---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="aviola/DominoDataset" src="https://huggingface.co/datasets/aviola/DominoDataset/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15']
```
### Number of Images
```json
{'valid': 11, 'test': 10, 'train': 249}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("aviola/DominoDataset", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/2](https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/2?ref=roboflow2huggingface)
### Citation
```
@misc{
dominos-6ptm5_dataset,
title = { dominos Dataset },
type = { Open Source Dataset },
author = { Virginia Tech },
howpublished = { \\url{ https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5 } },
url = { https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { sep },
note = { visited on 2024-09-14 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on September 14, 2024 at 6:38 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 270 images.
Dominos are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
* 50% probability of horizontal flip
* Randomly crop between 0 and 20 percent of the image
* Random rotation of between -15 and +15 degrees
* Random Gaussian blur of between 0 and 1.5 pixels
* Salt and pepper noise was applied to 0.1 percent of pixels
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