---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface

---

<div align="center">
  <img width="640" alt="aviola/DominoDataset2" src="https://huggingface.co./datasets/aviola/DominoDataset2/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': 23, 'test': 11, 'train': 240}
```


### 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/DominoDataset2", name="full")
example = ds['train'][0]
```

### Roboflow Dataset Page
[https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/3](https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/3?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 3:57 PM 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 274 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)
* Grayscale (CRT phosphor)

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 shear of between -10° to +10° horizontally and -10° to +10° vertically
* Random Gaussian blur of between 0 and 1.5 pixels
* Salt and pepper noise was applied to 0.1 percent of pixels