metadata
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
Dataset Labels
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15']
Number of Images
{'valid': 30, 'test': 10, 'train': 222}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("aviola/DominoDataset3", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/virginia-tech-xente/dominos-6ptm5/dataset/5
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 4:04 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 262 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
- 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