DominoDataset / README.md
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metadata
task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow2huggingface
aviola/DominoDataset

Dataset Labels

['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15']

Number of Images

{'valid': 11, 'test': 10, 'train': 249}

How to Use

pip install datasets
  • Load the dataset:
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

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