| --- |
| 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 |
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