--- 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 ```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