--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface ---
manot/pothole-segmentation2
### Dataset Labels ``` ['pothole'] ``` ### Number of Images ```json {'valid': 133, 'test': 66, 'train': 466} ``` ### 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("manot/pothole-segmentation2", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/gurgen-hovsepyan-mbrnv/pothole-detection-gilij/dataset/2](https://universe.roboflow.com/gurgen-hovsepyan-mbrnv/pothole-detection-gilij/dataset/2?ref=roboflow2huggingface) ### Citation ``` @misc{ pothole-detection-gilij_dataset, title = { pothole-detection Dataset }, type = { Open Source Dataset }, author = { Gurgen Hovsepyan }, howpublished = { \\url{ https://universe.roboflow.com/gurgen-hovsepyan-mbrnv/pothole-detection-gilij } }, url = { https://universe.roboflow.com/gurgen-hovsepyan-mbrnv/pothole-detection-gilij }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { jun }, note = { visited on 2023-06-13 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on June 13, 2023 at 12:48 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 665 images. Pothole 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) No image augmentation techniques were applied.