manot/pothole-segmentation

Dataset Labels

['potholes', 'object', 'pothole', 'potholes']

Number of Images

{'valid': 157, 'test': 80, 'train': 582}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("manot/pothole-segmentation", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d/dataset/3

Citation

@misc{ road-damage-xvt2d_dataset,
    title = { road damage Dataset },
    type = { Open Source Dataset },
    author = { abdulmohsen fahad },
    howpublished = { \\url{ https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d } },
    url = { https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d },
    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 8:47 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 819 images. Potholes are annotated in COCO format.

The following pre-processing was applied to each image:

• Auto-orientation of pixel data (with EXIF-orientation stripping)

No image augmentation techniques were applied.