README.md

metadata

task_categories:
  - image-segmentation
tags:
  - roboflow
  - roboflow2huggingface
  - Construction
  - Self Driving
  - Transportation
  - Damage Risk

Dataset Labels

['pothole']

Number of Images

{'test': 5, 'train': 80, 'valid': 5}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

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

Roboflow Dataset Page

https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4

Citation

@misc{ pothole-detection-irkz9_dataset,
    title = { Pothole Detection Dataset },
    type = { Open Source Dataset },
    author = { IMACS Pothole Detection },
    howpublished = { \\url{ https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 } },
    url = { https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9 },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jan },
    note = { visited on 2023-01-15 },
}

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on January 15, 2023 at 6:38 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 90 images. Pothole are annotated in COCO format.

The following pre-processing was applied to each image:

No image augmentation techniques were applied.