README.md

metadata

task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow2huggingface

Dataset Labels

['pothole']

Number of Images

{'valid': 133, 'test': 66, 'train': 466}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

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

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.