Datasets:
Tasks:
Object Detection
Size:
n<1K
image_id
int64 0
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End of preview. Expand
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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.
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