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---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="lipi17/building-cracks" src="https://huggingface.co/datasets/lipi17/building-cracks/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['crack']
```
### Number of Images
```json
{'valid': 433, 'test': 211, 'train': 1490}
```
### 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("lipi17/building-cracks", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg/dataset/2](https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg/dataset/2?ref=roboflow2huggingface)
### Citation
```
@misc{ crack-detection-y5kyg_dataset,
title = { Crack Detection Dataset },
type = { Open Source Dataset },
author = { António Raimundo },
howpublished = { \\url{ https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg } },
url = { https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { feb },
note = { visited on 2023-10-21 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on February 10, 2023 at 3:51 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 2134 images.
Soil 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.
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