elliemci/building_damages

Dataset Labels

['combined damage', 'flexural', 'minorrotation', 'moderaterotation', 'severerotation', 'shear', 'undamage', 'cracks', 'water']

Number of Images

{'valid': 1266, 'test': 736, 'train': 5255}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("elliemci/building_damages", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/elliemci/building-damages-nnk2b/dataset/2

Citation

@misc{
                            building-damages-nnk2b_dataset,
                            title = { building damages Dataset },
                            type = { Open Source Dataset },
                            author = { EllieMci },
                            howpublished = { \\url{ https://universe.roboflow.com/elliemci/building-damages-nnk2b } },
                            url = { https://universe.roboflow.com/elliemci/building-damages-nnk2b },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2025 },
                            month = { feb },
                            note = { visited on 2025-02-03 },
                            }

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on February 3, 2025 at 6:53 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 7257 images. Damage-in-structures-Undamage-Flexural-Shear-Combined-water--defects-cracks-waterdamage-crack are annotated in COCO format.

The following pre-processing was applied to each image:

No image augmentation techniques were applied.