--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface ---
lipi17/building-cracks-merged
### Dataset Labels ``` ['crack', 'stairstep_crack'] ``` ### Number of Images ```json {'test': 11, 'valid': 433, 'train': 947} ``` ### 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-merged", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks/dataset/1](https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks/dataset/1?ref=roboflow2huggingface) ### Citation ``` @misc{ merged-building-cracks_dataset, title = { Merged-Building-Cracks Dataset }, type = { Open Source Dataset }, author = { Lipi Deepaakshi Patnaik }, howpublished = { \\url{ https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks } }, url = { https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { oct }, note = { visited on 2023-10-21 }, } ``` ### License MIT ### Dataset Summary This dataset was exported via roboflow.com on October 21, 2023 at 12:21 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 1391 images. Cracks 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.