CODEBRIM: COncrete DEfect BRidge IMage Dataset

<strong>CODEBRIM: COncrete DEfect BRidge IMage Dataset</strong> for multi-target multi-class concrete defect classification in computer vision and machine learning.

Dataset as presented and detailed in our CVPR 2019 publication: http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html or https://arxiv.org/abs/1904.08486 . If you make use of the dataset <strong>please cite it as follows</strong>:

<strong>"Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh. <em>Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset</em>. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019"</strong>

We offer a supplementary GitHub repository with code to reproduce the paper and data loaders: https://github.com/ccc-frankfurt/meta-learning-CODEBRIM

For ease of use we provide the dataset in multiple different versions.

Files contained:<br>
* CODEBRIM_original_images: contains the original full-resolution images and bounding box annotations<br>
* CODEBRIM_cropped_dataset: contains the extracted crops/patches with corresponding class labels from the bounding boxes <br>
* CODEBRIM_classification_dataset: contains the cropped patches with corresponding class labels split into training, validation and test sets for machine learning<br>
* CODEBRIM_classification_balanced_dataset: similar to "CODEBRIM_classification_dataset" but with the exact replication of training images to balance the dataset in order to reproduce results obtained in the paper.