CODEBRIM: COncrete DEfect BRidge IMage Dataset 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 please cite it as follows: "Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh. Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019" 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: * CODEBRIM_original_images: contains the original full-resolution images and bounding box annotations * CODEBRIM_cropped_dataset: contains the extracted crops/patches with corresponding class labels from the bounding boxes * CODEBRIM_classification_dataset: contains the cropped patches with corresponding class labels split into training, validation and test sets for machine learning * 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.