{"url":"/dataset/ccic","name":"CCIC","full_name":"Concrete Crack Images for Classification","description_markdown":"The dataset contains concrete images having cracks. The data is collected from various METU Campus Buildings.\r\nThe dataset is divided into two as negative and positive crack images for image classification. \r\nEach class has 20000images with a total of 40000 images with 227 x 227 pixels with RGB channels. \r\nThe dataset is generated from 458 high-resolution images (4032x3024 pixel) with the method proposed by Zhang et al (2016). \r\nHigh-resolution images have variance in terms of surface finish and illumination conditions. \r\nNo data augmentation in terms of random rotation or flipping is applied. \r\n\r\nIf you use this dataset please cite: \r\n2018 – Özgenel, Ç.F., Gönenç Sorguç, A. “Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings”, ISARC 2018, Berlin.\r\n\r\nLei Zhang , Fan Yang , Yimin Daniel Zhang, and Y. J. Z., Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016). Road Crack Detection Using Deep Convolutional Neural Network. In 2016 IEEE International Conference on Image Processing (ICIP). http://doi.org/10.1109/ICIP.2016.7533052","description_withheld":null,"homepage":"https://data.mendeley.com/datasets/5y9wdsg2zt/2","introduced_date":"2019-07-23","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY 4.0","url":null},"modalities":[],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"}],"languages":[],"variants":["CCIC"],"data_loaders":[{"repo":"https://github.com/MILIND-RAJ/Concrete-Crack-Images-Classification-Using-ResNet50","url":"https://github.com/MILIND-RAJ/Concrete-Crack-Images-Classification-Using-ResNet50","frameworks":["pytorch"]}],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}