DOI: 10.5281/zenodo.5020566
title: Cu dataset – A copper ore labeled images dataset for segmentation training and testing

description: This dataset is composed of 121 pairs of correlated images. Each pair contains one image of a copper ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255). The sample came from a copper ore from Yauri Cusco (Peru) with a complex mineralogy, mainly composed of sulfides, oxides, silicates, and native copper. It was classified by size. The fraction +74-100 μm was cold mounted with epoxy resin and subsequently ground and polished. Correlative microscopy was employed for image acquisition. Thus, 121 fields were imaged on a reflected light microscope with a 20× (NA 0.40) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 1017×753 pixels with a resolution of 0.53 µm/pixel. As matter of fact, some images (the images No. 2, 3, 24, 25, 46, 47, 69, 91, and 113) have slightly smaller sizes because they were cropped during the registration procedure to correct co-localization errors of the order of a few pixels. Finally, the images from SEM were thresholded to generate the reference images. Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012). This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model (Chen et al., 2018) that reached mean values of 90.56% and 92.12% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights. For further questions and suggestions, please do not hesitate to contact us. <strong>Contact email</strong>: ogomes@gmail.com If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5020566 Please also cite this paper, which provides additional details about the dataset: Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.
description: The support of Brazilian funding agencies CAPES, CNPq, FAPERJ, and Finep is gratefully acknowledged.
description: {"references": ["Gomes, O.D.M., Paciornik. S., 2012. Multimodal microscopy for ore characterization. In Kazmiruk, V., (Ed.) Scanning Electron Microscopy. IntechOpen, London. p. 313\u201334. https://doi.org/10.5772/34180.", "Filippo, M.P.; Gomes, O.D.M.; da Costa, G.A.O.P.; Mota, G.L.A. Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images. Minerals Engineering, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.", "Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H., 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (Eds.), Computer Vision \u2013 ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11211. Springer, Cham. https://doi.org/10.1007/978-3-030-01234-2_49."]}