Copper and Iron composition

The data sets contain one folder dedicated to copper and one to iron. Each of them can be used for two distinct purposes: regression and image segmentation.
For regression, the microscopical images from the "copper-images" and "iron-images" folders can be used directly. The file names contain the XRF approximation of elemental composition for several elements. These represent the ground truth. The string encoding has the following format:
element1_value1__element2_value2__element3_value3 - object name__i.jpg
Example: Cu_0.03__Fe_98.16__P_0.14__Si_1.49__6 - Cui roman - fier - inainte__1.jpg
This reads that the copper is 0.03%, iron composition is 98.16% etc. and the image is the first (last part, "__1", before ".jpg") from the object "Cui roman - fier - inainte". The images made for the same object will contain the same string for the object name. The information can be extracted using a string tokenizer.
For image segmentation, the same images from "copper-images" and "iron-images" folders can be used together with the associated masks from "copper-labels" and "iron-labels". The label files have the same resolution and each pixel value corresponds to a certain class. The classes are provided in the text files "copper-codes.txt" and "iron-codes.txt".
The file names of the images used for validation and test purposes are given in the text files "copper-valid.txt", "iron-valid.txt", "copper-test.txt", and "iron-test.txt", respectively.
The data set is described in the article below. Results on the data can be found in the article, as well.
Ruxandra Stoean, Nebojsa Bacanin, Catalin Stoean, Leonard Ionescu, Miguel Atencia, Gonzalo Joya, Computational Framework for the Evaluation of the Composition and Degradation State of Metal Heritage Assets by Deep Learning, Journal of Cultural Heritage, 64, pp.198-206, 2023, https://doi.org/10.1016/j.culher.2023.10.007.
If you publish material based on the current database, please refer the paper above within the References.
The study took part within the project Object PErception and Reconstruction with deep neural Architectures (OPERA) https://sites.google.com/view/pce-opera/.