Eight minerals were labeled: K-feldspar (Afs), amphibole (Amp), biotite (Bt), muscovite (Ms), olivine (Ol), pyroxene (Px), plagioclase (Pl), and quartz (Qz), following the abbreviations of Whitney & Evans (2010). Figure 2 shows representative examples and the number of labels per mineral. Labels created on XPL images were duplicated onto the corresponding PPL images using the “Repeat Previous” option. Considering both XPL and PPL images, a total of 62178 minerals were labeled and exported as a JSON file. 2.3 Mineral identification and measurement The model consists of two components (Figure 3): the first identifies eight mineral categories using Detectron2, while the second performs object measurements using scikit-image. Matplotlib is used for histogram visualization, Python-Ternary for ternary diagrams, and FPDF for automated report generation. 2.3.1 Detectron 2-v0.6 (2021) It is a deep learning model developed by Facebook AI Research and implemented in PyTorch. It provides high-quality code for the implementation and evaluation of object detection and segmentation research (Girshick, 2018). Its three modules are described below. Backbone Network and Feature Pyramid Network (FPN): It is based on a CNN (ResNetX101) and extracts feature maps from the input image at multiple scales using the FPN. The convolutional layers (res2-res5) reduce the image resolution, producing multiscale feature maps (P2-P6). Lateral convolutions adjust the number of channels to 256, and upsampling fuses low- and high-resolution information. Figure 2 – Rocks with labeled minerals (left), red scale bar = 1 mm. Number of mineral labels (right)
RkJQdWJsaXNoZXIy MTM0Mzk2