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Recent studies have shown the interest of automating the classification of rock images using deep learning architectures. The biggest issue for practitioners when applying these methods to real-world data sets generated during mineral exploration is the long time required to create and label a data set. This study proposes a complete workflow to label and classify drill core photographs with minimal time required for labeling through five successive steps: i) using exploration drill core photographs, rock cores are separated from wooden trays using morphological operators; ii) feature descriptors are then extracted from rock images using color histograms for colorimetric information and Gabor filters for texture information; iii) features extractors then serve as input data for self-organizing maps (SOM) for generating clusters that can be partially labeled by geologists for generating a labeled data set with limited efforts, generating a data set made of labeled and unlabeled images; iv) the partially labeled data set can then be used to train either fully supervised or semi-supervised deep learning architectures for generating classifications; v) the classification model obtained can then be re-used on unseen data to automate logging process. This study presents this workflow separately for two different geological domains, namely a data set of sedimentary rocks classified according to the intensity of bleaching features and a data set of crystalline basement rocks classified by lithological domains. Software code and data set are made publicly available. • Development of a complete workflow to transform drill-core photographs datasets (directly obtained from mineral exploration) into a labeled dataset usable for deep-learning tasks. • Evaluation of the possibility of inferring lithological (for the basement subset) and alteration (for the basin subset) classes based on a self-labeling strategy. • Evaluation of the potential to improve classification accuracy by using unlabeled samples in a semi-supervised way.