PlutonicRocks-13: An Class-imbalanced Image Dataset of Plutonic Rocks

Due to the rise of artificial intelligence (AI), a fundamental challenge and opportunity in geosciences lies in translating expert geological knowledge into AI models capable of delivering intelligent lithological recognition services, enabling geoscience enthusiasts or non-geologists to accurately identify rock types. In natural environments, the spatial distribution of surface rocks is highly heterogeneous, resulting in rock image datasets that typically follow a long-tailed distribution.

Taking plutonic rocks as an example, this study adopts the classification and nomenclature scheme from the textbook Petrology (edited by Yu Bingsong et al.), to presents PlutonicRocks-13, an imbalanced dataset for rock image recognition. The dataset includes 13 common types of plutonic rocks, with a total of 4,785 images and an original data size of 2.49 GB. The rock types included in this dataset are: olivine, pyroxenite, hornblendite, gabbro, diorite, monzonite, syenite, nepheline syenite, granodiorite, monzogranite, syenogranite, plagiogranite and graphic granite.