## 1 Introduction  
Rock classification is an essential and critical task in various fields, such as geology, resource exploration, geotechnical investigation, rock mechanics, mineral resource prospecting [1], and constructional engineering [2]. It plays a vital role in supporting mineral and petroleum resource exploration, and in guiding design scheme optimization, safety assessment, and risk assessment in geotechnical engineering. Traditional methods of rock classification can be broadly categorized into physical tests and numerical statistical analysis. Physical tests analyze rock samples using techniques such as X-ray powder diffraction, scanning electron microscopy, and infrared spectroscopy [3], while numerical statistical analysis employs mathematical methods like the nearest-neighbor algorithm [4] and principal component analysis [5] to extract rock classification features. These traditional methods heavily rely on the expertise of professionals and specific equipment to extract useful information from rocks [6]. Therefore, the accuracy of rock classification can be greatly affected by poor experimental conditions or low-quality personnel, resulting in significant fluctuations. In addition, traditional methods are cumbersome and time-consuming, and they are incompatible with the trend of the widespread use of remote exploration devices such as drones in geological surveys [7] since they cannot meet the needs of geological survey personnel to classify rocks directly from images collected by the equipment. With the advancement of computer technology, computer vision methods based on machine learning and deep learning have begun to show excellent performance in image classification and have been applied in the field of rock classification. Researchers initially applied traditional machine learning methods, which train classifiers using artificially extracted rock image features for automatic classification. For example, [PERSON] et al. [8] extracted image feature values from more than 1000 carbonate slices using image processing and input them into a multi-layer perceptron neural network model to achieve the intelligent classification of carbonate rocks, with an accuracy of 93.3%. Similarly, [PERSON] et al. [9] extracted 27 features from thin-section images of basalt rock and were able to recognize and classify 140 thin-section images of rock samples with an accuracy of 92.22%. Although these traditional machine learning methods can produce satisfactory results, they cannot automatically extract features directly from unprocessed images due to the limited capacity of the shallow models. [10] Therefore, before training the classification model, manual pre-processing of images is necessary to extract features such as color, shape, and texture. This significantly reduces the level of automation in type classification. In recent years, deep learning has been applied in rock-type classification to overcome the limitations of traditional machine learning methods in image feature extraction [11]. Researchers have utilized various deep learning models to automatically extract image features, which leads to more intelligent image classification. [PERSON] et al. [12] designed a U-net convolutional neural network for one mineral recognition under the microscope with a test set success rate of above 90%. [PERSON] et al. [13] developed a rock image recognition model based on the Inception-V3 deep convolutional neural network for various rock types, achieving a classification accuracy of over 85%. [PERSON] et al. [14] used the ResNet50 and ResNet101 models for automatic feature extraction and classification of rock slice images, with an accuracy of 90.24% and 91.63% in the test set, respectively. [PERSON] et al. [15] proposed a deep residual neural network model with transfer learning to establish an automatic rock classification model with over 90% accuracy. [PERSON] et al. [16] proposed a transformer-based model for automatic core-face classification, eliminating the need for pre-processing and manual feature extraction.