## 1 Introduction  
The role of lithology identification is central in the field of geological engineering [1]. Lithologic information serves as a crucial foundation for geologists, enabling the assessment of regional geological evolution history, the determination of deep mineral and oil and gas resource types, and the estimation of various resource reserves [2, 3]. Furthermore, it supplies reference data for the prevention of geological disasters [4]. As such, the ability to identify rock lithology carries immense engineering and application value swiftly, efficiently, and accurately. In practical engineering, the accuracy of onsite manual visual inspection often falls short, necessitating more precise lithology identification to be conducted within the laboratory setting. Typically, this goal is facilitated by equipment such as scanning electron microscopes (SEM), X-ray diffraction (XRD), and electron probe microanalyzers (EPMA), which identify lithology based on characteristics such as rock density, magnetism, conductivity, and elemental content [5]. Nevertheless, these disparate devices may yield different types of data [6]. Given the high costs and time-intensive nature associated with most of this equipment, lithology identification methods based on thin sections as the primary approach. Thin sections identification is a traditional method of identifying lithology based on image recognition. This method involves cutting rock samples to make thin sections and then determining the rock type, genesis characteristics, and lithology under a polarizing microscope according to the rock structure and mineral sequence. Usually, this work requires experienced geologists, so the objectivity and efficiency of the research are limited. If intelligent recognition can be achieved, it would not only reduce the workload of researchers but also allow more practitioners to obtain efficient and objective identification results. [PERSON] [7] applied artificial neural networks (ANN) to identify thin sections of 10 prevalent minerals. [PERSON] [8] utilized a multi-layer perceptron algorithm to establish feature extraction rules. They extracted features from RGB or grayscale rock images and classified 140 thin rock sections, achieving an accuracy of 92.2%. Employing color, shape, and texture features extracted from rock images, [PERSON] [9] fed these features into the SVM model for rock type identification and reported an accuracy of 96.2%. [PERSON] [10] selected the three highest-performing models from five machine learning models for rock and mineral image identification. They then used stacking to enhance the performance of these models. The advent of deep learning technology aimed to heighten the degree of automation in the identification process and alleviate image processing challenges. [PERSON] [11] utilized DenseNet 121 and ResNet 50 to identify six categories of volcanic rocks and assessed the influence of four optimizers on model accuracy. [PERSON] [12] applied the ResNeXt-50 architecture for identifying rock types in oil and gas well logging core images, reaching a final accuracy of 93.12%. To augment identification accuracy, [PERSON] [13] proposed the MaSEResNeXt model to bolster the feature connectivity across different channels, achieving an identification accuracy of 90.89% for three types of thin rock section images. [PERSON] [14] explored the impact of three distinct optimization algorithms and two learning rate decay methods on identification performance. Utilizing transfer learning, [PERSON] [15] identified five types of thin rock sections and compared several advanced convolutional neural network models, including VGG 19, MobileNet V2, Inception V3, and ResNet 50. Among these, ResNet 50 recorded the highest accuracy of 95% in their study. The Ultra-Fine-Grained Visual Classification (Ultra-FGVC) endeavor seeks to identify objects with heightened precision, distinguishing various subcategories within a single species.