Abstract: We proposed a new classification of rock thin section images based on mixture of expert model by using  five common  rock thin sections as the research object to construct a dataset. The model learned the characteristics of each rock image from the thin section images and classified them. Firstly, multiple image classification models based on convolutional neural network(CNN) and Transformer (such as ResNet50, MobileNetV3, InceptionV3, DeiT, etc.) were used to train the data. Secondly, models with better performance were selected,  a mixture of experts model was built to obtain the final prediction result. The  ACC and AUC of lithology recognition reached 85.33% and 96.69% on the validation set and 87.16% and 96.75% on the test set. Finally, by combining a mixture of experts model with  multiple models, combining  advantage of each model,  balancing their contributions between each model, we improved the accuracy and robustness of classification results, making the obtained classification results more reliable and stable.

Key words: classification of rock thin section, mixture of expert model, image classification