Rock Image Classification Based on EfficientNet and Triplet Attention Mechanism

Rock image classification is a fundamental and crucial task in the creation of geological surveys. Traditional rock methods mainly rely on manual operation, resulting high costs unstable accuracy. While existing based deep learning models have overcome limitations traditional achieved intelligent classification, they still suffer from low accuracy due to suboptimal network structures. In this study, model EfficientNet triplet attention mechanism proposed achieve accurate end-to-end classification. The was built EfficientNet, which boasts an efficient structure thanks NAS technology compound scaling method, thus achieving for Additionally, introduced address shortcoming feature expression enable fully capture channel spatial information images, further improving During training, transfer employed by loading pre-trained parameters into model, accelerated convergence reduced training time. results show that with 92.6% set 93.2% Top-1 test set, outperforming other mainstream demonstrating strong robustness generalization ability.