## 4 Experiments and Results
### Results Analysis
#### 4.4.4 Performance Comparison for Different Models  
In this study, we aimed to evaluate the robustness and generalization ability of the classification models based on Triplet-EfficientNet and to demonstrate the superiority of our proposed method by comparing its performance with other models. We not only introduced mainstream classification models such as AlexNet, GoogleNet, and VGG16, but also replicated recent similar image classification methods, including a deep residual network model (ResNet34) proposed by [PERSON] et al. [15] and CA-EfficientNet proposed by [PERSON] et al. [32]. CA-EfficientNet is an improved EfficientNet model by incorporating the coordinate attention mechanism. Seven models, including the Triplet-EfficientNet model \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Initial** & \multirow{2}{*}{**Initial Loss**} & **Final Accuracy (Epoch = 60)** & **Final Loss (Epoch = 60)** \\ \hline AlexNet & 32.7\% & 1.638 & 65.5\% & 0.818 \\ \hline GoogleNet & 40.9\% & 2.870 & 83.3\% & 0.501 \\ \hline VGG16 & 35.7\% & 1.744 & 84.6\% & 0.289 \\ \hline EfficientNet-B7 (+SE attention mechanism) & 75.2\% & 1.225 & 90.4\% & 0.255 \\ \hline Triplet-EfficientNet (+Triplet attention mechanism) & 76.6\% & 1.215 & 92.6\% & 0.214 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy and loss value of different model in the training set. Figure 14: The changes in accuracy and loss value of different model during training. (**a**) Accuracy; (**b**) Loss. proposed in this paper, were tested with consistent training sets, training strategy, and input image size. In performance testing, we employed seven trained models for image inference in the test set and used the widely accepted Top-1 accuracy metric for evaluating the inference results. Top-1 accuracy refers to the accuracy with which the type with the highest probability of prediction matches the actual result [33]. As shown in Table 4, for input images of the same size, EfficientNet-B7, CA-EfficientNet, and Triplet-EfficientNet models greatly outperform other models in Top-1 accuracy due to the efficient network structure of EfficientNet. Among them, CA-EfficientNet and Triplet-EfficientNet models benefit from the introduction of the coordinate attention mechanism and triplet attention mechanism, respectively, and exhibit better performance. Compared with EfficientNet-B7, they have stronger spatial feature characterization ability and can obtain more effective feature information in rock images, thus further improving their Top-1 accuracy. Through further comparison, it was found that the triplet attention mechanism of Triplet-EfficientNet outperforms the coordinate attention mechanism of CA-EfficientNet in terms of model performance. This is because the triplet attention mechanism has more attention branches compared with the coordinate attention mechanism, allowing the model to more comprehensively extract feature information across image dimensions. Therefore, Triplet-EfficientNet which incorporates the triplet attention mechanism has the highest Top-1 accuracy. In addition to testing the performance of the models, we also used two indicators, Parameters and FLOPs, to calculate the computational complexity of each model. Parameters refer to the total number of parameters that need to be trained during model training and are used to measure the computational space complexity of the model. FLOPs (Floating-point Operations) refer to the number of floating-point operations that need to be performed in a neural network model, which is used to measure the computational time complexity of the model. As shown in Table 4, EfficientNet-B7, CA-EfficientNet, and Triplet-EfficientNet achieved higher accuracy with moderate parameters and FLOPs, thanks to the efficiency of the EfficientNet network structure and its parameters. Furthermore, the triplet attention mechanism of Triplet-EfficientNet is more efficient compared to the coordinate attention mechanism of CA-EfficientNet and the SE attention mechanism of EfficientNet-B7.