## 4 Experimental Design and Results
### Comparative Experimental Analysis
#### 4.4.2 Model Computational Comparison  
When evaluating deep learning models, two important metrics to consider are the number of parameters (Param) and the computational complexity (FLOPs) [43]. Different models exhibit distinct differences in terms of parameter count and computational complexity. Generally, a higher parameter count indicates a higher modeling capacity, but it also increases the computational burden. Similarly, a higher computational complexity requires more computing resources for training and inference. Therefore, when selecting a model, it is crucial to balance the number of parameters and computational complexity to meet the specific requirements of the task while considering the limitations of computing resources [44]. As shown in Table 3, we can compare the computational aspects of the given models.  
As shown in the table above, among the transformer-based models, the Swin Transformer model has a parameter count of 0.38 M and a computational complexity of 8.7 G FLOPs. On the other hand, the ViT model has a lower parameter count of 0.25 M but a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Model** & **Accuracy** & **F1 Score** & **Precision** & **Recall** & **ROC AUC** & **K** \\ \hline \multirow{3}{*}{Transformer} & Swin-T & 0.952 & 0.952 & 0.954 & 0.952 & 0.972 & 0.923 \\  & ViT & 0.944 & 0.944 & 0.945 & 0.944 & 0.957 & 0.901 \\  & ResNet & 0.912 & 0.906 & 0.918 & 0.915 & 0.937 & 0.872 \\ CNN & DenseNet & 0.897 & 0.896 & 0.902 & 0.897 & 0.940 & 0.885 \\  & LeNet & 0.890 & 0.887 & 0.898 & 0.884 & 0.918 & 0.862 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results of the test sets for each model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **Param (M)** & **FLOPs (G)** \\ \hline \multirow{3}{*}{Transformer} & Swin-T & 0.38 & 8.7 \\  & ViT & 0.25 & 743.0 \\  & ResNet & 24.5 & 3.9 \\ CNN & DenseNet & 6.69 & 2.91 \\  & LeNet & 0.06 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results for each model.  
significantly higher computational complexity of 743.0 G FLOPs. This indicates that the ViT model is computationally more expensive and requires larger computational resources compared to the Swin Transformer model.  
Among the CNN-based models, the ResNet model has a larger parameter count of 24.51 M but a relatively lower computational complexity of 3.9 G FLOPs. The DenseNet model has a parameter count of 6.69 M, slightly lower than ResNet, and a computational complexity of 2.91 G FLOPs. LeNet, being a very simple model, has the smallest parameter count and computational complexity, with only 0.06 M parameters and 0.005 G FLOPs.  
The Swin Transformer has a higher parameter count compared to ViT. This is because the Swin Transformer employs a block-based strategy, dividing the image into smaller blocks and then performing self-attention operations on these blocks, reducing the computational complexity of the model. In contrast, ViT uses a global self-attention mechanism, requiring self-attention operations across the entire image, resulting in higher computational complexity. Although the LeNet model is relatively simple, oilfield paragraph data typically contain rich information and complex patterns, necessitating a more powerful model to accurately capture these features for effective fault diagnosis. The Swin Transformer offers superior modeling capabilities and performance, contributing to improved diagnostic accuracy, especially when dealing with large-scale paragraph cards, whereas the simplicity of the LeNet model may not fully leverage these data to achieve the same level of performance. Therefore, for complex tasks like oilfield paragraph fault diagnosis, the Swin Transformer is more conducive to enhancing diagnostic efficiency and accuracy.