# ResnetCPS for Power Equipment and Defect Detection
## 4 Experiment
### Ablation Study  
A holistic ablation experiment was used to verify the relationship between the model architecture, sharing ratio, parameter amount, memory read and write amount, model size, and accuracy. The number of memory reads and writes, model size, and accuracy are compared with the parameters under different sharing ratios. The model architecture and sharing ratio are analyzed. The experimental data are substation data. Table 3 shows the experimental results of different models of resnetcps architecture. Because this parameter is the denominator to participate in the shared convolution calculation, the more significant the number, the smaller the proportion of parameters participating in the sharing. Params represents the number of parameters. The number of parameters determines the model's size and the training's complexity. The lower the number of parameters, the faster the model training. Memr + w represents the amount of memory read and write. The amount of memory read and write is enough to affect the response speed of the model. The smaller the memory read and write, the faster the model responds. Model size represents the space occupied by the storage model. Top-1 ACC represents the model's classification \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Params (M)** & **Top-1 Acc (\%)** \\ \hline wide\_resnet50 [26] & 68.88 & 81.46 \\ wide\_resnet101 [26] & 126.89 & 78.85 \\ resnetv2\_101\(\times\)1\_bitm [46] & 44.54 & 82.33 \\ resnetv2\_101\(\times\)3\_bitm [46] & 387.93 & 84.44 \\ nfnret\_10 [28] & 35.07 & 82.75 \\ sesresnet152 [31] & 66.82 & 78.65 \\ res2 net50 [29] & 48.4 & 78.95 \\ cearesnet101 [32] & 44.57 & 82.17 \\ cearesnet269 [32] & 102.09 & 87.97 \\ resnest200 [47] & 70.2 & 83.83 \\ resnest101 [47] & 48.28 & 82.89 \\ \hline resnetcps200 \(ratio=1/16\) & 125.84 & 88.36 \\ resnetcps350 \(ratio=1/8\) & 153.02 & 89.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of parameter quantity and accuracy of ResNet variant models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Params (M)** & **Top-1 Acc (\%)** \\ \hline RepVGG-B2 [48] & 80.31 & 81.71 \\ Multiscale DEQ [49] & 81 & 82.25 \\ PVTv2-B4 [50] & 82 & 84.65 \\ RevBiFPN-S5 [51] & 82 & 84.14 \\ Conformer-B [52] & 83.3 & 85.65 \\ XCIT-M24 [53] & 84 & 86.75 \\ DeiT-B [14] & 86 & 84.82 \\ ConViT-B [54] & 86 & 84.66 \\ ViT-B@224 [55] & 86.6 & 84.42 \\ ViT-B @384 [55] & 87 & 85.35 \\ SwinV2-B [56] & 88 & 86.68 \\ Swin-B [56] & 88 & 85.79 \\ Mega [57] & 90 & 83.36 \\ DiNAT-Base [58] & 90 & 85.14 \\ Pyramid ViG-B [59] & 92.6 & 84.84 \\ CoE-Large [60] & 95.3 & 83.47 \\ \hline resnetcps200 \(ratio=1/4\) & 82.21 & 87.16 \\ resnetcps200 \(ratio=1/8\) & 87.71 & 87.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of parameter quantity and accuracy of each model. accuracy at the maximum probability value. The optimal parameters in the table are bolded. Figure 7 is the corresponding diagram of parameters and accuracy of each model of resnetcps architecture. In addition, we also compared the accuracy impact of different channel self-attention modules on resnetcps.