# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 4 Experimental Results and Analysis
### Performance of Different Models during the Training Process  
Figure 6 presents the loss curves for various models operating in 7-shot and 10-shot configurations throughout iterative progression.  
During the training epoch, there was a noticeable decrease in aggregate loss across the four models, with models trained on smaller sample sizes converging more quickly. In the initial phase, the models exhibited higher total loss, which can be attributed to the early stage of parameter initialization where useful information was lacking. As training progressed into the mid-phase, the loss of all models notably decreased, indicating that the models were learning beneficial information from the data. At this stage, Faster R-CNN with a ResNet101 backbone demonstrated superiority due to its sophisticated network architecture, which was proficient in capturing the features of the data. As training approached its conclusion, there was a continued, though gradual, decrease in loss across the models. This slowdown characterized the model's gradual approach towards their maximum potential, making further improvements increasingly challenging. At this point, Res-Net101-based Faster R-CNN maintained its superiority by demonstrating the lowest cumulative loss among the models. Furthermore, under both 7-shot and 10-shot conditions, Faster R-CNN with ResNet101 as the backbone consistently exhibited the lowest losses, particularly under the 7-shot setting, where it achieved the optimal loss. This underlines  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Training Labels** & **mAP before Optimization (\%)** & **mAP after Optimization (\%)** \\ \hline
1000 & 52.49 & 55.07 \\
2000 & 61.84 & 63.73 \\
3000 & 69.83 & 71.38 \\
4000 & 75.61 & 77.24 \\
5000 & 76.05 & 77.92 \\
6000 & 76.58 & 78.21 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimization results of pseudo-label-based SSL algorithm.  
Figure 6: Loss values of various models at different epochs. (**a**) 7-shot. (**b**) 10-shot.  
that the highest level of accuracy was achieved when utilizing the 7-shot configuration in combination with ResNet101-based Faster-RCNN.  
Additionally, we observed some other trends in the entire training process. As training progressed, the performance of all models gradually stabilized, indicating that they were gradually finding a suitable state to detect targets accurately. Particularly in the case of Faster R-CNN (ResNet101), due to its advanced network architecture, the model excelled in capturing data features, maintaining its lead throughout the training process.  
However, in later stages, the performance differences among models began to narrow gradually. This can be attributed to the fact that they were all approaching their best performance on current training data. This also implies that further improvements will become increasingly challenging, requiring more refined tuning.  
In summary, these observations provide us with a deeper understanding of the dynamic changes during the training process. They also emphasize the superiority of ResNet 101 in combination with the 7-shot configuration, which will have a positive impact on practical applications.