# A Few Shot Classification Methods Based on Multiscale Relational Networks
## 3 Results  
All samples were processed into a size of 28 \(\times\) 28. A rotation for enhancement was performed, and 1200, 211, and 212 were randomly selected as training, verification, and test sets. In addition, this paper conducts single-sample experiments of 5-ways 1-shot (5W1S) and 20-ways 1-shot (20W1S) and small-sample experiments of 5-ways 5-shot (5W5S) and 20-ways 5-shot (20W5S). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{_Single Scale Characteristic_} & \multicolumn{2}{c}{_Multi-Scale Characteristic_} \\  & \multicolumn{2}{c}{_(5-Way 5-Shot with Small Samples)_} \\ \hline Three-dimensional splicing & 50.16 \(\pm\) 1.34\% & 51.1 \(\pm\) 0.97\% (64.70 \(\pm\) 0.61\%) \\ Three-dimensional subtraction & 49.45 \(\pm\) 1.28\% & 50.21 \(\pm\) 1.08\% (65.89 \(\pm\) 0.32\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification results with a 95% confidence interval of single sample images. Figure 7 shows that the network achieved the highest accuracy of 99.76% in the 5W1S experiment when the number of iterations was 325,000. Figure 8 shows that MSRN achieves the highest accuracy of 99.72% in the 5W1S experiment on the verification set when the number of iterations is 114,000. Therefore, for the 5W1S experiment, MSRN converges to the highest accuracy faster than the relational network. However, the highest accuracy on the verification set is slightly lower than the relational network. The trained model is tested on the test set, and the results are shown in Table 2 with a 95% confidence interval. The accuracy rate of the MSRN in the 5W1S experiment on the test set is higher than that of the other three methods. Figure 8: Accuracy and loss iteration curves of the 5-way 1-shot multi-scale relational network. Figure 7: The 5-way 1-shot accuracy and loss iteration curve of the relational network. In Figure 9, when the iterations achieve 565,000, the relational network has the highest accuracy of 97.51% in the 20W1S experiment. On the other hand, as shown in Figure 10, when the iteration is 89,000, MSRN achieves 97.43% accuracy (highest) in the 20W1S experiment. Therefore, for the 20W1S experiment, although the highest accuracy rate of the relational network is higher than that of the MSRN, MSRN converges to the highest accuracy much faster. As shown in Table 2, the accuracy rate of the 20W1S experiment on the test set of the MSRN is higher than that of the matching network, the prototype network, and the relational network. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Fine-Tuning**} & \multicolumn{2}{c}{**Accuracy (95\% Convidence Interval)**} \\ \cline{3-4}  & & **5-Way 1-Shot** & **20-Way 1-Shot** \\ \hline The Matching network & No & 98.10 \(\pm\) 0.23\% & 93.80 \(\pm\) 0.33\% \\ The Prototype network & No & 98.80 \(\pm\) 0.12\% & 96.00 \(\pm\) 0.28\% \\ The Relational network & No & 99.32 \(\pm\) 0.16\% & 96.73 \(\pm\) 0.15\% \\ The Multi-scale relational network & No & 99.35 \(\pm\) 0.25\% & 97.41 \(\pm\) 0.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of few-shot classification of Omniglot data set. Figure 10: Accuracy and loss iteration curves of the 20-way 1-shot multi-scale relational network. Figure 9: The 20-way 1-shot accuracy and loss iteration curve of the relational network. Figure 11 shows that when iterations achieve 625,000, the relational network has the highest accuracy of the 5W5S experiment at 99.824%. Figure 12 shows that when the iteration reaches 293,500, the MSRN (the method in this paper) achieves the highest accuracy of 99.824% in the 5W5S experiment. Therefore, for the 5W5S experiment, the relational network's highest accuracy rate on the verification set is similar to that of MSRN. However, MSRN converges to the highest accuracy rate faster than the relational network.