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Table 5 Comparison of accuracies under different algorithms

From: Ancient mural classification methods based on a multichannel separable network

  Reference [12] Reference [13] Reference [14] GoogLeNet VGG MCSN
Animals 65.63%/75.89%/65.13% 64.16%/73.56%/66.22% 66.61%/75.61%/60.61% 67.34%/67.34%/67.34% 70.63%/67.34%/67.34% 75.16%/82.16%/75.16%
Buildings 61.13%/72.54%/64.28% 63.51%/79.98%/73.24% 64.55%/77.45%/68.35% 65.98%/74.26%/68.33% 66.31%/68.43%/64.25% 69.45%/75.35%/70.24%
Clouds 68.24%/85.69%/76.22% 70.24%/84.67%/72.67% 70.36%/88.69%/74.65% 72.35%/84.34%/73.25% 74.64%/84.01%/72.59% 77.38%/90.36%/79.31%
Disciples 63.35%/88.54%/70.13% 69.41%/84.68%/74.61% 66.63%/87.19%/74.04% 62.31%/78.83%/76.21% 65.56%/77.21%/71.06% 70.33%/81.03%/78.61%
fo 61.34%/79.24%/76.13% 64.98%/82.44%/72.14% 67.15%/89.36%/77.24% 69.21%/86.54%/74.48% 64.58%/78.87%/71.69% 73.25%/87.36%/76.39%
People 62.33%/75.33%/66.16% 67.27%/81.49%/72.46% 67.20%/84.31%/71.89% 70.16%/81.59%/72.31% 69.24%/79.21%/71.59% 69.07%/78.82%/75.56%
Plants 67.19%/83.16%/69.35% 65.18%/75.84%/67.94% 68.85%/82.16%/69.71% 68.21%/82.41%/69.61% 61.55%/72.46%/69.21% 70.22%/86.66%/73.21%
pusa 69.99%/87.25%/76.41% 66.64%/89.66%/71.97% 71.45%/79.97%/74.69% 66.29%/83.46%/71.24% 67.15%/70.05%/67.16% 68.11%/91.00%/73.21%