# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 3 Results
As it appears from Figure 5, one potential explanation for this error might be the presence of lines that could be mistaken for roads. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Rank** & **Model Name** & **Epoch** & \
\begin{tabular}{c} **Train** \\ **Time (s)** \\ \end{tabular} & **Loss** & **Accuracy** & **Rank** & **Model Name** & **Epoch** &
\begin{tabular}{c} **Train** \\ **Time (s)** \\ \end{tabular} & **Loss** & **Accuracy** \\ \hline
1 & ResNet50 & 20 & 644.5 & 0.091 & 0.975 & 21 & RegNetX004 & 26 & 390.1 & 0.138 & 0.954 \\
2 & EfficientNetV2B0 & 20 & 367.5 & 0.087 & 0.972 & 22 & RegNetX006 & 17 & 249.2 & 0.138 & 0.954 \\
3 & ResNet152 & 17 & 1265.2 & 0.087 & 0.971 & 23 & DenseNet169 & 11 & 454.3 & 0.138 & 0.953 \\
4 & ResNetRS50 & 19 & 742.9 & 0.094 & 0.969 & 24 & EfficientNetB4 & 15 & 784.9 & 0.139 & 0.953 \\
5 & ResNetRS152 & 17 & 1419.3 & 0.106 & 0.968 & 25 & ResNetRS350 & 15 & 3049.3 & 0.150 & 0.951 \\
6 & ResNet101 & 16 & 858.7 & 0.103 & 0.967 & 26 & MobileNet & 12 & 152.7 & 0.146 & 0.950 \\
7 & RegNetX008 & 23 & 423.7 & 0.120 & 0.966 & 27 & VGG19 & 14 & 658.6 & 0.156 & 0.949 \\
8 & ResNetRS200 & 16 & 1988.2 & 0.106 & 0.964 & 28 & ResNet101V2 & 12 & 650.6 & 0.156 & 0.948 \\
9 & ResNetRS270 & 18 & 2991.4 & 0.109 & 0.964 & 29 & ResNetRS420 & 14 & 3430.0 & 0.163 & 0.946 \\
10 & EfficientNetV2B1 & 15 & 340.2 & 0.116 & 0.963 & 30 & VGG16 & 12 & 463.1 & 0.165 & 0.945 \\
11 & ResNetR5101 & 12 & 728.2 & 0.121 & 0.960 & 31 & MobileNetV2 & 15 & 229.2 & 0.169 & 0.944 \\
12 & EfficientNetV2B2 & 12 & 308.6 & 0.132 & 0.957 & 32 & EfficientNetB5 & 14 & 979.9 & 0.168 & 0.944 \\
13 & ResNetX032 & 13 & 534.7 & 0.125 & 0.957 & 33 & EfficientNetB7 & 17 & 2106.8 & 0.167 & 0.943 \\
14 & EfficientNetV2B3 & 13 & 416.3 & 0.124 & 0.957 & 34 & RegNetX002 & 19 & 194.4 & 0.165 & 0.943 \\
15 & EfficientNetB2 & 14 & 441.2 & 0.128 & 0.957 & 35 & ResNet50V2 & 11 & 332.1 & 0.179 & 0.942 \\
16 & DenseNet201 & 11 & 558.2 & 0.128 & 0.956 & 36 & EfficientNetB6 & 22 & 2054.6 & 0.172 & 0.942 \\
17 & EfficientNetB0 & 12 & 264.6 & 0.133 & 0.956 & 37 & ResNet152V2 & 11 & 812.1 & 0.187 & 0.937 \\
18 & DenseNet121 & 13 & 419.4 & 0.133 & 0.955 & 38 & NASNetMobile & 13 & 324.0 & 0.224 & 0.925 \\
19 & EfficientNetB1 & 14 & 438.6 & 0.133 & 0.955 & 39 & InceptionV3 & 14 & 278.4 & 0.256 & 0.918 \\
20 & EfficientNetB3 & 13 & 526.0 & 0.136 & 0.955 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Epochs, training time (s), cross-entropy loss, and accuracy which was averaged over the train, validation, and test sets. Models were trained (separately) on an NVIDIA RTX2080S GPU. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Set** & **Kappa** & **Loss** & **Accuracy** \\ \hline \multirow{3}{*}{ResNet50} & Train & 0.9900 & 0.0304 & 0.9910 \\  & Validation & 0.9629 & 0.1226 & 0.9667 \\  & Test & 0.9641 & 0.1189 & 0.9678 \\ \hline \multirow{3}{*}{EfficientNetV2B0} & Train & 0.9789 & 0.0572 & 0.9811 \\  & Validation & 0.9662 & 0.0969 & 0.9696 \\  & Test & 0.9600 & 0.1064 & 0.9641 \\ \hline \multirow{3}{*}{ResNet152} & Train & 0.9864 & 0.0395 & 0.9878 \\  & Validation & 0.9613 & 0.1063 & 0.9652 \\ \cline{1-1}  & Test & 0.9559 & 0.1153 & 0.9604 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics: train, validation, and test sets.