###### Abstract
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Initial ds** & **small\_ds\_1** & **small\_ds\_2** & **small\_ds\_3** & **aug\_ds** \\ \hline F1 & 0.71 & 0.71 & 0.75 & 0.69 & 0.76 \\ Accuracy & - & 0.72 & 0.75 & 0.69 & 0.76 \\ TPR & 0.75 & 0.99 & 0.99 & 0.76 & 0.87 \\ TNR & 0.96 & 0.99 & 1.00 & 0.97 & 0.99 \\ TPR & 0.72 & 0.80 & 0.70 & 0.69 & 0.82 \\ TNR & 0.90 & 0.98 & 0.98 & 0.95 & 0.96 \\ TPR & 0.52 & 0.63 & 0.81 & 0.72 & 0.79 \\ TNR & 0.93 & 0.92 & 0.95 & 0.93 & 0.94 \\ TPR & 0.81 & 0.67 & 0.79 & 0.79 & 0.70 \\ TNR & 0.93 & 0.99 & 0.95 & 0.95 & 0.97 \\ TPR & 0.70 & 0.75 & 0.68 & 0.70 & 0.72 \\ TNR & 0.95 & 0.92 & 0.95 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Metrics for Xception. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Initial ds** & **small\_ds\_1** & **small\_ds\_2** & **small\_ds\_3** & **aug\_ds** \\ \hline F1 & 0.69 & 0.74 & 0.73 & 0.66 & 0.70 \\ Accuracy & - & 0.74 & 0.73 & 0.67 & 0.70 \\  & & & C0 & & \\ TPR & 0.73 & 1.00 & 0.99 & 0.74 & 0.91 \\ TNR & 0.97 & 0.99 & 1.00 & 0.96 & 0.99 \\ TPR & 0.71 & 0.73 & 0.86 & 0.84 & 0.74 \\ TNR & 0.89 & 0.97 & 0.97 & 0.94 & 0.98 \\ TNR & 0.89 & 0.97 & 0.97 & 0.94 & 0.98 \\ TNR & 0.51 & 0.58 & 0.787 & 0.60 & 0.58 \\ TNR & 0.93 & 0.94 & 0.968 & 0.96 & 0.92 \\ TNR & 0.80 & 0.76 & 0.87 & 0.70 & 0.59 \\ TNR & 0.91 & 0.99 & 0.95 & 0.98 & 0.94 \\ TNR & 0.95 & 0.92 & 0.64 & 0.66 & 0.75 \\ TNR & 0.95 & 0.92 & 0.90 & 0.92 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Metrics for Inception_v3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Initial ds** & **small\_ds\_1** & **small\_ds\_2** & **small\_ds\_3** & **aug\_ds** \\ \hline F1 & 0.71 & 0.71 & 0.75 & 0.69 & 0.76 \\ Accuracy & - & 0.72 & 0.75 & 0.69 & 0.76 \\ TNR & 0.75 & 0.99 & 0.99 & 0.76 & 0.87 \\ TNR & 0.96 & 0.99 & 1.00 & 0.97 & 0.99 \\ TNR & 0.72 & 0.80 & 0.70 & 0.69 & 0.82 \\ TNR & 0.90 & 0.98 & 0.98 & 0.95 & 0.96 \\ TNR & 0.52 & 0.63 & 0.81 & 0.72 & 0.79 \\ TNR & 0.93 & 0.92 & 0.95 & 0.93 & 0.94 \\ TNR & 0.81 & 0.67 & 0.79 & 0.79 & 0.70 \\ TNR & 0.93 & 0.99 & 0.95 & 0.95 & 0.97 \\ TNR & 0.70 & 0.75 & 0.68 & 0.70 & 0.72 \\ TNR & 0.95 & 0.92 & 0.95 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._As can be seen in Tables 5-15, training on an unbalanced dataset leads to the fact that the model can poorly see the small classes, or not see at all. ResNet models are extremely sensitive to class imbalance. So, the ResNet34 model does not see the C5 class at all when training on the initial dataset (TPR = 0.00), but TPR \(\approx\) 0.46 after training on small datasets (small_ds_1, small_ds_2, small_ds_3 and aug_ds). For ResNet50, we can see an increase of TPR for class C5 from 0.29 (initial ds) to \(\approx\)0.58(small ds) and 0.60 (aug_ds). For the C6 class, the TPR increases from 0.56 (initial ds) to 0.77 (aug_ds). TPR for class C5 for ResNet101 increases from 0.15 (initial ds) to 0.67(aug_ds). For VIT models we can improve TPR by 0.5 for small classes if we will use a balanced dataset. For example, VIT-base-patch16-224: TPR = 0.12(initial ds) and TPR = 0.61 (aug_ds). DeIT models are more stabilized for the training on an unbalanced dataset. For them, the improvement in metrics is no more than 0.2. DenseNet proved to be overly sensitive to training on an unbalanced dataset. For them, training on a balanced dataset is preferable, then class recognition metrics will be significantly improved (by 0.6 or more). A similar conclusion can be drawn for Inception_v3, Xception, and VGG. The training of these models on a balanced dataset allows improved metrics by 0.2 for some classes. It should be noted that there is no deterioration in the recognition of classes that contain a large number of objects in the initial data set. It can also be seen from the obtained results that the metrics on the \"small_ds_1\", \"small_ds_2\", and \"small_ds_3\" datasets are different and depend on those objects that remained in these datasets after the initial dataset was cut off.