# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 5 Results and Validation
### Analysis of Model Progress and Performance During Training  
Following the training of the YOLOv12 model, a file named results.csv is generated, which contains the results obtained for each epoch based on the metrics defined in Section 4.3. These results are computed using the validation set specified in the configuration file and reflect the model's performance on data not seen during training but used for evaluation throughout the learning process. The results obtained during the last 5 epochs, out of the 20 total training epochs, are presented in Table 3.  
As observed, the model maintained very strong performance throughout the last five training epochs. The values for precision, recall, and the calculated F1 score were high, indicating accurate and efficient detection. Moreover, the [EMAIL_ADDRESS] remained high, while [EMAIL_ADDRESS]:0.95 progressively increased, reflecting an increasingly better generalization capability under varied conditions. In parallel, the loss values on the validation set (_box_loss_, _cls_loss_, and _df_loss_, represented in the lower graphs of Figure 9 by the blue, green, and red curves, respectively) showed a slight decrease, indicating ongoing refinement and a reduction in localization and classification errors. In the final epoch (epoch 20), relatively low loss values were recorded confirming stable model convergence. The best generalization results were achieved in epoch 19, which yielded the highest [EMAIL_ADDRESS]:0.95 score. Training was not extended beyond 20 epochs because the model had reached a point where its performance had stabilized, and subsequent epochs did not yield significant improvements. The learning rate had also decreased considerably by epoch 20 (0.000119), indicating that the model was approaching convergence. Throughout the YOLOv12 training process, the model's performance is continuously monitored after each epoch, and the system automatically retains the checkpoint corresponding to the best validation performance. As a result, the most optimal version of the model is available at the end of training and can be directly used for detection tasks. This approach ensures an optimal selection of trained parameters, helping to prevent overfitting and maximize accuracy in real-world application scenarios. Over the course of the 20 training epochs (Figure 9), the YOLOv12s model exhibited a clear positive trend, reflected both in the reduction in loss values and in the overall improvement in performance. The _box_loss_ decreased from 1.353 to 0.903 on the training set and from 1.357 to 0.930 on the validation set, indicating increasingly accurate localization of license plates (blue line in Figure 9). In parallel, the _cls_loss_ showed a significant reduction from 2.396 to \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Epoch** & **Time** & **box_loss** & **cls_loss** & **df_loss** & **Precision** & **Recall** & **F1** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline
16 & 8861.030 & 1.049 & 0.616 & 0.992 & 0.988 & 0.950 & 0.969 & 0.975 & 0.701 \\
17 & 9428.170 & 1.006 & 0.598 & 0.971 & 1.000 & 0.951 & 0.975 & 0.973 & 0.720 \\
18 & 9996.460 & 0.985 & 0.589 & 0.960 & 0.984 & 0.952 & 0.968 & 0.972 & 0.726 \\
19 & 10,530.200 & 0.988 & 0.564 & 0.956 & 1.000 & 0.952 & 0.975 & 0.979 & 0.739 \\
20 & 11,068.000 & 0.903 & 0.534 & 0.937 & 0.998 & 0.940 & 0.968 & 0.981 & 0.737 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics on the validation set during the last 5 epochs of YOLOv12 training. 0.534 (train) and from 18.901 to 0.468 (val), clearly reflecting improved classification of the detected objects (green line in Figure 9). Additionally, the _dfl_loss_ (Distribution Focal Loss), which contributes to the precision of bounding box position prediction, dropped from 1.151 to 0.937 (train) and from 1.331 to 1.015 (val), confirming increasingly accurate object positioning (red line in Figure 9).