## 4 Results  
This section presents the results obtained from the trained models. During the training process and the evaluation of the models, an environment composed of an NVIDIA RTX 4090 graphics card, an AMD Ryzen 9 7950K processor with 16 cores, and 128 GB of DDR4 RAM is utilized. Table 1 demonstrates the parameters employed during the training phase. Figure 3 illustrates the training and convergence of the four models.  
Regarding the YOLO model (Figure 3a), it is trained for a total of 100 epochs, achieving convergence at a loss value of 0.4. The batch size is set to 10, and 8 workers are utilized. This configuration proves to be the most efficient in terms of speed and performance, based on preliminary tests conducted with fewer epochs. Throughout the training process, an iterative optimization procedure is employed, determining that the most appropriate optimizer for this model is Adam. This optimizer adjusts the model's parameter weights to minimize the loss function, thereby facilitating the model's ability to learn more rapidly and efficiently.  
Since YOLO requires 100 epochs to achieve a reasonable point of convergence, GELAN is trained directly for 75 epochs. Beginning from epoch 68, it is observed that the model starts to converge, exhibiting minimal learning loss. In the final 7 epochs, the validation set loss fluctuates by only 0.01, while the training set loss continues to decrease, except in the last epoch, where it is increased by 0.02. This behavior suggests that the model may be beginning to overfit (see Figure 2(b)). The batch size is set to 10, and 8 workers are utilized, resulting in the optimal combination of speed and efficiency based on the tests conducted during the initial epochs.  
The DETR model achieves convergence around Epoch 50, demonstrating signs of overfitting around Epoch 54 (Figure 2(c)). A batch size of 2 with 2 workers appears to attain the optimal speed-efficiency balance based on tests conducted with fewer epochs. For this model, the optimizer is changed to AdamW, which is more effective for transformer architectures.  
Considering that the DETR model requires 55 epochs to achieve convergence, ViTDet is trained for a similar number of epochs. ViTDet exhibits a tendency to converge slightly earlier, suggesting comparable or potentially superior learning efficiency. However, in the final 1-2 epochs, a marginal increase is observed in both the validation loss (0.1) and the training loss (0.01), which may indicate the onset of overfitting during training (see Figure 2(d)). Regarding hardware configuration, a batch size of 2 and 2 workers provide the optimal balance between speed and efficiency, based on preliminary tests conducted with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Epochs** & **Batch Size** & **Workers** & **Optimizer** \\ \hline YOLO & 50 + 25 + 25 & 10 & 8 & [PERSON] \\ \hline GELAN & 75 & 10 & 8 & Adam \\ \hline DETR & 30 + 25 & 2 & 2 & AdamW \\ \hline ViTDet & 55 & 2 & 2 & AdamW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used during the training phase.  
Figure 3: Convergence of the models during training.  
fewer epochs. For optimization, the AdamW optimizer is employed, as it is particularly well suited for transformer-based architectures, such as ViTDet.