## 5 Discussions  
This research focuses on the recognition of fine-grained vehicles, which are almost visually identical. Although promising results have been achieved using a one-stage object detector, vision transformers are known for their computational and memory requirements. The self-attention mechanism used in the transformer encoder block computes pairwise \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**mAP @0.5**} & \multirow{2}{*}{**mAP 0.5:0.95**} & \multirow{2}{*}{**Time (s)**, **Test Images = 2530**, **Batch Size = 10**} \\ \hline Yolov51\_SGD & 0.874 & 0.899 & 0.845 & 40 \\ Yolov51\_ADAM & 0.871 & 0.896 & 0.847 & 40 \\ Yolov55\_SGD & 0.88 & 0.9 & 0.844 & 47 \\ Yolov55\_ADAM & 0.891 & 0.901 & 0.848 & 47 \\ [PERSON]\_tr\_SGD & 0.926 & 0.919 & 0.864 & 56 \\ [PERSON]\_tr\_ADAM & 0.927 & 0.919 & 0.871 & 56 \\ [PERSON] & 0.899 & 0.882 & 0.831 & 37 \\ [PERSON] & 0.901 & 0.9 & 0.858 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: All models’ performance comparison on the Stanford car test set, where Yolov7 and Yolov8 performed at a minimum inference speed with lower mAP compared to the proposed model, which performed better than all, scoring 0.919 at mAP@50 and 0.871 at [EMAIL_ADDRESS]:95 thresholds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Train Anno**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Image Resolution**} & \multirow{2}{*}{**Accuracy**} \\ \cline{1-1} \cline{4-5} RA-CNN & & VGG-19 & 448 \(\times\) 448 & 92.5\% \\ BoT & & Alex-Net & Not given & 92.5\% \\ WPA & BBox & CaffeNet & 224\(\times\)224 & 92.6\% \\ MA-CNN & & VGG-19 & 448 \(\times\) 448 & 92.8\% \\ PA-CNN & & VGG-19 & 448 \(\times\) 448 & 93.3\% \\ M2 DRL & & VGG-16 & 448 \(\times\) 448 & 93.3\% \\ Yolov5-Trans & BBox & CSP-Darknet53 & 640 \(\times\) 640 & 93.4\% \\ DFL-CNN & & VGG-16 & 448 \(\times\) 448 & 93.8\% \\ TASN & & ResNet-50 & 224 \(\times\) 224 & 93.8\% \\ Hsnet & Parts & GoogleNet & 224 \(\times\) 224 & 93.9\% \\ MGE-CNN & & ResNet-50 & 448 \(\times\) 448 & 93.9\% \\ NTSNet & & ResNet-50 & 448 \(\times\) 448 & 93.9\% \\ GCL & & ResNet-50+BN & 448 \(\times\) 448 & 94.0\% \\ FDL & & ResNet-50 & 448 \(\times\) 448 & 94.3\% \\ S3N & & ResNet-50 & 448 \(\times\) 448 & 94.7\% \\ DF-GMM & & ResNet-50 & 448 \(\times\) 448 & 94.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy comparison of our model with the state of the art on Stanford car dataset. interactions between all elements in the input feature map, resulting in quadratic complexity with respect to the input size. During the training stage, our model performed considerably better but still consumed a lot of time because of the higher number of training parameters as compared to the other models, and when we tested the model on the test set, the inference speed was slightly increased as well. The implementation of a visual transformer, which costs additional speed and memory resources, is a drawback of this study. However, as we know, vision transformers divide the input image into fixed-size patches and process them individually. Through reducing the patch size, the number of patches and the subsequent memory requirements can be decreased. But taking into consideration that this reduction should be balanced with the model's ability to capture fine-grained details, smaller patches might result in a loss of information. Various methods have already been proposed to make transformer attention mechanism more efficient, such as utilizing sparse attention patterns or approximating attention mechanisms with lower complexity operations such as kernelized self-attention or linear attention. These approaches can significantly reduce memory requirements and computational overhead and can be considered to align with our future research work.