## 4 Experiments
### Main Result  
We present our results on three benchmark datasets: VeRi-776 [3], VehicleID [11], and PKU-VD [12] and compare the results with those of state-of-the-art vehicle re-ID methods. Table 1 shows the flops counter for each parts in LFASM. **VeRi-776:** The total numbers of query and gallery images were 1678 and 11,579, respectively. We compared the proposed LFASM with the state-of-the-art vehicle re-ID methods. First, we considered LOMO [39], which utilizes a handcrafted local feature for person re-ID; it solves the problems associated with view and illumination variations. The GoogLeNet fine-tuned on the CompCars dataset [40] can extract high-level semantic attributes of the vehicle appearance, while VAMI [41] is a viewpoint-aware attention model used to extract the core area from different views through an adversarial network, and QD-DLF [42] has different directional feature pooling layers. Siamese-CNN + Path-LSTM [18] is a two-stage framework that combines complex spatiotemporal information and effectively regularizes the re-ID results. The comparison results on the VeRi-776, presented in Table 2 show that our proposed LFASM model achieves accuracies of 61.92%, 90.11%, and 92.91% mAP, top-1, and top-5, respectively. The ROC curves for the VeRi-776 are plotted in Figure 9, and the Area Under the Curve (AUC) is 0.974. The standard deviation values (std) of ap is 0.236. To determine the effect of each component in our model, we conducted an ablation study on VeRi-776. Our framework comprises three components: a PTN, local aware features (LAF), and Siamese feature match (SFM). We removed one component at a time and retrained the remaining network to evaluate the model performance in the absence of the removed component. Our model was able to achieve accuracies of 52.69%, 83.41%, and 90.81% for mAP, top-1, and top-5, respectively, without either PTN, SFM, or LAF, and the results were considered to be the baseline. The results of other comparative experiments are detailed in Table 3. The performance shows that the attention module has the most significant influence on the learning process; the other modules were also found to have improve the experimental results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Input Resolution** & **Params (M)** & **MACs (G)** \\ \hline LAF & 234 \(\times\) 234 & 51.69 & 19.46 \\ \hline PTN & 234 \(\times\) 234 & 0.97 & 0.09 \\ \hline SFM & 234 \(\times\) 234 & 8.40 & 0.008 \\ \hline LFASM & 234 \(\times\) 234 & 60.09 & 19.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flops counter for each parts in LFASM. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mAP** & **Top-1** & **Top-5** \\ \hline BOW-SIFT [43] & 1.51 & 1.91 & 4.53 \\ \hline LOMO [39] & 9.78 & 23.9 & 39.1 \\ \hline GoogleNet [40] & 17.8 & 50.7 & 67.5 \\ \hline VAMI [41] & 50.1 & 77 & 90.8 \\ \hline QD-DLF [42] & 61.8 & 88.5 & **94.5** \\ \hline Siamese-CNN + Path-LSTM [18] & 58.3 & 83.5 & 90.0 \\ \hline LFASM (Ours) & 61.92 & 90.11 & 92.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance (%) comparison of different methods on the VeRi-776 dataset. **VehicleID:** VehicleID has a larger number of images than that of VeRi-776, with both front and rear views of the vehicles. The testing data of VehicleID were split into three subsets, as detailed in Table 4.  
Table 5 presents the comparison results on the VehicleID dataset. As shown, our model achieves the highest top-1 rate and exhibits robust performance with respect to other evaluation indices. **PKU-VD:** Furthermore, we tested our method on the PKU-VD dataset, in turn, the two subdatasets: VD1 and VD2. Each subdataset is further divided into test sets of the following three sizes: small, medium, and large. Table 6 presents the number of test images in each subdataset. We followed the official setting provided by [12] for our model evaluation.