## 5 Experimental Results
### Ablation Studies
\begin{table}
\begin{tabular}{|l|c|c|c|} \hline
**Methods** & **R@1** & **R@5** & **R@10** & **mAP** \\ \hline \(PCB\) & 92.64 & - & - & 77.47 \\ \(PCB\)T\({}_{(L_{3}M_{3}h_{4})}\) & 86.58 & 94.42 & 96.26 & 66.00 \\ \hline \(PCB\)T\({}_{(L_{1}M_{3}h_{4})}\) & 90.08 & 95.72 & 97.15 & 71.95 \\ \(PCB\)T\({}_{(L_{2}M_{3}h_{4})}\) & 88.00 & 95.25 & 96.67 & 68.23 \\ \(PCB\)T\({}_{(L_{3}M_{3}h_{4})}\) & 72.36 & 86.67 & 90.53 & 42.64 \\ \hline \end{tabular}
\end{table} 
Table 3: The performance of the PCB Transformers (PCBTr) and baseline (PCB) for Market-1501 [39] datasets. The best results are in bold. The subscripts of “L”, “M” and “h” represent the presence of transformers after that block, the number of transformers after each block, and the number of heads in the model, respectively. **The Impact of Using Different Number of Transformers:** Another essential component to determine is the number of transformers required to boost the performance of the baselines. We have incorporated between 1 to 25 transformers for RTr and 1 to 16 transformers in DTr as shown in Tables 1 and 2. There is no specific number of transformers that gives the highest performance on all datasets over all the baselines. However, it can be seen that single transformers provide a considerable improvement over baselines. **Effect of Transformers Locations:** The role of integrating Transformers into the model is essential. We have placed the transformers in \(RTr\) after blocks while in \(DTr\) at the network's end before the classification layer. In \(RTr\), the best results are obtained when a single transformer is placed after each in the baseline for both datasets, as shown in Table 1. Similarly, the best performance is achieved using a single transformer for DukeMTMC and 10 transformers for Market-1501 datasets, as shown in Table 2. However, it should be noted that the transformer is not used in the baseline for DukeMTMC and 10 transformers for Market-1501 datasets, as shown in Table 2. However, it should be noted that the transformer is used in the baseline for DukeMTMC and 10 transformers for Market-1501 datasets, as shown in Table 2. However, it should be noted that the transformer is used in the baseline for DukeMTMC and 10 transformers for Market-1501 datasets, as shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & & **Level-1 (L)** & & & & **Level-2 (L)** & & & & **Level-3 (L)** & \\  & **R@1** & **R@5** & **R@10** & **mAP** & **R@1** & **R@5** & **R@10** & **mAP** & **R@1** & **R@5** & **R@10** & **mAP** \\ \hline \(h_{1}\) & 89.40 & 95.78 & 97.65 & 74.41 & 90.23 & 96.11 & 97.65 & 75.29 & 90.74 & 96.41 & 97.83 & 75.47 \\ \(h_{2}\) & 89.46 & 96.32 & 97.57 & 74.33 & 90.32 & 96.47 & 97.65 & 75.80 & 90.59 & 96.41 & 97.65 & 75.45 \\ \(h_{4}\) & 90.26 & 96.41 & 97.65 & 75.14 & 90.26 & 96.23 & 97.45 & 75.59 & 90.53 & 96.08 & 97.42 & 74.87 \\ \(h_{16}\) & 0.06 & 0.06 & 0.06 & 0.20 & 0.06 & 0.06 & 0.06 & 0.20 & 0.06 & 0.06 & 0.06 & 0.20 \\ \hline \end{tabular}
\end{table} 
Table 5: The effect of the number of heads on various Residual transformers trained and evaluated on Market-1501 dataset. The worst performance is given when the number of heads is 16. Figure 7: Sample images from the Market-1501 dataset to show the visual explanations of Residual-Transformers against the baseline method. Our proposed architecture focused on the fine-grained details to re-identify. The visual attention maps are generated by baseline and transformer-based architectures using Grad-CAM++ [44] (1 st row), Score-CAM [45] (2 nd row), and Eigen-CAM [46] (3 rd row). that irrespective of the integration location of transformers, improvement is achieved in most cases.