# PVTReID: A Quick Person Reidentification-Based Pyramid Vision Transformer
## 4 Experiments
### Comparison to State-of-the-Art Methods  
In Table 6, we compare our PVTReID to state-of-the-art methods on the three benchmarks datasets Market1501, MSMT17, and DukeMTMC-reID. On large datasets, the overall performance of PVTReID is significantly better than previous state-of-the-art methods. Specifically, on MSMT17, PVTReID achieves a 2.4% improvement in mAP. On DukeMTMC-reID, PVTReID obtains a 0.5% improvement in mAP. On smaller datasets such as Market-1501, PVTReID lags slightly behind a few other state-of-the-art methods. Although CNN-based ReID methods mostly use the ResNet50 backbone to extract person features, they may contain several branches (e.g., attention modules, pose estimation models, and other modules) which increase computational cost. We conducted a fair comparison of inference speed between PVTReID and CNN-based ReID on the same hardware. OSNet does not use ResNet50 as the backbone, instead using a self-designed CNN. Compared to state-of-the-art methods using ResNet50, PVTReID is 0.8% slower than ISP in terms of mAP and 8% faster than ISP in terms of inference speed on the Market-1501 dataset; on MSMT17, PVTReID shows an improvement of 2.4% in mAP compared to ABDNet and a 52% improvement in inference speed, while on the DukeMTMC-reID dataset PVTReID shows an improvement of 0.5% in mAP and 8% in inference speed compared to ISP. The reasons for these results are as follows: first, most CNN-based methods use complex branches to improve their fine-grained discrimination ability, which generates additional resource consumption; second, several ReID methods (e.g., ABDNet) use 384 \(\times\) 128 size images, which increases resource consumption during feature extraction; \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Inference** **(Images/s)**} & \multicolumn{2}{c}{**Market1501**} & \multicolumn{2}{c}{**MSMT17**} & \multicolumn{2}{c}{**DukeMTMC-reID**} \\ \cline{6-9}  & & & & **mAP** & **R1** & **mAP** & **R1** & **mAP** & **R1** \\ \hline \multirow{8}{*}{CNN} & CBN [2] & 256 \(\times\) 128 & 338 & 77.3 & 91.3 & 42.9 & 72.8 & 67.3 & 82.5 \\  & OSNet [38] & 256 \(\times\) 128 & 2028 & 84.9 & 94.8 & 52.9 & 78.7 & 73.5 & 88.6 \\  & SAN [39] & 256 \(\times\) 128 & 290 & 88.0 & **96.1** & 55.7 & 79.2 & 75.7 & 87.9 \\  & PGFA [40] & 256 \(\times\) 128 & 263 & 76.8 & 91.2 & - & - & 65.5 & 82.6 \\  & HOReID [41] & 256 \(\times\) 128 & 310 & 84.9 & 94.2 & - & - & 75.6 & 86.9 \\  & ISP [42] & 256 \(\times\) 128 & 315 & **88.6** & 95.3 & - & - & **80.0** & 89.6 \\  & MGN [43] & 384 \(\times\) 128 & 287 & 86.9 & 95.7 & 52.1 & 76.9 & 78.4 & 88.7 \\  & SCSN [8] & 384 \(\times\) 128 & 267 & 88.5 & 95.7 & 58.5 & **83.8** & 79.0 & **91.0** \\  & ABDNet [9] & 384 \(\times\) 128 & 223 & 88.3 & 95.6 & **60.8** & 82.3 & 78.6 & 89.0 \\ \hline \multirow{2}{*}{PVT} & Basic & 256 \(\times\) 128 & 359 & 86.3 & 94.9 & 60.2 & 81.2 & 77.8 & 87.9 \\  & PVTReID & 256 \(\times\) 128 & 341 & 87.8 & 95.3 & 63.2 & 82.3 & 80.5 & 90.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**LFM**} & \multirow{2}{*}{**SIE**} & \multicolumn{2}{c}{**Market1501**} & \multicolumn{2}{c}{**MSMT17**} & \multicolumn{2}{c}{**DukeMTMC-reID**} \\ \cline{3-10}  & & & **mAP** & **R1** & **mAP** & **R1** & **mAP** & **R1** \\ \hline Basic & ✗ & ✗ & 86.3 & 94.9 & 60.2 & 81.2 & 77.8 & 87.9 \\  & ✗ & ✗ & 87.6 & 95.1 & 62.1 & 82.1 & 79.3 & 88.6 \\  & ✗ & ✗ & 86.7 & 94.7 & 61.8 & 81.9 & 79.7 & 89.3 \\ PVTReID & ✗ & ✗ & 87.8 & 95.0 & 63.2 & 82.3 & 80.5 & 90.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of PVTReID ablation study. ✗ means that the module is not used at method. ✗ means that the module is used at method.