# An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
## 4 Experiments
### Results  
**Data Augmentation Ablation Analysis. ** It can be seen from the data in Table 2 that the data augmentation methods have a significant impact on the performance of MSFANet. We test the model's mAP and Rank-1 with different data augmentation method combinations, including flipping, cropping, erasing, color jitter, rotation, and autoaugment, etc. We set flipping, cropping, erasing, and patching as a basic data augmentation combination, which can be combined with color jitter, affine, rotation, cutout, and autoaugment, respectively. As shown in Figure 5(a), we compare these different combinations' mAP. We can find that the best performance can be achieved when adopting a novel combination of basic data augmentation with autoaugment. As shown in the last row of the following table, it can achieve 82% mAP and 92.5% Rank-1 that obviously surpass other data augmentation combinations. Therefore, in the subsequent experiments, this data augmentation combination is carried out for the proposed MSFANet training. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Time** & **Camera** & **Resolution** & **IDs (T-Q-G)** & **Images (T-Q-G)** \\ \hline Market1501 [1] & 2015 & 6 & fixed & 751-750-751 & 12936-3368-15913 \\ DukeMTMC [31] & 2017 & 8 & fixed & 702-702-1110 & 16522-2228-17661 \\ VIPeR [32] & 2007 & 2 & fixed & 316-316-316 & 632-632 \\ GRID [33] & 2009 & 8 & vary & 125-125-900 & 250-125-900 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistics of experimental datasets. (T: Train; Q: Query; G: Gallery.) **Results of loss function. ** To verify the proposed fusion loss function's effectiveness, illustrated in Equation (6), we first set \(\alpha\) as a decimal between 0 and 1 and \(\beta\) as 1 to find the optimal coefficient \(\alpha\). Figure 6(b) compares the mAP based on fusion loss functions with different coefficients, and it can be observed that the mAP achieves the best when \(\alpha\) is set to 0.5. Then, in order to further verify this fusion loss function, we train MSFANet with softmax loss function and fusion loss function on Market1501 and DukeMTMC datasets, respectively. As shown in Table 3, the mAP and Rank-1 increase 0.6% and 0.3% on Market1501 dataset while increase 2.2% and 0.9% on DukeMTMC datasets. These results thus demonstrate the effectiveness and necessity of our fusion loss function. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**MSFANet Data Augmentation on Market1501**} \\ \hline Horizontal flip & Crop & Erase & Patch & Color jitter & Affine & Rotation & Cutout & AutoAugment & mAP (\%) & Rank-1 (\%) \\ \hline  & Y & Y & Y & Y & & & & 81.3 & 91.0 \\ Y & Y & Y & & & & & & 81.8 & 91.1 \\ Y & & & & & & & & 82.2 & 92.4 \\ Y & Y & & & & & & & 83.1 & 92.5 \\ Y & Y & Y & Y & & & Y & & 83.5 & 92.6 \\ Y & Y & Y & Y & & Y & & & 83.9 & 92.8 \\ Y & Y & Y & Y & Y & & & & 84.6 & 93.3 \\ Y & Y & Y & Y & & & & & 85.0 & 93.9 \\ Y & Y & Y & Y & Y & & & & 85.1 & 93.9 \\ Y & Y & Y & Y & Y & & & Y & 86.0 & 94.5 \\ Y & Y & Y & Y & & & & Y & **86.8** & **94.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of data augmentation methods on MSFANet. The mAP and Rank-1 of this model are calculated by combining different data augmentation methods. Figure 6: mAP (\(\%\)) under epochs based on different data augmentation combinations (**a**). mAP (\(\%\)) under epochs based on fusion loss function with different coefficients (**b**), where T\(\alpha\) denotes the result of the fusion loss function with weight \(\alpha\). **Results on big ReID datasets. ** As shown in Table 4, the experimental results of MSFANet on big ReID datasets are presented, including the model's complexities and inference abilities. The table compares the performance of MSFANet with other state-of-the-art methods, which contains top and bottom two parts, representing training from scratch or not, respectively. * **Model complexity analysis.