# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 4 Experiments and Results
### Performance Results
#### 4.2.2 Results on CUHK-03 Dataset  
The CUHK-03 dataset includes both manually cropped images (referred to as the labeled version) and automatically cropped images by the detector [32] (denoted as the detected version).  
Following the protocol in [23], we randomly divided the dataset into the training set of 1160 persons and the set of 100 persons. The experiments were conducted with five random splits with the results computed by the single shot-setting. The learning rate was set as \(4\times 10^{-3}\) for the first \(5\times 10^{3}\) epochs; then, it decreased to \(1\times 10^{-3}\). The momentum was set as 0.9, and the weight decay was \(5\times 10^{-4}\). The batch-size was set as 256. The experimental results for the detected version and the labeled version are shown in Tables 2 and 3, respectively. It shows that our method reached the best performance for rank@1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank = 1** & **Rank = 5** & **Rank = 10** & **Rank = 20** \\ \hline FPNN [23] & 27.87 & 59.64 & 73.53 & 87.34 \\ MLF [27] & 34.30 & 55.06 & 64.96 & 74.94 \\ kLFDA [28] & 42.76 & 69.01 & 79.63 & - \\ IDLA [29] & 47.53 & 71.20 & 80.02 & - \\ DeepRank [30] & 50.41 & 75.53 & - & - \\ MVLDM+ [31] & 61.37 & 82.74 & 88.88 & 93.85 \\ MuDeep [5] & 79.01 & 94.00 & 96.84 & - \\ \hline Ours & 83.92 & 92.82 & 97.45 & 99.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on CUHK-01.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank = 1** & **Rank = 5** & **Rank = 10** & **Rank = 20** \\ \hline FPNN [23] & 19.89 & 49.41 & 64.79 & 81.14 \\ HA-CNN [32] & 41.70 & - & - & - \\ DeepSimilarity [33] & 46.20 & 73.77 & 84.46 & 90.07 \\ LOMO+XQDA [34] & 46.25 & 78.90 & 88.55 & 94.25 \\ MGC+X [35] & 46.29 & - & - & - \\ LOMO+SSM [36] & 49.05 & 81.25 & 90.30 & - \\ PCB [37] & 63.70 & - & - & - \\ TRFDWCAM [38] & 66.60 & - & - & - \\ CASN+PCB [39] & 71.50 & - & - & - \\ P\({}^{2}\)-Net [40] & 74.90 & - & - & - \\ MuDeep [5] & 75.64 & 94.36 & 97.46 & - \\ DSA-reID [41] & 78.20 & - & - & - \\ \hline Ours & 78.50 & 92.10 & 95.02 & 98.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results for CUHK-03 on the detected version.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank = 1** & **Rank = 5** & **Rank = 10** & **Rank = 20** \\ \hline FPNN [23] & 20.65 & 50.77 & 66.15 & 82.31 \\ HA-CNN [32] & 44.40 & - & - & - \\ DeepSimilarity [33] & 48.92 & 80.77 & 90.46 & 92.07 \\ MGC+XQDA [34] & 49.29 & - & - & - \\ LOMO+XQDA [34] & 52.20 & 82.23 & 92.14 & 96.25 \\ LOMO+SSM [36] & 52.50 & 84.53 & 92.49 & - \\ TRFDWCAM [38] & 70.10 & - & - & - \\ CASN+PCB [39] & 73.70 & - & - & - \\ MuDeep [5] & 76.87 & 96.12 & 98.41 & - \\ P\({}^{2}\)-Net [40] & 78.30 & - & - & - \\ DSA-reID [41] & 78.90 & - & - & - \\ \hline Ours & 82.50 & 92.15 & 95.22 & 99.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for CUHK-03 on the labeled version.  
We compared the existing methods (like FPNN [23], MGCA [35], and DeepSimilarity [33]), HA-CNN [32], and MuDeep [5]. From Tables 2 and 3, the rank@1 matching rate of our method exceeded the counterparts and was reached at 78.50% and 82.50%, respectively. For the labeled version in Table 3, our matching rates at rank@5, 10, and 20 were lower than those of LOMO+SSM [36]. They both regard the person re-identification as a binary classification task and consider the person image pairs. However, in our method, we just considered the deep features for the single person image.