## 4 Experiments
### Architecture Evaluation  
To enable the model to obtain the best performance, the latest Re-ID algorithm will be pre-trained on ImageNet first. In order to ensure a fair comparison with other state-of-the-art algorithms, we also put our algorithm on ImageNet for pre-training. Results on Market-1501. We compare our method with the state-of-the-art Re-ID model in Table 2. The CNN found by our KDAS-ReID reached a rank-1 of 94.7% and a mAP of 85.3%, which is better than other state-of-the-art Re-ID models. Our KDAS-ReID obtains higher accuracy and mAP than the teacher model based on ResNet-50, and reduces the parameters of the teacher model by more than 43%. In addition, after using the same augmentation technology, our KDAS-ReID also outperforms other Re-ID models. Results on CUHK03. Table 3 shows the comparison between KDAS-ReID and other models. Whether in the bounding box of manually marked or automatically detected people, our KDAS-ReID has shown better performance than other models. Results on MSMT17 in Table 4. On MSMT17, the rank-1 accuracy of our KDAS-ReID achieves 78.4%, and the mAP achieves 53.2%. In addition, our model outperforms the teacher model and the previous state-of-the-art method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Backbone** & **Params(M)** & \multicolumn{3}{c}{**Market-1501**} \\ \cline{3-6}  & **R-1** & **mAP** \\ \hline PAN [33] & ResNet50 & \textgreater{}25.1 & 82.8 & 63.3 \\ TriNet [34] & ResNet50 & 25.1 & 84.9 & 69.1 \\ AOS [35] & ResNet50 & \textgreater{}25.1 & 86.4 & 70.4 \\ MLFN [36] & ResNeXt-50 & \textgreater{}25.0 & 90.0 & 74.3 \\ DuATM [37] & DenseNet-121 & \textgreater{}8.0 & 91.4 & 76.6 \\ PCB [11] & ResNet50 & 27.2 & 93.8 & 81.6 \\ Mancs [38] & ResNet50 & \textgreater{}25.1 & 93.1 & 82.3 \\ HPM [39] & ResNet50 & 25.1 & 94.2 & 82.7 \\ \hline Auto-ReID [4] & - & 13.1 & 94.5 & 85.1 \\ \hline
**Teacher Model** & ResNet50 & \textgreater{}25.1 & 93.7 & 83.2 \\
**KDAS-ReID** & - & 14.3 & 94.7 & 85.3 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline TriNet [34] & ResNet50 & 25.1 & 86.7 & 81.1 \\ AOS [35] & ResNet50 & \textgreater{}25.1 & 88.7 & 83.3 \\ AACN [40] & GoogleNet & \textgreater{}8.0 & 88.7 & 83.0 \\ PSE+ECN [10] & ResNet50 & \textgreater{}25.1 & 90.3 & 84.0 \\ PCB [11] & ResNet50 & 27.2 & 95.1 & 91.9 \\ \hline Auto-ReID [4] & - & 13.1 & 95.4 & 94.2 \\ \hline
**Teacher Model** & ResNet50 & \textgreater{}25.1 & 94.7 & 93.1 \\
**KDAS-ReID** & - & 14.3 & **95.6** & **94.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The current state-of-the-art model is compared with our model on the Market-1501 dataset. We compare each model in terms of parameters, R-1 (Rank-1) accuracy, and mAP. We have boldfaced the best results in the table. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architectures** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** & **Params(M)** \\ \hline ResNet-18 [1] & 66.0 & 85.2 & 94.6 & 96.5 & 11.6 \\ ResNet-34 [1] & 68.0 & 86.7 & 94.8 & 96.6 & 21.7 \\ ResNet-50 [1] & 68.5 & 87.2 & 95.5 & 97.1 & 25.1 \\ \hline DARTS [15] & 65.2 & 85.6 & 94.3 & 96.4 & **9.1** \\ GDAS [32] & 66.8 & 86.5 & 94.7 & 96.9 & 13.5 \\ \hline Baseline 1 & 71.7 & 87.9 & 95.9 & 97.4 & 11.2 \\ Baseline 2 & 71.5 & 87.0 & 95.7 & 97.3 & 9.8 \\ Baseline 3 & 72.3 & 89.0 & 96.5 & 97.9 & 12.0 \\ Baseline 4 & 72.1 & 88.6 & 96.4 & 97.9 & 11.4 \\ \hline Baseline + Distillation 1 & 74.7 & 89.7 & 96.2 & 97.6 & 16.5 \\ Baseline + Distillation 2 & 75.8 & 91.0 & 96.6 & 98.0 & 15.0 \\ Baseline + Distillation 3 & 75.3 & 89.9 & 96.5 & **98.1** & 14.3 \\ Baseline + Distillation 4 & 74.9 & 89.3 & 96.5 & 97.7 & 12.1 \\ \hline Distillation + Dynamic Temperature 1 & **77.0** & **91.1** & **97.0** & 97.9 & 14.3 \\ Distillation + Dynamic Temperature 2 & 76.9 & **91.1** & 96.7 & 97.9 & 15.7 \\ Distillation + Dynamic Temperature 3 & 76.0 & 90.0 & 96.6 & 98.0 & 16.4 \\ Distillation + Dynamic Temperature 4 & 75.7 & 90.1 & 96.5 & 98.0 & 15.4 \\ \hline \hline \end{tabular}
\end{table}