# Metric Embedding Learning on Multi-Directional Projections
## 4 Results
### Object Re-Identification  
Metric embedding learning of image representations is often used for re-identification of people [63; 64] or vehicles [65]. To analyze the performance of the model in a real-life application, vehicle images are used as inputs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Pretraining** & **Architecture** & **Mining** & \
\begin{tabular}{c} **Training Time** \\ **(Seconds)** \\ \end{tabular} & **Iterations** &
\begin{tabular}{c} **Time per Iteration** \\ **(Seconds)** \\ \end{tabular} \\ \hline \multirow{6}{*}{Original \(128\times 128\)} & Yes & Siamese & & 7488.01 & 75 & 99.84 \\  & Yes & Triplet & Semi-hard & 2528.65 & 16 & 158.04 \\  & Yes & Triplet & Hardest & 2907.28 & 18 & 161.52 \\  & No & Siamese & & 10,263.05 & 102 & 100.62 \\  & No & Triplet & Semi-hard & 9047.23 & 47 & 192.49 \\  & No & Triplet & Hardest & 9720.34 & 60 & 162.01 \\ \hline \multirow{6}{*}{Highlighted \(96\times 96\)} & Yes & Siamese & & 2976.65 & 73 & 40.78 \\  & Yes & Triplet & Semi-hard & 1399.09 & 21 & 66.62 \\  & Yes & Triplet & Hardest & 1454.04 & 21 & 69.24 \\  & No & Siamese & & 1984.10 & 120 & 16.53 \\  & No & Triplet & Semi-hard & 518.04 & 16 & 32.38 \\  & No & Triplet & Hardest & 1212.54 & 32 & 37.89 \\ \hline \multirow{6}{*}{MDIPFL50 \(50\times 37\)} & Yes & Siamese & & 2430.68 & 86 & 28.26 \\  & Yes & Triplet & Semi-hard & 890.85 & 16 & 55.68 \\ \cline{1-1}  & Yes & Triplet & Hardest & 2500.35 & 52 & 48.08 \\ \cline{1-1}  & No & Siamese & & 3017.49 & 104 & 29.01 \\ \cline{1-1}  & No & Triplet & Semi-hard & 888.62 & 19 & 46.77 \\ \cline{1-1}  & No & Triplet & Hardest & 2425.68 & 46 & 52.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The measured runtimes of the experiments for training on the NIST SD19. Each iteration took 500 steps. Times of pretraining not included. The dataset published in the International Workshop on Automatic Traffic Surveillance of CVPR 2016 [66] is processed, and the collected samples are used for the experiment. The two different viewpoints are presented on Figure 8.  
The records of the dataset are labeled and annotated, the extraction of the regions of interest was done accordingly. For every instance, a total of 30 observations from two different viewpoints are used. For those objects where the number of observations is inadequate, records are removed from the dataset. The remaining set is split into a training and a validation subset using a \(9:1\) ratio; the total number of training instances is 629, with 69 test classes. Samples of the prepared dataset are shown in Figure 9.  
As a backbone to the architecture, the smallest version of ResNet [23] is applied to map input images to encodings with a length of 128 elements. During training, a maximum number of 1000 epochs are done, with 200 steps per epoch. If pretraining is relevant, a maximum of 100 epochs with 500 steps on each is done. Early stopping could occur if loss convergence stops. Measured one-shot classification accuracy is presented in Table 3. To correctly interpret these data, it is important to emphasize that the vehicle instances in the test set were not seen during training.