## 4 Results and Discussion  
### Automated Detection  
Results from the first 2 training stages can be found in Table 2; these refer to the results on labeled simulated images (before and after domain adaptation) in terms of the mean average precision (mAP) over all intersection over union (IoU) thresholds from 50% until 95% with 5% increments and AP at 50% and 75% IoU. Although the new version of the simulated dataset has around 17 times less examples, the performance of the detector is not severely hindered, having gone from 86.76 [10] to 82.13 mAP, and, more importantly, it improved on the real test dataset, as will be discussed later. As expected, the performance of the detector trained on unaltered simulated images decreases when testing with domain converted images (2 nd versus 3 rd and 4 th rows of Table 2) due to the domain gap introduced. When fine-tuning on the adapted data, mAP increases without significantly decreasing on original images; for example, after fine-tuning with CUT converted images, the performance improves from 42.89 to 77.26, while the mAP on the original set only decreases from 82.13 to 75.25. Interestingly, this improvement (and the decrease on original images) is more accentuated with CUT than with CycleGAN. This hints that CUT is able to better approximate the simulated to the real domain, which is also corroborated by the Frechet Inception Distances (FID) [44] presented in Table 3: after the conversion with CUT, the FID between the resulting distribution and the real domain is lower than with CycleGAN (56.87 vs. 60.04), while being higher for the original domain (58.17 vs. 35.40) (The closer two distributions are, the lower the FID.). These conclusions can also be visually verified in Figure 6, where the progression from the simulated (\"Simulated (new)\") to the real domain via CycleGAN and CUT is shown, as well as a comparison with the old version of the simulated dataset. In general, both methods tend to darken the original image, make the surface finishes less dull/more metallic, and introduce reflections, but these are more accentuated with CUT (see the 3 rd and 6 th rows). It is also interesting to note that CUT introduces more texture to the floors (1 st and 5 th rows), transforms the background pillars into vertical lights (rows 2, 3, 4 and 6), and makes the glass windows more see-through (2 nd row). The results regarding the performance of the system on real unlabeled images, the main focus of this work, can be found in Table 4 and Figure 7. Similarly to previous works [10; 30], due to the lack of ground truth bounding boxes for real images, we frame the problem as multi-label multi-class classification, in which for each vehicle we obtain a list of detected parts and compare it to the ground-truth list. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Data**} & \multicolumn{3}{c}{**Detection**} \\ \cline{3-5}  & & **mAP** & **AP@50** & **AP@75** \\ \hline \multirow{3}{*}{From scratch} & simulated & 82.13 & 93.99 & 90.38 \\  & CycleGAN & 69.23 & 83.91 & 78.66 \\  & CUT & 42.89 & 58.43 & 49.15 \\ \hline \multirow{2}{*}{Fine-tuning w/CycleGAN} & simulated & 77.41 & 90.80 & 86.71 \\  & CycleGAN & 80.53 & 93.08 & 89.53 \\ \hline \multirow{2}{*}{Fine-tuning w/CUT} & simulated & 75.25 & 89.45 & 84.43 \\  & CUT & 77.26 & 92.15 & 86.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection results obtained on simulated images, before and after domain adaptation, in terms of mAP and AP at 50% and 75% of IoU. The first column refers to the training process and the second specifies the test data for each scenario; for example, the 3 rd row refers to the performance when training on simulated images and testing on CycleGAN converted images, while the 6 th row refers to fine-tuning and testing on simulated images converted by CycleGAN. We include the results from [10] for comparison.