## 3 Results
### Accuracy Compared to Weather  
To evaluate the impact of the conditioning branch on the performance with respect to the different weather conditions used for auxiliary prediction and optimization, Figures 7-9 detail the relation between mAP and the three meta-variables chosen (namely temperature, humidity, and time of day). Visual examples to accompany the accuracy overview of Tables 1 and 2 can be seen in Figures 10 and 11 (ground truth labels and the image without bounding boxes can be found Figure A3), while visualizations of accuracy with respect to the different weather variables can be seen in Figures 7-9. In Figure 7, it can be observed that training models with a temperature-focused auxiliary branch do not change the performance of said model in any significant way (other than a generally lowered mAP). It can be seen that all models follow a curve that is similar to the distribution of samples seen in Figure 4b, and it can be expected that this is happening as the model optimization is simpler when regressing to the mean of the dataset. In addition, it can be observed that the indirect conditioning method is generally more agnostic to variation in the meta-variables. Similarly to the temperature-focused auxiliary branch, humidity- and time-of-day conditioning do not seem to improve the overall performance of the models. However, interestingly, the models seem to be generally agnostic to the distribution of samples (shown in Figure 4a,c). This indicates that the model has trouble extracting meaningful information with regard to the auxiliary optimization task. This is also present in Table 2, which is where it can be seen that the networks have difficulty with accurately predicting their respective weather conditions (specifically humidity and the time of day), whereas temperature prediction is rather accurate and falls close to the acceptable deviation of the \(L1_{e}\) loss. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & \(mAP_{voc}\) & \(mAP_{oco}\) & \(mAP_{L}\) & \(mAP_{M}\) & \(mAP_{S}\) & MR \\ \hline YOLOv5 (Baseline) & **0.604** & **0.465** & **0.825** & **0.640** & 0.491 & 0.342 \\ YOLOv5 (Pretrain) & 0.600 & 0.454 & 0.831 & 0.621 & 0.489 & 0.324 \\ YOLOv5 (Temp.) & 0.584 & 0.410 & 0.796 & 0.590 & 0.468 & 0.322 \\ YOLOv5 (Hum.) & 0.493 & 0.293 & 0.675 & 0.560 & 0.268 & 0.357 \\ YOLOv5 (ToD) & 0.549 & 0.439 & 0.805 & 0.566 & 0.431 & 0.356 \\ \hline DN-DETR Baseline & 0.378 & 0.348 & 0.123 & 0.344 & 0.563 & 0.421 \\ DN-DETR (Temp.) & 0.225 & 0.148 & 0.100 & 0.190 & 0.682 & 0.389 \\ DN-DETR (Hum.) & 0.191 & 0.132 & 0.100 & 0.160 & 0.671 & 0.415 \\ DN-DETR (ToD) & 0.219 & 0.142 & 0.00 & 0.169 & 0.661 & 0.410 \\ \hline Def. DETR Baseline & 0.332 & 0.202 & 0.005 & 0.051 & 0.637 & 0.383 \\ Def. DETR (Temp.) & 0.297 & 0.184 & 0.001 & 0.045 & 0.620 & 0.351 \\ Def. DETR (Hum.) & 0.213 & 0.114 & 0.000 & 0.020 & 0.517 & 0.416 \\ Def. DETR (ToD) & 0.289 & 0.178 & 0.001 & 0.040 & 0.619 & 0.395 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: In this table, the mAP and MR of the direct (YOLOv5) and indirect conditioning (DETR) variants are detailed. Highlighted with **bold** is the best performing model across all of the models, and highlighted with underline is the best performing model for a given architecture. Temperature, humidity, and time of day are abbreviated as Temp., Hum., and ToD, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model** & **MAE** & **MAPE** & **Std. ** \\ \hline \multirow{4}{*}{\(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{ \text{ \text{ \textFigure 8: mAP of **(a)** YOLO and **(b)** DETR variants, with respect to the humidity of samples in the test-set. Please note the Y-axis ranges differ between architectures. Figure 7: mAP of **(a)** YOLO and **(b)** DETR variants, with respect to temperature of samples in the test-set. Please note the Y-axis ranges differ between architectures. Figure 11: Example of indirect conditioning performance for each conditioned model.