## 3 Experiment and Results
### Evaluation Metrics  
Model evaluation is very important to determine the model performance and model compatibility with the research objectives. There are several evaluation metrics that can be used in bus object detection, which include evaluation metrics related to the accuracy, speed, and efficiency of the model. Evaluation metrics related to accuracy are usually the main focus in detecting bus objects, because the model must be able to identify buses with high accuracy. The basic evaluation metrics that are often used are precision (P) and recall (R). Precision is the ratio between the number of true positives (TP) and the number of positive predictions (TP + false positives (FP)). This metric measures how accurately the model predicts the positive class. Recall is the ratio between the number of true positives (TP) and the number of true positive classes (TP + false negatives (FN)). This metric measures how many positive classes the model can identify. Apart from precision and recall, there are other evaluation metrics that are more commonly used in object detection, namely average precision (AP) and mean average precision (mAP). The AP measures how well the model finds relevant objects and ignores the irrelevant objects. The AP is calculated by plotting the precision-recall curve and calculating the area under the curve. The mAP, on the other hand, is the average of the APs for all the object classes found by the model. The mAP provides a more comprehensive picture of the model's performance because it measures the accuracy for all classes of objects, not just for one class. The equations are shown in Equations (11)-(14).  
\begin{table}
\begin{tabular}{c c} \hline
**Device** & **Configuration** \\ \hline System & Windows 11 Pro \\ CPU & 12 th Gen Intel(R) Core (TM) i7-12700 2.10 GHz \\ GPU & NVIDIA GeForce RTX 3060 Ti \\ Framework & PyTorch 1.13.1 \\ IDE & Visual Studio Code \\ Python version & version 3.7.9 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental setting.  
\[P=\frac{TP}{TP+FP^{\prime}} \tag{11}\]  
\[R=\frac{TP}{TP+FN^{\prime}} \tag{12}\]  
\[AP=\int_{0}^{1}P(r)dr \tag{13}\]  
\[mAP=\frac{\sum_{i=1}^{K}AP_{i}}{k} \tag{14}\]  
In addition to the evaluation metrics related to accuracy, there are evaluation metrics related to the model speed and efficiency. For example, gigaflops per second (GFLOPS) measures the number of floating-point operations a model can perform in one second, and the inference time measures the time it takes a model to process a single image or video. The evaluation metrics related to the model speed and efficiency are critical in real-time applications such as object detection in video.  
Apart from that, there are other evaluation metrics such as the number of model parameters and the model size. The number of model parameters is the number of parameters (weights) used by the model to learn patterns in the training data. The more parameters used, the more complex the model. This metric gives an idea of the complexity of the model. Model size, on the other hand, measures the size of the model file in bytes or megabytes (MB). This metric gives an idea of the complexity and practicality of the model, especially in applications that require fast and space-efficient file transfers.