# Swin Transformer-Based Object Detection Model Using Explainable Meta-Learning Mining
## 4 Results and Performance Evaluation
### Performance Evaluation  
In this paper, 35,159 fire prediction image data points provided by AI hub are used for performance evaluation. Accordingly, to prevent overfitting, the training dataset is 50%; the validation dataset is 10%; and the test dataset is 40%. In addition, the implementation environment uses AMD Ryzen 9 5900X 12-Core Processor 3.70 GHz, 96 GB RAM, NVIDIA GeForce RTX 3090, Python 3.6 version, etc. For the performance evaluation of the  
Figure 7: The results drawn from the Vision Transformer-based explainable object detection using meta-learning mining. (**a,b**) Grad-CAM normally captures the highest importance in fire prediction as fire. (**c**) incorrectly detected results. proposed method, its accuracy for object detection is compared. Average Precision (AP) for the performance comparison of object detectors uses AUC using the precision-recall relation [36]. Generally, an AUC curve has many repetitions up and down. In the curves that have the tendency of crossing each other, it is hard to compare with a different detector in the same plot. Therefore, AP is suitable for comparison with a different detector [37]. In this study, APs for multiple classes are needed. So, the AP value for each class is calculated, and their mean value is extracted. To do that, Mean Average Precision (mAP) is applied. mAP is capable of extracting the mean of AP values by interpolating all points. Equation (1) presents the interpolation of all points. \[\text{AP}=\sum_{i}(r_{i+1}-r_{i})\rho_{inpolation}(r_{i+1}) \tag{1}\]  
The higher mAP value, the more accuracy. The lower value, the less accuracy. Equation (2) presents mAP. \(AP_{i}\) represents the AP value of \(i\)th class, and \(N\) means the total number of classes to evaluate. \[\text{mAP}=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{2}\]  
For performance evaluation, firstly, the performance of the proposed method is evaluated. Standard Swin Transformer, Swin Transformer + YOLO, and Swin Transformer + YOLO + Few-Shot Learning are evaluated in terms of performance. Table 1 shows the results of the performance evaluation for each method [38]. In the evaluation results of Table 1, the mAP result of the standard Swin Transformer is evaluated as 46.5. Additionally, in the case of Swin Transformer + YOLO, the mAP is 50.54, which is higher than standard Swin Transformer [39]. This is considered to be because Swin Transformer + YOLO can detect object information and pixel information in various scales. In addition, in the case of the proposed Swin Transformer + YOLO + Few-Shot Learning (ours), the extracted mAP was the highest at 51.2. Secondly, the proposed method is compared with a conventional object detection method in order the evaluation of its excellency. The network used in the proposed method has a feature pyramid structure. Therefore, the performance of the object detection model based on the feature pyramid structure is compared. The object detection models used for comparison are YOLOACT, YOLOACT++, and YOLOS models. Table 2 shows the results of the performance comparison between object detection algorithms. Table 2 compares the mAP of the YOLACT, YOLACT++, and YOLOS models and the proposed model. The YOLACT and YOLACT++ models have the advantage of real-time detection [42]. As a result of performance evaluation, the proposed method is evaluated highly by about 17. Therefore, through the proposed method, it is possible to predict and monitor fire occurrence in real-time. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mAP** \\ \hline Swin Transformer [28] & 46.5 \\ Swin Transformer + YOLO & 50.54 \\ Swin Transformer + YOLO + Few-Shot Learning (ours) & 51.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of the performance evaluation for each method. \begin{table}
\begin{tabular}{c c} \hline \hline
**Object Detection Model** & **mAP** \\ \hline YOLACT [40] & 30.45 \\ YOLACT++ [41] & 34.65 \\ YOLOS [15] & 30.04 \\ Swin Transformer + YOLO + Few-Shot Learning (ours) & 51.52 \\ \hline \hline \end{tabular}
\end{table}