## 3 Materials and Methods
### Experimental Setup and Procedure Description
While the size of the image is represented by \(\hat{W}\) and \(\hat{H}\) as the width and height of the retrained results, which are obtained from Formulas (6) and (7). Furthermore, the results of this \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Image Size** & **Batch** & **Momentum** & **Initial Learning Rate** & **Decay** & **Training Steps** \\ \hline
416 \(\times\) 416 & 64 & 0.949 & 0.001 & 0.0005 & 8000, 9000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initialization parameters of YOLOv4. Figure 7: Research Methodology. retraining will be the input for drawing the bounding box in Figure 9c. The estimated bounding box values must be computed in YOLO format according to the draw bounding box Equations (8)-(11), where, respectively, \(l\), \(r\), \(t\), and \(b\) are the left, right, top, and bottom sides of the box. \[l =\left(x-\frac{\hat{W}}{2}\right)*W \tag{8}\] \[r =\left(x+\frac{\hat{W}}{2}\right)*W\] (9) \[t =\left(y-\frac{\hat{H}}{2}\right)*H\] (10) \[b =\left(y+\frac{\hat{H}}{2}\right)*H \tag{11}\]  
Algorithm 1 is the pseudocode of pseudo-labeling to change from detection output to YOLO format. We define \"classes\" as an array of category lists for \"pants,\" \"midlength dress,\" \"hoodie,\" \"jacket,\" and \"mid-length skirt.\" After saving the result, we deleted some lines we did not need to calculate. B is an array of the total number of bounding boxes from the text file because some images have many categories to detect. For every bounding box, we put the values of the predicted class of B[i] to the \"idx.\" \"left_x\" is for the values of the left_x of B in the i\({}^{\text{th}}\) Bounding box, and top_y is for the values of top_y in i\({}^{\text{th}}\) Bounding Box. w and h are the width and height values of the bounding box in i\({}^{\text{th}}\) iterations shown in Algorithm 1. After that, we identify the actual value of the class that the LabellImg tool already defined by comparing the \"classes\" array. Furthermore, the conversion process from the detection output format into the YOLOV4 format begins by  
Figure 8: Detection result of the YOLOv4 traditional model (Mid-length dress: 95% (left_x: 42 top_y: 13 width: 142 height: 196)). Figure 9: Conversion from Detection Output to YOLO Form. (**a**) YOLOv4 Detection Result; (**b**) Pseudolabeling Result; (**c**) Input Result for YOLOv4. performing calculations to get the value of \(\hat{x}\), \(\hat{y}\), \(\hat{W}\), and \(\hat{H}\). These values are then executed using the boundingBoxYOLO function, which is concatenated with a, \(\hat{x}\), \(\hat{y}\), \(\hat{W}\), and \(\hat{H}\). ```
Input: the image and its corresponding text File Output: the text file with YOLO bounding box format Procedure:
1. classes \(\leftarrow\) an array of categories list ['pants,''mid-length dress,' 'hoodie,' 'jacket,''mid-length skirt']
2. H \(\leftarrow\) the height of the image
3. W \(\leftarrow\) the width of the image
4. read the text file
5. B \(\leftarrow\) an array of the total number of bounding box values from the text file
6. IF the length of B > 0:
7. FOR i = 0 to the length of B:
8. idx \(\leftarrow\) the value of the predicted classes of B[i]
9. left_x \(\leftarrow\) the value of the left_x of B[i]
10. top_y \(\leftarrow\) the value of the top_y of B[i]
11. w \(\leftarrow\) the value of the width of B[i]
12. h \(\leftarrow\) the value of the height of B[i]
13. a = the index of the idx value in classes []
14. \(\hat{x}\) = (left_x + w/2)/W
15. \(\hat{y}\) = (top_y + h/2)/H
16. \(\hat{W}\) = w / W
17. \(\hat{H}\) = h / H
18. boundingBoxYOLO \(\leftarrow\) concatenate ( a + - \"+ \(\hat{x}\) + \" + \"+ \(\hat{y}\) + \" - \(\hat{W}\) + \" + \(\hat{H}\))
19. write boundingBoxYOLO value in the text file
20. ENDIF
```  
**Algorithm 1** The pseudocode of pseudo-labeling to change from detection output format to YOLO format  
After the pseudo-labeling process, we annotated only 663 images and obtained a total of 3453 labeled images of Dataset A and Dataset B.