## 4 Results and Discussion
### Comparison of Our FC-YOLOv4 with YOLOv4 and YOLOv3  
In this part, we conducted a series of tests using the trained FC-YOLOv4 model and test images for validating the algorithm's performance. We used 80 percent of the combination of datasets from before pseudo-labeling, after pseudo-labeling, Brightening, Mosaic, and CLAHE as training datasets, and a total of 16,276 images were used. We divided our dataset into training and testing subsets to ensure that the images in each category were balanced. For the testing dataset, we used the collection of the rest of 20 percent of our dataset and collection of the images from the DeepFashion2 dataset, second-hand images from Google Image, and Augmented images. When evaluating the model, we also collected clutter  
Figure 14: Detection time comparison for Before and After Augmentation. Figure 12: Detection results with different augmentation results. (**a**) Result After Training with Dataset B; (**b**) Result After Training with Dataset C; (**c**) Result After Training with Dataset D; (**d**) Result After Training with Dataset E.  
Figure 13: Precision-Recall Curve for different image augmentation. backdrop photos with multiple categories. The suggested model is compared against the YOLOv3 and YOLOv4 two-stage conventional models to demonstrate the proposed model's superiority. The primary purpose of this research is that our model aims to be able to categorize the closest category when it detects an image that is not in the trained dataset. The precision-recall curve, or P-R curve, can be generated by plotting the precision ratio against the recall ratio using the precision ratio as the vertical axis and the recall ratio as the horizontal axis. The precision-recall curves for our three models are depicted in Figure 15. While YOLOv4 is more accurate at lower IOU thresholds, our FC-YOLO v4 is more efficient at higher IOU thresholds. As shown in Table 7, after data augmentation, our proposed FC-YOLOv4 model gets 0.007 percent and 40.2 percent higher than the original YOLOv4 and YOLOv3 models with nearly the same detection time. The detection results of our suggested model were compared to that of the YOLOv3 and Yolov4 conventional models. We evaluated photos with many categories to verify the model's correctness. The detection results for the YOLOv3 model are described in Figure 16a,d,g. The detection findings for YOLOv4 are illustrated in Figure 16b,e,h, whereas the detection findings for our FC-YOLOv4 model are shown in Figure 16c,f,i. In Figure 16a-c, we discovered that our model could classify the pattern as a \"skirt\" and can recognize two bounding box skirts even when the training dataset has a different pattern and part of the skirt is covered by the coat. When we examine the findings of the three models in Figure 16d-f, we can see that even though one of the jackets was not photographed from the front view, only the FC-YOLOv4 model correctly detects it. In Figure 16g-i, our FC-YOLOv4 model correctly classifies items as \"hoodies\", while the other two models' bounding boxes overlap. Our FC-YOLOv4 model can also recognize multiple categories and adjacent items with the same color within an image with greater precision than the other two models, as shown in Figure 16j-l. Table 8 represents the number of true positive results with different thresholds. Our FC-YOLOv4 model has more True Positive numbers than another two models. Figure 17 depicts the outcomes of our suggested FC-YOLOv4 model's detection. As displayed in Figure 17a-d, our proposed model can detect overlapping clothing with varying hue/saturation levels. As illustrated in Figure 17e-g, our model can recognize even a white outfit with a high brightness level. Figure 17h-k illustrates that our suggested model can also be used to identify second-hand clothing with a cluttered background. As depicted in Figure 17l-n, our proposed model can accurately detect rotated images. Using the Mosaic Augmentation Method, we integrated four images with various categories into