## 3 Materials and Methods
### Experimental Setup and Procedure Description  
Incomplete learning trains model parameters using partial points, and prediction accuracy are significantly worse than fully supervised learning. This is because a small number of labeled points cannot sufficiently describe the general distribution of the data. As a result, employing more training data can result in more accurate predictions. As a semisupervised learning approach, pseudo-labels may successfully utilize unlabeled data. As shown in Figure 7, data collection is the first step in this study. The traditional YOLOv4 model is developed by manually labeling and training Dataset A, which is collected from Shopee Data. Testing is carried out on dataset B, which has not been labeled, using this model. The format conversion to the YOLO format is then performed based on the findings of the label prediction. After labeling the image collection, it is necessary to feed the annotated data into the YOLOv4 model. The quantity of the training dataset is a critical aspect to consider when assessing the accuracy of the object recognition model. Even though several datasets are available, we cannot always train using them. Additionally, the data annotation process is manual, which explains why labeling a huge dataset takes so long. Pseudo-Labelling is one of the most effective methods for resolving this issue. Three types of learning exist; supervised, unsupervised, and semi-supervised. While supervised learning is the process of generating a model from labeled data, unsupervised learning is developing a model from unlabeled data. Semi-supervised learning is a strategy that entails training the  
Figure 6: The network architecture of FC-YOLOv4. model on a small set of labeled data and then predicting on a large set of unlabeled data. Pseudo-Labelling is semi-supervised learning. In our research, we will also propose a Fashion category detector trained on our small dataset to identify categories with similar patterns. We utilized Google Colab Notebook Pro Version for this investigation, and our model was trained and validated on an NVIDIA-SMI 495.46 Telsa P100-PCIE server running NVIDIA Driver 460.32.03 and CUDA 11.2. We employed our YOLOv4 model with the CSPDarknet53. The parameters used to initialize the network are listed in Table 3. To improve the model's detection accuracy and alter the input required by the Darknet framework, we utilized the size of the input images in our research to 416 \(\times\) 416. To evaluate the training procedure, the batch size was set to 64, capturing 64 images every iteration for 8000 and 9000 training steps. Momentum, initial learning rate, weight decay regularization, and other parameters were the original parameters of the YOLOv4 model. We set the maximum batch size to 10,000 (the number of classes multiplied by 2000) and increase it in 80 and 90 percent increments. We apply filters based on the (number of classes + 5) * 3 formula. It will process 64 images every iteration during training and simultaneously transmit 24 subdivisions to the GPU. YOLOv4 predicted the objects in the image using the detection output format, as shown in Figure 8. Therefore, it must be converted to YOLOv4 format. We captured the detection result in the (.txt) format with the same filename as the image after training with dataset A using YOLOv4. Figure 9a represents the detection result of the YOLOv4 model after training. When forecasting, the YOLO model specifies the top left corner coordinates, followed by the width (w) and height (h) of the enclosing box (h). The width and height of the original image are denoted by (W) and (H), respectively. This result will be the input for Figure 9b. \[\hat{x}=\frac{left\_x+\frac{w}{2}}{W} \tag{4}\]  
\[\hat{y}=\frac{top\_y+\frac{h}{2}}{W} \tag{5}\]  
\[\hat{W}=\frac{w}{W} \tag{6}\]  
\[\hat{H}=\frac{h}{H} \tag{7}\]  
Figure 9b is the YOLO format for retraining the model. The center point of the image is represented by \(\hat{x}\), which is obtained from calculating Formulas (4) and (5).