## 3 Materials and Methods
### Dataset Collection and Pre-Processing
(**a**) different point of view image; (**b**) different photo model position image; (**c**) different perspectives and illumination image. Figure 2: Samples of our Dataset. photos with a single bounding box and images with multiple bounding boxes that had multiple categories, as shown in Figure 3b (Hoodies and Pant). Afterward, it allows the model to differentiate those things into new or never-seen-before objects. \[L=\{\;L_{B1},\;L_{B2},\;\ldots\ldots\ldots,\;L_{Bi}\},\;B_{i}\cap B_{i+1}<0.5 \tag{1}\]  
In this research, when we choose a category, we select two categories that best match each pattern. Pant, dress, skirt, hoodie, and jacket are included. When gathering photographs, we collected various types of images uploaded by vendors in order from the website's list. Typically, when sellers submit a product, they include a single design as well as variations of the product in the photograph. The number of photographs in each category is determined by the number of bounding boxes for each category within an image. Table 1 displays the number of photos and bounding boxes within each category for the total of Dataset A and Dataset B. The collected image collection includes a maximum of 14 bounding boxes per image, according to Table 2.  
Each category contains a variety of designs, colors, and patterns. Figure 4 represents the visualization of the distribution of our obtained dataset. Our collection includes product photographs with indoor, outdoor, solid, and other backgrounds. We collected photographs from many perspectives, including back, front, side, and others. Some models are photographed while standing, while others are photographed while sitting and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Number of Images** & **Number of Bounding Boxes** \\ \hline \hline Pants & 741 & 1212 \\ \hline Dress & 699 & 1048 \\ \hline Hoodie & 1059 & 1843 \\ \hline Jacket & 684 & 977 \\ \hline Skirt & 933 & 1685 \\ \hline Total & 4116 & 6765 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of pictures and bounding boxes per category. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Categories** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** \\ \hline Pants & 428 & 173 & 76 & 19 & 12 & 4 & 3 & 4 & & 2 & & & \\ \hline Dress & 485 & 142 & 41 & 11 & 5 & 7 & 4 & & & & & 1 & \\ \hline Hoodie & 725 & 177 & 54 & 46 & 25 & 18 & 5 & 4 & 6 & 2 & & 2 & & 2 & \\ \hline Jacket & 561 & 62 & 39 & 22 & 9 & 1 & 1 & & 1 & & & & & \\ \hline Skirt & 605 & 177 & 63 & 18 & 19 & 13 & 16 & 7 & 4 & 3 & 1 & 2 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of images according to the bounding boxes per category. Figure 3: Image Labelling Process. (**a**) Overlapping Dress Image; (**b**) Multiple Category Within One Image; (**c**) Overlapping Hoodie Image. promoting the product. There are also products with hanging styles, especially in the Hoodie category. Due to the fact that it is a garment, we have collected product images with various colors, lengths, and patterns. Because the majority of products are advertised with models, the models' accessories and hand positions cover a portion of the product. Lower-body garments, such as pants and skirts, are sometimes obscured by upper-body garments, resulting in partial occlusion. Depending on the model's posture, particular pants have similar patterns to skirts. Therefore, it would be more difficult to distinguish types of clothing from others.