# Fashion Product Classification through Deep Learning and Computer Vision
## 3 System Overview  
The proposed system aims at automatizing the classification of seven features, as previously mentioned. In the following, there is a more detailed description of these features:  
1. _Logo size and type._ There are three main Adidas logos, namely, the original \"Badge Of Sports\" (BOS), the trefoil, and the linear one (as shown in Figure 1); each of them could appear as a small, medium or large logo;
2. _Three-stripes presence and colors._ A mark that is characteristic of Adidas consists of three equidistant bands of the same color, typically on the sleeves of shirts or on the side of pants. The three-stripes  
Figure 1: Types of Adidas logos. can be contrasted or tonal: this refers to the difference between their color and the color of the background;
3. _Three main colors palette._ For each article, a maximum of three visible colors should be extracted, ordered by their importance, and the percentage of the area covered by such colors should be provided;
4. _Prints and clothing patterns._ Besides colors, there could be prints over the clothing, textured fabrics, or geometric patterns: this is called the main-execution of the article (examples are shown in Figure 2);
5. _Neck shape._ The type of the collar of a jacket, a sweatshirt or a shirt can be classified in major categories, like U-neck, V-neck, hooded, bomber, and so on (illustrated in Figure 3);
6. _Sleeves shape._ Adidas teams need to differentiate between \"raglan\" and \"set-in\" sleeves, i.e., the shape of the junction between sleeves and body of the garment, as in Figure 4;
7. _Material of the clothing._ Clothes have a lot of fabric types, but at this stage (recognition from final renderings), only cotton, (generic) synthetic, and viscose fabrics have to be distinguished. Figure 4: In “raglan” shirts, the junction between the body and the sleeves starts from the armpits and goes to the neck; the term “set-in”, instead, refers to those shirts that have the sleeves sewn with the body of the garment with a vertical closure. Figure 3: Types of neck shapes. Figure 2: Prints and clothing patterns’ examples. Some preliminary considerations help at understanding the main problems that have to be faced in the development of the system. Thus, when choosing the right method for each feature, the following points have been carefully taken in account:  
* The complete dataset that Adidas teams provide us with consists of about 11k images, where articles could be duplicated (e.g., for many of them, clothes back-view and front-view are depicted separately). The great majority of these images are unlabeled, i.e., no classification is provided for the aforementioned seven features. Not considering duplicates, only 2616 images of this dataset were manually labeled by Adidas data experts, thus indicating the correct classification of each feature. This small portion of 2616 images of the dataset was used as ground truth for all the proposed algorithms. * Classifications are sometimes difficult to perform even by a human observer, since they depend on very small details, or not well-defined classes: e.g., recognizing the material (synthetic, cotton, or viscose) of a garment only from its rendering image is very hard;
* For some features, classes range in a high variety of values, as for color names, and examples for each class are very few; moreover, having many classes could mean that inter-class differences are small, and this may lead to inaccurate results. For these reasons, a different module was developed for each feature. Features (1)-(3) have been approached with classic image processing methods, while the others have been approached with deep learning. As an exception, the logo search was also reinforced by using a deep network for certain types of logos. In Figure 5, a detailed schema illustrates the structure of the system.