# Fashion Product Classification through Deep Learning and Computer Vision
## 1 Introduction  
In fashion industries, obtaining a visual analysis of the overall production is a key aspect, both in developing marketing strategies and for helping fashion designers in the creative workflow of new products. As a first important step in order to proceed with a visual analysis, the various outcomes of the designers' work must be collected and categorized. This applies especially if there are many different designer teams which are employed with outsourcing contracts, and located all around the world, as it usually happens with large companies. In particular, in Adidas AG(tm), the designers' production comprises a consistent number of images which represent a very large variety of products, from clothing to footwear. Moreover, these works are usually independent from each other. Then, articles are of different types and come from different sources, but their characteristics must be analyzed and classified as a whole by data experts and analysts. Hence, a significant step in visual analysis is recognizing, classifying, and extracting features directly from final images or 3D-renderings of products, collected before the actual fabrication of the clothing. Categorizing apparel products is also useful for e-commerce purposes, as well as for avoiding duplicates, ranking product types, or doing statistical analysis [1]. The categorization of apparel products was, until now, manually performed by Adidas teams, since it requires both domain expertise and a comprehensive knowledge over the range of products. Such a manual classification is an error-prone task that may cause incorrect results, misleading the subsequent visual analysis, and which also requires too much time. As a matter of fact, over the years, the number of produced articles has grown significantly, and, currently, Adidas teams invent \(\sim\)20 k different articles per season (twice a year). For all these products, about two dozen main attributes (or categories) were identified by Adidas data experts, such as the presence and position of logos or the three-stripes, which is the primary color, the presence of prints or clothing patterns, and so on. Each of these categories takes values in a different domain, and ranges across a set of possible classes. As expected, manually performing the classification of attributes for each article has become an unfeasible task. Due to the large amount of data, and the diversity of the source of images, the task of classifying and recognizing features of clothing images is not only hard to perform manually, but, at the same time, it is also difficult to automate properly. A subset of seven key attributes were chosen, which we will call _features_ in the next, namely, (_i_) logo type and size, (_ii_) three-stripes presence and colors, (_iii_) three main colors palette, (_iv_) prints and patterns, (_v_) neck shape, (_vi_) sleeve shape, and (_vii_) material of the clothing. These features are the most important both for business reasons (e.g., detecting logos in clothing is a fundamental information for a brand), and for their significance in identifying a particular garment, so as to avoid or substantially reduce duplicates or similar products. The characteristics of each feature and the reasons for their importance are detailed later in the paper. The core idea is to automate the classification process of these seven features by using machine learning techniques and computer vision, but many aspects in such a task raise concrete research problems. In fact, each clothing feature could eventually require a different method of classification, spacing from segmentation [2; 3], image retrieval [4], to machine learning techniques, such as deep learning [5; 6; 7; 8]. These algorithms must be applied to the under-investigated area of fashion, and must be refined for the specific domain. In addition, the automatization must have an accuracy comparable to manual methods in order to be useful for business purposes.