Using Pre Trained Models for Fine Grained Image Classification in Fashion Field
Fashion image classification offers online stores a fast and effective way to manage the volume of their products. It could be used in product categorization by their brands, types, styles, etc. It could be also used to make fashion ensembles of clothes and even to create product recommendations. The results of apparel image classification are also useful for such purposes as product filtering and search. A wide range of different models have been successfully applied to coarse level image classification, but only a few of them could be applied to fashion images due to the nature of the data: such images could belong to the one high level topic such as 't shirt' 'skirt' or 'sport shoes' but at the lower level be fine grained (e.g. 'high heels' 'medium heels' Each apparel shop can have its own fine grained hierarchy leading to difficulties to acquire a big dataset. This work shows necessity of pre trained model usage to achieve a good predictive quality. In addition, the article contains a description and an experimental study of an approach to stack features from pretrained model and features learned directly from an image pixel representation. This approach significantly outperforms baseline methods in fine grained image classification [1, 2, 3] and a classification based only on a pre trained model.