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Fashion is one of the largest world's industries and computer visiontechniques have been becoming more popular in recent years, in particular, fortasks such as object detection and apparel segmentation. Even with the rapidgrowth in computer vision solutions, specifically for the fashion industry,many problems are far for being resolved. Therefore, not at all times,adjusting out-of-the-box pre-trained computer vision models will provide thedesired solution. In the present paper is proposed a pipeline that takes anoisy image with a person and specifically detects the regions with garmentsthat are bottoms or tops. Our solution implements models that are capable offinding human parts in an image e.g. full-body vs half-body, or no human isfound. Then, other models knowing that there's a human and its composition(e.g. not always we have a full-body) finds the bounding boxes/regions of theimage that very likely correspond to a bottom or a top. For the creation ofbounding boxes/regions task, a benchmark dataset was specifically prepared. Theresults show that the Mask RCNN solution is robust, and generalized enough tobe used and scalable in unseen apparel/fashion data.

Fashion Object Detection for Tops & Bottoms