  Fashion retrieval is the challenging task of finding an exact match for
fashion items contained within an image. Difficulties arise from the
fine-grained nature of clothing items, very large intra-class and inter-class
variance. Additionally, query and source images for the task usually come from
different domains - street photos and catalogue photos respectively. Due to
these differences, a significant gap in quality, lighting, contrast, background
clutter and item presentation exists between domains. As a result, fashion
retrieval is an active field of research both in academia and the industry.
  Inspired by recent advancements in Person Re-Identification research, we
adapt leading ReID models to be used in fashion retrieval tasks. We introduce a
simple baseline model for fashion retrieval, significantly outperforming
previous state-of-the-art results despite a much simpler architecture. We
conduct in-depth experiments on Street2Shop and DeepFashion datasets and
validate our results. Finally, we propose a cross-domain (cross-dataset)
evaluation method to test the robustness of fashion retrieval models.
