Products-6K: A Large-Scale Groceries Product Recognition Dataset

Product recognition is a task that receives continuous attention by the computer vision/deep learning community mainly with the scope of providing robust solutions for automatic checkout supermarkets. One of the main challenges is the lack of images that illustrate in realistic conditions a high number of products. Here the product recognition task is perceived slightly differently compared to the automatic checkout paradigm but the challenges encountered are the same. The setting under which this dataset is captured is with the aim to help individuals with visual impairment in doing their daily grocery in order to increase their autonomy. In particular, we propose a large-scale dataset utilized to tackle the product recognition problem in a supermarket environment. The dataset is characterized by (a) large scale in terms of unique products associated with one or more photos from different viewpoints, (b) rich textual descriptions linked to different levels of annotation and, (c) images acquired both in laboratory conditions and in a realistic supermarket scenario portrayed in various clutter and lighting conditions. A direct comparison with existing datasets of this category demonstrates the significantly higher number of the available unique products, as well as the richness of its annotation enabling different recognition scenarios. Finally, the dataset is also benchmarked using various approaches based both on visual and textual descriptors.商品识别是计算机视觉与深度学习领域持续关注的研究任务，其核心目标是为超市自动结账场景提供鲁棒的解决方案。该领域的核心挑战之一，在于缺乏能够在真实场景下展示大量商品的图像数据。本数据集所面向的商品识别任务，虽与自动结账范式下的任务定义略有差异，但面临的核心挑战一致。本数据集的采集初衷，是帮助视障人士完成日常采购，以提升其生活自主性。具体而言，本文提出了一款面向超市环境商品识别任务的大规模数据集。该数据集具备以下三大特征：(a) 规模庞大，涵盖多款独特商品，且每款商品均配有多张不同视角的拍摄照片；(b) 附带丰富的文本描述，并关联多粒度标注信息；(c) 图像采集场景覆盖实验室环境与真实超市场景，后者包含多样的杂乱摆放与光照条件。与同类现有数据集直接对比可知，本数据集的独特商品数量显著更多，且标注信息更为丰富，可支撑多种不同的识别任务场景。最后，本数据集已基于视觉与文本描述子的多种方法完成了基准测试。