RP2K

We introduce RP2K, a new large-scale retail product dataset for fine-grained image classification. Unlike previous datasets focusing on relatively few products, we collect more than 500,000 images of retail products on shelves belonging to 2000 different products. Our dataset aims to advance the research in retail object recognition, which has massive applications such as automatic shelf auditing and image-based product information retrieval. Our dataset enjoys following properties: (1) It is by far the largest scale dataset in terms of product categories. (2) All images are captured manually in physical retail stores with natural lightings, matching the scenario of real applications. (3) We provide rich annotations to each object, including the sizes, shapes and flavors/scents. We believe our dataset could benefit both computer vision research and retail industry. Pipeline of our data collection process. Our photo collectors were first distributed in over 500 different retail stores and collected over 10k high-resolution shelf images. Then we use a pre-trained detection model to extract the bounding boxes of potential objects of interests. After that, our human annotators discard the incorrect bounding boxes, including heavily occluded images and images that is not a valid retail product. The remaining images are annotated by the annotators.

本研究提出RP2K，一款面向细粒度图像分类任务的大规模零售商品数据集。与此前聚焦少量商品的现有数据集不同，本次构建的数据集共收录了覆盖2000种不同零售商品的货架场景图像超50万张。本数据集旨在推动零售物体识别领域的研究发展，该领域拥有自动货架盘点、基于图像的商品信息检索等诸多规模化应用场景。本数据集具备如下特性：(1) 就商品品类规模而言，本数据集是目前同类数据集里体量最大的；(2) 所有图像均在实体零售门店中以自然光环境手动拍摄，与实际应用场景高度契合；(3) 为每个目标对象提供了丰富的标注信息，涵盖尺寸、形态以及风味/气味特征。我们相信本数据集将同时为计算机视觉研究与零售行业带来助力。以下为本数据集的采集流程：首先，数据采集人员奔赴超过500家不同的零售门店，采集了超1万张高分辨率货架图像。随后，借助预训练检测模型提取潜在目标的边界框。此后，人工标注人员会剔除不合格的边界框，包括遮挡严重的图像以及非合规零售商品的图像。剩余图像将由标注人员完成后续标注工作。