Synthetic Dataset

本研究中使用的数据集名为‘Synthetic Dataset’，由Paralleldots, Inc.创建，主要用于训练和测试对象定位算法。该数据集包含354张图像，这些图像通过在空货架上随机放置商品并应用逻辑参数来生成。数据集的创建旨在解决零售环境中商品定位的难题，特别是在商品种类繁多、包装频繁变化且商品密度高的情况下。通过使用此数据集，研究者能够训练模型以识别和定位零售货架上的商品，从而在实际应用中提高商品管理的自动化水平。

The dataset employed in this study is designated as 'Synthetic Dataset', developed by Paralleldots, Inc., and is primarily utilized for training and testing object localization algorithms. This dataset comprises 354 images, which are generated by randomly placing products on empty shelves and applying logical parameters. The dataset was created to address the challenge of product localization in retail environments, particularly in scenarios with a wide variety of products, frequent packaging changes, and high product density. By leveraging this dataset, researchers can train models to identify and localize products on retail shelves, thereby improving the automation level of product management in real-world applications.