AmazonScience/RobustAD

RobustAD数据集是一个专为评估现实世界场景中异常检测模型鲁棒性而设计的图像数据集。它包括三个子数据集，分别针对印刷电路板（PCB）、金属零部件和包装袋计数任务。每个子数据集都有训练用的源域数据和多个测试用的目标域数据，这些目标域数据包含了不同的分布偏移。该数据集旨在确保应用案例的多样性和鼓励通用方法的开发，反映了现实世界异常检测任务的复杂性。

The RobustAD dataset is an image dataset specifically designed to evaluate the robustness of anomaly detection models in real-world scenarios. It consists of three sub-datasets targeting printed circuit boards (PCB), metal automotive parts, and packet counting tasks in the pharmaceutical industry. Each sub-dataset includes source domain data for training and multiple target domains with different distribution shifts for testing, aiming to ensure diversity in use cases and encourage the development of generalizable methods, reflecting the complexity of real-world anomaly detection tasks.