MVTec D2S Dataset

MVTec D2S is a benchmark for instance-aware semantic segmentation in an industrial domain. It contains 21,000 high-resolution images with pixel-wise labels of all object instances. The objects comprise groceries and everyday products from 60 categories. The benchmark is designed such that it resembles the real-world setting of an automatic checkout, inventory, or warehouse system. The training images only contain objects of a single class on a homogeneous background, while the validation and test sets are much more complex and diverse.

MVTec D2S 是工业领域实例感知语义分割的基准数据集。该数据集包含21,000张高分辨率图像，并对所有物体实例进行了像素级的标签标注。其中物体包括来自60个类别的食品杂货和日常用品。该基准数据集的设计旨在模拟真实世界自动结账、库存或仓库系统的环境。训练图像仅包含单一类别的物体，背景均匀，而验证集和测试集则具有更高的复杂性和多样性。