Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection.

翻译：尽管基于视觉的检测算法取得了进展，但现实工业场景中的挑战——特别是在数据可用性、数据质量以及复杂生产需求方面——往往仍未得到充分解决。我们提出VISION Datasets，这是一个包含14个工业检测数据集的多元化集合，能够独特地应对这些挑战。与先前数据集不同，VISION为缺陷检测带来了通用性，在所有数据划分中均提供标注掩码，并适用于多种检测方法。我们的数据集还包含实例分割标注，从而能够实现精准的缺陷识别。共计18,000张图像涵盖44种缺陷类型，VISION力求镜像广泛的实际生产场景。通过支持基于VISION Datasets的两项持续进行的挑战赛，我们希望进一步推动基于视觉的工业检测领域的发展。