Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets, the data scale for AD is relatively small, and evaluation metrics are still deficient compared to classic vision tasks, such as object detection and semantic segmentation. To fill these gaps, this work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. This enables fair evaluation and sustainable development for different methods on this challenging benchmark. Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which prevents a comprehensive evaluation of different methods. Inspired by the metrics in the segmentation field, we further propose several more practical threshold-dependent AD-specific metrics, ie, m$F_1$$^{.2}_{.8}$, mAcc$^{.2}_{.8}$, mIoU$^{.2}_{.8}$, and mIoU-max. Motivated by GAN inversion's high-quality reconstruction capability, we propose a simple but more powerful InvAD framework to achieve high-quality feature reconstruction. Our method improves the effectiveness of reconstruction-based methods on popular MVTec AD, VisA, and our newly proposed COCO-AD datasets under a multi-class unsupervised setting, where only a single detection model is trained to detect anomalies from different classes. Extensive ablation experiments have demonstrated the effectiveness of each component of our InvAD. Full codes and models are available at https://github.com/zhangzjn/ader.

翻译：异常检测（AD）常聚焦于工业质检和医学病变检查中的异常区域检测。然而，由于特定场景目标的限制，异常检测的数据规模相对较小，且与目标检测、语义分割等经典视觉任务相比，评估指标仍显不足。为填补这些空白，本工作首先通过将COCO数据集扩展至异常检测领域，构建了一个大规模、通用的COCO-AD数据集，从而为不同方法在该挑战性基准上的公平评估和可持续发展提供支持。此外，当前指标（如AU-ROC）在简单数据集上已近乎饱和，无法全面评估不同方法。受分割领域指标启发，我们进一步提出了若干更实用的阈值依赖型异常检测专用指标，即m$F_1$$^{.2}_{.8}$、mAcc$^{.2}_{.8}$、mIoU$^{.2}_{.8}$和mIoU-max。受GAN反演高质量重建能力的启发，我们提出了一种简单但更强大的InvAD框架，以实现高质量特征重建。我们的方法在多类无监督设置下（即仅训练单一检测模型以检测不同类别的异常），在流行的MVTec AD、VisA及新提出的COCO-AD数据集上提升了基于重建方法的有效性。大量消融实验证明了InvAD各组成部分的有效性。完整代码和模型已开源至https://github.com/zhangzjn/ader。