构建380类异常检测大基准，推动跨领域泛化研究
ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories
- 整合49个数据集构建380类异常检测基准
- 现有方法在全量类别下性能从90.6%降至78.5%
- 提出Dinomaly-m模型，在不增加推理成本下提升性能
异常检测旨在仅使用正常样本训练数据识别缺陷。现有基准（如MVTec-AD含15类）覆盖类别过窄，限制了跨场景泛化与可扩展性评估。本文提出ADNet，一个大规模多领域基准，涵盖电子、工业、农食、基础设施和医疗等领域的380个类别，源自49个公开数据集，共包含196,294张RGB图像：其中116,192张为正常训练样本，80,102张测试图像中60,311张为异常样本。所有图像均采用MVTec风格的像素级标注和结构化文本描述，支持多模态异常检测任务。大量实验揭示显著可扩展性挑战：当前最优方法在一对一设置下达到90.6% I-AUROC，但在380类多类别设置下下降至78.5%。为此，我们提出Dinomaly-m，基于Dinomaly的上下文引导专家混合架构，扩展解码器容量而不增加推理开销，实现83.2% I-AUROC与93.1% P-AUROC，优于现有方法。ADNet设计为标准化且可扩展的基准，支持社区拓展多样化异常检测数据集，并为未来异常检测基础模型提供可扩展支撑。
原文摘要 · Abstract (English)
Anomaly detection (AD) aims to identify defects using normal-only training data. Existing anomaly detection benchmarks (e.g., MVTec-AD with 15 categories) cover only a narrow range of categories, limiting the evaluation of cross-context generalization and scalability. We introduce ADNet, a large-scale, multi-domain benchmark comprising 380 categories aggregated from 49 publicly available datasets across Electronics, Industry, Agrifood, Infrastructure, and Medical domains. The benchmark includes a total of 196,294 RGB images, consisting of 116,192 normal samples for training and 80,102 test images, of which 60,311 are anomalous. All images are standardized with MVTec-style pixel-level annotations and structured text descriptions spanning both spatial and visual attributes, enabling multimodal anomaly detection tasks. Extensive experiments reveal a clear scalability challenge: existing state-of-the-art methods achieve 90.6% I-AUROC in one-for-one settings but drop to 78.5% when scaling to all 380 categories in a multi-class setting. To address this, we propose Dinomaly-m, a context-guided Mixture-of-Experts extension of Dinomaly that expands decoder capacity without increasing inference cost. It achieves 83.2% I-AUROC and 93.1% P-AUROC, demonstrating superior performance over existing approaches. ADNet is designed as a standardized and extensible benchmark, supporting the community in expanding anomaly detection datasets across diverse domains and providing a scalable foundation for future anomaly detection foundation models. Dataset: https://grainnet.github.io/ADNet
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