UniAD: A Real-World Multi-Category Industrial Anomaly Detection Dataset with a Unified CLIP-Based Framework

This is the authors' abstract. We don't add key points for this paper.

Industrial image anomaly detection is critical for automated manufacturing. However, most existing methods rely on single-category training paradigms, resulting in poor scalability and limited cross-category generalization. These approaches require separate models for each product type and fail to model the complex multi-modal distribution of normal samples in multi-category scenarios. To overcome these limitations, we propose UniCLIP-AD, a unified anomaly detection framework that leverages the general semantic knowledge of CLIP and adapts it to the industrial domain using Low-Rank Adaptation (LoRA). This design enables a single model to effectively handle diverse industrial parts. In addition, we introduce UniAD, a large-scale industrial anomaly detection dataset collected from real production lines. It contains over 25,000 high-resolution images across 7 categories of electronic components, with both pixel-level and image-level annotations. UniAD captures fine-grained, diverse, and realistic defects, making it a strong benchmark for unified anomaly detection. Experiments show that UniCLIP-AD achieves superior performance on UniAD, with an AU-ROC of 92.1% and F1-score of 89.8% in cross-category tasks, outperforming the strongest baselines (CFA and DSR) by 3% AU-ROC and 23.9% F1-score.