MoViAD: A Modular Library for Visual Anomaly Detection
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Abstract (arXiv)
VAD is a critical field in machine learning focused on identifying deviations from normal patterns in images, often challenged by the scarcity of anomalous data and the need for unsupervised training. To accelerate research and deployment in this domain, we introduce MoViAD, a comprehensive and highly modular library designed to provide fast and easy access to state-of-the-art VAD models, trainers, datasets, and VAD utilities. MoViAD supports a wide array of scenarios, including continual, semi-supervised, few-shots, noisy, and many more. In addition, it addresses practical deployment challenges through dedicated Edge and IoT settings, offering optimized models and backbones, along with quantization and compression utilities for efficient on-device execution and distributed inference. MoViAD integrates a selection of backbones, robust evaluation VAD metrics (pixel-level and image-level) and useful profiling tools for efficiency analysis. The library is designed for fast, effortless deployment, enabling machine learning engineers to easily use it for their specific setup with custom models, datasets, and backbones. At the same time, it offers the flexibility and extensibility researchers need to develop and experiment with new methods.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 31 references that are in the map. First posted on arXiv 16 Jul 2025.
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References · 27
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- 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
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- 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
- 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
- 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
- 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
- 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
- 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
- 2024PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edgecited by 16
- 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
- 2024SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detectioncited by 12
- 2022Continual Learning Approaches for Anomaly Detectioncited by 9
- 2024ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shiftcited by 5
- 2025Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detectioncited by 4
- 2024ONER: Online Experience Replay for Incremental Anomaly Detectioncited by 3
- 2025From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithmscited by 1
- 2025Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compressioncited by 1
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