mlsanityData to 7 Oct 2026

Anomalib: A Deep Learning Library for Anomaly Detection

arXiv 2202.08341 ↗First posted 16 Feb 2022cs.CV cs.LGcited by 75

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Abstract (arXiv)

This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib comprises state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks and that can be used off-the-shelf. In addition, the library provides components to design custom algorithms that could be tailored towards specific needs. Additional tools, including experiment trackers, visualizers, and hyper-parameter optimizers, make it simple to design and implement anomaly detection models. The library also supports OpenVINO model optimization and quantization for real-time deployment. Overall, anomalib is an extensive library for the design, implementation, and deployment of unsupervised anomaly detection models from data to the edge.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

13 of 23 references.

References · 13

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,379
  3. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  4. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  5. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  6. 2018Albumentations: fast and flexible image augmentationscited by 558
  7. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  8. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  9. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  10. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  11. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
  12. 2019Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detectioncited by 39
1 more
  1. 2020Neural Network Compression Framework for fast model inferencecited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 12; 2023: 10; 2024: 26; 2025: 16; 2026: 11.

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  1. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  2. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  3. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  4. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  5. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  6. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  7. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  8. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  9. 2022MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detectioncited by 19
  10. 2024Supervised Anomaly Detection for Complex Industrial Imagescited by 18
  11. 2022U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Thresholdcited by 16
  12. 2022Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentcited by 11
38 more
  1. 2024CSAD: Unsupervised Component Segmentation for Logical Anomaly Detectioncited by 11
  2. 2023Optimizing PatchCore for Few/many-shot Anomaly Detectioncited by 10
  3. 2024Dynamic Addition of Noise in a Diffusion Model for Anomaly Detectioncited by 10
  4. 2024RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detectioncited by 8
  5. 2022PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentationcited by 6
  6. 2022FRE: A Fast Method For Anomaly Detection And Segmentationcited by 5
  7. 2023Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Visioncited by 5
  8. 2024Blind Localization and Clustering of Anomalies in Texturescited by 5
  9. 2024RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observationscited by 5
  10. 2024Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detectioncited by 5
  11. 2025Kaputt: A Large-Scale Dataset for Visual Defect Detectioncited by 5
  12. 2022AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detectioncited by 4
  13. 2024AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerancecited by 4
  14. 2024Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Modelscited by 4
  15. 2022Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detectioncited by 3
  16. 2024Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flowscited by 3
  17. 2025Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selectioncited by 3
  18. 2025An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contaminationcited by 3
  19. 2023Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networkscited by 2
  20. 2024Distillation-based fabric anomaly detectioncited by 2
  21. 2024Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detectioncited by 2
  22. 2024View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesiscited by 2
  23. 2024FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentationcited by 2
  24. 2025ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defectscited by 2
  25. 2022UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessmentcited by 1
  26. 2023FABLE : Fabric Anomaly Detection Automation Processcited by 1
  27. 2024Quantum Patch-Based Autoencoder for Anomaly Segmentationcited by 1
  28. 2024Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertaintycited by 1
  29. 2024Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detectioncited by 1
  30. 2024AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Propertiescited by 1
  31. 2024PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classificationcited by 1
  32. 20253D-ADAM: A Dataset for 3D Anomaly Detection in Additive Manufacturingcited by 1
  33. 2026Integration of deep generative Anomaly Detection algorithm in high-speed industrial linecited by 1
  34. 2026SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detectioncited by 1
  35. 2023Defect Detection Approaches Based on Simulated Reference Imagecited by 0
  36. 2023Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysiscited by 0
  37. 2024CSE: Surface Anomaly Detection with Contrastively Selected Embeddingcited by 0
  38. 2024OCR is All you need: Importing Multi-Modality into Image-based Defect Detection Systemcited by 0

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