mlsanityData to 7 Oct 2026

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

arXiv 2301.13359 ↗First posted 31 Jan 2023cs.CV cs.AIcited by 64

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Abstract (arXiv)

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently, many advanced algorithms have been reported, but their performance deviates considerably with various IM settings. We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications. In addition, it is difficult for researchers to analyze IAD algorithms without a uniform benchmark. To solve this problem, we propose a uniform IM benchmark, for the first time, to assess how well these algorithms perform, which includes various levels of supervision (unsupervised versus fully supervised), learning paradigms (few-shot, continual and noisy label), and efficiency (memory usage and inference speed). Then, we construct a comprehensive image anomaly detection benchmark (IM-IAD), which includes 19 algorithms on seven major datasets with a uniform setting. Extensive experiments (17,017 total) on IM-IAD provide in-depth insights into IAD algorithm redesign or selection. Moreover, the proposed IM-IAD benchmark challenges existing algorithms and suggests future research directions. To foster reproducibility and accessibility, the source code of IM-IAD is uploaded on the website, https://github.com/M-3LAB/IM-IAD.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). Builds on 68 of its 132 references that are in the map. First posted on arXiv 31 Jan 2023.

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68 of 132 references.

References · 68

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  9. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
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  11. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  12. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
38 more
  1. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  2. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  3. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  4. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  5. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  6. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  7. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  8. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  9. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  10. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  11. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  12. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  13. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  14. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  15. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  16. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  17. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  18. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  19. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  20. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  21. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  22. 2020Explainable Deep One-Class Classificationcited by 126
  23. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  24. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  25. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  26. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  27. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 89
  28. 2019Curriculum Loss: Robust Learning and Generalization against Label Corruptioncited by 87
  29. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  30. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  31. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  32. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  33. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  34. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  35. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  36. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  37. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  38. 2020DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentationcited by 53

50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 16; 2024: 17; 2025: 24; 2026: 7.

64 papers.

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  1. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  2. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  3. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  4. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
  5. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  6. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  7. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 47
  8. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  9. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  10. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  11. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  12. 2023Component-aware anomaly detection framework for adjustable and logical industrial visual inspectioncited by 22
38 more
  1. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21
  2. 2023PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentationcited by 21
  3. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  4. 2024PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edgecited by 16
  5. 2024Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detectioncited by 16
  6. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  7. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  8. 2024SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detectioncited by 14
  9. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  10. 2024Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspectivecited by 12
  11. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  12. 2023Optimizing PatchCore for Few/many-shot Anomaly Detectioncited by 10
  13. 2024VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modelingcited by 9
  14. 2024RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detectioncited by 8
  15. 2024FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Datacited by 8
  16. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  17. 2024Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interferencecited by 7
  18. 2025INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learningcited by 7
  19. 2023Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Visioncited by 5
  20. 2025CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detectioncited by 5
  21. 2025Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detectioncited by 4
  22. 2025Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detectioncited by 4
  23. 2023Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flowcited by 3
  24. 2024AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelinescited by 3
  25. 2025Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusioncited by 3
  26. 2025RoBiS: Robust Binary Segmentation for High-Resolution Industrial Imagescited by 3
  27. 2025CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environmentscited by 3
  28. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  29. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  30. 2025Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detectioncited by 2
  31. 2026VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detectioncited by 2
  32. 2023Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detectioncited by 1
  33. 2024Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detectioncited by 1
  34. 2025BenchReAD: A systematic benchmark for retinal anomaly detectioncited by 1
  35. 2025ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detectioncited by 1
  36. 2025AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRIcited by 1
  37. 2026Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applicationscited by 1
  38. 2024Fractals as Pre-training Datasets for Anomaly Detection and Localizationcited by 0

50 most cited of 64 All 64 (JSON)