mlsanityData to 7 Oct 2026

Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection

arXiv 2403.12580 ↗First posted 19 Mar 2024cs.CVcited by 138

Not on the map. Why?

Abstract (arXiv)

Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However, the recent development of IAD approach has encountered certain difficulties due to dataset limitations. On the one hand, most of the state-of-the-art methods have achieved saturation (over 99% in AUROC) on mainstream datasets such as MVTec, and the differences of methods cannot be well distinguished, leading to a significant gap between public datasets and actual application scenarios. On the other hand, the research on various new practical anomaly detection settings is limited by the scale of the dataset, posing a risk of overfitting in evaluation results. Therefore, we propose a large-scale, Real-world, and multi-view Industrial Anomaly Detection dataset, named Real-IAD, which contains 150K high-resolution images of 30 different objects, an order of magnitude larger than existing datasets. It has a larger range of defect area and ratio proportions, making it more challenging than previous datasets. To make the dataset closer to real application scenarios, we adopted a multi-view shooting method and proposed sample-level evaluation metrics. In addition, beyond the general unsupervised anomaly detection setting, we propose a new setting for Fully Unsupervised Industrial Anomaly Detection (FUIAD) based on the observation that the yield rate in industrial production is usually greater than 60%, which has more practical application value. Finally, we report the results of popular IAD methods on the Real-IAD dataset, providing a highly challenging benchmark to promote the development of the IAD field.

Key facts. Cited by 138 papers in the map (data through 7 Oct 2026). Builds on 34 of its 42 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

34 of 42 references.

References · 34

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2017Mask R-CNNcited by 7,259
  3. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  4. 2015Fast R-CNNcited by 2,930
  5. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  6. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
  7. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  8. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  9. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  10. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  11. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  12. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
22 more
  1. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  2. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  3. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  4. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  5. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  6. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  7. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  8. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  9. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
  10. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  11. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 128
  12. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  13. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  14. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  15. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  16. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  17. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  18. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  19. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  20. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  21. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  22. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 22; 2025: 58; 2026: 58.

138 papers.

Sort:

By area:

  1. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 126
  2. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  3. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 92
  4. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 47
  5. 2024MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detectioncited by 46
  6. 2024GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detectioncited by 42
  7. 2024Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generationcited by 31
  8. 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 28
  9. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 28
  10. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  11. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  12. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
38 more
  1. 2024CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generationcited by 16
  2. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 15
  3. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  4. 2024VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detectioncited by 13
  5. 2025MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learningcited by 12
  6. 2025Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detectioncited by 12
  7. 2025EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Modelscited by 11
  8. 2025Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detectioncited by 11
  9. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  10. 2025Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detectioncited by 9
  11. 2024RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detectioncited by 8
  12. 2025Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Processcited by 8
  13. 2024A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detectioncited by 7
  14. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  15. 2025INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learningcited by 7
  16. 2025FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detectioncited by 6
  17. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  18. 2024RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observationscited by 5
  19. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  20. 2025Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shiftcited by 5
  21. 2025Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detectioncited by 5
  22. 2025SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmarkcited by 5
  23. 2026AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparisoncited by 5
  24. 2025Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detectioncited by 4
  25. 2025Towards Personalized Quantum Federated Learning for Anomaly Detectioncited by 4
  26. 2026One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detectioncited by 4
  27. 2026JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QAcited by 4
  28. 2024IPAD: Industrial Process Anomaly Detection Datasetcited by 3
  29. 2024Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detectioncited by 3
  30. 2025Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusioncited by 3
  31. 2025Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmarkcited by 3
  32. 2025Towards High-Resolution Industrial Image Anomaly Detectioncited by 3
  33. 2025An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contaminationcited by 3
  34. 2025Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detectioncited by 3
  35. 2025VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusioncited by 3
  36. 2026Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselinescited by 3
  37. 2024MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detectioncited by 2
  38. 2025ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defectscited by 2

50 most cited of 138 All 138 (JSON)