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Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。
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| 2026-09-20 | 3792 |
Today
+3 stars today
This week
+24 stars this week
This month
+49 stars this month
Momentum
36.0
growth rate 0.64%/day
# Awesome Industrial Anomaly Detection [](https://github.com/sindresorhus/awesome)
We discuss public datasets and related studies in detail. Welcome to read our paper and make comments.
[Deep Industrial Image Anomaly Detection: A Survey (Machine Intelligence Research)](https://link.springer.com/article/10.1007/s11633-023-1459-z)
[A Survey of Recent Advances in Industrial Anomaly Detection: From Normal-Only Training to Foundation-Model Priors [2026]](https://www.preprints.org/manuscript/202606.0719)
[IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [TCYB 2024]](https://arxiv.org/abs/2301.13359)[[code]](https://github.com/M-3LAB/open-iad)[[中文]](https://blog.csdn.net/m0_63828250/article/details/136891730)
We will keep focusing on this field and updating relevant information.
Keywords: anomaly detection, anomaly segmentation, industrial image, defect detection
<!-- [[Main Page]](https://github.com/M-3LAB) [[Survey]](https://github.com/M-3LAB/awesome-industrial-anomaly-detection) [[Benchmark]](https://github.com/M-3LAB/open-iad) [[Result]](https://github.com/M-3LAB/IM-IAD) -->
🔥🔥🔥 Contributions to our repository are welcome. Feel free to categorize the papers and [pull requests](https://github.com/M-3LAB/awesome-industrial-anomaly-detection/pulls).
---
🔥🔥🔥 We have released AD-Copilot, an end-to-end trained MLLM for industrial anomaly detection. Most impressively, AD-Copilot surpasses humans on real industrial inspection tasks! Try it at [[Code]](https://github.com/jam-cc/AD-Copilot)[[Demo]](https://huggingface.co/spaces/jiang-cc/AD-Copilot)
🔥🔥🔥 How well are current MLLMs performing as industrial quality inspectors? Which MLLM performs best in industrial anomaly detection? Please refer to our recent research. [[ICLR 2025]](https://arxiv.org/abs/2410.09453)[[Github]](https://github.com/jam-cc/MMAD)
🔥🔥🔥 We compare different types of anomaly synthesis methods in detail. Welcome to make comments.
ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection [[paper]](https://arxiv.org/abs/2510.07927)
A Survey on Industrial Anomalies Synthesis [[paper]](https://arxiv.org/abs/2502.16412)[[github]](https://github.com/M-3LAB/awesome-anomaly-synthesis)
🔥🔥🔥 3D Anomaly Detection: A Survey [[paper]](https://www.researchgate.net/publication/398334588_3D_Anomaly_Detection_A_Survey?_tp=eyJjb250ZXh0Ijp7InBhZ2UiOiJzcG90bGlnaHQiLCJwcmV2aW91c1BhZ2UiOiJwcm9maWxlIiwicG9zaXRpb24iOiJwYWdlQ29udGVudCJ9fQ) [[github]](https://github.com/M-3LAB/awesome-3d-anomaly-detection)
---
## Table of Contents
- [Awesome Industrial Anomaly Detection ](#awesome-industrial-anomaly-detection-)
- [Table of Contents](#table-of-contents)
- [SOTA methods with code](#sota-methods-with-code)
- [Recommended Benchmarks](#recommended-benchmarks)
- [Recent research \[survey\]](#recent-research-survey)
- [ECCV 2026](#eccv-2026)
- [ICML 2026](#icml-2026)
- [CVPR 2026](#cvpr-2026)
- [ICLR 2026](#iclr-2026)
- [AAAI 2026](#aaai-2026)
- [NeurIPS 2025](#neurips-2025)
- [KDD 2025](#kdd-2025)
- [ICCV 2025](#iccv-2025)
- [ICML 2025](#icml-2025)
- [CVPR 2025](#cvpr-2025)
- [Paper Tree (Classification of representative methods)](#paper-tree-classification-of-representative-methods)
- [Timeline](#timeline)
- [Paper list for industrial image anomaly detection](#paper-list-for-industrial-image-anomaly-detection)
- [Related Survey, Benchmark, and Framework](#related-survey-benchmark-and-framework)
- [2 Unsupervised AD](#2-unsupervised-ad)
- [2.1 Feature-Embedding-based Methods](#21-feature-embedding-based-methods)
- [2.1.1 Teacher-Student](#211-teacher-student)
- [2.1.2 One-Class Classification (OCC)](#212-one-class-classification-occ)
- [2.1.3 Distribution-Map](#213-distribution-map)
- [2.1.4 Memory Bank](#214-memory-bank)
- [2.1.5 Vison Language AD](#215-vison-language-ad)
- [2.2 ReconstExcerpt of 222,021 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b744ac1face72c05, topic:dataset, desc:datasets, readme:datasets
matched fp:b744ac1face72c05, topic:deep-learning
matched fp:b744ac1face72c05, topic:computer-vision
matched fp:b744ac1face72c05, topic:medical
