A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection (2406.03262v3)

Abstract: Visual anomaly detection aims to identify anomalous regions in images through unsupervised learning paradigms, with increasing application demand and value in fields such as industrial inspection and medical lesion detection. Despite significant progress in recent years, there is a lack of comprehensive benchmarks to adequately evaluate the performance of various mainstream methods across different datasets under the practical multi-class setting. The absence of standardized experimental setups can lead to potential biases in training epochs, resolution, and metric results, resulting in erroneous conclusions. This paper addresses this issue by proposing a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework that is highly extensible for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. Additionally, we have proposed the GPU-assisted ADEval package to address the slow evaluation problem of metrics like time-consuming mAU-PRO on large-scale data, significantly reducing evaluation time by more than \textit{1000-fold}. Through extensive experimental results, we objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection. We hope that ADer will become a valuable resource for researchers and practitioners in the field, promoting the development of more robust and generalizable anomaly detection systems. Full codes are open-sourced at https://github.com/zhangzjn/ader.

• The paper introduces ADer, a comprehensive framework that standardizes evaluation of multi-class visual anomaly detection methods.
• ADer integrates 15 state-of-the-art techniques with GPU-assisted ADEval to efficiently benchmark large-scale datasets.
• The detailed analysis reveals performance strengths and limitations, providing actionable insights for advancing VAD research.

A Comprehensive Library for Benchmarking Multi-Class Visual Anomaly Detection

The paper in focus provides a notable contribution to the field of Visual Anomaly Detection (VAD) by addressing the gap in standardized benchmarking methods. This research proposes ADer, a comprehensive library designed for evaluating multi-class visual anomaly detection methods across diverse datasets. The absence of standardized benchmarks in this domain has led to difficulties in comparing the performance of different techniques accurately across various datasets and settings. This paper aims to rectify these inconsistencies and provide a robust framework for the evaluation of VAD methods.

The ADer framework is structured to incorporate various datasets from industrial, medical, and general-purpose domains. It supports 15 state-of-the-art anomaly detection methods and includes nine comprehensive evaluation metrics, ensuring a detailed performance comparison for each method. One of the paper’s significant contributions is the GPU-assisted ADEval package, which drastically reduces evaluation time, making it feasible to perform detailed assessments on large-scale datasets with greater efficiency than before.

Key Findings and Contributions

1. Methodological Diversity: The ADer library encompasses a wide range of VAD methods, categorized into augmentation-based, embedding-based, and reconstruction-based techniques, as well as hybrid methods. This diversity allows researchers to analyze the different strengths and limitations of various approaches.
2. Efficient Benchmarking: The introduction of GPU-assisted evaluation has enhanced the speed and feasibility of benchmarking, especially for complex metrics such as mAU-PRO, that could be previously cumbersome on large datasets.
3. Detailed Analysis:
   • Quantitative Results: The methods like InvAD, ViTAD, and MambaAD exhibit strong performances across most datasets, particularly emphasizing their capability in the multi-class setting. In contrast, certain single-class methods show significant performance gaps, highlighting their limitations in this context.
   • Convergence and Stability: The paper provides insights into the convergence patterns of different methods, demonstrating that some reach saturation faster than others. This analysis is crucial for understanding the training dynamics of VAD models.
4. Cross-Domain and Dataset Correlation: The paper explores the correlation between different datasets and methods, providing valuable insights into how different anomaly detection strategies perform across domains like industrial, medical, and general-purpose settings.

Implications

The open-source ADer library and the extensive evaluation framework offer the research community indispensable tools for developing and comparing VAD methods. This work not only enhances the rigor in the evaluation process but also stimulates future developments by providing a baseline across multiple datasets and metrics. As the field progresses, such standardized frameworks will become increasingly important in driving advancements and ensuring comparability between novel methods.

Challenges and Future Directions

The paper also outlines critical challenges faced by current VAD methodologies, such as the need for more robust algorithms, efficiency concerns related to model design, and the demand for larger and more diverse datasets to drive technological development further. The report encourages the exploration of VAD-specific metrics and augmentation techniques to improve model performance and interpretability.

In conclusion, this paper significantly impacts visual anomaly detection by delivering a structured and efficient benchmarking framework, extensively analyzing state-of-the-art methods, and setting a precedent for future research. Researchers now have a substantial basis from which to evaluate new approaches, ultimately promoting the development of more robust and generalized VAD systems. The insights gained from this paper will influence practitioners and aid in the technology's practical applications in various real-world scenarios.