MoViAD: A Modular Library for Visual Anomaly Detection

TL;DR

MoViAD is a modular library that simplifies the development, evaluation, and deployment of visual anomaly detection models across various scenarios and hardware settings.

Contribution

It introduces a flexible, comprehensive library supporting multiple VAD models, datasets, and deployment utilities, facilitating research and practical applications.

Findings

Supports diverse VAD scenarios including semi-supervised and few-shots

Provides optimized models and utilities for edge and IoT deployment

Includes robust evaluation metrics and profiling tools

Abstract

VAD is a critical field in machine learning focused on identifying deviations from normal patterns in images, often challenged by the scarcity of anomalous data and the need for unsupervised training. To accelerate research and deployment in this domain, we introduce MoViAD, a comprehensive and highly modular library designed to provide fast and easy access to state-of-the-art VAD models, trainers, datasets, and VAD utilities. MoViAD supports a wide array of scenarios, including continual, semi-supervised, few-shots, noisy, and many more. In addition, it addresses practical deployment challenges through dedicated Edge and IoT settings, offering optimized models and backbones, along with quantization and compression utilities for efficient on-device execution and distributed inference. MoViAD integrates a selection of backbones, robust evaluation VAD metrics (pixel-level and image-level) and useful profiling tools for efficiency analysis. The library is designed for fast, effortless deployment, enabling machine learning engineers to easily use it for their specific setup with custom models, datasets, and backbones. At the same time, it offers the flexibility and extensibility researchers need to develop and experiment with new methods.