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Reads images and video clips from disk, validates file paths, and formats data for anomaly detection models.
Distinct from Multi-Format Data Loading: Distinct from general Multi-Format Data Loading by focusing on image and video data with validation for anomaly detection.
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Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings. The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi
Reads images and video clips from disk, validates paths, and formats data for anomaly detection models.