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Dataclasses that hold input images, ground truth, masks, and prediction outputs for a single anomaly detection sample.
Distinct from Anomaly Detection: Distinct from Anomaly Detection: focuses on the data container for a single sample, not the detection algorithms.
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- open-edge-platform/anomalib
open-edge-platform/anomalib
5,871Auf GitHub ansehenAnomalib 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
Defines dataclasses holding input images, ground truth, masks, and predictions for each sample.
Pythonanomaly-detectionanomaly-localizationanomaly-segmentation
