abc-125/viad-benchmark
Benchmark for Visual Industrial Anomaly Detection
This project helps operations engineers and quality control managers evaluate visual industrial anomaly detection models for real-world manufacturing and inspection tasks. It takes various datasets and anomaly detection models as input and produces a benchmark of their performance, highlighting how practical aspects like input size and noisy data affect defect detection. This helps users understand which models are most reliable for identifying manufacturing defects or unusual patterns on production lines.
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Use this if you need to objectively compare different visual anomaly detection models to find the best one for your industrial inspection pipeline, moving beyond academic claims.
Not ideal if you are looking for a pre-built, ready-to-deploy anomaly detection system rather than a tool for evaluating existing models.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Aug 16, 2025
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