mala-lab/deviation-network-image
Official PyTorch implementation of the paper “Explainable Deep Few-shot Anomaly Detection with Deviation Networks”, weakly/partially supervised anomaly detection, few-shot anomaly detection, image defect detection.
This tool helps quality control inspectors and manufacturing engineers automatically identify defects in products using images. You provide a small set of images of good products and a few examples of known defects, and it learns to spot anomalies in new images. The output highlights where the defects are located, making it easier to inspect goods on an assembly line or in a quality assurance process.
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Use this if you need to detect unusual patterns or defects in image data, especially when you have very few examples of what constitutes a 'defect'.
Not ideal if your data is not image-based, such as numerical tables or text, or if you have a very large, well-labeled dataset of both normal and anomalous items.
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Language
Python
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Last pushed
Oct 29, 2022
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