liznerski/eoe
Repository for the Exposing Outlier Exposure paper
This project helps data scientists and machine learning engineers analyze image data to detect anomalies. It takes in a dataset of normal images and a small collection of random 'outlier' images, then outputs a model that can identify unusual images with high accuracy. The primary users are researchers and practitioners working on computer vision tasks that involve identifying unexpected or rare items.
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Use this if you need to build a robust anomaly detection system for image data, especially when you only have a limited number of examples of what constitutes an anomaly.
Not ideal if your anomaly detection problem does not involve images or if you require real-time, high-throughput inference on embedded systems.
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Language
Python
License
MIT
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Last pushed
Aug 20, 2024
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