Hydra is a computer vision system designed to manage data quality monitoring processes in experimental halls. Initially developed for Hall-D in 2019, it has been successfully deployed across all experimental halls at Jefferson Lab. The system consists of back-end processes managing models, inferences, and data flow, with front-end components accessible via web pages for user interaction. Hydra aims to alleviate the burden on humans by automating tasks such as image classification using AI and computer vision technologies. By utilizing Python back-end supported by a MySQL database, Hydra can efficiently process images through a multi-step workflow. The system includes components like Feeder for image analysis and Load Balancer for distributing inference orders among Predict processes. The Keeper process takes actions based on the Predict reports received, recording inferences and determining which images require further labeling or training. The web-based front end allows users to label images, evaluate model performance, monitor real-time classifications, and inspect data quality status.
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