We aggregate positive queries to train and improve our models
Partium collects usage information to continuously improve its performance.
Specifically, with every confirmation that a particular part has been found, the system keeps accumulating knowledge with the query images taken, the filters (if available and applied), and the used text keywords. The system is not retrained with every single search query, but we accumulate data and deploy newly trained models every time we see a meaningful boost in performance.