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Converting machine learning models into the standardized ONNX format.
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This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring serve
Exports complex models into standardized formats to enable robust, multithreaded inference within web applications.