D4.2 Model deployment analysis V2

This document presents the final outcomes of the VOXReality project in relation to the training, optimization, deployment, and sharing of its AI models, as well as the implementation of the VOXReality eXtended Reality (XR) applications. It provides a detailed retrospective analysis of the adopted “once-for-all” (OFA) training methodology, along with the optimization techniques that were applied to reduce model size and computational requirements while preserving performance. The VOXReality model optimization approach, developed during the project, successfully supported model pruning, quantization, and export to common formats such as ONNX, facilitating deployment across diverse hardware platforms. The document also reports experimental results validating the effectiveness of the implemented optimization strategies. Deployment and sharing mechanisms for the pretrained AI models were defined and realized, including source code-based deployment and containerized solutions, ensuring accessibility, portability, and reproducibility. Furthermore, the document defines the deployment and sharing options for the pretrained VOXReality AI models, providing clear guidelines on effective deployment and access, including source code-based deployment and containerization strategies. It also includes the architecture, design principles, and implementation details of VOXReality's XR applications, specifically the VR Conferences, Augmented Theatre, and Training Assistant.