Lindamulage, AKodagoda, NReyal, SSamarasinghe, PYogarajah, P2023-01-242023-01-242022-11-04A. Lindamulage, N. Kodagoda, S. Reyal, P. Samarasinghe and P. Yogarajah, "Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation," TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON), Hong Kong, Hong Kong, 2022, pp. 1-6, doi: 10.1109/TENCON55691.2022.9977637.21593442https://rda.sliit.lk/handle/123456789/3145Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model for head-pose estimationenEdge InferenceHead Pose estimationNetwork PruningOptimisationQuantisationTensorFlowArticle10.1109/TENCON55691.2022.9977637