We report on a novel application of computer vision techniques to extract beyond the Standard Model (BSM) parameters directly from high energy physics (HEP) flavor data. We develop a method of transforming angular and kinematic distributions into ``quasi-images" that can be used to train a convolutional neural network to perform regression tasks, similar to fitting. This contrasts with the usual classification functions performed using ML/AI in HEP. As a proof-of-concept, we train a 34-layer Residual Neural Network (ResNet) to regress on these images and determine the Wilson Coefficient $C_{9}$ in MC (Monte Carlo) simulations of $B^{0} \rightarrow K^{*} \mu^{+} \mu^{-}$ decays. The technique described here can be generalized and may find applicability across various HEP experiments and elsewhere.