We study the convergence of Nesterov Accelerated Gradient (NAG) minimization algorithm applied to a class of non convex functions called strongly quasar convex functions, which can exhibit highly non convex behaviour. We show that in the case of strongly quasar convex functions, NAG can achieve an accelerated convergence speed at the cost of a lower curvature assumption. We provide a continuous analysis through high resolution ODEs, in which negative friction may appear. Finally, we investigate connections with a weaker class of non convex functions (smooth Polyak-\L ojasiewicz functions) by characterizing the gap between this class and the one of smooth strongly quasar convex functions.