We consider N i.i.d. one-dimensional inhomogeneous diusion processes (X_i(t), i = 1,. .. , N) with drift µ(t, x) = \sum_{ j=1}^K \alpha_jj(t)g_j(x) and diffusion coefficient σ(t, x), where K, the functions g_j(x) and σ(t, x) are known. Our concern is the nonparametric estimation of the K-dimensional unknown function (\alpha_j(t), j = 1,. .. , k) from the continuous observation of the sample paths (X_i(t)) throughout a fixed time interval [0, τ]. A collection of projection estimators belonging to a product of finite-dimensional subspaces of L2 ([0, τ ]) is built. The L2-risk is defined by the expectation of either an empirical norm or a deterministic norm fitted to the problem. Rates of convergence for large N are discussed. A data-driven choice of the dimensions of the projection spaces is proposed. The theoretical results are illustrated by numerical experiments on simulated data.