The vibration of a circular cylinder due to fluid forces is of interest in various engineering fields. In this study, we investigate an approach to estimate fluid forces acting on a circular cylinder in a flow field based on experimental flow visualizations using a deep neural network (DNN). Specifically, the wake patterns and fluid forces are correlated in a computational fluid dynamics (CFD) simulation, and the forces in the experiment are estimated by comparing experimental and computational wake patterns with DNN. The approach is tested via dye-ink visualization around a circular cylinder at Reynolds number of 560, referring Seyed-Aghazadeh et al. (Physics of Fluids, 2015). First, the CFD simulation of a circular cylinder with forced vibration in the crossflow direction is conducted with various vibration frequencies. Subsequently, the visualized wake images of the resulting flow fields and corresponding fluid forces are used as training data for DNN. In the estimation, the images from the experiment are detected by the CFD-trained DNN. With this, we can also recall the fluid forces correlated by CFD. The average drag coefficient and the peak value of lift coefficient estimated from streaming experimental images have the standard deviations of 2.1 to 13.7% and 6.6 to 18.6%, respectively, depending on the number of training images. The root-mean-square value of the lift coefficient obtained from the present estimation is 0.82, which is similar to the experimental value of 0.8 under the same flow and oscillation conditions.