Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad point spread function of modern space‐based instruments. In certain cases, even finding of point sources on the image becomes a non‐trivial task. We present a method for point sources extraction using a convolution neural network (CNN) trained on our own artificial dataset, which imitates images from the Fermi Large Area Telescope. These images are raw count photon maps of 10 × 10 deg2 covering energies from 1 to 10 GeV. We compare different CNN architectures that demonstrate accuracy increase by ≈15% and reduces the inference time by at least the factor of four accuracy improvement with respect to a similar state‐of‐the‐art models.