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.

Computer vision technologies are widely used in sports to control the quality of training. However, there are only a few approaches to recognizing the punches of a person engaged in boxing training. All existing approaches have used manual feature selection and trained on insufficient datasets. We introduce a new approach for recognizing actions in an untrimmed video based on three stages: removing frames without actions, action localization and action classification. Furthermore, we collected a sufficient dataset that contains five classes in total represented by more than 1000 punches in total. On each stage, we compared existing approaches and found the optimal model that allowed us to recognize actions in untrimmed videos with an accuracy 87%.