In the past years neural networks have surpassed classic approaches in a number of tasks and achieved state-of-the-art results. The main domains of application were image classification, object detection / segmentation and speech recognition. This became possible by virtue of two factors: development of hardware and emergence of large open-sourced datasets, such as Imagenet. The medical domain has been difficult to lever with neural networks for quite a long time, mostly due to the lack of large quality datasets. The tables have turned a few years ago, and since then a number of large datasets has been released, mostly in a form of competition. This allowed many engineers to contribute in solving medical problems, such as knee osteoarthritis diagnosis [11] and bone age assessment [5]. But in some tasks, such as ECG classification, there are few large open datasets, and most of the publications are either supported by private data, or make use of small datasets, which may not be representative and unbiased [6]. In this paper we apply variations of convolutional neural network architecture - ResNet [4] - to the task of atrial fibrillation classification to obtain benchmarks and get intuition behind performance of those variations.