  Although automatic shot transition detection approaches are already
investigated for more than two decades, an effective universal human-level
model was not proposed yet. Even for common shot transitions like hard cuts or
simple gradual changes, the potential diversity of analyzed video contents may
still lead to both false hits and false dismissals. Recently, deep
learning-based approaches significantly improved the accuracy of shot
transition detection using 3D convolutional architectures and artificially
created training data. Nevertheless, one hundred percent accuracy is still an
unreachable ideal. In this paper, we share the current version of our deep
network TransNet V2 that reaches state-of-the-art performance on respected
benchmarks. A trained instance of the model is provided so it can be instantly
utilized by the community for a highly efficient analysis of large video
archives. Furthermore, the network architecture, as well as our experience with
the training process, are detailed, including simple code snippets for
convenient usage of the proposed model and visualization of results.
