  We propose a dense image prediction out-of-distribution detection algorithm,
called PixOOD, which does not require training on samples of anomalous data and
is not designed for a specific application which avoids traditional training
biases. In order to model the complex intra-class variability of the
in-distribution data at the pixel level, we propose an online data condensation
algorithm which is more robust than standard K-means and is easily trainable
through SGD. We evaluate PixOOD on a wide range of problems. It achieved
state-of-the-art results on four out of seven datasets, while being competitive
on the rest. The source code is available at https://github.com/vojirt/PixOOD.
