  In the deep-learning community new algorithms are published at an incredible
pace. Therefore, solving an image classification problem for new datasets
becomes a challenging task, as it requires to re-evaluate published algorithms
and their different configurations in order to find a close to optimal
classifier. To facilitate this process, before biasing our decision towards a
class of neural networks or running an expensive search over the network space,
we propose to estimate the classification difficulty of the dataset. Our method
computes a single number that characterizes the dataset difficulty 27x faster
than training state-of-the-art networks. The proposed method can be used in
combination with network topology and hyper-parameter search optimizers to
efficiently drive the search towards promising neural-network configurations.
