  In recent years, several companies and researchers have started to tackle the
problem of damage recognition within the scope of automated inspection of built
structures. While companies are neither willing to publish associated data nor
models, researchers are facing the problem of data shortage on one hand and
inconsistent dataset splitting with the absence of consistent metrics on the
other hand. This leads to incomparable results. Therefore, we introduce the
building inspection toolkit -- bikit -- which acts as a simple to use data hub
containing relevant open-source datasets in the field of damage recognition.
The datasets are enriched with evaluation splits and predefined metrics,
suiting the specific task and their data distribution. For the sake of
compatibility and to motivate researchers in this domain, we also provide a
leaderboard and the possibility to share model weights with the community. As
starting point we provide strong baselines for multi-target classification
tasks utilizing extensive hyperparameter search using three transfer learning
approaches for state-of-the-art algorithms. The toolkit and the leaderboard are
available online.
