Abstract Automated monitoring of insects and other arthropods is vital for ecological research and conservation; yet current image recognition tools often lack generalizability across diverse imaging conditions and struggle with varying specimen sizes. This limits their practical utility and wide adoption by ecologists. We present flatbug , an open‐source Python package developed to provide a robust, generalizable and user‐friendly solution for arthropod detection and instance segmentation. flatbug employs an adaptive tiling framework for both training and inference, enabling scale‐ and size‐agnostic detection. It leverages instance segmentation, allowing for automated background removal and precise body‐size estimation. The package includes pre‐trained models and is accompanied by a uniquely diverse benchmark dataset of over 113,000 annotated arthropods from laboratory‐ and field‐based imaging systems. This dataset facilitates rigorous cross‐validation and evaluation. Our best flatbug model achieves an average F1 score of 94.2% across these diverse datasets. Crucially, it demonstrates strong out‐of‐the‐box generalizability, with performance reduced by only 7.1% on average when tested against entirely novel imaging systems excluded from its training. This confirms flatbug 's robust performance in new contexts without retraining. flatbug offers ecologists and practitioners a ready‐to‐use, efficient and accurate tool for arthropod monitoring that addresses common limitations of existing methods. With comprehensive documentation, tutorials and an online demo, it is designed for straightforward integration and use. By providing a generalizable solution and a new standard for evaluating cross‐domain performance, flatbug aims to accelerate advancements in automated arthropod detection and ecological computer vision. The package, dataset and models are freely available at https://github.com/darsa‐group/flat‐bug/ and https://doi.org/10.5281/zenodo.18164125 .
Svenning et al. (2026) studied this question.