ImageGrains 2.0 models

Model weights for the default segmentation model of ImageGrains 2.0 (https://github.com/dmair1989/imagegrains), i.e., the fine-tuned Cellpose-SAM default, and the other Cellpose-2-based models. The ImageGrains 2 dataset is available here: 10.5281/zenodo.17866827 If you use these models, please cite: Mair, D., Witz, G., Do Prado, A., Garefalakis, P., Wild, A., Ville, F., Schuster, B., Horn, M., Österle, J., Fabbri, S. C., Litty, C., Achleitner, S., Leistner, S., Hiller, C., and Schlunegger, F. (2026): ImageGrains 2.0: Improved precision and generalization for grain segmentation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6346. Pachitariu, M., Rariden, M., Stringer, C.. Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv 2025.04.28.651001; https://doi.org/10.1101/2025.04.28.651001. If you use legacy versions of Cellpose (e.g., by using ImageGrains v1.x models), please cite: Mair, D., Witz, G., Do Prado, A.H., Garefalakis, P. & Schlunegger, F. (2023) Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning. Earth Surface Processes and Landforms, 1–18. https://doi.org/10.1002/esp.5755. Stringer, C.A., Pachitariu, M., (2021). Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106. https://doi.org/10.1038/s41592-020-01018-x. If you use ImageGrains to calculate percentile uncertainties, please also cite: Mair, D., Henrique, A., Prado, D., Garefalakis, P., Lechmann, A., Whittaker, A., and Schlunegger, F. (2022): Grain size of fluvial gravel bars from close-range UAV imagery-uncertainty in segmentation-based data, Earth Surf. Dyn., 10,953-973. https://doi.org/10.5194/esurf-10-953-2022.