This repository contains the benchmark dataset proposed in: CellTypeGraph: A New Geometric Computer Vision Benchmark, Cerrone et al., CVPR2022 Paper Abstract: Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Raw Data Source: The benchmark raw data is derived from "A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule, A. Vijayan et al., eLife 2021". Source code: Repository for using the benchmark: https://github.com/hci-unihd/celltype-graph-benchmark Repository for reproducing the experiments in the manuscript: https://github.com/hci-unihd/plant-celltype Index: Repository for using the benchmark (data-loaders, transforms, metrics): https://github.com/hci-unihd/celltype-graph-benchmark Repository for reproducing the experiments in the manuscript (experiments, training, GNN models, features extraction, visualization, inference): https://github.com/hci-unihd/plant-celltype Download integrity check (md5sum): raw_data.zip: fd0ecccdea684d156fd8ac182a22251b label_grs_surface.zip: 7aa603f3d3cddbf766679d388076269b