Dendritic spines are the seat of most excitatory synapses in the brain, and a cellular structure considered central to learning, memory, and activity-dependent plasticity. Typically, humans quantify dendritic spines from light microscopy data in a manual painstaking, error-prone process. We found that human-to-human variability is substantial (inter-rater reliability 82.2±6.4 %), raising concerns about the reproducibility of experiments and the validity of using human-annotated 'ground truth' as an evaluation method for computational approaches of spine identification. To address this, we present DeepD3, an open deep learning-based framework to robustly quantify dendritic spines in microscopy data automatically. DeepD3's neural networks have been trained on data from different sources and experimental conditions, which were annotated and segmented by multiple experts, such that they can precisely predict the presence of dendrites and dendritic spines. Importantly, these networks were validated in several datasets on varying acquisition modalities, species, anatomical locations, and fluorescent indicators. The entire DeepD3 open framework, including the fully segmented training data, a benchmark that multiple experts have annotated on a pixel and single spine level, and the DeepD3 neural network model zoo is fully available, addressing the lack of openly available datasets of dendritic spines while offering a ready-to-use, flexible, transparent, and reproducible spine quantification method.