Machine-learning models for quantum-dot state recognition are commonly trained using synthetic data, where large labeled datasets can be generated efficiently. However, strong performance on simulated data does not guarantee equally strong performance in experiments. Here, we benchmark five neural network architectures for quantum-dot state recognition using a shared synthetic training dataset and independent synthetic and experimental test sets. We compare convolutional and fully connected neural networks, a mixture density network, a vision transformer, and a U-Net across multiple training set sizes and two input normalization schemes. We find that model performance and rankings are closely preserved when moving from held-out synthetic patches to previously unseen simulated devices, but change substantially when the same models are evaluated on experimental data. In particular, the architecture achieving the highest synthetic performance is not the one that performs best experimentally. Input normalization further affects training behavior and, for some architectures, synthetic-to-experimental transfer. These results show that synthetic benchmark performance alone is insufficient to select models for experimental deployment and highlight the need to jointly evaluate architecture, preprocessing, data efficiency, model complexity, and experimental transfer when developing machine-learning tools for automated quantum-dot characterization and control.

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