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January 10, 2026 17:09
This PR implements a ResUNet architecture as an alternative to the existing ResNet model. The original ResNet operates in the data dimension space, where an explicit initial downsampling step is required to accelerate training. In the ResNet/QM9 workflow, this was achieved by removing grid cells (pixels), which can lead to a loss of information. In contrast, the proposed ResUNet architecture includes both encoder and decoder components, allowing it to handle downsampling and upsampling internally and more efficiently. Initial tests on the QM9 dataset indicate that the ResUNet achieves comparable performance and training speed to the ResNet/QM9 model. A more thorough comparison, however, will require further hyperparameter tuning for the ResUNet architecture. --------- Co-authored-by: Hananeh Oliaei <ho0950@della-vis1.princeton.edu>
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Handle model instantiation using Hydra, enabling: