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Data were fed into a fully convolutional network, based on the SegNet architecture (31) with the final soft-max layer removed, as we did not perform a classification problem. The depth of the network allows for long-range interactions to be incorporated without fully connected layers. The network was implemented in Mathematica, and optimization was performed using the Adam optimizer (38) on a Tesla 40c graphics processing unit (GPU) with 256 GB of random-access memory (RAM) and a computer with a Titan V GPU and 128 GB of RAM. Code is freely available. See “materials availability” below.

For training, the in silico–generated input data were augmented with standard data augmentation methods: Symmetric copies of each original were generated by reflection and rotation. All images were down sampled to have dimensions of 224 by 224 pixels. For crumpling data, creases were also linearized to look more similar to the experimental input. An example of the effect of linearizing is shown in fig. S2.

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