A set of prepared datasets for running experiments to replicate paper (see below).

List of data:

Training capspix2pix:

crops256.zip - folder containing 256x256 crops from the original dataset for training capspix2pix. Images are in the "train/original" folder, and labels are in the "train/mask" folder.
syn256_x_data_val.npy + syn256_y_data_val.npy + syn256_y_points_data_val.npy (images + labels + centrelines) - validation synthetic dataset, used while training capspix2pix for plotting
Training u-net:

capspix2pix_AR_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from real labels
capspix2pix_SSM_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from synthetic labels
PBAM_SSM_data_train.npy + PBAM_SSM_mask_train.npy (images + labels) - data generated from PBAM (Physics-based model) for training u-net
pix2pix_AR_data_train.npy + pix2pix_AR_mask_train.npy (images + labels) - data generated from a pix2pix model from real labels for training u-net
pix2pix_SSM_data_train.npy + pix2pix_SSM_mask_train.npy (images + labels) - data generated from a pix2pix model from synthetic labels for training u-net
real_data_data_train.npy + real_data_mask_train.npy (images + labels) - augmented real dataset for training u-net
Testing u-net:

org64_data_test.npy + org64_mask_test.npy (images + labels) - crops from original test dataset for testing u-net
Interpolation:

crops256_inter_data_train.npy + crops256_inter_mask_train.npy (images + labels) - example data for interpolation
Please cite the following paper when using this dataset:

Bass, C., Dai, T., Billot, B., Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. “Image synthesis with a convolutional capsule generative adversarial network,” Medial Imaging with Deep Learning.

See Github page for further instructions:

https://github.com/CherBass/CapsPix2Pix