Open NimaMojtahedi opened 1 year ago
Most self-supervised learning methods typically operate on feature vectors, making it possible to work with 3D data. However, when working with 3D data, two specific modifications are required: 1) applying augmentation transformations directly to the 3D data (instead of 2D) and 2) modifying the feature extractor backbone to accept 3D data as input and produce a 1D latent vector as output.
Thanks for the comment. Are you planning to add this feature in the next releases?
TBH, I haven't thought about it yet. However, I might consider including it in the near future!
Cool! Looking forward
Great package. I am wondering if there is possibility to use this package for 3D volumetric data (images)?