Speakers
Description
DaCapo is a deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. It addresses the need for scalability and 3D-aware segmentation networks, and is developed with a modular and open-source framework for training and deploying deep learning solutions at scale. DaCapo breaks through terabyte-sized (teravoxels) segmentation ceilings by incorporating established segmentation approaches with blockwise distributed deployment using local, cluster, or cloud deployment infrastructures. The different aspects of DaCapo's functionality have been separated and encapsulated into submodules that can be selected depending on a user's specific needs. These submodules include mechanisms particular to the task type (e.g., semantic vs. instance segmentation), neural network architecture (including pretrained models), compute infrastructure and paradigm (e.g., cloud or cluster, local or distributed), and data loading, among others.
| Target audience | Intermediate users, Advanced users, Intermediate developers, Advanced developers |
|---|---|
| Keywords | deep learning, machine learning, segmentation, big data, open source, cloud, batch processing, cluster computing |
