{"package": "nnunet-inference-on-cpu-and-gpu", "summary": "nnU-Net. Framework for out-of-the box biomedical image segmentation. Can do inference on both gpu(if cuda available) and cpu(if cuda not available)", "pypi_url": "https://pypi.org/project/nnunet-inference-on-cpu-and-gpu", "piwheels_url": "https://www.piwheels.org/project/nnunet-inference-on-cpu-and-gpu", "releases": {"1.6.6": {"released": "2021-05-28 16:06:38", "prerelease": false, "yanked": false, "skip_reason": "", "files": {"nnunet_inference_on_cpu_and_gpu-1.6.6-py3-none-any.whl": {"file_url": "https://archive1.piwheels.org/simple/nnunet-inference-on-cpu-and-gpu/nnunet_inference_on_cpu_and_gpu-1.6.6-py3-none-any.whl", "filehash": "7f2880519c7cda58c0867806d5d82784fef90145145f758dcb468c86ae136771", "filesize": 445649, "builder_abi": "cp35m", "file_abi_tag": "none", "platform": "any", "requires_python": null, "apt_dependencies": [], "pip_dependencies": ["batchgenerators", "dicom2nifti", "medpy", "nibabel", "numpy", "pandas", "requests", "scikit-image", "scipy", "simpleitk", "sklearn", "tifffile", "torch", "tqdm"]}}}}}