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Metadata-Version: 2.1
Name: nnunetv2-bm-custom
Version: 2.5.25
Summary: nnU-Net is a framework for out-of-the box image segmentation.
Author: Helmholtz Imaging Applied Computer Vision Lab, Roman Fitzjalen BlueMind AI Inc
Author-Email: Fabian Isensee <f.isensee[at]dkfz-heidelberg.de>
Project-Url: homepage, https://github.com/MIC-DKFZ/nnUNet
Project-Url: repository, https://github.com/bluemindai/nnUNet
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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2019] [Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Keywords: deep learning,image segmentation,semantic segmentation,medical image analysis,medical image segmentation,nnU-Net,nnunet
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
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Requires-Dist: pre-commit; extra == "dev"
Provides-Extra: dev
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 979 characters]

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nnunetv2_bm_custom-2.5.25.dist-info/RECORD — —

top_level.txt

configs
dist
documentation
nnunetv2
scripts
vm-yc

entry_points.txt

[console_scripts]
nnUNetv2_accumulate_crossval_results = nnunetv2.evaluation.find_best_configuration:accumulate_crossval_results_entry_point
nnUNetv2_apply_postprocessing = nnunetv2.postprocessing.remove_connected_components:entry_point_apply_postprocessing
nnUNetv2_convert_MSD_dataset = nnunetv2.dataset_conversion.convert_MSD_dataset:entry_point
nnUNetv2_convert_old_nnUNet_dataset = nnunetv2.dataset_conversion.convert_raw_dataset_from_old_nnunet_format:convert_entry_point
nnUNetv2_determine_postprocessing = nnunetv2.postprocessing.remove_connected_components:entry_point_determine_postprocessing_folder
nnUNetv2_download_pretrained_model_by_url = nnunetv2.model_sharing.entry_points:download_by_url
nnUNetv2_ensemble = nnunetv2.ensembling.ensemble:entry_point_ensemble_folders
nnUNetv2_evaluate_folder = nnunetv2.evaluation.evaluate_predictions:evaluate_folder_entry_point
nnUNetv2_evaluate_simple = nnunetv2.evaluation.evaluate_predictions:evaluate_simple_entry_point
nnUNetv2_export_model_to_zip = nnunetv2.model_sharing.entry_points:export_pretrained_model_entry
nnUNetv2_extract_fingerprint = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:extract_fingerprint_entry
nnUNetv2_find_best_configuration = nnunetv2.evaluation.find_best_configuration:find_best_configuration_entry_point
nnUNetv2_install_pretrained_model_from_zip = nnunetv2.model_sharing.entry_points:install_from_zip_entry_point
nnUNetv2_move_plans_between_datasets = nnunetv2.experiment_planning.plans_for_pretraining.move_plans_between_datasets:entry_point_move_plans_between_datasets
nnUNetv2_plan_and_preprocess = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_and_preprocess_entry
nnUNetv2_plan_experiment = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_experiment_entry
nnUNetv2_plot_overlay_pngs = nnunetv2.utilities.overlay_plots:entry_point_generate_overlay
nnUNetv2_predict = nnunetv2.inference.predict_from_raw_data:predict_entry_point
nnUNetv2_predict_from_modelfolder = nnunetv2.inference.predict_from_raw_data:predict_entry_point_modelfolder
nnUNetv2_preprocess = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:preprocess_entry
nnUNetv2_train = nnunetv2.run.run_training:run_training_entry