--- a
+++ b/setup.py
@@ -0,0 +1,50 @@
+from setuptools import setup, find_namespace_packages
+
+setup(name='nnunet',
+      packages=find_namespace_packages(include=["nnunet", "nnunet.*"]),
+      version='1.6.6',
+      description='nnU-Net. Framework for out-of-the box biomedical image segmentation.',
+      url='https://github.com/MIC-DKFZ/nnUNet',
+      author='Division of Medical Image Computing, German Cancer Research Center',
+      author_email='f.isensee@dkfz-heidelberg.de',
+      license='Apache License Version 2.0, January 2004',
+      install_requires=[
+            "torch>=1.6.0a",
+            "tqdm",
+            "dicom2nifti",
+            "scikit-image>=0.14",
+            "medpy",
+            "scipy",
+            "batchgenerators==0.21",
+            "numpy",
+            "sklearn",
+            "SimpleITK",
+            "pandas",
+            "requests",
+            "nibabel", 'tifffile','axial_attention'
+      ],
+      entry_points={
+          'console_scripts': [
+              'nnUNet_convert_decathlon_task = nnunet.experiment_planning.nnUNet_convert_decathlon_task:main',
+              'nnUNet_plan_and_preprocess = nnunet.experiment_planning.nnUNet_plan_and_preprocess:main',
+              'nnUNet_train = nnunet.run.run_training:main',
+              'nnUNet_train_DP = nnunet.run.run_training_DP:main',
+              'nnUNet_train_DDP = nnunet.run.run_training_DDP:main',
+              'nnUNet_predict = nnunet.inference.predict_simple:main',
+              'nnUNet_ensemble = nnunet.inference.ensemble_predictions:main',
+              'nnUNet_find_best_configuration = nnunet.evaluation.model_selection.figure_out_what_to_submit:main',
+              'nnUNet_print_available_pretrained_models = nnunet.inference.pretrained_models.download_pretrained_model:print_available_pretrained_models',
+              'nnUNet_print_pretrained_model_info = nnunet.inference.pretrained_models.download_pretrained_model:print_pretrained_model_requirements',
+              'nnUNet_download_pretrained_model = nnunet.inference.pretrained_models.download_pretrained_model:download_by_name',
+              'nnUNet_download_pretrained_model_by_url = nnunet.inference.pretrained_models.download_pretrained_model:download_by_url',
+              'nnUNet_determine_postprocessing = nnunet.postprocessing.consolidate_postprocessing_simple:main',
+              'nnUNet_export_model_to_zip = nnunet.inference.pretrained_models.collect_pretrained_models:export_entry_point',
+              'nnUNet_install_pretrained_model_from_zip = nnunet.inference.pretrained_models.download_pretrained_model:install_from_zip_entry_point',
+              'nnUNet_change_trainer_class = nnunet.inference.change_trainer:main',
+              'nnUNet_evaluate_folder = nnunet.evaluation.evaluator:nnunet_evaluate_folder',
+              'nnUNet_plot_task_pngs = nnunet.utilities.overlay_plots:entry_point_generate_overlay',
+          ],
+      },
+      keywords=['deep learning', 'image segmentation', 'medical image analysis',
+                'medical image segmentation', 'nnU-Net', 'nnunet']
+      )