| ### 1. Requirements | |
| Some of the examples may require optional dependencies. In case of any optional import errors, | |
| please install the relevant packages according to the error message. | |
| Or install all optional requirements by: | |
| ``` | |
| pip install -r https://raw.githubusercontent.com/Project-MONAI/MONAI/master/requirements-dev.txt | |
| ``` | |
| ### 2. List of examples | |
| #### [classification_3d](./classification_3d) | |
| Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset). | |
| The examples are standard PyTorch programs and have both dictionary-based and array-based transformation versions. | |
| #### [classification_3d_ignite](./classification_3d_ignite) | |
| Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset). | |
| The examples are PyTorch Ignite programs and have both dictionary-based and array-based transformation versions. | |
| #### [distributed_training](./distributed_training) | |
| The examples show how to execute distributed training and evaluation based on 3 different frameworks: | |
| - PyTorch native `DistributedDataParallel` module with `torch.distributed.launch`. | |
| - Horovod APIs with `horovodrun`. | |
| - PyTorch ignite and MONAI workflows. | |
| They can run on several distributed nodes with multiple GPU devices on every node. | |
| #### [segmentation_3d](./segmentation_3d) | |
| Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset. | |
| The examples are standard PyTorch programs and have both dictionary-based and array-based versions. | |
| #### [segmentation_3d_ignite](./segmentation_3d_ignite) | |
| Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset. | |
| The examples are PyTorch Ignite programs and have both dictionary-base and array-based transformations. | |
| #### [workflows](./workflows) | |
| Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset. | |
| The examples are built with MONAI workflows, mainly contain: trainer/evaluator, handlers, post_transforms, etc. | |
| #### [synthesis](./synthesis) | |
| A GAN training and evaluation example for a medical image generative adversarial network. Easy run training script uses `GanTrainer` to train a 2D CT scan reconstruction network. Evaluation script generates random samples from a trained network. | |
| ### 3. List of tutorials | |
| Please check out https://github.com/Project-MONAI/Tutorials | |