| ### 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 |
| |
