testbed/Project-MONAI__MONAI/examples/README.md

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

Training and evaluation examples of 3D classification based on DenseNet3D and IXI dataset. The examples are standard PyTorch programs and have both dictionary-based and array-based transformation versions.

classification_3d_ignite

Training and evaluation examples of 3D classification based on DenseNet3D and IXI dataset. The examples are PyTorch Ignite programs and have both dictionary-based and array-based transformation versions.

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

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

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

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

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