The repository contains 3D variants of popular models for segmentation like FPN, Unet, Linknet and PSPNet.

This repository is based on great segmentation_models repo by @qubvel

• Unet
• FPN
• Linknet
• PSPNet

• keras >= 2.2.0 and tensorflow >= 1.13
• classification_models_3D
• efficientnet_3D

pip install segmentation-models-3D

import segmentation_models_3D as sm

model1 = sm.Unet('resnet34', encoder_weights='imagenet')
# binary segmentation (this parameters are default when you call Unet('resnet34')
model2 = sm.FPN('densenet121', classes=1, activation='sigmoid')
# multiclass segmentation with non overlapping class masks (your classes + background)
model3 = sm.Linknet('resnet34', classes=3, activation='softmax')
# multiclass segmentation with independent overlapping/non-overlapping class masks
model4 = sm.PSPNet('resnet34', classes=3, activation='sigmoid')
# If you need to specify non-standard input shape
model5 = sm.Unet('resnet50', input_shape=(96, 128, 128, 6), encoder_weights=None)

All possible backbones: 'resnet18, 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'seresnet18', 'seresnet34', 'seresnet50', 'seresnet101', 'seresnet152', 'seresnext50', 'seresnext101', 'senet154', 'resnext50', 'resnext101', 'vgg16', 'vgg19', 'densenet121', 'densenet169', 'densenet201', 'inceptionresnetv2', 'inceptionv3', 'mobilenet', 'mobilenetv2' Also available: 'efficientnetb0', 'efficientnetb1', 'efficientnetb2', 'efficientnetb3', 'efficientnetb4', 'efficientnetb5', 'efficientnetb6', 'efficientnetb7'

• https://github.com/qubvel/classification_models - original classification 2D repo
• https://github.com/qubvel/segmentation_models - original segmentation 2D repo
• classification_models_3D - models for classification in 3D
• volumentations - 3D augmentations

• There is no 'bilinear' interpolation for UpSample3D layer, so it uses Nearest Neighbour upsampling.