Segmentation models Zoo

Segmentation models with pretrained backbones

Unet and FPN like models

Backbone model	Name	Weights	UNet	FPN
VGG16	vgg16	imagenet	+	+
VGG19	vgg19	imagenet	+	+
ResNet18	resnet18	imagenet	+	+
ResNet34	resnet34	imagenet	+	+
ResNet50	resnet50	imagenet imagenet11k-places365ch	+	+
ResNet101	resnet101	imagenet	+	+
ResNet152	resnet152	imagenet imagenet11k	+	+
ResNeXt50	resnext50	imagenet	+	+
ResNeXt101	resnext101	imagenet	+	+
DenseNet121	densenet121	imagenet	+	+
DenseNet169	densenet169	imagenet	+	+
DenseNet201	densenet201	imagenet	+	+
Inception V3	inceptionv3	imagenet	+	+
Inception ResNet V2	inceptionresnetv2	imagenet	+	+

Installation

Installing via pip

$ pip install segmentation_models

Using latest version in your project

$ git clone https://github.com/qubvel/segmentation_models.git
$ cd segmentation_models
$ git submodule update --init --recursive

Code examples

Train Unet model:

from segmentation_models import Unet

# prepare data
x, y = ...

# prepare model
model = Unet(backbone_name='resnet34', encoder_weigths='imagenet')
model.compile('Adam', 'binary_crossentropy', ['binary_accuracy'])

# train model
model.fit(x, y)

Train FPN model:

from segmentation_models import FPN

model = FPN(backbone_name='resnet34', encoder_weigths='imagenet')

Useful trick

Freeze encoder weights for fine-tuning during first epochs of training:

from segmentation_models import FPN
from segmentation_models.utils import set_trainable

model = FPN(backbone_name='resnet34', encoder_weigths='imagenet', freeze_encoder=True)
model.compile('Adam', 'binary_crossentropy', ['binary_accuracy'])

# pretrain model decoder
model.fit(x, y, epochs=2)

# release all layers for training
set_trainable(model) # set all layers trainable and recompile model

# continue training
model.fit(x, y, epochs=100)

TODO

• Update Unet API
• Update FPN API
• Add Linknet models
• Add PSP models
• Add DPN backbones