miaecle/segmentation_models

Segmentation models with pretrained backbones. Keras.

Segmentation models Zoo

Segmentation models is python library with Neural Networks for Image Segmentation based on Keras (Tensorflow) framework.

The main features of this library are:

• High level API (just two lines to create NN)
• 4 models architectures for binary and multi class segmentation (including legendary Unet)
• 15 available backbones for each architecture
• All backbones have pre-trained weights for faster and better convergence

Latest documentation is avaliable on Read the Docs

Avaliable models:

• Unet
• FPN
• Linknet
• PSPNet

Avaliable backbones:

Backbone model	Name	Weights
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

Requirements

1. Python 3.6 or higher
2. Keras >=2.1.0
3. Tensorflow >= 1.4

Installation

Installing via pip

$ pip install segmentation-models

Using latest version in your project

$ pip install git+https://github.com/qubvel/segmentation_models

Code examples

Train Unet model:

from segmentation_models import Unet
from segmentation_models.backbones import get_preprocessing

# prepare data
x, y = ...

preprocessing_fn = get_preprocessing('resnet34')
x = preprocessing_fn(x)

# prepare model
model = Unet(backbone_name='resnet34', encoder_weights='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_weights='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_weights='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

Change Log

Version 0.1.2

Areas of improvement

• Added PSPModel
• Prepocessing functions for all backbones:

from segmentation_models.backbones import get_preprocessing

preprocessing_fn = get_preprocessing('resnet34')
X = preprocessing_fn(x)

API changes

• Default param use_batchnorm=True for all decoders
• FPN model Upsample2D layer renamed to ResizeImage

Version 0.1.1

• Added Linknet model
• Keras 2.2+ compatibility (fixed import of _obtain_input_shape)
• Small code improvements and bug fixes

Version 0.1.0

• Unet and FPN models