from segmentation_models import Unet

model = Unet()

model = Unet('resnet34', encoder_weights='imagenet')

# binary segmentation (this parameters are default when you call Unet('resnet34')
model = Unet('resnet34', classes=1, activation='sigmoid')

# multiclass segmentation with non overlapping class masks (your classes + background)
model = Unet('resnet34', classes=3, activation='softmax')

# multiclass segmentation with independent overlapping/non-overlapping class masks
model = Unet('resnet34', classes=3, activation='sigmoid')

# if you set input channels not equal to 3, you have to set encoder_weights=None
# how to handle such case with encoder_weights='imagenet' described in docs
model = Unet('resnet34', input_shape=(None, None, 6), encoder_weights=None)

from segmentation_models import Unet
from segmentation_models.backbones import get_preprocessing
from segmentation_models.losses import bce_jaccard_loss
from segmentation_models.metrics import iou_score

BACKBONE = 'resnet34'
preprocess_input = get_preprocessing(BACKBONE)

# load your data
x_train, y_train, x_val, y_val = load_data(...)

# preprocess input
x_train = preprocess_input(x_train)
x_val = preprocess_input(x_val)

# define model
model = Unet(BACKBONE, encoder_weights='imagenet')
model.compile('Adam', loss=bce_jaccard_loss, metrics=[iou_score])

# fit model
# if you use data generator use model.fit_generator(...) instead of model.fit(...)
# more about `fit_generator` here: https://keras.io/models/sequential/#fit_generator
model.fit(
    x=x_train,
    y=y_train,
    batch_size=16,
    epochs=100,
    validation_data=(x_val, y_val),
)