# Segmentation Models Python API¶

Getting started with segmentation models is easy.

## Unet¶

segmentation_models.Unet(backbone_name='vgg16', input_shape=(None, None, 3), classes=1, activation='sigmoid', weights=None, encoder_weights='imagenet', encoder_freeze=False, encoder_features='default', decoder_block_type='upsampling', decoder_filters=(256, 128, 64, 32, 16), decoder_use_batchnorm=True, **kwargs)

Unet is a fully convolution neural network for image semantic segmentation

Parameters: backbone_name – name of classification model (without last dense layers) used as feature extractor to build segmentation model. input_shape – shape of input data/image (H, W, C), in general case you do not need to set H and W shapes, just pass (None, None, C) to make your model be able to process images af any size, but H and W of input images should be divisible by factor 32. classes – a number of classes for output (output shape - (h, w, classes)). activation – name of one of keras.activations for last model layer (e.g. sigmoid, softmax, linear). weights – optional, path to model weights. encoder_weights – one of None (random initialization), imagenet (pre-training on ImageNet). encoder_freeze – if True set all layers of encoder (backbone model) as non-trainable. encoder_features – a list of layer numbers or names starting from top of the model. Each of these layers will be concatenated with corresponding decoder block. If default is used layer names are taken from DEFAULT_SKIP_CONNECTIONS. decoder_block_type – one of blocks with following layers structure: upsampling: UpSampling2D -> Conv2D -> Conv2D transpose: Transpose2D -> Conv2D decoder_filters – list of numbers of Conv2D layer filters in decoder blocks decoder_use_batchnorm – if True, BatchNormalisation layer between Conv2D and Activation layers is used. Unet keras.models.Model

segmentation_models.Linknet(backbone_name='vgg16', input_shape=(None, None, 3), classes=1, activation='sigmoid', weights=None, encoder_weights='imagenet', encoder_freeze=False, encoder_features='default', decoder_block_type='upsampling', decoder_filters=(None, None, None, None, 16), decoder_use_batchnorm=True, **kwargs)

Linknet is a fully convolution neural network for fast image semantic segmentation

Note

This implementation by default has 4 skip connections (original - 3).

Parameters: backbone_name – name of classification model (without last dense layers) used as feature extractor to build segmentation model. input_shape – shape of input data/image (H, W, C), in general case you do not need to set H and W shapes, just pass (None, None, C) to make your model be able to process images af any size, but H and W of input images should be divisible by factor 32. classes – a number of classes for output (output shape - (h, w, classes)). activation – name of one of keras.activations for last model layer (e.g. sigmoid, softmax, linear). weights – optional, path to model weights. encoder_weights – one of None (random initialization), imagenet (pre-training on ImageNet). encoder_freeze – if True set all layers of encoder (backbone model) as non-trainable. encoder_features – a list of layer numbers or names starting from top of the model. Each of these layers will be concatenated with corresponding decoder block. If default is used layer names are taken from DEFAULT_SKIP_CONNECTIONS. decoder_filters – list of numbers of Conv2D layer filters in decoder blocks, for block with skip connection a number of filters is equal to number of filters in corresponding encoder block (estimates automatically and can be passed as None value). decoder_use_batchnorm – if True, BatchNormalisation layer between Conv2D and Activation layers is used. decoder_block_type – one of - upsampling: use UpSampling2D keras layer - transpose: use Transpose2D keras layer Linknet keras.models.Model

## FPN¶

segmentation_models.FPN(backbone_name='vgg16', input_shape=(None, None, 3), classes=21, activation='softmax', weights=None, encoder_weights='imagenet', encoder_freeze=False, encoder_features='default', pyramid_block_filters=256, pyramid_use_batchnorm=True, pyramid_aggregation='concat', pyramid_dropout=None, **kwargs)

FPN is a fully convolution neural network for image semantic segmentation

Parameters: backbone_name – name of classification model (without last dense layers) used as feature extractor to build segmentation model. input_shape – shape of input data/image (H, W, C), in general case you do not need to set H and W shapes, just pass (None, None, C) to make your model be able to process images af any size, but H and W of input images should be divisible by factor 32. classes – a number of classes for output (output shape - (h, w, classes)). weights – optional, path to model weights. activation – name of one of keras.activations for last model layer (e.g. sigmoid, softmax, linear). encoder_weights – one of None (random initialization), imagenet (pre-training on ImageNet). encoder_freeze – if True set all layers of encoder (backbone model) as non-trainable. encoder_features – a list of layer numbers or names starting from top of the model. Each of these layers will be used to build features pyramid. If default is used layer names are taken from DEFAULT_FEATURE_PYRAMID_LAYERS. pyramid_block_filters – a number of filters in Feature Pyramid Block of FPN. pyramid_use_batchnorm – if True, BatchNormalisation layer between Conv2D and Activation layers is used. pyramid_aggregation – one of ‘sum’ or ‘concat’. The way to aggregate pyramid blocks. pyramid_dropout – spatial dropout rate for feature pyramid in range (0, 1). FPN keras.models.Model

## PSPNet¶

segmentation_models.PSPNet(backbone_name='vgg16', input_shape=(384, 384, 3), classes=21, activation='softmax', weights=None, encoder_weights='imagenet', encoder_freeze=False, downsample_factor=8, psp_conv_filters=512, psp_pooling_type='avg', psp_use_batchnorm=True, psp_dropout=None, **kwargs)

PSPNet is a fully convolution neural network for image semantic segmentation

Parameters: backbone_name – name of classification model used as feature extractor to build segmentation model. input_shape – shape of input data/image (H, W, C). H and W should be divisible by 6 * downsample_factor and NOT None! classes – a number of classes for output (output shape - (h, w, classes)). activation – name of one of keras.activations for last model layer (e.g. sigmoid, softmax, linear). weights – optional, path to model weights. encoder_weights – one of None (random initialization), imagenet (pre-training on ImageNet). encoder_freeze – if True set all layers of encoder (backbone model) as non-trainable. downsample_factor – one of 4, 8 and 16. Downsampling rate or in other words backbone depth to construct PSP module on it. psp_conv_filters – number of filters in Conv2D layer in each PSP block. psp_pooling_type – one of ‘avg’, ‘max’. PSP block pooling type (maximum or average). psp_use_batchnorm – if True, BatchNormalisation layer between Conv2D and Activation layers is used. psp_dropout – dropout rate between 0 and 1. PSPNet keras.models.Model

## metrics¶

segmentation_models.metrics.IOUScore(class_weights=None, class_indexes=None, threshold=None, per_image=False, smooth=1e-05, name=None)

The Jaccard index, also known as Intersection over Union and the Jaccard similarity coefficient (originally coined coefficient de communauté by Paul Jaccard), is a statistic used for comparing the similarity and diversity of sample sets. The Jaccard coefficient measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets:

$J(A, B) = \frac{A \cap B}{A \cup B}$
Parameters: class_weights – or np.array of class weights (len(weights) = num_classes). class_indexes – Optional integer or list of integers, classes to consider, if None all classes are used. smooth – value to avoid division by zero per_image – if True, metric is calculated as mean over images in batch (B), else over whole batch threshold – value to round predictions (use > comparison), if None prediction will not be round A callable iou_score instance. Can be used in model.compile(...) function.

Example:

metric = IOUScore()
model.compile('SGD', loss=loss, metrics=[metric])

segmentation_models.metrics.FScore(beta=1, class_weights=None, class_indexes=None, threshold=None, per_image=False, smooth=1e-05, name=None)

The F-score (Dice coefficient) can be interpreted as a weighted average of the precision and recall, where an F-score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1-score are equal. The formula for the F score is:

$F_\beta(precision, recall) = (1 + \beta^2) \frac{precision \cdot recall} {\beta^2 \cdot precision + recall}$

The formula in terms of Type I and Type II errors:

$L(tp, fp, fn) = \frac{(1 + \beta^2) \cdot tp} {(1 + \beta^2) \cdot fp + \beta^2 \cdot fn + fp}$
where:
• tp - true positives;
• fp - false positives;
• fn - false negatives;
Parameters: beta – Integer of float f-score coefficient to balance precision and recall. class_weights – or np.array of class weights (len(weights) = num_classes) class_indexes – Optional integer or list of integers, classes to consider, if None all classes are used. smooth – Float value to avoid division by zero. per_image – If True, metric is calculated as mean over images in batch (B), else over whole batch. threshold – Float value to round predictions (use > comparison), if None prediction will not be round. name – Optional string, if None default f{beta}-score name is used. A callable f_score instance. Can be used in model.compile(...) function.

Example:

metric = FScore()
model.compile('SGD', loss=loss, metrics=[metric])


## losses¶

segmentation_models.losses.JaccardLoss(class_weights=None, class_indexes=None, per_image=False, smooth=1e-05)

Creates a criterion to measure Jaccard loss:

$L(A, B) = 1 - \frac{A \cap B}{A \cup B}$
Parameters: class_weights – Array (np.array) of class weights (len(weights) = num_classes). class_indexes – Optional integer or list of integers, classes to consider, if None all classes are used. per_image – If True loss is calculated for each image in batch and then averaged, else loss is calculated for the whole batch. smooth – Value to avoid division by zero. A callable jaccard_loss instance. Can be used in model.compile(...) function or combined with other losses.

Example:

loss = JaccardLoss()
model.compile('SGD', loss=loss)

segmentation_models.losses.DiceLoss(beta=1, class_weights=None, class_indexes=None, per_image=False, smooth=1e-05)

Creates a criterion to measure Dice loss:

$L(precision, recall) = 1 - (1 + \beta^2) \frac{precision \cdot recall} {\beta^2 \cdot precision + recall}$

The formula in terms of Type I and Type II errors:

$L(tp, fp, fn) = \frac{(1 + \beta^2) \cdot tp} {(1 + \beta^2) \cdot fp + \beta^2 \cdot fn + fp}$
where:
• tp - true positives;
• fp - false positives;
• fn - false negatives;
Parameters: beta – Float or integer coefficient for precision and recall balance. class_weights – Array (np.array) of class weights (len(weights) = num_classes). class_indexes – Optional integer or list of integers, classes to consider, if None all classes are used. per_image – If True loss is calculated for each image in batch and then averaged, loss is calculated for the whole batch. (else) – smooth – Value to avoid division by zero. A callable dice_loss instance. Can be used in model.compile(...) function or combined with other losses.

Example:

loss = DiceLoss()
model.compile('SGD', loss=loss)

segmentation_models.losses.BinaryCELoss()

Creates a criterion that measures the Binary Cross Entropy between the ground truth (gt) and the prediction (pr).

$L(gt, pr) = - gt \cdot \log(pr) - (1 - gt) \cdot \log(1 - pr)$
Returns: A callable binary_crossentropy instance. Can be used in model.compile(...) function or combined with other losses.

Example:

loss = BinaryCELoss()
model.compile('SGD', loss=loss)

segmentation_models.losses.CategoricalCELoss(class_weights=None, class_indexes=None)

Creates a criterion that measures the Categorical Cross Entropy between the ground truth (gt) and the prediction (pr).

$L(gt, pr) = - gt \cdot \log(pr)$
Parameters: class_weights – Array (np.array) of class weights (len(weights) = num_classes). class_indexes – Optional integer or list of integers, classes to consider, if None all classes are used. A callable categorical_crossentropy instance. Can be used in model.compile(...) function or combined with other losses.

Example:

loss = CategoricalCELoss()
model.compile('SGD', loss=loss)

segmentation_models.losses.BinaryFocalLoss(alpha=0.25, gamma=2.0)

Creates a criterion that measures the Binary Focal Loss between the ground truth (gt) and the prediction (pr).

$L(gt, pr) = - gt \alpha (1 - pr)^\gamma \log(pr) - (1 - gt) \alpha pr^\gamma \log(1 - pr)$
Parameters: alpha – Float or integer, the same as weighting factor in balanced cross entropy, default 0.25. gamma – Float or integer, focusing parameter for modulating factor (1 - p), default 2.0. A callable binary_focal_loss instance. Can be used in model.compile(...) function or combined with other losses.

Example:

loss = BinaryFocalLoss()
model.compile('SGD', loss=loss)

segmentation_models.losses.CategoricalFocalLoss(alpha=0.25, gamma=2.0, class_indexes=None)

Creates a criterion that measures the Categorical Focal Loss between the ground truth (gt) and the prediction (pr).

$L(gt, pr) = - gt \cdot \alpha \cdot (1 - pr)^\gamma \cdot \log(pr)$
Parameters: alpha – Float or integer, the same as weighting factor in balanced cross entropy, default 0.25. gamma – Float or integer, focusing parameter for modulating factor (1 - p), default 2.0. class_indexes – Optional integer or list of integers, classes to consider, if None all classes are used. A callable categorical_focal_loss instance. Can be used in model.compile(...) function or combined with other losses.

Example

loss = CategoricalFocalLoss()
model.compile('SGD', loss=loss)


## utils¶

segmentation_models.utils.set_trainable(model, recompile=True, **kwargs)

Set all layers of model trainable and recompile it

Note

Model is recompiled using same optimizer, loss and metrics:

model.compile(
model.optimizer,
loss=model.loss,
metrics=model.metrics,
loss_weights=model.loss_weights,
sample_weight_mode=model.sample_weight_mode,
weighted_metrics=model.weighted_metrics,
)

Parameters: model (keras.models.Model`) – instance of keras model