def SSD300(input_shape=(300, 300, 3), num_classes=21, softmax=True):
"""SSD300 architecture.
# Arguments
input_shape: Shape of the input image.
num_classes: Number of classes including background.
# Notes
In order to stay compatible with pre-trained models, the parameters
were chosen as in the caffee implementation.
# References
https://arxiv.org/abs/1512.02325
"""
x = input_tensor = Input(shape=input_shape)
source_layers = ssd300_body(x)
# Add multibox head for classification and regression
num_priors = [4, 6, 6, 6, 4, 4]
normalizations = [20, -1, -1, -1, -1, -1]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
# stay compatible with caffe models
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
model.minmax_sizes = [(30, 60), (60, 111), (111, 162), (162, 213), (213, 264), (264, 315)]
model.steps = [8, 16, 32, 64, 100, 300]
model.special_ssd_boxes = True
return model
def SSD512(input_shape=(512, 512, 3), num_classes=21, softmax=True):
"""SSD512 architecture.
# Arguments
input_shape: Shape of the input image.
num_classes: Number of classes including background.
# Notes
In order to stay compatible with pre-trained models, the parameters
were chosen as in the caffee implementation.
# References
https://arxiv.org/abs/1512.02325
"""
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_body(x)
# Add multibox head for classification and regression
num_priors = [4, 6, 6, 6, 6, 4, 4]
normalizations = [20, -1, -1, -1, -1, -1, -1]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
# stay compatible with caffe models
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
#model.minmax_sizes = [(35, 76), (76, 153), (153, 230), (230, 307), (307, 384), (384, 460), (460, 537)]
model.minmax_sizes = [(20.48, 51.2), (51.2, 133.12), (133.12, 215.04), (215.04, 296.96), (296.96, 378.88), (378.88, 460.8), (460.8, 542.72)]
model.steps = [8, 16, 32, 64, 128, 256, 512]
model.special_ssd_boxes = True
return model
def DSOD512(input_shape=(512, 512, 3), num_classes=21, activation='relu', softmax=True):
"""DSOD, DenseNet based SSD512 architecture.
# Arguments
input_shape: Shape of the input image.
num_classes: Number of classes including background.
activation: Type of activation functions.
# References
https://arxiv.org/abs/1708.01241
"""
x = input_tensor = Input(shape=input_shape)
source_layers = dsod512_body(x, activation=activation)
num_priors = [4, 6, 6, 6, 6, 4, 4]
normalizations = [20, 20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
model.minmax_sizes = [(35, 76), (76, 153), (153, 230), (230, 307), (307, 384), (384, 460), (460, 537)]
model.steps = [8, 16, 32, 64, 128, 256, 512]
model.special_ssd_boxes = True
return model
def DSODSL512(input_shape=(512, 512, 3), activation='relu', softmax=True):
"""DenseNet based Architecture for SegLink512.
# Arguments
input_shape: Shape of the input image.
# References
https://arxiv.org/abs/1708.01241
"""
# DSOD body
x = input_tensor = Input(shape=input_shape)
source_layers = dsod512_body(x, activation=activation)
# Add multibox head for classification and regression
num_priors = [1, 1, 1, 1, 1, 1, 1]
normalizations = [20, -1, -1, -1, -1, -1, -1]
output_tensor = multibox_head(source_layers, num_priors, normalizations,
softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
return model
def SL512(input_shape=(512, 512, 3), softmax=True):
"""SegLink512 architecture.
# Arguments
input_shape: Shape of the input image.
# References
https://arxiv.org/abs/1703.06520
"""
# SSD body
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_body(x)
# Add multibox head for classification and regression
num_priors = [1, 1, 1, 1, 1, 1, 1]
normalizations = [20, -1, -1, -1, -1, -1, -1]
output_tensor = multibox_head(source_layers, num_priors, normalizations,
softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
return model
def TBPP512(input_shape=(512, 512, 3), softmax=True):
"""TextBoxes++512 architecture.
# Arguments
input_shape: Shape of the input image.
# References
- [TextBoxes++: A Single-Shot Oriented Scene Text Detector](https://arxiv.org/abs/1801.02765)
"""
# SSD body
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_body(x)
num_maps = len(source_layers)
# Add multibox head for classification and regression
num_priors = [14] * num_maps
normalizations = [1] * num_maps
output_tensor = multibox_head(source_layers, num_priors, normalizations,
softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1, 2, 3, 5, 1 / 2, 1 / 3, 1 / 5] * 2] * num_maps
#model.shifts = [[(0.0, 0.0)] * 7 + [(0.0, 0.5)] * 7] * num_maps
model.shifts = [[(0.0, -0.25)] * 7 + [(0.0, 0.25)] * 7] * num_maps
model.special_ssd_boxes = False
model.scale = 0.5
return model
def TBPP512_dense_separable(input_shape=(512, 512, 3), softmax=True):
"""TextBoxes++512 architecture with dense blocks and separable convolution.
"""
# custom body
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_dense_separable_body(x)
num_maps = len(source_layers)
# Add multibox head for classification and regression
num_priors = [14] * num_maps
normalizations = [1] * num_maps
output_tensor = multibox_head_separable(source_layers, num_priors,
normalizations, softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1, 2, 3, 5, 1 / 2, 1 / 3, 1 / 5] * 2] * num_maps
#model.shifts = [[(0.0, 0.0)] * 7 + [(0.0, 0.5)] * 7] * num_maps
model.shifts = [[(0.0, -0.25)] * 7 + [(0.0, 0.25)] * 7] * num_maps
model.special_ssd_boxes = False
model.scale = 0.5
return model
def DSODTBPP512(input_shape=(512, 512, 3), softmax=True):
"""DenseNet based Architecture for TextBoxes++512.
"""
# DSOD body
x = input_tensor = Input(shape=input_shape)
source_layers = dsod512_body(x)
num_maps = len(source_layers)
# Add multibox head for classification and regression
num_priors = [14] * num_maps
normalizations = [1] * num_maps
output_tensor = multibox_head(source_layers, num_priors, normalizations,
softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1, 2, 3, 5, 1 / 2, 1 / 3, 1 / 5] * 2] * num_maps
#model.shifts = [[(0.0, 0.0)] * 7 + [(0.0, 0.5)] * 7] * num_maps
model.shifts = [[(0.0, -0.25)] * 7 + [(0.0, 0.25)] * 7] * num_maps
model.special_ssd_boxes = False
model.scale = 0.5
return model
def SL384x512_dense(input_shape=(384, 512, 3), activation='relu'):
# body
x = input_tensor = Input(shape=input_shape)
source_layers = ssd384x512_dense_body(x, activation=activation)
# Add multibox head for classification and regression
num_priors = [1, 1, 1, 1, 1, 1]
normalizations = [20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, normalizations)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
return model
def SL512_resnet(input_shape=(512, 512, 3), activation='relu', softmax=True):
# TODO: it does not converge!
# DSOD body
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_resnet_body(x, activation=activation)
# Add multibox head for classification and regression
num_priors = [1, 1, 1, 1, 1, 1, 1]
normalizations = [20, 20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, normalizations,
softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
return model
def SSD512_resnet(input_shape=(512, 512, 3), num_classes=21, softmax=True):
# TODO: it does not converge!
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_resnet_body(x)
# Add multibox head for classification and regression
num_priors = [4, 6, 6, 6, 6, 4, 4]
normalizations = [20, 20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
# stay compatible with caffe models
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
model.minmax_sizes = [(35, 76), (76, 153), (153, 230), (230, 307), (307, 384), (384, 460), (460, 537)]
model.steps = [8, 16, 32, 64, 128, 256, 512]
model.special_ssd_boxes = True
return model
