def TinyYOLO_Siamese(input_shape=(3, 416, 416), num_classes=80, num_priors=5):
"""Tiny YOLO (v2) architecture
# Arguments
input_shape: Shape of the input image
num_classes: Number of classes (excluding background)
# References
https://arxiv.org/abs/1612.08242
https://arxiv.org/abs/1506.02640
"""
K.set_image_dim_ordering('th')
input_tensor_1 = Input(shape=input_shape)
input_tensor_2 = Input(shape=input_shape)
net_1 = {}
net_2 = {}
net_1 = create_base_network(input_tensor_1, net_1)
net_2 = create_base_network(input_tensor_2, net_2, True)
merge_nets = (concat())([net_1['relu7'], net_2['relu7']])
conv = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(merge_nets)
ReLU = (LeakyReLU(alpha=0.1))(conv)
outNet = (Convolution2D(num_priors * (4 + num_classes + 1),
1,
1,
border_mode='same',
subsample=(1, 1)))(ReLU)
model = Model([net_1['input'], net_2['input']], outNet)
return model
def TinyYOLO(input_shape=(3,416,416),num_classes=80,num_priors=5):
"""Tiny YOLO (v2) architecture
# Arguments
input_shape: Shape of the input image
num_classes: Number of classes (excluding background)
# References
https://arxiv.org/abs/1612.08242
https://arxiv.org/abs/1506.02640
"""
K.set_image_dim_ordering('th')
net={}
input_tensor = Input(shape=input_shape)
net['input'] = input_tensor
net['conv1'] = (YOLOConvolution2D(16, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['input'])
net['relu1'] = (LeakyReLU(alpha=0.1))(net['conv1'])
net['pool1'] = (MaxPooling2D(pool_size=(2, 2),padding='valid'))(net['relu1'])
net['conv2'] = (YOLOConvolution2D(32, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['pool1'])
net['relu2'] = (LeakyReLU(alpha=0.1))(net['conv2'])
net['pool2'] = (MaxPooling2D(pool_size=(2, 2),padding='valid'))(net['relu2'])
net['conv3'] = (YOLOConvolution2D(64, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['pool2'])
net['relu3'] = (LeakyReLU(alpha=0.1))(net['conv3'])
net['pool3'] = (MaxPooling2D(pool_size=(2, 2),padding='valid'))(net['relu3'])
net['conv4'] = (YOLOConvolution2D(128, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['pool3'])
net['relu4'] = (LeakyReLU(alpha=0.1))(net['conv4'])
net['pool4'] = (MaxPooling2D(pool_size=(2, 2),padding='valid'))(net['relu4'])
net['conv5'] = (YOLOConvolution2D(256, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['pool4'])
net['relu5'] = (LeakyReLU(alpha=0.1))(net['conv5'])
net['pool5'] = (MaxPooling2D(pool_size=(2, 2),padding='valid'))(net['relu5'])
net['conv6'] = (YOLOConvolution2D(512, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['pool5'])
net['relu6'] = (LeakyReLU(alpha=0.1))(net['conv6'])
net['pool6'] = (MaxPooling2D(pool_size=(2, 2),strides=(1,1),padding='same'))(net['relu6'])
net['conv7'] = (YOLOConvolution2D(1024, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['pool6'])
net['relu7'] = (LeakyReLU(alpha=0.1))(net['conv7'])
net['conv8'] = (YOLOConvolution2D(1024, 3, 3, border_mode='same',subsample=(1,1),
epsilon=0.000001))(net['relu7'])
net['relu8'] = (LeakyReLU(alpha=0.1))(net['conv8'])
net['conv9'] = (Conv2D(num_priors*(4+num_classes+1), (1, 1), padding='same',
strides=(1,1)))(net['relu8'])
model = Model(net['input'], net['conv9'])
return model
def YOLO(input_shape=(3, 416, 416), num_classes=80, num_priors=5):
"""YOLO (v2) architecture
# Arguments
input_shape: Shape of the input image
num_classes: Number of classes (excluding background)
# References
https://arxiv.org/abs/1612.08242
https://arxiv.org/abs/1506.02640
"""
K.set_image_dim_ordering('th')
net = {}
input_tensor = Input(shape=input_shape)
net['input'] = input_tensor
net['conv1'] = (YOLOConvolution2D(32,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['input'])
net['relu1'] = (LeakyReLU(alpha=0.1))(net['conv1'])
net['pool1'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu1'])
net['conv2'] = (YOLOConvolution2D(64,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool1'])
net['relu2'] = (LeakyReLU(alpha=0.1))(net['conv2'])
net['pool2'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu2'])
net['conv3_1'] = (YOLOConvolution2D(128,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool2'])
net['relu3_1'] = (LeakyReLU(alpha=0.1))(net['conv3_1'])
net['conv3_2'] = (YOLOConvolution2D(64,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu3_1'])
net['relu3_2'] = (LeakyReLU(alpha=0.1))(net['conv3_2'])
net['conv3_3'] = (YOLOConvolution2D(128,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu3_2'])
net['relu3_3'] = (LeakyReLU(alpha=0.1))(net['conv3_3'])
net['pool3'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu3_3'])
net['conv4_1'] = (YOLOConvolution2D(256,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool3'])
net['relu4_1'] = (LeakyReLU(alpha=0.1))(net['conv4_1'])
net['conv4_2'] = (YOLOConvolution2D(128,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu4_1'])
net['relu4_2'] = (LeakyReLU(alpha=0.1))(net['conv4_2'])
net['conv4_3'] = (YOLOConvolution2D(256,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu4_2'])
net['relu4_3'] = (LeakyReLU(alpha=0.1))(net['conv4_3'])
net['pool4'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu4_3'])
net['conv5_1'] = (YOLOConvolution2D(512,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool4'])
net['relu5_1'] = (LeakyReLU(alpha=0.1))(net['conv5_1'])
net['conv5_2'] = (YOLOConvolution2D(256,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu5_1'])
net['relu5_2'] = (LeakyReLU(alpha=0.1))(net['conv5_2'])
net['conv5_3'] = (YOLOConvolution2D(512,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu5_2'])
net['relu5_3'] = (LeakyReLU(alpha=0.1))(net['conv5_3'])
net['conv5_4'] = (YOLOConvolution2D(256,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu5_3'])
net['relu5_4'] = (LeakyReLU(alpha=0.1))(net['conv5_4'])
net['conv5_5'] = (YOLOConvolution2D(512,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu5_4'])
net['relu5_5'] = (LeakyReLU(alpha=0.1))(net['conv5_5'])
net['pool5'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu5_5'])
net['conv6_1'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool5'])
net['relu6_1'] = (LeakyReLU(alpha=0.1))(net['conv6_1'])
net['conv6_2'] = (YOLOConvolution2D(512,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_1'])
net['relu6_2'] = (LeakyReLU(alpha=0.1))(net['conv6_2'])
net['conv6_3'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_2'])
net['relu6_3'] = (LeakyReLU(alpha=0.1))(net['conv6_3'])
net['conv6_4'] = (YOLOConvolution2D(512,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_3'])
net['relu6_4'] = (LeakyReLU(alpha=0.1))(net['conv6_4'])
net['conv6_5'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_4'])
net['relu6_5'] = (LeakyReLU(alpha=0.1))(net['conv6_5'])
net['conv6_6'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_5'])
net['relu6_6'] = (LeakyReLU(alpha=0.1))(net['conv6_6'])
net['conv6_7'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_6'])
net['relu6_7'] = (LeakyReLU(alpha=0.1))(net['conv6_7'])
net['reorg7'] = (Reorg())(net['relu5_5'])
net['merge7'] = (merge([net['reorg7'], net['relu6_7']],
mode='concat',
concat_axis=1))
net['conv8'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['merge7'])
net['relu8'] = (LeakyReLU(alpha=0.1))(net['conv8'])
net['conv9'] = (Convolution2D(num_priors * (4 + num_classes + 1),
1,
1,
border_mode='same',
subsample=(1, 1)))(net['relu8'])
model = Model(net['input'], net['conv9'])
return model
def TinyYOLT(input_shape=(3, 416, 416), num_classes=80, num_priors=5):
"""Tiny TinyYOLT architecture
# Arguments
input_shape: Shape of the input image
num_classes: Number of classes (excluding background)
# References
https://arxiv.org/abs/1612.08242
https://arxiv.org/abs/1506.02640
"""
K.set_image_dim_ordering('th')
input_tensor = Input(shape=input_shape)
net = {}
net['input'] = input_tensor
# Scale 1
net['input_scale1'] = (YOLOConvolution2D(16,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['input'])
# Scale 2
net['deconv'] = Deconvolution2D(3,
4,
4,
net['input']._keras_shape,
'glorot_uniform',
'linear',
border_mode='valid',
subsample=(2, 2),
name='deconv')(net['input'])
net['deconv'] = UpSampling2D(size=(2, 2))((net['input']))
net['conv1'] = (YOLOConvolution2D(16,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['deconv'])
net['input_scale2'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['conv1'])
# Merge scales
# net['merge'] = (concat())([net['input_scale1'], net['input_scale2']])
net['merge'] = merge([net['input_scale1'], net['input_scale2']],
mode='concat',
name='merge',
concat_axis=1)
net['relu1'] = (LeakyReLU(alpha=0.1))(net['merge'])
net['pool1'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu1'])
net['conv2'] = (YOLOConvolution2D(32,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool1'])
net['relu2'] = (LeakyReLU(alpha=0.1))(net['conv2'])
net['pool2'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu2'])
net['conv3'] = (YOLOConvolution2D(64,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool2'])
net['relu3'] = (LeakyReLU(alpha=0.1))(net['conv3'])
net['pool3'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu3'])
net['conv4'] = (YOLOConvolution2D(128,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool3'])
net['relu4'] = (LeakyReLU(alpha=0.1))(net['conv4'])
net['pool4'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu4'])
net['conv5'] = (YOLOConvolution2D(256,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool4'])
net['relu5'] = (LeakyReLU(alpha=0.1))(net['conv5'])
net['pool5'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu5'])
net['conv6'] = (YOLOConvolution2D(512,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool5'])
net['relu6'] = (LeakyReLU(alpha=0.1))(net['conv6'])
net['pool6'] = (MaxPooling2D(pool_size=(2, 2),
strides=(1, 1),
border_mode='same'))(net['relu6'])
net['conv7'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool6'])
net['relu7'] = (LeakyReLU(alpha=0.1))(net['conv7'])
net['conv8'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu7'])
net['relu8'] = (LeakyReLU(alpha=0.1))(net['conv8'])
net['output'] = (Convolution2D(num_priors * (4 + num_classes + 1),
1,
1,
border_mode='same',
subsample=(1, 1)))(net['relu8'])
model = Model(net['input'], net['output'])
return model
def create_base_network(input_dim, net, doubleScaling=False):
net['input'] = input_dim
if doubleScaling:
output_shape_tensor = getShapeLayer(net['input'])
net['deconv'] = (YOLODeconvolution2D(3,
3,
3,
batch_size=16,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['input'])
net['conv1'] = (YOLOConvolution2D(16,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['deconv'])
net['input1'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['conv1'])
else:
net['input1'] = (YOLOConvolution2D(16,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['input'])
net['relu1'] = (LeakyReLU(alpha=0.1))(net['input1'])
net['pool1'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu1'])
net['conv2'] = (YOLOConvolution2D(32,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool1'])
net['relu2'] = (LeakyReLU(alpha=0.1))(net['conv2'])
net['pool2'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu2'])
net['conv3'] = (YOLOConvolution2D(64,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool2'])
net['relu3'] = (LeakyReLU(alpha=0.1))(net['conv3'])
net['pool3'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu3'])
net['conv4'] = (YOLOConvolution2D(128,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool3'])
net['relu4'] = (LeakyReLU(alpha=0.1))(net['conv4'])
net['pool4'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu4'])
net['conv5'] = (YOLOConvolution2D(256,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool4'])
net['relu5'] = (LeakyReLU(alpha=0.1))(net['conv5'])
net['pool5'] = (MaxPooling2D(pool_size=(2, 2),
border_mode='valid'))(net['relu5'])
net['conv6'] = (YOLOConvolution2D(512,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool5'])
net['relu6'] = (LeakyReLU(alpha=0.1))(net['conv6'])
net['pool6'] = (MaxPooling2D(pool_size=(2, 2),
strides=(1, 1),
border_mode='same'))(net['relu6'])
net['conv7'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool6'])
net['relu7'] = (LeakyReLU(alpha=0.1))(net['conv7'])
return net
def vgg16YOLO(input_shape=(3, 416, 416), num_classes=80, num_priors=5):
"""YOLO (v2) architecture
# Arguments
input_shape: Shape of the input image
num_classes: Number of classes (excluding background)
# References
https://arxiv.org/abs/1612.08242
https://arxiv.org/abs/1506.02640
"""
K.set_image_dim_ordering('th')
net = {}
# Block 1
input_tensor = input_tensor = Input(shape=input_shape)
img_size = (input_shape[1], input_shape[0])
net['input'] = input_tensor
net['conv1_1'] = Convolution2D(64,
3,
3,
activation='relu',
border_mode='same',
name='conv1_1')(net['input'])
net['conv1_2'] = Convolution2D(64,
3,
3,
activation='relu',
border_mode='same',
name='conv1_2')(net['conv1_1'])
net['pool1'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool1')(net['conv1_2'])
# Block 2
net['conv2_1'] = Convolution2D(128,
3,
3,
activation='relu',
border_mode='same',
name='conv2_1')(net['pool1'])
net['conv2_2'] = Convolution2D(128,
3,
3,
activation='relu',
border_mode='same',
name='conv2_2')(net['conv2_1'])
net['pool2'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool2')(net['conv2_2'])
# Block 3
net['conv3_1'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv3_1')(net['pool2'])
net['conv3_2'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv3_2')(net['conv3_1'])
net['conv3_3'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv3_3')(net['conv3_2'])
net['pool3'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool3')(net['conv3_3'])
# Block 4
net['conv4_1'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv4_1')(net['pool3'])
net['conv4_2'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv4_2')(net['conv4_1'])
net['conv4_3'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv4_3')(net['conv4_2'])
net['pool4'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool4')(net['conv4_3'])
# Block 5
net['conv5_1'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv5_1')(net['pool4'])
net['conv5_2'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv5_2')(net['conv5_1'])
net['conv5_3'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv5_3')(net['conv5_2'])
# before are from VGG, below are from YOLO
net['relu5_5'] = (LeakyReLU(alpha=0.1))(net['conv5_3'])
net['pool5'] = (MaxPooling2D(pool_size=(2, 2),
padding='valid'))(net['relu5_5'])
net['conv6_1'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['pool5'])
net['relu6_1'] = (LeakyReLU(alpha=0.1))(net['conv6_1'])
net['conv6_2'] = (YOLOConvolution2D(512,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_1'])
net['relu6_2'] = (LeakyReLU(alpha=0.1))(net['conv6_2'])
net['conv6_3'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_2'])
net['relu6_3'] = (LeakyReLU(alpha=0.1))(net['conv6_3'])
net['conv6_4'] = (YOLOConvolution2D(512,
1,
1,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_3'])
net['relu6_4'] = (LeakyReLU(alpha=0.1))(net['conv6_4'])
net['conv6_5'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_4'])
net['relu6_5'] = (LeakyReLU(alpha=0.1))(net['conv6_5'])
net['conv6_6'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_5'])
net['relu6_6'] = (LeakyReLU(alpha=0.1))(net['conv6_6'])
net['conv6_7'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['relu6_6'])
net['relu6_7'] = (LeakyReLU(alpha=0.1))(net['conv6_7'])
net['reorg7'] = (Reorg())(net['relu5_5'])
net['merge7'] = (Concatenate(axis=1)([net['reorg7'], net['relu6_7']]))
net['conv8'] = (YOLOConvolution2D(1024,
3,
3,
border_mode='same',
subsample=(1, 1),
epsilon=0.000001))(net['merge7'])
net['relu8'] = (LeakyReLU(alpha=0.1))(net['conv8'])
net['conv9'] = (Conv2D(num_priors * (4 + num_classes + 1), (1, 1),
padding='same',
strides=(1, 1)))(net['relu8'])
model = Model(net['input'], net['conv9'])
return model
def build_own_det(img_shape=(3, 224, 224), n_classes=1000, num_priors=5):
K.set_image_dim_ordering('th')
img_input = Input(shape=img_shape)
# Steem
x = Convolution2D(64, 7, 7, border_mode='same', subsample=(1,1), name='block1_conv1', activation='relu')(img_input)
x = MaxPooling2D(pool_size=(3, 3), strides=(4, 4), padding='same', name='MaxPool_1')(x)
x = BatchNormalization(name='Batch_1')(x)
x = Convolution2D(64, 1, 1, border_mode='same', subsample=(1,1), name='block2_conv1', activation='relu')(x)
x = Convolution2D(256, 3, 3, border_mode='same', subsample=(1,1), name='block2_conv2', activation='relu')(x)
x = BatchNormalization(name='Batch_2')(x)
steem_output = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='steem_output')(x)
# Inception 1
inc_1_1 = Convolution2D(64, 1, 1, border_mode='same', subsample=(1,1), name='inc_1_1', activation='relu')(steem_output)
inc_1_2_a = Convolution2D(96, 1, 1, border_mode='same', subsample=(1,1), name='inc_1_2_a', activation='relu')(steem_output)
inc_1_2_b = Convolution2D(128, 3, 3, border_mode='same', subsample=(1,1), name='inc_1_2_b', activation='relu')(inc_1_2_a)
inc_1_3_a = Convolution2D(16, 1, 1, border_mode='same', subsample=(1,1), name='inc_1_3_a', activation='relu')(steem_output)
inc_1_3_b = Convolution2D(32, 5, 5, border_mode='same', subsample=(1,1), name='inc_1_3_b', activation='relu')(inc_1_3_a)
inc_1_4_a = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inc_1_4_a')(steem_output)
inc_1_4_b = Convolution2D(32, 1, 1, border_mode='same', subsample=(1,1), name='inc_1_4_b', activation='relu')(inc_1_4_a)
out_inc_1 = merge([inc_1_1, inc_1_2_b, inc_1_3_b, inc_1_4_b], mode='concat', concat_axis=1, name='out_inc_1')
# Residual 1
add_r1 = Add(name='add_r1')([steem_output, out_inc_1])
add_1 = Convolution2D(480, 1, 1, border_mode='same', subsample=(1,1), name='add_1', activation='relu')(add_r1)
# Inception 2
inc_2_1 = Convolution2D(128, 1, 1, border_mode='same', subsample=(1,1), name='inc_2_1', activation='relu')(add_1)
inc_2_2_a = Convolution2D(128, 1, 1, border_mode='same', subsample=(1,1), name='inc_2_2_a', activation='relu')(add_1)
inc_2_2_b = Convolution2D(192, 3, 3, border_mode='same', subsample=(1,1), name='inc_2_2_b', activation='relu')(inc_2_2_a)
inc_2_3_a = Convolution2D(32, 1, 1, border_mode='same', subsample=(1,1), name='inc_2_3_a', activation='relu')(add_1)
inc_2_3_b = Convolution2D(96, 5, 5, border_mode='same', subsample=(1,1), name='inc_2_3_b', activation='relu')(inc_2_3_a)
inc_2_4_a = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), border_mode='same', name='inc_2_4_a')(add_1)
inc_2_4_b = Convolution2D(64, 1, 1, border_mode='same', subsample=(1,1), name='inc_2_4_b', activation='relu')(inc_2_4_a)
out_inc_2 = merge([inc_2_1, inc_2_2_b, inc_2_3_b, inc_2_4_b], mode='concat', concat_axis=1, name='out_inc_2')
# Residual 2
add_2 = Add(name='add_2')([add_1, out_inc_2])
# Parallel block
out_inc = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='out_inc')(add_2)
conv_3 = YOLOConvolution2D(512, 3, 3, border_mode='same', subsample=(1,1), name='block3_conv1')(out_inc)
conv_3 = LeakyReLU(alpha=0.1)(conv_3)
conv_4 = YOLOConvolution2D(1024, 3, 3, border_mode='same', subsample=(1,1), name='block4_conv1')(conv_3)
conv_4 = LeakyReLU(alpha=0.1)(conv_4)
conv_5 = YOLOConvolution2D(512, 3, 3, border_mode='same', subsample=(1,1), name='block5_conv1')(conv_4)
conv_5 = LeakyReLU(alpha=0.1)(conv_5)
conv_6 = YOLOConvolution2D(1024, 3, 3, border_mode='same', subsample=(1,1), name='block6_conv1')(conv_5)
conv_6 = LeakyReLU(alpha=0.1)(conv_6)
conv_7 = YOLOConvolution2D(512, 3, 3, border_mode='same', subsample=(1,1), name='block7_conv1')(conv_6)
conv_7 = LeakyReLU(alpha=0.1)(conv_7)
conv_8 = YOLOConvolution2D(1024, 3, 3, border_mode='same', subsample=(1,1), name='block8_conv1')(conv_7)
conv_8 = LeakyReLU(alpha=0.1)(conv_8)
reorg = (Reorg())(add_2)
concat = Concatenate(axis=1)([conv_8, reorg])
conv_9 = Convolution2D(1024, 3, 3, border_mode='same', subsample=(1,1), name='block9_conv1', activation='relu')(concat)
last_conv = Convolution2D(num_priors * (4 + n_classes + 1), (1, 1), padding='same', strides=(1, 1), activation='relu')(conv_9)
model = Model(input=img_input, output=last_conv)
plot(model, to_file='team7_model_det.png', show_shapes=True, show_layer_names=True)
return model