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YOLOv2 on Pascal VOC2012 – 网络模型(Model)
source link: http://www.banbeichadexiaojiubei.com/index.php/2020/12/13/yolov2-on-pascal-voc2012-网络模型model/
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YOLOv2模型结构
YOLO的输入输出很复杂，Loss函数更加复杂，但是它的网络结构比较简单，只是Convolusion + Batch Normalization + Leaky Relu的重复叠加。
下面是 experiencor/keras-yolo2 定义的网络模型。
from keras.models import Sequential, Model
from keras.layers import Reshape, Activation, Conv2D, Input, MaxPooling2D, BatchNormalization, Flatten, Dense, Lambda
from keras.layers.advanced_activations import LeakyReLU
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
from keras.optimizers import SGD, Adam, RMSprop
from keras.layers.merge import concatenate
import keras.backend as K
import tensorflow as tf
# the function to implement the orgnization layer (thanks to github.com/allanzelener/YAD2K)
def space_to_depth_x2(x):
return tf.space_to_depth(x, block_size=2)
def ConvBatchLReLu(x,filters,kernel_size,index,trainable):
# when strides = None, strides = pool_size.
x = Conv2D(filters, kernel_size, strides=(1,1),
padding='same', name='conv_{}'.format(index),
use_bias=False, trainable=trainable)(x)
x = BatchNormalization(name='norm_{}'.format(index), trainable=trainable)(x)
x = LeakyReLU(alpha=0.1)(x)
return(x)
def ConvBatchLReLu_loop(x,index,convstack,trainable):
for para in convstack:
x = ConvBatchLReLu(x,para["filters"],para["kernel_size"],index,trainable)
index += 1
return(x)
def define_YOLOv2(IMAGE_H,IMAGE_W,GRID_H,GRID_W,TRUE_BOX_BUFFER,BOX,CLASS, trainable=False):
convstack3to5 = [{"filters":128, "kernel_size":(3,3)}, # 3
{"filters":64, "kernel_size":(1,1)}, # 4
{"filters":128, "kernel_size":(3,3)}] # 5
convstack6to8 = [{"filters":256, "kernel_size":(3,3)}, # 6
{"filters":128, "kernel_size":(1,1)}, # 7
{"filters":256, "kernel_size":(3,3)}] # 8
convstack9to13 = [{"filters":512, "kernel_size":(3,3)}, # 9
{"filters":256, "kernel_size":(1,1)}, # 10
{"filters":512, "kernel_size":(3,3)}, # 11
{"filters":256, "kernel_size":(1,1)}, # 12
{"filters":512, "kernel_size":(3,3)}] # 13
convstack14to20 = [{"filters":1024, "kernel_size":(3,3)}, # 14
{"filters":512, "kernel_size":(1,1)}, # 15
{"filters":1024, "kernel_size":(3,3)}, # 16
{"filters":512, "kernel_size":(1,1)}, # 17
{"filters":1024, "kernel_size":(3,3)}, # 18
{"filters":1024, "kernel_size":(3,3)}, # 19
{"filters":1024, "kernel_size":(3,3)}] # 20
input_image = Input(shape=(IMAGE_H, IMAGE_W, 3),name="input_image")
true_boxes = Input(shape=(1, 1, 1, TRUE_BOX_BUFFER , 4),name="input_hack")
# Layer 1
x = ConvBatchLReLu(input_image,filters=32,kernel_size=(3,3),index=1,trainable=trainable)
x = MaxPooling2D(pool_size=(2, 2),name="maxpool1_416to208")(x)
# Layer 2
x = ConvBatchLReLu(x,filters=64,kernel_size=(3,3),index=2,trainable=trainable)
x = MaxPooling2D(pool_size=(2, 2),name="maxpool1_208to104")(x)
# Layer 3 - 5
x = ConvBatchLReLu_loop(x,3,convstack3to5,trainable)
x = MaxPooling2D(pool_size=(2, 2),name="maxpool1_104to52")(x)
# Layer 6 - 8
x = ConvBatchLReLu_loop(x,6,convstack6to8,trainable)
x = MaxPooling2D(pool_size=(2, 2),name="maxpool1_52to26")(x)
# Layer 9 - 13
x = ConvBatchLReLu_loop(x,9,convstack9to13,trainable)
skip_connection = x
x = MaxPooling2D(pool_size=(2, 2),name="maxpool1_26to13")(x)
# Layer 14 - 20
x = ConvBatchLReLu_loop(x,14,convstack14to20,trainable)
# Layer 21
skip_connection = ConvBatchLReLu(skip_connection,filters=64,
kernel_size=(1,1),index=21,trainable=trainable)
skip_connection = Lambda(space_to_depth_x2)(skip_connection)
x = concatenate([skip_connection, x])
# Layer 22
x = ConvBatchLReLu(x,filters=1024,kernel_size=(3,3),index=22,trainable=trainable)
# Layer 23
x = Conv2D(BOX * (4 + 1 + CLASS), (1,1), strides=(1,1), padding='same', name='conv_23')(x)
output = Reshape((GRID_H, GRID_W, BOX, 4 + 1 + CLASS),name="final_output")(x)
# small hack to allow true_boxes to be registered when Keras build the model
# for more information: https://github.com/fchollet/keras/issues/2790
output = Lambda(lambda args: args[0],name="hack_layer")([output, true_boxes])
model = Model([input_image, true_boxes], output)
return(model, true_boxes)
IMAGE_H, IMAGE_W = 416, 416
GRID_H, GRID_W = 13 , 13
TRUE_BOX_BUFFER = 50
BOX = int(len(ANCHORS)/2)
CLASS = len(LABELS)
## true_boxes is the tensor that takes "b_batch"
model, true_boxes = define_YOLOv2(IMAGE_H,IMAGE_W,GRID_H,GRID_W,TRUE_BOX_BUFFER,BOX,CLASS,
trainable=False)
model.summary()
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_image (InputLayer) (None, 416, 416, 3) 0
__________________________________________________________________________________________________
conv_1 (Conv2D) (None, 416, 416, 32) 864 input_image[0][0]
__________________________________________________________________________________________________
norm_1 (BatchNormalization) (None, 416, 416, 32) 128 conv_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 416, 416, 32) 0 norm_1[0][0]
__________________________________________________________________________________________________
maxpool1_416to208 (MaxPooling2D (None, 208, 208, 32) 0 leaky_re_lu_1[0][0]
__________________________________________________________________________________________________
conv_2 (Conv2D) (None, 208, 208, 64) 18432 maxpool1_416to208[0][0]
__________________________________________________________________________________________________
norm_2 (BatchNormalization) (None, 208, 208, 64) 256 conv_2[0][0]
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 208, 208, 64) 0 norm_2[0][0]
__________________________________________________________________________________________________
maxpool1_208to104 (MaxPooling2D (None, 104, 104, 64) 0 leaky_re_lu_2[0][0]
__________________________________________________________________________________________________
conv_3 (Conv2D) (None, 104, 104, 128 73728 maxpool1_208to104[0][0]
__________________________________________________________________________________________________
norm_3 (BatchNormalization) (None, 104, 104, 128 512 conv_3[0][0]
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 104, 104, 128 0 norm_3[0][0]
__________________________________________________________________________________________________
conv_4 (Conv2D) (None, 104, 104, 64) 8192 leaky_re_lu_3[0][0]
__________________________________________________________________________________________________
norm_4 (BatchNormalization) (None, 104, 104, 64) 256 conv_4[0][0]
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, 104, 104, 64) 0 norm_4[0][0]
__________________________________________________________________________________________________
conv_5 (Conv2D) (None, 104, 104, 128 73728 leaky_re_lu_4[0][0]
__________________________________________________________________________________________________
norm_5 (BatchNormalization) (None, 104, 104, 128 512 conv_5[0][0]
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU) (None, 104, 104, 128 0 norm_5[0][0]
__________________________________________________________________________________________________
maxpool1_104to52 (MaxPooling2D) (None, 52, 52, 128) 0 leaky_re_lu_5[0][0]
__________________________________________________________________________________________________
conv_6 (Conv2D) (None, 52, 52, 256) 294912 maxpool1_104to52[0][0]
__________________________________________________________________________________________________
norm_6 (BatchNormalization) (None, 52, 52, 256) 1024 conv_6[0][0]
__________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU) (None, 52, 52, 256) 0 norm_6[0][0]
__________________________________________________________________________________________________
conv_7 (Conv2D) (None, 52, 52, 128) 32768 leaky_re_lu_6[0][0]
__________________________________________________________________________________________________
norm_7 (BatchNormalization) (None, 52, 52, 128) 512 conv_7[0][0]
__________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, 52, 52, 128) 0 norm_7[0][0]
__________________________________________________________________________________________________
conv_8 (Conv2D) (None, 52, 52, 256) 294912 leaky_re_lu_7[0][0]
__________________________________________________________________________________________________
norm_8 (BatchNormalization) (None, 52, 52, 256) 1024 conv_8[0][0]
__________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 52, 52, 256) 0 norm_8[0][0]
__________________________________________________________________________________________________
maxpool1_52to26 (MaxPooling2D) (None, 26, 26, 256) 0 leaky_re_lu_8[0][0]
__________________________________________________________________________________________________
conv_9 (Conv2D) (None, 26, 26, 512) 1179648 maxpool1_52to26[0][0]
__________________________________________________________________________________________________
norm_9 (BatchNormalization) (None, 26, 26, 512) 2048 conv_9[0][0]
__________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU) (None, 26, 26, 512) 0 norm_9[0][0]
__________________________________________________________________________________________________
conv_10 (Conv2D) (None, 26, 26, 256) 131072 leaky_re_lu_9[0][0]
__________________________________________________________________________________________________
norm_10 (BatchNormalization) (None, 26, 26, 256) 1024 conv_10[0][0]
__________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU) (None, 26, 26, 256) 0 norm_10[0][0]
__________________________________________________________________________________________________
conv_11 (Conv2D) (None, 26, 26, 512) 1179648 leaky_re_lu_10[0][0]
__________________________________________________________________________________________________
norm_11 (BatchNormalization) (None, 26, 26, 512) 2048 conv_11[0][0]
__________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU) (None, 26, 26, 512) 0 norm_11[0][0]
__________________________________________________________________________________________________
conv_12 (Conv2D) (None, 26, 26, 256) 131072 leaky_re_lu_11[0][0]
__________________________________________________________________________________________________
norm_12 (BatchNormalization) (None, 26, 26, 256) 1024 conv_12[0][0]
__________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU) (None, 26, 26, 256) 0 norm_12[0][0]
__________________________________________________________________________________________________
conv_13 (Conv2D) (None, 26, 26, 512) 1179648 leaky_re_lu_12[0][0]
__________________________________________________________________________________________________
norm_13 (BatchNormalization) (None, 26, 26, 512) 2048 conv_13[0][0]
__________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU) (None, 26, 26, 512) 0 norm_13[0][0]
__________________________________________________________________________________________________
maxpool1_26to13 (MaxPooling2D) (None, 13, 13, 512) 0 leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
conv_14 (Conv2D) (None, 13, 13, 1024) 4718592 maxpool1_26to13[0][0]
__________________________________________________________________________________________________
norm_14 (BatchNormalization) (None, 13, 13, 1024) 4096 conv_14[0][0]
__________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU) (None, 13, 13, 1024) 0 norm_14[0][0]
__________________________________________________________________________________________________
conv_15 (Conv2D) (None, 13, 13, 512) 524288 leaky_re_lu_14[0][0]
__________________________________________________________________________________________________
norm_15 (BatchNormalization) (None, 13, 13, 512) 2048 conv_15[0][0]
__________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU) (None, 13, 13, 512) 0 norm_15[0][0]
__________________________________________________________________________________________________
conv_16 (Conv2D) (None, 13, 13, 1024) 4718592 leaky_re_lu_15[0][0]
__________________________________________________________________________________________________
norm_16 (BatchNormalization) (None, 13, 13, 1024) 4096 conv_16[0][0]
__________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU) (None, 13, 13, 1024) 0 norm_16[0][0]
__________________________________________________________________________________________________
conv_17 (Conv2D) (None, 13, 13, 512) 524288 leaky_re_lu_16[0][0]
__________________________________________________________________________________________________
norm_17 (BatchNormalization) (None, 13, 13, 512) 2048 conv_17[0][0]
__________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU) (None, 13, 13, 512) 0 norm_17[0][0]
__________________________________________________________________________________________________
conv_18 (Conv2D) (None, 13, 13, 1024) 4718592 leaky_re_lu_17[0][0]
__________________________________________________________________________________________________
norm_18 (BatchNormalization) (None, 13, 13, 1024) 4096 conv_18[0][0]
__________________________________________________________________________________________________
leaky_re_lu_18 (LeakyReLU) (None, 13, 13, 1024) 0 norm_18[0][0]
__________________________________________________________________________________________________
conv_19 (Conv2D) (None, 13, 13, 1024) 9437184 leaky_re_lu_18[0][0]
__________________________________________________________________________________________________
norm_19 (BatchNormalization) (None, 13, 13, 1024) 4096 conv_19[0][0]
__________________________________________________________________________________________________
conv_21 (Conv2D) (None, 26, 26, 64) 32768 leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU) (None, 13, 13, 1024) 0 norm_19[0][0]
__________________________________________________________________________________________________
norm_21 (BatchNormalization) (None, 26, 26, 64) 256 conv_21[0][0]
__________________________________________________________________________________________________
conv_20 (Conv2D) (None, 13, 13, 1024) 9437184 leaky_re_lu_19[0][0]
__________________________________________________________________________________________________
leaky_re_lu_21 (LeakyReLU) (None, 26, 26, 64) 0 norm_21[0][0]
__________________________________________________________________________________________________
norm_20 (BatchNormalization) (None, 13, 13, 1024) 4096 conv_20[0][0]
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, 13, 13, 256) 0 leaky_re_lu_21[0][0]
__________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU) (None, 13, 13, 1024) 0 norm_20[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 13, 13, 1280) 0 lambda_1[0][0]
leaky_re_lu_20[0][0]
__________________________________________________________________________________________________
conv_22 (Conv2D) (None, 13, 13, 1024) 11796480 concatenate_1[0][0]
__________________________________________________________________________________________________
norm_22 (BatchNormalization) (None, 13, 13, 1024) 4096 conv_22[0][0]
__________________________________________________________________________________________________
leaky_re_lu_22 (LeakyReLU) (None, 13, 13, 1024) 0 norm_22[0][0]
__________________________________________________________________________________________________
conv_23 (Conv2D) (None, 13, 13, 100) 102500 leaky_re_lu_22[0][0]
__________________________________________________________________________________________________
final_output (Reshape) (None, 13, 13, 4, 25 0 conv_23[0][0]
__________________________________________________________________________________________________
input_hack (InputLayer) (None, 1, 1, 1, 50, 0
__________________________________________________________________________________________________
hack_layer (Lambda) (None, 13, 13, 4, 25 0 final_output[0][0]
input_hack[0][0]
==================================================================================================
Total params: 50,650,436
Trainable params: 102,500
Non-trainable params: 50,547,936
_________________________________________________________________________________
参考链接
https://fairyonice.github.io/Part_3_Object_Detection_with_Yolo_using_VOC_2012_data_model.html
除非注明，否则均为[半杯茶的小酒杯]原创文章，转载必须以链接形式标明本文链接
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