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FrankJule
License Plate Detect Recognition Via Deep Neural Networks Accuracy Up To 99.9
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05a2501f
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License Plate Detect Recognition Via Deep Neural Networks Accuracy Up To 99.9
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License Plate Detect Recognition Via Deep Neural Networks Accuracy Up To 99.9
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License Plate Detect Recognition Via Deep Neural Networks Accuracy Up To 99.9
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05a2501f
编写于
7月 23, 2018
作者:
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朱本福
提交者:
GitHub
7月 23, 2018
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车牌mtcnn检测模型
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ocr_test/model_platecar/det1.caffemodel
ocr_test/model_platecar/det1.caffemodel
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-0
ocr_test/model_platecar/det1.prototxt
ocr_test/model_platecar/det1.prototxt
+185
-0
ocr_test/model_platecar/det2.caffemodel
ocr_test/model_platecar/det2.caffemodel
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-0
ocr_test/model_platecar/det2.prototxt
ocr_test/model_platecar/det2.prototxt
+228
-0
ocr_test/model_platecar/det2_input.prototxt
ocr_test/model_platecar/det2_input.prototxt
+237
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ocr_test/model_platecar/det3.caffemodel
ocr_test/model_platecar/det3.caffemodel
+0
-0
ocr_test/model_platecar/det3.prototxt
ocr_test/model_platecar/det3.prototxt
+300
-0
ocr_test/model_platecar/det3_input.prototxt
ocr_test/model_platecar/det3_input.prototxt
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未找到文件。
ocr_test/model_platecar/det1.caffemodel
0 → 100644
浏览文件 @
05a2501f
文件已添加
ocr_test/model_platecar/det1.prototxt
0 → 100644
浏览文件 @
05a2501f
name: "PNet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 12
input_dim: 30
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 10
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "PReLU1"
type: "PReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "PReLU2"
type: "PReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "conv3"
type: "Convolution"
bottom: "conv2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "PReLU3"
type: "PReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4j-1"
type: "Convolution"
bottom: "conv3"
top: "conv4-1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 2
kernel_h:1
kernel_w:10
stride:1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4j-2"
type: "Convolution"
bottom: "conv3"
top: "conv4-2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4
kernel_h:1
kernel_w:10
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv4-1"
top: "prob1"
}
\ No newline at end of file
ocr_test/model_platecar/det2.caffemodel
0 → 100644
浏览文件 @
05a2501f
文件已添加
ocr_test/model_platecar/det2.prototxt
0 → 100644
浏览文件 @
05a2501f
name: "RNet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 24
input_dim: 60
################################################
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 28
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu1"
type: "PReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 48
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu2"
type: "PReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
####################################
##################################
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
kernel_size: 2
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu3"
type: "PReLU"
bottom: "conv3"
top: "conv3"
}
###############################
###############################
layer {
name: "conv4i"
type: "InnerProduct"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
num_output: 128
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu4"
type: "PReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5i-1"
type: "InnerProduct"
bottom: "conv4"
top: "conv5-1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
num_output: 2
#kernel_size: 1
#stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv5i-2"
type: "InnerProduct"
bottom: "conv4"
top: "conv5-2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
num_output: 4
#kernel_size: 1
#stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv5-1"
top: "prob1"
}
\ No newline at end of file
ocr_test/model_platecar/det2_input.prototxt
0 → 100644
浏览文件 @
05a2501f
name: "RNet"
layer {
top: "data"
top: "label"
name: "data"
type: "MemoryData"
memory_data_param{
batch_size: 10
height: 24
width: 60
channels: 3
}
include: { phase: TEST }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 28
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu1"
type: "PReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 48
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu2"
type: "PReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
####################################
##################################
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
kernel_size: 2
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu3"
type: "PReLU"
bottom: "conv3"
top: "conv3"
}
###############################
###############################
layer {
name: "conv4i"
type: "InnerProduct"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
num_output: 128
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu4"
type: "PReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5i-1"
type: "InnerProduct"
bottom: "conv4"
top: "conv5-1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
num_output: 2
#kernel_size: 1
#stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv5i-2"
type: "InnerProduct"
bottom: "conv4"
top: "conv5-2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
num_output: 4
#kernel_size: 1
#stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv5-1"
top: "prob1"
}
\ No newline at end of file
ocr_test/model_platecar/det3.caffemodel
0 → 100644
浏览文件 @
05a2501f
文件已添加
ocr_test/model_platecar/det3.prototxt
0 → 100644
浏览文件 @
05a2501f
name: "ONet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 48
input_dim: 120
##################################
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 32
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu1"
type: "PReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu2"
type: "PReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu3"
type: "PReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4"
type: "Convolution"
bottom: "pool3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 128
kernel_size: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu4"
type: "PReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5i"
type: "InnerProduct"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 3
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "drop5i"
type: "Dropout"
bottom: "conv5"
top: "conv5"
dropout_param {
dropout_ratio: 0.25
}
}
layer {
name: "prelu5"
type: "PReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "conv6i-1"
type: "InnerProduct"
bottom: "conv5"
top: "conv6-1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6i-2"
type: "InnerProduct"
bottom: "conv5"
top: "conv6-2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 4
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6-3"
type: "InnerProduct"
bottom: "conv5"
top: "conv6-3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 8
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv6-1"
top: "prob1"
}
ocr_test/model_platecar/det3_input.prototxt
0 → 100644
浏览文件 @
05a2501f
name: "ONet"
layer {
top: "data"
top: "label"
name: "data"
type: "MemoryData"
memory_data_param{
batch_size: 10
height: 48
width: 120
channels: 3
}
include: { phase: TEST }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 32
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu1"
type: "PReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu2"
type: "PReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu3"
type: "PReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4"
type: "Convolution"
bottom: "pool3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 128
kernel_size: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu4"
type: "PReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5i"
type: "InnerProduct"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 3
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "drop5i"
type: "Dropout"
bottom: "conv5"
top: "conv5"
dropout_param {
dropout_ratio: 0.25
}
}
layer {
name: "prelu5"
type: "PReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "conv6i-1"
type: "InnerProduct"
bottom: "conv5"
top: "conv6-1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6i-2"
type: "InnerProduct"
bottom: "conv5"
top: "conv6-2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 4
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6-3"
type: "InnerProduct"
bottom: "conv5"
top: "conv6-3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 8
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv6-1"
top: "prob1"
}
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