Created
December 29, 2018 01:41
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deploy_hands17_baseline.prototxt
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name: "HandBaselineNet" | |
input: "data" | |
input_shape { | |
dim: 1 | |
dim: 1 | |
dim: 96 | |
dim: 96 | |
} | |
layer { | |
name: "conv0" | |
type: "Convolution" | |
bottom: "data" | |
top: "conv0" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 16 | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu0" | |
type: "ReLU" | |
bottom: "conv0" | |
top: "conv0" | |
} | |
layer { | |
name: "conv1" | |
type: "Convolution" | |
bottom: "conv0" | |
top: "conv1" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 16 | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "pool1" | |
type: "Pooling" | |
bottom: "conv1" | |
top: "pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "relu2" | |
type: "ReLU" | |
bottom: "pool1" | |
top: "pool1" | |
} | |
layer { | |
name: "conv2_0" | |
type: "Convolution" | |
bottom: "pool1" | |
top: "conv2_0" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 32 | |
kernel_size: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu2_0" | |
type: "ReLU" | |
bottom: "conv2_0" | |
top: "conv2_0" | |
} | |
layer { | |
name: "conv2" | |
type: "Convolution" | |
bottom: "conv2_0" | |
top: "conv2" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 32 | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu2" | |
type: "ReLU" | |
bottom: "conv2" | |
top: "conv2" | |
} | |
layer { | |
name: "conv3" | |
type: "Convolution" | |
bottom: "conv2" | |
top: "conv3" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 32 | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "res1" | |
type: "Eltwise" | |
bottom: "conv2_0" | |
bottom: "conv3" | |
top: "res1" | |
} | |
layer { | |
name: "pool2" | |
type: "Pooling" | |
bottom: "res1" | |
top: "pool2" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "relu3" | |
type: "ReLU" | |
bottom: "pool2" | |
top: "pool2" | |
} | |
layer { | |
name: "conv3_0" | |
type: "Convolution" | |
bottom: "pool2" | |
top: "conv3_0" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu3_0" | |
type: "ReLU" | |
bottom: "conv3_0" | |
top: "conv3_0" | |
} | |
layer { | |
name: "conv4" | |
type: "Convolution" | |
bottom: "conv3_0" | |
top: "conv4" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu4" | |
type: "ReLU" | |
bottom: "conv4" | |
top: "conv4" | |
} | |
layer { | |
name: "conv5" | |
type: "Convolution" | |
bottom: "conv4" | |
top: "conv5" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "res2" | |
type: "Eltwise" | |
bottom: "conv3_0" | |
bottom: "conv5" | |
top: "res2" | |
} | |
layer { | |
name: "pool3" | |
type: "Pooling" | |
bottom: "res2" | |
top: "pool3" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "relu5" | |
type: "ReLU" | |
bottom: "pool3" | |
top: "pool3" | |
} | |
layer { | |
name: "fc1" | |
type: "InnerProduct" | |
bottom: "pool3" | |
top: "fc1" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 2048 | |
weight_filler { | |
type: "gaussian" | |
std: 0.001 | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu6" | |
type: "ReLU" | |
bottom: "fc1" | |
top: "fc1" | |
} | |
layer { | |
name: "drop1" | |
type: "Dropout" | |
bottom: "fc1" | |
top: "fc1" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc2" | |
type: "InnerProduct" | |
bottom: "fc1" | |
top: "fc2" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 2048 | |
weight_filler { | |
type: "gaussian" | |
std: 0.001 | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "relu7" | |
type: "ReLU" | |
bottom: "fc2" | |
top: "fc2" | |
} | |
layer { | |
name: "drop2" | |
type: "Dropout" | |
bottom: "fc2" | |
top: "fc2" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc3" | |
type: "InnerProduct" | |
bottom: "fc2" | |
top: "predict" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 63 | |
weight_filler { | |
type: "gaussian" | |
std: 0.001 | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} |
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