Last active
February 24, 2017 03:48
-
-
Save oopsno/56c78d46a6c1faae421d2f591a4d4e3d to your computer and use it in GitHub Desktop.
TestNet from Fisher Yu @ PASCAL VOC 2012
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
name: "Fisher Yu's Frontend Test Net @ PASCAL VOC 2012" | |
layer { | |
name: "data" | |
type: "ImageLabelData" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: false | |
crop_size: 500 | |
mean_value: 102.93 | |
mean_value: 111.36 | |
mean_value: 116.52 | |
} | |
image_label_data_param { | |
image_list_path: "PASCAL_VOC2012" | |
label_list_path: "PASCAL_VOC2012" | |
batch_size: 2 | |
shuffle: true | |
label_slice { | |
dim: 16 | |
dim: 16 | |
stride: 8 | |
stride: 8 | |
offset: 186 | |
offset: 186 | |
} | |
padding: REFLECT | |
} | |
} | |
layer { | |
name: "conv1_1" | |
type: "Convolution" | |
bottom: "data" | |
top: "conv1_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu1_1" | |
type: "ReLU" | |
bottom: "conv1_1" | |
top: "conv1_1" | |
} | |
layer { | |
name: "conv1_2" | |
type: "Convolution" | |
bottom: "conv1_1" | |
top: "conv1_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu1_2" | |
type: "ReLU" | |
bottom: "conv1_2" | |
top: "conv1_2" | |
} | |
layer { | |
name: "pool1" | |
type: "Pooling" | |
bottom: "conv1_2" | |
top: "pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv2_1" | |
type: "Convolution" | |
bottom: "pool1" | |
top: "conv2_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu2_1" | |
type: "ReLU" | |
bottom: "conv2_1" | |
top: "conv2_1" | |
} | |
layer { | |
name: "conv2_2" | |
type: "Convolution" | |
bottom: "conv2_1" | |
top: "conv2_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu2_2" | |
type: "ReLU" | |
bottom: "conv2_2" | |
top: "conv2_2" | |
} | |
layer { | |
name: "pool2" | |
type: "Pooling" | |
bottom: "conv2_2" | |
top: "pool2" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv3_1" | |
type: "Convolution" | |
bottom: "pool2" | |
top: "conv3_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_1" | |
type: "ReLU" | |
bottom: "conv3_1" | |
top: "conv3_1" | |
} | |
layer { | |
name: "conv3_2" | |
type: "Convolution" | |
bottom: "conv3_1" | |
top: "conv3_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_2" | |
type: "ReLU" | |
bottom: "conv3_2" | |
top: "conv3_2" | |
} | |
layer { | |
name: "conv3_3" | |
type: "Convolution" | |
bottom: "conv3_2" | |
top: "conv3_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_3" | |
type: "ReLU" | |
bottom: "conv3_3" | |
top: "conv3_3" | |
} | |
layer { | |
name: "pool3" | |
type: "Pooling" | |
bottom: "conv3_3" | |
top: "pool3" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv4_1" | |
type: "Convolution" | |
bottom: "pool3" | |
top: "conv4_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_1" | |
type: "ReLU" | |
bottom: "conv4_1" | |
top: "conv4_1" | |
} | |
layer { | |
name: "conv4_2" | |
type: "Convolution" | |
bottom: "conv4_1" | |
top: "conv4_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_2" | |
type: "ReLU" | |
bottom: "conv4_2" | |
top: "conv4_2" | |
} | |
layer { | |
name: "conv4_3" | |
type: "Convolution" | |
bottom: "conv4_2" | |
top: "conv4_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_3" | |
type: "ReLU" | |
bottom: "conv4_3" | |
top: "conv4_3" | |
} | |
layer { | |
name: "conv5_1" | |
type: "Convolution" | |
bottom: "conv4_3" | |
top: "conv5_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 | |
dilation: 2 | |
} | |
} | |
layer { | |
name: "relu5_1" | |
type: "ReLU" | |
bottom: "conv5_1" | |
top: "conv5_1" | |
} | |
layer { | |
name: "conv5_2" | |
type: "Convolution" | |
bottom: "conv5_1" | |
top: "conv5_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 | |
dilation: 2 | |
} | |
} | |
layer { | |
name: "relu5_2" | |
type: "ReLU" | |
bottom: "conv5_2" | |
top: "conv5_2" | |
} | |
layer { | |
name: "conv5_3" | |
type: "Convolution" | |
bottom: "conv5_2" | |
top: "conv5_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 | |
dilation: 2 | |
} | |
} | |
layer { | |
name: "relu5_3" | |
type: "ReLU" | |
bottom: "conv5_3" | |
top: "conv5_3" | |
} | |
layer { | |
name: "fc6" | |
type: "Convolution" | |
bottom: "conv5_3" | |
top: "fc6" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 4096 | |
kernel_size: 7 | |
dilation: 4 | |
} | |
} | |
layer { | |
name: "relu6" | |
type: "ReLU" | |
bottom: "fc6" | |
top: "fc6" | |
} | |
layer { | |
name: "drop6" | |
type: "Dropout" | |
bottom: "fc6" | |
top: "fc6" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc7" | |
type: "Convolution" | |
bottom: "fc6" | |
top: "fc7" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 4096 | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "relu7" | |
type: "ReLU" | |
bottom: "fc7" | |
top: "fc7" | |
} | |
layer { | |
name: "drop7" | |
type: "Dropout" | |
bottom: "fc7" | |
top: "fc7" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "final" | |
type: "Convolution" | |
bottom: "fc7" | |
top: "final" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.001 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "loss" | |
type: "SoftmaxWithLoss" | |
bottom: "final" | |
bottom: "label" | |
top: "loss" | |
loss_param { | |
ignore_label: 255 | |
normalization: VALID | |
} | |
} | |
layer { | |
name: "accuracy" | |
type: "Accuracy" | |
bottom: "final" | |
bottom: "label" | |
top: "accuracy" | |
accuracy_param { | |
ignore_label: 255 | |
} | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment