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Created September 22, 2016 19:07
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Caffe Training Log
I0922 01:37:07.665272 20192 caffe.cpp:217] Using GPUs 0
I0922 01:37:07.926897 20192 caffe.cpp:222] GPU 0: GRID K520
I0922 01:37:08.066320 20192 solver.cpp:48] Initializing solver from parameters:
test_iter: 50
test_interval: 100
base_lr: 5e-07
display: 10
max_iter: 600000
lr_policy: "step"
gamma: 0.001
momentum: 0.9
weight_decay: 0.0005
stepsize: 5000
snapshot: 100
snapshot_prefix: "examples/imagenet/VGG16_SOD_finetune_nArt"
solver_mode: GPU
device_id: 0
net: "/home/ubuntu/caffe/models/vgg16_finetune/vgg16_train_val.prototxt"
train_state {
level: 0
stage: ""
}
I0922 01:37:08.066504 20192 solver.cpp:91] Creating training net from net file: /home/ubuntu/caffe/models/vgg16_finetune/vgg16_train_val.prototxt
I0922 01:37:08.066902 20192 upgrade_proto.cpp:52] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/ubuntu/caffe/models/vgg16_finetune/vgg16_train_val.prototxt
I0922 01:37:08.067068 20192 upgrade_proto.cpp:60] Successfully upgraded file specified using deprecated V1LayerParameter
I0922 01:37:08.067191 20192 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0922 01:37:08.067224 20192 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0922 01:37:08.067243 20192 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy/top1
I0922 01:37:08.067248 20192 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy/top2
I0922 01:37:08.067539 20192 net.cpp:58] Initializing net from parameters:
name: "VGG16_SOD_finetune"
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
crop_size: 224
mean_file: "/home/ubuntu/caffe/examples/imagenet/n_art_train_mean.binaryproto"
}
data_param {
source: "/home/ubuntu/caffe/examples/imagenet/n_art_train_lmdb"
batch_size: 14
backend: LMDB
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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: "fc8_2"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8_2"
bottom: "label"
include {
phase: TRAIN
}
}
I0922 01:37:08.067703 20192 layer_factory.hpp:77] Creating layer data
I0922 01:37:08.068073 20192 net.cpp:100] Creating Layer data
I0922 01:37:08.068095 20192 net.cpp:408] data -> data
I0922 01:37:08.068120 20192 net.cpp:408] data -> label
I0922 01:37:08.068148 20192 data_transformer.cpp:25] Loading mean file from: /home/ubuntu/caffe/examples/imagenet/n_art_train_mean.binaryproto
I0922 01:37:08.068644 20204 db_lmdb.cpp:35] Opened lmdb /home/ubuntu/caffe/examples/imagenet/n_art_train_lmdb
I0922 01:37:08.080431 20192 data_layer.cpp:41] output data size: 14,3,224,224
I0922 01:37:08.097286 20192 net.cpp:150] Setting up data
I0922 01:37:08.097339 20192 net.cpp:157] Top shape: 14 3 224 224 (2107392)
I0922 01:37:08.097347 20192 net.cpp:157] Top shape: 14 (14)
I0922 01:37:08.097352 20192 net.cpp:165] Memory required for data: 8429624
I0922 01:37:08.097367 20192 layer_factory.hpp:77] Creating layer conv1_1
I0922 01:37:08.097403 20192 net.cpp:100] Creating Layer conv1_1
I0922 01:37:08.097417 20192 net.cpp:434] conv1_1 <- data
I0922 01:37:08.097434 20192 net.cpp:408] conv1_1 -> conv1_1
I0922 01:37:08.283529 20192 net.cpp:150] Setting up conv1_1
I0922 01:37:08.283583 20192 net.cpp:157] Top shape: 14 64 224 224 (44957696)
I0922 01:37:08.283591 20192 net.cpp:165] Memory required for data: 188260408
I0922 01:37:08.283620 20192 layer_factory.hpp:77] Creating layer relu1_1
I0922 01:37:08.283641 20192 net.cpp:100] Creating Layer relu1_1
I0922 01:37:08.283649 20192 net.cpp:434] relu1_1 <- conv1_1
I0922 01:37:08.283661 20192 net.cpp:395] relu1_1 -> conv1_1 (in-place)
I0922 01:37:08.283840 20192 net.cpp:150] Setting up relu1_1
I0922 01:37:08.283859 20192 net.cpp:157] Top shape: 14 64 224 224 (44957696)
I0922 01:37:08.283864 20192 net.cpp:165] Memory required for data: 368091192
I0922 01:37:08.283870 20192 layer_factory.hpp:77] Creating layer conv1_2
I0922 01:37:08.283888 20192 net.cpp:100] Creating Layer conv1_2
I0922 01:37:08.283917 20192 net.cpp:434] conv1_2 <- conv1_1
I0922 01:37:08.283938 20192 net.cpp:408] conv1_2 -> conv1_2
I0922 01:37:08.285308 20192 net.cpp:150] Setting up conv1_2
I0922 01:37:08.285331 20192 net.cpp:157] Top shape: 14 64 224 224 (44957696)
I0922 01:37:08.285336 20192 net.cpp:165] Memory required for data: 547921976
I0922 01:37:08.285351 20192 layer_factory.hpp:77] Creating layer relu1_2
I0922 01:37:08.285367 20192 net.cpp:100] Creating Layer relu1_2
I0922 01:37:08.285372 20192 net.cpp:434] relu1_2 <- conv1_2
I0922 01:37:08.285379 20192 net.cpp:395] relu1_2 -> conv1_2 (in-place)
I0922 01:37:08.285630 20192 net.cpp:150] Setting up relu1_2
I0922 01:37:08.285651 20192 net.cpp:157] Top shape: 14 64 224 224 (44957696)
I0922 01:37:08.285656 20192 net.cpp:165] Memory required for data: 727752760
I0922 01:37:08.285661 20192 layer_factory.hpp:77] Creating layer pool1
I0922 01:37:08.285670 20192 net.cpp:100] Creating Layer pool1
I0922 01:37:08.285677 20192 net.cpp:434] pool1 <- conv1_2
I0922 01:37:08.285686 20192 net.cpp:408] pool1 -> pool1
I0922 01:37:08.285748 20192 net.cpp:150] Setting up pool1
I0922 01:37:08.285779 20192 net.cpp:157] Top shape: 14 64 112 112 (11239424)
I0922 01:37:08.285784 20192 net.cpp:165] Memory required for data: 772710456
I0922 01:37:08.285789 20192 layer_factory.hpp:77] Creating layer conv2_1
I0922 01:37:08.285804 20192 net.cpp:100] Creating Layer conv2_1
I0922 01:37:08.285809 20192 net.cpp:434] conv2_1 <- pool1
I0922 01:37:08.285818 20192 net.cpp:408] conv2_1 -> conv2_1
I0922 01:37:08.288187 20192 net.cpp:150] Setting up conv2_1
I0922 01:37:08.288210 20192 net.cpp:157] Top shape: 14 128 112 112 (22478848)
I0922 01:37:08.288216 20192 net.cpp:165] Memory required for data: 862625848
I0922 01:37:08.288228 20192 layer_factory.hpp:77] Creating layer relu2_1
I0922 01:37:08.288244 20192 net.cpp:100] Creating Layer relu2_1
I0922 01:37:08.288250 20192 net.cpp:434] relu2_1 <- conv2_1
I0922 01:37:08.288257 20192 net.cpp:395] relu2_1 -> conv2_1 (in-place)
I0922 01:37:08.288512 20192 net.cpp:150] Setting up relu2_1
I0922 01:37:08.288533 20192 net.cpp:157] Top shape: 14 128 112 112 (22478848)
I0922 01:37:08.288538 20192 net.cpp:165] Memory required for data: 952541240
I0922 01:37:08.288543 20192 layer_factory.hpp:77] Creating layer conv2_2
I0922 01:37:08.288560 20192 net.cpp:100] Creating Layer conv2_2
I0922 01:37:08.288571 20192 net.cpp:434] conv2_2 <- conv2_1
I0922 01:37:08.288579 20192 net.cpp:408] conv2_2 -> conv2_2
I0922 01:37:08.291851 20192 net.cpp:150] Setting up conv2_2
I0922 01:37:08.291893 20192 net.cpp:157] Top shape: 14 128 112 112 (22478848)
I0922 01:37:08.291905 20192 net.cpp:165] Memory required for data: 1042456632
I0922 01:37:08.291915 20192 layer_factory.hpp:77] Creating layer relu2_2
I0922 01:37:08.291923 20192 net.cpp:100] Creating Layer relu2_2
I0922 01:37:08.291930 20192 net.cpp:434] relu2_2 <- conv2_2
I0922 01:37:08.291939 20192 net.cpp:395] relu2_2 -> conv2_2 (in-place)
I0922 01:37:08.292094 20192 net.cpp:150] Setting up relu2_2
I0922 01:37:08.292114 20192 net.cpp:157] Top shape: 14 128 112 112 (22478848)
I0922 01:37:08.292119 20192 net.cpp:165] Memory required for data: 1132372024
I0922 01:37:08.292124 20192 layer_factory.hpp:77] Creating layer pool2
I0922 01:37:08.292135 20192 net.cpp:100] Creating Layer pool2
I0922 01:37:08.292145 20192 net.cpp:434] pool2 <- conv2_2
I0922 01:37:08.292152 20192 net.cpp:408] pool2 -> pool2
I0922 01:37:08.292201 20192 net.cpp:150] Setting up pool2
I0922 01:37:08.292217 20192 net.cpp:157] Top shape: 14 128 56 56 (5619712)
I0922 01:37:08.292222 20192 net.cpp:165] Memory required for data: 1154850872
I0922 01:37:08.292227 20192 layer_factory.hpp:77] Creating layer conv3_1
I0922 01:37:08.292239 20192 net.cpp:100] Creating Layer conv3_1
I0922 01:37:08.292250 20192 net.cpp:434] conv3_1 <- pool2
I0922 01:37:08.292263 20192 net.cpp:408] conv3_1 -> conv3_1
I0922 01:37:08.297400 20192 net.cpp:150] Setting up conv3_1
I0922 01:37:08.297425 20192 net.cpp:157] Top shape: 14 256 56 56 (11239424)
I0922 01:37:08.297430 20192 net.cpp:165] Memory required for data: 1199808568
I0922 01:37:08.297461 20192 layer_factory.hpp:77] Creating layer relu3_1
I0922 01:37:08.297472 20192 net.cpp:100] Creating Layer relu3_1
I0922 01:37:08.297477 20192 net.cpp:434] relu3_1 <- conv3_1
I0922 01:37:08.297484 20192 net.cpp:395] relu3_1 -> conv3_1 (in-place)
I0922 01:37:08.297747 20192 net.cpp:150] Setting up relu3_1
I0922 01:37:08.297780 20192 net.cpp:157] Top shape: 14 256 56 56 (11239424)
I0922 01:37:08.297785 20192 net.cpp:165] Memory required for data: 1244766264
I0922 01:37:08.297791 20192 layer_factory.hpp:77] Creating layer conv3_2
I0922 01:37:08.297804 20192 net.cpp:100] Creating Layer conv3_2
I0922 01:37:08.297816 20192 net.cpp:434] conv3_2 <- conv3_1
I0922 01:37:08.297827 20192 net.cpp:408] conv3_2 -> conv3_2
I0922 01:37:08.307003 20192 net.cpp:150] Setting up conv3_2
I0922 01:37:08.307027 20192 net.cpp:157] Top shape: 14 256 56 56 (11239424)
I0922 01:37:08.307032 20192 net.cpp:165] Memory required for data: 1289723960
I0922 01:37:08.307041 20192 layer_factory.hpp:77] Creating layer relu3_2
I0922 01:37:08.307054 20192 net.cpp:100] Creating Layer relu3_2
I0922 01:37:08.307065 20192 net.cpp:434] relu3_2 <- conv3_2
I0922 01:37:08.307080 20192 net.cpp:395] relu3_2 -> conv3_2 (in-place)
I0922 01:37:08.307373 20192 net.cpp:150] Setting up relu3_2
I0922 01:37:08.307394 20192 net.cpp:157] Top shape: 14 256 56 56 (11239424)
I0922 01:37:08.307399 20192 net.cpp:165] Memory required for data: 1334681656
I0922 01:37:08.307404 20192 layer_factory.hpp:77] Creating layer conv3_3
I0922 01:37:08.307418 20192 net.cpp:100] Creating Layer conv3_3
I0922 01:37:08.307425 20192 net.cpp:434] conv3_3 <- conv3_2
I0922 01:37:08.307436 20192 net.cpp:408] conv3_3 -> conv3_3
I0922 01:37:08.316660 20192 net.cpp:150] Setting up conv3_3
I0922 01:37:08.316686 20192 net.cpp:157] Top shape: 14 256 56 56 (11239424)
I0922 01:37:08.316692 20192 net.cpp:165] Memory required for data: 1379639352
I0922 01:37:08.316700 20192 layer_factory.hpp:77] Creating layer relu3_3
I0922 01:37:08.316715 20192 net.cpp:100] Creating Layer relu3_3
I0922 01:37:08.316722 20192 net.cpp:434] relu3_3 <- conv3_3
I0922 01:37:08.316730 20192 net.cpp:395] relu3_3 -> conv3_3 (in-place)
I0922 01:37:08.316982 20192 net.cpp:150] Setting up relu3_3
I0922 01:37:08.317001 20192 net.cpp:157] Top shape: 14 256 56 56 (11239424)
I0922 01:37:08.317006 20192 net.cpp:165] Memory required for data: 1424597048
I0922 01:37:08.317011 20192 layer_factory.hpp:77] Creating layer pool3
I0922 01:37:08.317023 20192 net.cpp:100] Creating Layer pool3
I0922 01:37:08.317028 20192 net.cpp:434] pool3 <- conv3_3
I0922 01:37:08.317036 20192 net.cpp:408] pool3 -> pool3
I0922 01:37:08.317095 20192 net.cpp:150] Setting up pool3
I0922 01:37:08.317111 20192 net.cpp:157] Top shape: 14 256 28 28 (2809856)
I0922 01:37:08.317116 20192 net.cpp:165] Memory required for data: 1435836472
I0922 01:37:08.317121 20192 layer_factory.hpp:77] Creating layer conv4_1
I0922 01:37:08.317137 20192 net.cpp:100] Creating Layer conv4_1
I0922 01:37:08.317147 20192 net.cpp:434] conv4_1 <- pool3
I0922 01:37:08.317155 20192 net.cpp:408] conv4_1 -> conv4_1
I0922 01:37:08.335126 20192 net.cpp:150] Setting up conv4_1
I0922 01:37:08.335161 20192 net.cpp:157] Top shape: 14 512 28 28 (5619712)
I0922 01:37:08.335167 20192 net.cpp:165] Memory required for data: 1458315320
I0922 01:37:08.335176 20192 layer_factory.hpp:77] Creating layer relu4_1
I0922 01:37:08.335189 20192 net.cpp:100] Creating Layer relu4_1
I0922 01:37:08.335194 20192 net.cpp:434] relu4_1 <- conv4_1
I0922 01:37:08.335201 20192 net.cpp:395] relu4_1 -> conv4_1 (in-place)
I0922 01:37:08.335475 20192 net.cpp:150] Setting up relu4_1
I0922 01:37:08.335496 20192 net.cpp:157] Top shape: 14 512 28 28 (5619712)
I0922 01:37:08.335501 20192 net.cpp:165] Memory required for data: 1480794168
I0922 01:37:08.335506 20192 layer_factory.hpp:77] Creating layer conv4_2
I0922 01:37:08.335523 20192 net.cpp:100] Creating Layer conv4_2
I0922 01:37:08.335530 20192 net.cpp:434] conv4_2 <- conv4_1
I0922 01:37:08.335541 20192 net.cpp:408] conv4_2 -> conv4_2
I0922 01:37:08.369307 20192 net.cpp:150] Setting up conv4_2
I0922 01:37:08.369338 20192 net.cpp:157] Top shape: 14 512 28 28 (5619712)
I0922 01:37:08.369344 20192 net.cpp:165] Memory required for data: 1503273016
I0922 01:37:08.369360 20192 layer_factory.hpp:77] Creating layer relu4_2
I0922 01:37:08.369377 20192 net.cpp:100] Creating Layer relu4_2
I0922 01:37:08.369384 20192 net.cpp:434] relu4_2 <- conv4_2
I0922 01:37:08.369390 20192 net.cpp:395] relu4_2 -> conv4_2 (in-place)
I0922 01:37:08.369653 20192 net.cpp:150] Setting up relu4_2
I0922 01:37:08.369674 20192 net.cpp:157] Top shape: 14 512 28 28 (5619712)
I0922 01:37:08.369679 20192 net.cpp:165] Memory required for data: 1525751864
I0922 01:37:08.369685 20192 layer_factory.hpp:77] Creating layer conv4_3
I0922 01:37:08.369699 20192 net.cpp:100] Creating Layer conv4_3
I0922 01:37:08.369710 20192 net.cpp:434] conv4_3 <- conv4_2
I0922 01:37:08.369724 20192 net.cpp:408] conv4_3 -> conv4_3
I0922 01:37:08.403653 20192 net.cpp:150] Setting up conv4_3
I0922 01:37:08.403695 20192 net.cpp:157] Top shape: 14 512 28 28 (5619712)
I0922 01:37:08.403702 20192 net.cpp:165] Memory required for data: 1548230712
I0922 01:37:08.403712 20192 layer_factory.hpp:77] Creating layer relu4_3
I0922 01:37:08.403723 20192 net.cpp:100] Creating Layer relu4_3
I0922 01:37:08.403729 20192 net.cpp:434] relu4_3 <- conv4_3
I0922 01:37:08.403738 20192 net.cpp:395] relu4_3 -> conv4_3 (in-place)
I0922 01:37:08.403900 20192 net.cpp:150] Setting up relu4_3
I0922 01:37:08.403919 20192 net.cpp:157] Top shape: 14 512 28 28 (5619712)
I0922 01:37:08.403924 20192 net.cpp:165] Memory required for data: 1570709560
I0922 01:37:08.403929 20192 layer_factory.hpp:77] Creating layer pool4
I0922 01:37:08.403941 20192 net.cpp:100] Creating Layer pool4
I0922 01:37:08.403951 20192 net.cpp:434] pool4 <- conv4_3
I0922 01:37:08.403959 20192 net.cpp:408] pool4 -> pool4
I0922 01:37:08.404018 20192 net.cpp:150] Setting up pool4
I0922 01:37:08.404036 20192 net.cpp:157] Top shape: 14 512 14 14 (1404928)
I0922 01:37:08.404041 20192 net.cpp:165] Memory required for data: 1576329272
I0922 01:37:08.404045 20192 layer_factory.hpp:77] Creating layer conv5_1
I0922 01:37:08.404058 20192 net.cpp:100] Creating Layer conv5_1
I0922 01:37:08.404069 20192 net.cpp:434] conv5_1 <- pool4
I0922 01:37:08.404080 20192 net.cpp:408] conv5_1 -> conv5_1
I0922 01:37:08.438071 20192 net.cpp:150] Setting up conv5_1
I0922 01:37:08.438097 20192 net.cpp:157] Top shape: 14 512 14 14 (1404928)
I0922 01:37:08.438103 20192 net.cpp:165] Memory required for data: 1581948984
I0922 01:37:08.438112 20192 layer_factory.hpp:77] Creating layer relu5_1
I0922 01:37:08.438122 20192 net.cpp:100] Creating Layer relu5_1
I0922 01:37:08.438130 20192 net.cpp:434] relu5_1 <- conv5_1
I0922 01:37:08.438138 20192 net.cpp:395] relu5_1 -> conv5_1 (in-place)
I0922 01:37:08.438405 20192 net.cpp:150] Setting up relu5_1
I0922 01:37:08.438426 20192 net.cpp:157] Top shape: 14 512 14 14 (1404928)
I0922 01:37:08.438431 20192 net.cpp:165] Memory required for data: 1587568696
I0922 01:37:08.438436 20192 layer_factory.hpp:77] Creating layer conv5_2
I0922 01:37:08.438452 20192 net.cpp:100] Creating Layer conv5_2
I0922 01:37:08.438458 20192 net.cpp:434] conv5_2 <- conv5_1
I0922 01:37:08.438469 20192 net.cpp:408] conv5_2 -> conv5_2
I0922 01:37:08.472337 20192 net.cpp:150] Setting up conv5_2
I0922 01:37:08.472368 20192 net.cpp:157] Top shape: 14 512 14 14 (1404928)
I0922 01:37:08.472373 20192 net.cpp:165] Memory required for data: 1593188408
I0922 01:37:08.472383 20192 layer_factory.hpp:77] Creating layer relu5_2
I0922 01:37:08.472393 20192 net.cpp:100] Creating Layer relu5_2
I0922 01:37:08.472398 20192 net.cpp:434] relu5_2 <- conv5_2
I0922 01:37:08.472409 20192 net.cpp:395] relu5_2 -> conv5_2 (in-place)
I0922 01:37:08.472678 20192 net.cpp:150] Setting up relu5_2
I0922 01:37:08.472698 20192 net.cpp:157] Top shape: 14 512 14 14 (1404928)
I0922 01:37:08.472710 20192 net.cpp:165] Memory required for data: 1598808120
I0922 01:37:08.472715 20192 layer_factory.hpp:77] Creating layer conv5_3
I0922 01:37:08.472733 20192 net.cpp:100] Creating Layer conv5_3
I0922 01:37:08.472760 20192 net.cpp:434] conv5_3 <- conv5_2
I0922 01:37:08.472771 20192 net.cpp:408] conv5_3 -> conv5_3
I0922 01:37:08.506752 20192 net.cpp:150] Setting up conv5_3
I0922 01:37:08.506777 20192 net.cpp:157] Top shape: 14 512 14 14 (1404928)
I0922 01:37:08.506783 20192 net.cpp:165] Memory required for data: 1604427832
I0922 01:37:08.506791 20192 layer_factory.hpp:77] Creating layer relu5_3
I0922 01:37:08.506803 20192 net.cpp:100] Creating Layer relu5_3
I0922 01:37:08.506819 20192 net.cpp:434] relu5_3 <- conv5_3
I0922 01:37:08.506826 20192 net.cpp:395] relu5_3 -> conv5_3 (in-place)
I0922 01:37:08.507024 20192 net.cpp:150] Setting up relu5_3
I0922 01:37:08.507046 20192 net.cpp:157] Top shape: 14 512 14 14 (1404928)
I0922 01:37:08.507051 20192 net.cpp:165] Memory required for data: 1610047544
I0922 01:37:08.507056 20192 layer_factory.hpp:77] Creating layer pool5
I0922 01:37:08.507067 20192 net.cpp:100] Creating Layer pool5
I0922 01:37:08.507072 20192 net.cpp:434] pool5 <- conv5_3
I0922 01:37:08.507082 20192 net.cpp:408] pool5 -> pool5
I0922 01:37:08.507146 20192 net.cpp:150] Setting up pool5
I0922 01:37:08.507165 20192 net.cpp:157] Top shape: 14 512 7 7 (351232)
I0922 01:37:08.507172 20192 net.cpp:165] Memory required for data: 1611452472
I0922 01:37:08.507177 20192 layer_factory.hpp:77] Creating layer fc6
I0922 01:37:08.507205 20192 net.cpp:100] Creating Layer fc6
I0922 01:37:08.507216 20192 net.cpp:434] fc6 <- pool5
I0922 01:37:08.507225 20192 net.cpp:408] fc6 -> fc6
I0922 01:37:09.967185 20192 net.cpp:150] Setting up fc6
I0922 01:37:09.967243 20192 net.cpp:157] Top shape: 14 4096 (57344)
I0922 01:37:09.967249 20192 net.cpp:165] Memory required for data: 1611681848
I0922 01:37:09.967264 20192 layer_factory.hpp:77] Creating layer relu6
I0922 01:37:09.967279 20192 net.cpp:100] Creating Layer relu6
I0922 01:37:09.967285 20192 net.cpp:434] relu6 <- fc6
I0922 01:37:09.967299 20192 net.cpp:395] relu6 -> fc6 (in-place)
I0922 01:37:09.967756 20192 net.cpp:150] Setting up relu6
I0922 01:37:09.967784 20192 net.cpp:157] Top shape: 14 4096 (57344)
I0922 01:37:09.967789 20192 net.cpp:165] Memory required for data: 1611911224
I0922 01:37:09.967794 20192 layer_factory.hpp:77] Creating layer drop6
I0922 01:37:09.967809 20192 net.cpp:100] Creating Layer drop6
I0922 01:37:09.967816 20192 net.cpp:434] drop6 <- fc6
I0922 01:37:09.967823 20192 net.cpp:395] drop6 -> fc6 (in-place)
I0922 01:37:09.967869 20192 net.cpp:150] Setting up drop6
I0922 01:37:09.967883 20192 net.cpp:157] Top shape: 14 4096 (57344)
I0922 01:37:09.967888 20192 net.cpp:165] Memory required for data: 1612140600
I0922 01:37:09.967893 20192 layer_factory.hpp:77] Creating layer fc7
I0922 01:37:09.967906 20192 net.cpp:100] Creating Layer fc7
I0922 01:37:09.967916 20192 net.cpp:434] fc7 <- fc6
I0922 01:37:09.967924 20192 net.cpp:408] fc7 -> fc7
I0922 01:37:10.207295 20192 net.cpp:150] Setting up fc7
I0922 01:37:10.207353 20192 net.cpp:157] Top shape: 14 4096 (57344)
I0922 01:37:10.207358 20192 net.cpp:165] Memory required for data: 1612369976
I0922 01:37:10.207373 20192 layer_factory.hpp:77] Creating layer relu7
I0922 01:37:10.207388 20192 net.cpp:100] Creating Layer relu7
I0922 01:37:10.207394 20192 net.cpp:434] relu7 <- fc7
I0922 01:37:10.207403 20192 net.cpp:395] relu7 -> fc7 (in-place)
I0922 01:37:10.207623 20192 net.cpp:150] Setting up relu7
I0922 01:37:10.207643 20192 net.cpp:157] Top shape: 14 4096 (57344)
I0922 01:37:10.207648 20192 net.cpp:165] Memory required for data: 1612599352
I0922 01:37:10.207653 20192 layer_factory.hpp:77] Creating layer drop7
I0922 01:37:10.207664 20192 net.cpp:100] Creating Layer drop7
I0922 01:37:10.207674 20192 net.cpp:434] drop7 <- fc7
I0922 01:37:10.207681 20192 net.cpp:395] drop7 -> fc7 (in-place)
I0922 01:37:10.207721 20192 net.cpp:150] Setting up drop7
I0922 01:37:10.207737 20192 net.cpp:157] Top shape: 14 4096 (57344)
I0922 01:37:10.207741 20192 net.cpp:165] Memory required for data: 1612828728
I0922 01:37:10.207746 20192 layer_factory.hpp:77] Creating layer fc8_2
I0922 01:37:10.207756 20192 net.cpp:100] Creating Layer fc8_2
I0922 01:37:10.207787 20192 net.cpp:434] fc8_2 <- fc7
I0922 01:37:10.207799 20192 net.cpp:408] fc8_2 -> fc8_2
I0922 01:37:10.208037 20192 net.cpp:150] Setting up fc8_2
I0922 01:37:10.208055 20192 net.cpp:157] Top shape: 14 2 (28)
I0922 01:37:10.208060 20192 net.cpp:165] Memory required for data: 1612828840
I0922 01:37:10.208067 20192 layer_factory.hpp:77] Creating layer loss
I0922 01:37:10.208082 20192 net.cpp:100] Creating Layer loss
I0922 01:37:10.208087 20192 net.cpp:434] loss <- fc8_2
I0922 01:37:10.208093 20192 net.cpp:434] loss <- label
I0922 01:37:10.208106 20192 net.cpp:408] loss -> (automatic)
I0922 01:37:10.208122 20192 layer_factory.hpp:77] Creating layer loss
I0922 01:37:10.208590 20192 net.cpp:150] Setting up loss
I0922 01:37:10.208609 20192 net.cpp:157] Top shape: (1)
I0922 01:37:10.208614 20192 net.cpp:160] with loss weight 1
I0922 01:37:10.208647 20192 net.cpp:165] Memory required for data: 1612828844
I0922 01:37:10.208652 20192 net.cpp:226] loss needs backward computation.
I0922 01:37:10.208662 20192 net.cpp:226] fc8_2 needs backward computation.
I0922 01:37:10.208667 20192 net.cpp:226] drop7 needs backward computation.
I0922 01:37:10.208672 20192 net.cpp:226] relu7 needs backward computation.
I0922 01:37:10.208675 20192 net.cpp:226] fc7 needs backward computation.
I0922 01:37:10.208680 20192 net.cpp:226] drop6 needs backward computation.
I0922 01:37:10.208684 20192 net.cpp:226] relu6 needs backward computation.
I0922 01:37:10.208688 20192 net.cpp:226] fc6 needs backward computation.
I0922 01:37:10.208694 20192 net.cpp:226] pool5 needs backward computation.
I0922 01:37:10.208699 20192 net.cpp:226] relu5_3 needs backward computation.
I0922 01:37:10.208703 20192 net.cpp:226] conv5_3 needs backward computation.
I0922 01:37:10.208708 20192 net.cpp:226] relu5_2 needs backward computation.
I0922 01:37:10.208714 20192 net.cpp:226] conv5_2 needs backward computation.
I0922 01:37:10.208719 20192 net.cpp:226] relu5_1 needs backward computation.
I0922 01:37:10.208722 20192 net.cpp:226] conv5_1 needs backward computation.
I0922 01:37:10.208727 20192 net.cpp:226] pool4 needs backward computation.
I0922 01:37:10.208734 20192 net.cpp:226] relu4_3 needs backward computation.
I0922 01:37:10.208737 20192 net.cpp:226] conv4_3 needs backward computation.
I0922 01:37:10.208742 20192 net.cpp:226] relu4_2 needs backward computation.
I0922 01:37:10.208746 20192 net.cpp:226] conv4_2 needs backward computation.
I0922 01:37:10.208755 20192 net.cpp:226] relu4_1 needs backward computation.
I0922 01:37:10.208760 20192 net.cpp:226] conv4_1 needs backward computation.
I0922 01:37:10.208765 20192 net.cpp:226] pool3 needs backward computation.
I0922 01:37:10.208770 20192 net.cpp:226] relu3_3 needs backward computation.
I0922 01:37:10.208775 20192 net.cpp:226] conv3_3 needs backward computation.
I0922 01:37:10.208780 20192 net.cpp:226] relu3_2 needs backward computation.
I0922 01:37:10.208783 20192 net.cpp:226] conv3_2 needs backward computation.
I0922 01:37:10.208789 20192 net.cpp:226] relu3_1 needs backward computation.
I0922 01:37:10.208793 20192 net.cpp:226] conv3_1 needs backward computation.
I0922 01:37:10.208798 20192 net.cpp:226] pool2 needs backward computation.
I0922 01:37:10.208803 20192 net.cpp:226] relu2_2 needs backward computation.
I0922 01:37:10.208808 20192 net.cpp:226] conv2_2 needs backward computation.
I0922 01:37:10.208813 20192 net.cpp:226] relu2_1 needs backward computation.
I0922 01:37:10.208817 20192 net.cpp:226] conv2_1 needs backward computation.
I0922 01:37:10.208822 20192 net.cpp:226] pool1 needs backward computation.
I0922 01:37:10.208828 20192 net.cpp:226] relu1_2 needs backward computation.
I0922 01:37:10.208832 20192 net.cpp:226] conv1_2 needs backward computation.
I0922 01:37:10.208837 20192 net.cpp:226] relu1_1 needs backward computation.
I0922 01:37:10.208842 20192 net.cpp:226] conv1_1 needs backward computation.
I0922 01:37:10.208847 20192 net.cpp:228] data does not need backward computation.
I0922 01:37:10.208873 20192 net.cpp:283] Network initialization done.
I0922 01:37:10.209368 20192 upgrade_proto.cpp:52] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/ubuntu/caffe/models/vgg16_finetune/vgg16_train_val.prototxt
I0922 01:37:10.209463 20192 upgrade_proto.cpp:60] Successfully upgraded file specified using deprecated V1LayerParameter
I0922 01:37:10.209501 20192 solver.cpp:181] Creating test net (#0) specified by net file: /home/ubuntu/caffe/models/vgg16_finetune/vgg16_train_val.prototxt
I0922 01:37:10.209550 20192 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer data
I0922 01:37:10.209579 20192 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer loss
I0922 01:37:10.209900 20192 net.cpp:58] Initializing net from parameters:
name: "VGG16_SOD_finetune"
state {
phase: TEST
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 224
mean_file: "/home/ubuntu/caffe/examples/imagenet/n_art_val_mean.binaryproto"
}
data_param {
source: "/home/ubuntu/caffe/examples/imagenet/n_art_val_lmdb"
batch_size: 6
backend: LMDB
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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: "fc8_2"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8_2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "accuracy/top1"
type: "Accuracy"
bottom: "fc8_2"
bottom: "label"
top: "accuracy@1"
include {
phase: TEST
}
accuracy_param {
top_k: 1
}
}
layer {
name: "accuracy/top2"
type: "Accuracy"
bottom: "fc8_2"
bottom: "label"
top: "accuracy@5"
include {
phase: TEST
}
accuracy_param {
top_k: 2
}
}
I0922 01:37:10.210072 20192 layer_factory.hpp:77] Creating layer data
I0922 01:37:10.210155 20192 net.cpp:100] Creating Layer data
I0922 01:37:10.210166 20192 net.cpp:408] data -> data
I0922 01:37:10.210180 20192 net.cpp:408] data -> label
I0922 01:37:10.210189 20192 data_transformer.cpp:25] Loading mean file from: /home/ubuntu/caffe/examples/imagenet/n_art_val_mean.binaryproto
I0922 01:37:10.211014 20206 db_lmdb.cpp:35] Opened lmdb /home/ubuntu/caffe/examples/imagenet/n_art_val_lmdb
I0922 01:37:10.212326 20192 data_layer.cpp:41] output data size: 6,3,224,224
I0922 01:37:10.219663 20192 net.cpp:150] Setting up data
I0922 01:37:10.219699 20192 net.cpp:157] Top shape: 6 3 224 224 (903168)
I0922 01:37:10.219707 20192 net.cpp:157] Top shape: 6 (6)
I0922 01:37:10.219712 20192 net.cpp:165] Memory required for data: 3612696
I0922 01:37:10.219719 20192 layer_factory.hpp:77] Creating layer label_data_1_split
I0922 01:37:10.219735 20192 net.cpp:100] Creating Layer label_data_1_split
I0922 01:37:10.219741 20192 net.cpp:434] label_data_1_split <- label
I0922 01:37:10.219750 20192 net.cpp:408] label_data_1_split -> label_data_1_split_0
I0922 01:37:10.219763 20192 net.cpp:408] label_data_1_split -> label_data_1_split_1
I0922 01:37:10.219779 20192 net.cpp:408] label_data_1_split -> label_data_1_split_2
I0922 01:37:10.219902 20192 net.cpp:150] Setting up label_data_1_split
I0922 01:37:10.219919 20192 net.cpp:157] Top shape: 6 (6)
I0922 01:37:10.219925 20192 net.cpp:157] Top shape: 6 (6)
I0922 01:37:10.219930 20192 net.cpp:157] Top shape: 6 (6)
I0922 01:37:10.219934 20192 net.cpp:165] Memory required for data: 3612768
I0922 01:37:10.219939 20192 layer_factory.hpp:77] Creating layer conv1_1
I0922 01:37:10.219954 20192 net.cpp:100] Creating Layer conv1_1
I0922 01:37:10.219959 20192 net.cpp:434] conv1_1 <- data
I0922 01:37:10.219969 20192 net.cpp:408] conv1_1 -> conv1_1
I0922 01:37:10.221808 20192 net.cpp:150] Setting up conv1_1
I0922 01:37:10.221830 20192 net.cpp:157] Top shape: 6 64 224 224 (19267584)
I0922 01:37:10.221835 20192 net.cpp:165] Memory required for data: 80683104
I0922 01:37:10.221849 20192 layer_factory.hpp:77] Creating layer relu1_1
I0922 01:37:10.221859 20192 net.cpp:100] Creating Layer relu1_1
I0922 01:37:10.221863 20192 net.cpp:434] relu1_1 <- conv1_1
I0922 01:37:10.221871 20192 net.cpp:395] relu1_1 -> conv1_1 (in-place)
I0922 01:37:10.222051 20192 net.cpp:150] Setting up relu1_1
I0922 01:37:10.222070 20192 net.cpp:157] Top shape: 6 64 224 224 (19267584)
I0922 01:37:10.222075 20192 net.cpp:165] Memory required for data: 157753440
I0922 01:37:10.222080 20192 layer_factory.hpp:77] Creating layer conv1_2
I0922 01:37:10.222093 20192 net.cpp:100] Creating Layer conv1_2
I0922 01:37:10.222098 20192 net.cpp:434] conv1_2 <- conv1_1
I0922 01:37:10.222107 20192 net.cpp:408] conv1_2 -> conv1_2
I0922 01:37:10.224200 20192 net.cpp:150] Setting up conv1_2
I0922 01:37:10.224222 20192 net.cpp:157] Top shape: 6 64 224 224 (19267584)
I0922 01:37:10.224228 20192 net.cpp:165] Memory required for data: 234823776
I0922 01:37:10.224241 20192 layer_factory.hpp:77] Creating layer relu1_2
I0922 01:37:10.224249 20192 net.cpp:100] Creating Layer relu1_2
I0922 01:37:10.224254 20192 net.cpp:434] relu1_2 <- conv1_2
I0922 01:37:10.224266 20192 net.cpp:395] relu1_2 -> conv1_2 (in-place)
I0922 01:37:10.225029 20192 net.cpp:150] Setting up relu1_2
I0922 01:37:10.225050 20192 net.cpp:157] Top shape: 6 64 224 224 (19267584)
I0922 01:37:10.225055 20192 net.cpp:165] Memory required for data: 311894112
I0922 01:37:10.225061 20192 layer_factory.hpp:77] Creating layer pool1
I0922 01:37:10.225070 20192 net.cpp:100] Creating Layer pool1
I0922 01:37:10.225075 20192 net.cpp:434] pool1 <- conv1_2
I0922 01:37:10.225083 20192 net.cpp:408] pool1 -> pool1
I0922 01:37:10.225138 20192 net.cpp:150] Setting up pool1
I0922 01:37:10.225155 20192 net.cpp:157] Top shape: 6 64 112 112 (4816896)
I0922 01:37:10.225160 20192 net.cpp:165] Memory required for data: 331161696
I0922 01:37:10.225164 20192 layer_factory.hpp:77] Creating layer conv2_1
I0922 01:37:10.225177 20192 net.cpp:100] Creating Layer conv2_1
I0922 01:37:10.225188 20192 net.cpp:434] conv2_1 <- pool1
I0922 01:37:10.225195 20192 net.cpp:408] conv2_1 -> conv2_1
I0922 01:37:10.227031 20192 net.cpp:150] Setting up conv2_1
I0922 01:37:10.227054 20192 net.cpp:157] Top shape: 6 128 112 112 (9633792)
I0922 01:37:10.227059 20192 net.cpp:165] Memory required for data: 369696864
I0922 01:37:10.227072 20192 layer_factory.hpp:77] Creating layer relu2_1
I0922 01:37:10.227082 20192 net.cpp:100] Creating Layer relu2_1
I0922 01:37:10.227087 20192 net.cpp:434] relu2_1 <- conv2_1
I0922 01:37:10.227095 20192 net.cpp:395] relu2_1 -> conv2_1 (in-place)
I0922 01:37:10.227859 20192 net.cpp:150] Setting up relu2_1
I0922 01:37:10.227877 20192 net.cpp:157] Top shape: 6 128 112 112 (9633792)
I0922 01:37:10.227883 20192 net.cpp:165] Memory required for data: 408232032
I0922 01:37:10.227888 20192 layer_factory.hpp:77] Creating layer conv2_2
I0922 01:37:10.227900 20192 net.cpp:100] Creating Layer conv2_2
I0922 01:37:10.227905 20192 net.cpp:434] conv2_2 <- conv2_1
I0922 01:37:10.227915 20192 net.cpp:408] conv2_2 -> conv2_2
I0922 01:37:10.231783 20192 net.cpp:150] Setting up conv2_2
I0922 01:37:10.231809 20192 net.cpp:157] Top shape: 6 128 112 112 (9633792)
I0922 01:37:10.231814 20192 net.cpp:165] Memory required for data: 446767200
I0922 01:37:10.231824 20192 layer_factory.hpp:77] Creating layer relu2_2
I0922 01:37:10.231833 20192 net.cpp:100] Creating Layer relu2_2
I0922 01:37:10.231843 20192 net.cpp:434] relu2_2 <- conv2_2
I0922 01:37:10.231853 20192 net.cpp:395] relu2_2 -> conv2_2 (in-place)
I0922 01:37:10.232008 20192 net.cpp:150] Setting up relu2_2
I0922 01:37:10.232028 20192 net.cpp:157] Top shape: 6 128 112 112 (9633792)
I0922 01:37:10.232033 20192 net.cpp:165] Memory required for data: 485302368
I0922 01:37:10.232036 20192 layer_factory.hpp:77] Creating layer pool2
I0922 01:37:10.232051 20192 net.cpp:100] Creating Layer pool2
I0922 01:37:10.232056 20192 net.cpp:434] pool2 <- conv2_2
I0922 01:37:10.232064 20192 net.cpp:408] pool2 -> pool2
I0922 01:37:10.232122 20192 net.cpp:150] Setting up pool2
I0922 01:37:10.232138 20192 net.cpp:157] Top shape: 6 128 56 56 (2408448)
I0922 01:37:10.232143 20192 net.cpp:165] Memory required for data: 494936160
I0922 01:37:10.232148 20192 layer_factory.hpp:77] Creating layer conv3_1
I0922 01:37:10.232183 20192 net.cpp:100] Creating Layer conv3_1
I0922 01:37:10.232189 20192 net.cpp:434] conv3_1 <- pool2
I0922 01:37:10.232198 20192 net.cpp:408] conv3_1 -> conv3_1
I0922 01:37:10.237370 20192 net.cpp:150] Setting up conv3_1
I0922 01:37:10.237395 20192 net.cpp:157] Top shape: 6 256 56 56 (4816896)
I0922 01:37:10.237401 20192 net.cpp:165] Memory required for data: 514203744
I0922 01:37:10.237412 20192 layer_factory.hpp:77] Creating layer relu3_1
I0922 01:37:10.237428 20192 net.cpp:100] Creating Layer relu3_1
I0922 01:37:10.237434 20192 net.cpp:434] relu3_1 <- conv3_1
I0922 01:37:10.237442 20192 net.cpp:395] relu3_1 -> conv3_1 (in-place)
I0922 01:37:10.237709 20192 net.cpp:150] Setting up relu3_1
I0922 01:37:10.237730 20192 net.cpp:157] Top shape: 6 256 56 56 (4816896)
I0922 01:37:10.237735 20192 net.cpp:165] Memory required for data: 533471328
I0922 01:37:10.237740 20192 layer_factory.hpp:77] Creating layer conv3_2
I0922 01:37:10.237771 20192 net.cpp:100] Creating Layer conv3_2
I0922 01:37:10.237777 20192 net.cpp:434] conv3_2 <- conv3_1
I0922 01:37:10.237787 20192 net.cpp:408] conv3_2 -> conv3_2
I0922 01:37:10.246842 20192 net.cpp:150] Setting up conv3_2
I0922 01:37:10.246866 20192 net.cpp:157] Top shape: 6 256 56 56 (4816896)
I0922 01:37:10.246871 20192 net.cpp:165] Memory required for data: 552738912
I0922 01:37:10.246881 20192 layer_factory.hpp:77] Creating layer relu3_2
I0922 01:37:10.246894 20192 net.cpp:100] Creating Layer relu3_2
I0922 01:37:10.246901 20192 net.cpp:434] relu3_2 <- conv3_2
I0922 01:37:10.246907 20192 net.cpp:395] relu3_2 -> conv3_2 (in-place)
I0922 01:37:10.247264 20192 net.cpp:150] Setting up relu3_2
I0922 01:37:10.247285 20192 net.cpp:157] Top shape: 6 256 56 56 (4816896)
I0922 01:37:10.247290 20192 net.cpp:165] Memory required for data: 572006496
I0922 01:37:10.247295 20192 layer_factory.hpp:77] Creating layer conv3_3
I0922 01:37:10.247311 20192 net.cpp:100] Creating Layer conv3_3
I0922 01:37:10.247323 20192 net.cpp:434] conv3_3 <- conv3_2
I0922 01:37:10.247331 20192 net.cpp:408] conv3_3 -> conv3_3
I0922 01:37:10.256526 20192 net.cpp:150] Setting up conv3_3
I0922 01:37:10.256551 20192 net.cpp:157] Top shape: 6 256 56 56 (4816896)
I0922 01:37:10.256556 20192 net.cpp:165] Memory required for data: 591274080
I0922 01:37:10.256564 20192 layer_factory.hpp:77] Creating layer relu3_3
I0922 01:37:10.256578 20192 net.cpp:100] Creating Layer relu3_3
I0922 01:37:10.256583 20192 net.cpp:434] relu3_3 <- conv3_3
I0922 01:37:10.256590 20192 net.cpp:395] relu3_3 -> conv3_3 (in-place)
I0922 01:37:10.256754 20192 net.cpp:150] Setting up relu3_3
I0922 01:37:10.256772 20192 net.cpp:157] Top shape: 6 256 56 56 (4816896)
I0922 01:37:10.256778 20192 net.cpp:165] Memory required for data: 610541664
I0922 01:37:10.256783 20192 layer_factory.hpp:77] Creating layer pool3
I0922 01:37:10.256793 20192 net.cpp:100] Creating Layer pool3
I0922 01:37:10.256798 20192 net.cpp:434] pool3 <- conv3_3
I0922 01:37:10.256804 20192 net.cpp:408] pool3 -> pool3
I0922 01:37:10.256865 20192 net.cpp:150] Setting up pool3
I0922 01:37:10.256880 20192 net.cpp:157] Top shape: 6 256 28 28 (1204224)
I0922 01:37:10.256886 20192 net.cpp:165] Memory required for data: 615358560
I0922 01:37:10.256889 20192 layer_factory.hpp:77] Creating layer conv4_1
I0922 01:37:10.256901 20192 net.cpp:100] Creating Layer conv4_1
I0922 01:37:10.256911 20192 net.cpp:434] conv4_1 <- pool3
I0922 01:37:10.256919 20192 net.cpp:408] conv4_1 -> conv4_1
I0922 01:37:10.274196 20192 net.cpp:150] Setting up conv4_1
I0922 01:37:10.274220 20192 net.cpp:157] Top shape: 6 512 28 28 (2408448)
I0922 01:37:10.274225 20192 net.cpp:165] Memory required for data: 624992352
I0922 01:37:10.274235 20192 layer_factory.hpp:77] Creating layer relu4_1
I0922 01:37:10.274248 20192 net.cpp:100] Creating Layer relu4_1
I0922 01:37:10.274255 20192 net.cpp:434] relu4_1 <- conv4_1
I0922 01:37:10.274262 20192 net.cpp:395] relu4_1 -> conv4_1 (in-place)
I0922 01:37:10.274531 20192 net.cpp:150] Setting up relu4_1
I0922 01:37:10.274554 20192 net.cpp:157] Top shape: 6 512 28 28 (2408448)
I0922 01:37:10.274580 20192 net.cpp:165] Memory required for data: 634626144
I0922 01:37:10.274587 20192 layer_factory.hpp:77] Creating layer conv4_2
I0922 01:37:10.274600 20192 net.cpp:100] Creating Layer conv4_2
I0922 01:37:10.274606 20192 net.cpp:434] conv4_2 <- conv4_1
I0922 01:37:10.274616 20192 net.cpp:408] conv4_2 -> conv4_2
I0922 01:37:10.308038 20192 net.cpp:150] Setting up conv4_2
I0922 01:37:10.308068 20192 net.cpp:157] Top shape: 6 512 28 28 (2408448)
I0922 01:37:10.308073 20192 net.cpp:165] Memory required for data: 644259936
I0922 01:37:10.308087 20192 layer_factory.hpp:77] Creating layer relu4_2
I0922 01:37:10.308104 20192 net.cpp:100] Creating Layer relu4_2
I0922 01:37:10.308110 20192 net.cpp:434] relu4_2 <- conv4_2
I0922 01:37:10.308118 20192 net.cpp:395] relu4_2 -> conv4_2 (in-place)
I0922 01:37:10.308401 20192 net.cpp:150] Setting up relu4_2
I0922 01:37:10.308421 20192 net.cpp:157] Top shape: 6 512 28 28 (2408448)
I0922 01:37:10.308426 20192 net.cpp:165] Memory required for data: 653893728
I0922 01:37:10.308431 20192 layer_factory.hpp:77] Creating layer conv4_3
I0922 01:37:10.308445 20192 net.cpp:100] Creating Layer conv4_3
I0922 01:37:10.308456 20192 net.cpp:434] conv4_3 <- conv4_2
I0922 01:37:10.308468 20192 net.cpp:408] conv4_3 -> conv4_3
I0922 01:37:10.342345 20192 net.cpp:150] Setting up conv4_3
I0922 01:37:10.342377 20192 net.cpp:157] Top shape: 6 512 28 28 (2408448)
I0922 01:37:10.342383 20192 net.cpp:165] Memory required for data: 663527520
I0922 01:37:10.342393 20192 layer_factory.hpp:77] Creating layer relu4_3
I0922 01:37:10.342409 20192 net.cpp:100] Creating Layer relu4_3
I0922 01:37:10.342416 20192 net.cpp:434] relu4_3 <- conv4_3
I0922 01:37:10.342423 20192 net.cpp:395] relu4_3 -> conv4_3 (in-place)
I0922 01:37:10.342603 20192 net.cpp:150] Setting up relu4_3
I0922 01:37:10.342622 20192 net.cpp:157] Top shape: 6 512 28 28 (2408448)
I0922 01:37:10.342627 20192 net.cpp:165] Memory required for data: 673161312
I0922 01:37:10.342631 20192 layer_factory.hpp:77] Creating layer pool4
I0922 01:37:10.342644 20192 net.cpp:100] Creating Layer pool4
I0922 01:37:10.342656 20192 net.cpp:434] pool4 <- conv4_3
I0922 01:37:10.342664 20192 net.cpp:408] pool4 -> pool4
I0922 01:37:10.342726 20192 net.cpp:150] Setting up pool4
I0922 01:37:10.342742 20192 net.cpp:157] Top shape: 6 512 14 14 (602112)
I0922 01:37:10.342747 20192 net.cpp:165] Memory required for data: 675569760
I0922 01:37:10.342751 20192 layer_factory.hpp:77] Creating layer conv5_1
I0922 01:37:10.342766 20192 net.cpp:100] Creating Layer conv5_1
I0922 01:37:10.342777 20192 net.cpp:434] conv5_1 <- pool4
I0922 01:37:10.342787 20192 net.cpp:408] conv5_1 -> conv5_1
I0922 01:37:10.376670 20192 net.cpp:150] Setting up conv5_1
I0922 01:37:10.376699 20192 net.cpp:157] Top shape: 6 512 14 14 (602112)
I0922 01:37:10.376705 20192 net.cpp:165] Memory required for data: 677978208
I0922 01:37:10.376714 20192 layer_factory.hpp:77] Creating layer relu5_1
I0922 01:37:10.376729 20192 net.cpp:100] Creating Layer relu5_1
I0922 01:37:10.376734 20192 net.cpp:434] relu5_1 <- conv5_1
I0922 01:37:10.376744 20192 net.cpp:395] relu5_1 -> conv5_1 (in-place)
I0922 01:37:10.377023 20192 net.cpp:150] Setting up relu5_1
I0922 01:37:10.377048 20192 net.cpp:157] Top shape: 6 512 14 14 (602112)
I0922 01:37:10.377053 20192 net.cpp:165] Memory required for data: 680386656
I0922 01:37:10.377060 20192 layer_factory.hpp:77] Creating layer conv5_2
I0922 01:37:10.377074 20192 net.cpp:100] Creating Layer conv5_2
I0922 01:37:10.377086 20192 net.cpp:434] conv5_2 <- conv5_1
I0922 01:37:10.377095 20192 net.cpp:408] conv5_2 -> conv5_2
I0922 01:37:10.411201 20192 net.cpp:150] Setting up conv5_2
I0922 01:37:10.411238 20192 net.cpp:157] Top shape: 6 512 14 14 (602112)
I0922 01:37:10.411244 20192 net.cpp:165] Memory required for data: 682795104
I0922 01:37:10.411255 20192 layer_factory.hpp:77] Creating layer relu5_2
I0922 01:37:10.411267 20192 net.cpp:100] Creating Layer relu5_2
I0922 01:37:10.411274 20192 net.cpp:434] relu5_2 <- conv5_2
I0922 01:37:10.411290 20192 net.cpp:395] relu5_2 -> conv5_2 (in-place)
I0922 01:37:10.411594 20192 net.cpp:150] Setting up relu5_2
I0922 01:37:10.411615 20192 net.cpp:157] Top shape: 6 512 14 14 (602112)
I0922 01:37:10.411620 20192 net.cpp:165] Memory required for data: 685203552
I0922 01:37:10.411625 20192 layer_factory.hpp:77] Creating layer conv5_3
I0922 01:37:10.411643 20192 net.cpp:100] Creating Layer conv5_3
I0922 01:37:10.411654 20192 net.cpp:434] conv5_3 <- conv5_2
I0922 01:37:10.411665 20192 net.cpp:408] conv5_3 -> conv5_3
I0922 01:37:10.445684 20192 net.cpp:150] Setting up conv5_3
I0922 01:37:10.445714 20192 net.cpp:157] Top shape: 6 512 14 14 (602112)
I0922 01:37:10.445719 20192 net.cpp:165] Memory required for data: 687612000
I0922 01:37:10.445729 20192 layer_factory.hpp:77] Creating layer relu5_3
I0922 01:37:10.445739 20192 net.cpp:100] Creating Layer relu5_3
I0922 01:37:10.445745 20192 net.cpp:434] relu5_3 <- conv5_3
I0922 01:37:10.445767 20192 net.cpp:395] relu5_3 -> conv5_3 (in-place)
I0922 01:37:10.445952 20192 net.cpp:150] Setting up relu5_3
I0922 01:37:10.445971 20192 net.cpp:157] Top shape: 6 512 14 14 (602112)
I0922 01:37:10.445976 20192 net.cpp:165] Memory required for data: 690020448
I0922 01:37:10.445982 20192 layer_factory.hpp:77] Creating layer pool5
I0922 01:37:10.446020 20192 net.cpp:100] Creating Layer pool5
I0922 01:37:10.446038 20192 net.cpp:434] pool5 <- conv5_3
I0922 01:37:10.446046 20192 net.cpp:408] pool5 -> pool5
I0922 01:37:10.446117 20192 net.cpp:150] Setting up pool5
I0922 01:37:10.446135 20192 net.cpp:157] Top shape: 6 512 7 7 (150528)
I0922 01:37:10.446140 20192 net.cpp:165] Memory required for data: 690622560
I0922 01:37:10.446144 20192 layer_factory.hpp:77] Creating layer fc6
I0922 01:37:10.446163 20192 net.cpp:100] Creating Layer fc6
I0922 01:37:10.446171 20192 net.cpp:434] fc6 <- pool5
I0922 01:37:10.446180 20192 net.cpp:408] fc6 -> fc6
I0922 01:37:11.909122 20192 net.cpp:150] Setting up fc6
I0922 01:37:11.909178 20192 net.cpp:157] Top shape: 6 4096 (24576)
I0922 01:37:11.909184 20192 net.cpp:165] Memory required for data: 690720864
I0922 01:37:11.909198 20192 layer_factory.hpp:77] Creating layer relu6
I0922 01:37:11.909214 20192 net.cpp:100] Creating Layer relu6
I0922 01:37:11.909220 20192 net.cpp:434] relu6 <- fc6
I0922 01:37:11.909230 20192 net.cpp:395] relu6 -> fc6 (in-place)
I0922 01:37:11.909706 20192 net.cpp:150] Setting up relu6
I0922 01:37:11.909726 20192 net.cpp:157] Top shape: 6 4096 (24576)
I0922 01:37:11.909732 20192 net.cpp:165] Memory required for data: 690819168
I0922 01:37:11.909737 20192 layer_factory.hpp:77] Creating layer drop6
I0922 01:37:11.909747 20192 net.cpp:100] Creating Layer drop6
I0922 01:37:11.909770 20192 net.cpp:434] drop6 <- fc6
I0922 01:37:11.909780 20192 net.cpp:395] drop6 -> fc6 (in-place)
I0922 01:37:11.909827 20192 net.cpp:150] Setting up drop6
I0922 01:37:11.909849 20192 net.cpp:157] Top shape: 6 4096 (24576)
I0922 01:37:11.909853 20192 net.cpp:165] Memory required for data: 690917472
I0922 01:37:11.909858 20192 layer_factory.hpp:77] Creating layer fc7
I0922 01:37:11.909868 20192 net.cpp:100] Creating Layer fc7
I0922 01:37:11.909874 20192 net.cpp:434] fc7 <- fc6
I0922 01:37:11.909884 20192 net.cpp:408] fc7 -> fc7
I0922 01:37:12.149355 20192 net.cpp:150] Setting up fc7
I0922 01:37:12.149410 20192 net.cpp:157] Top shape: 6 4096 (24576)
I0922 01:37:12.149415 20192 net.cpp:165] Memory required for data: 691015776
I0922 01:37:12.149430 20192 layer_factory.hpp:77] Creating layer relu7
I0922 01:37:12.149444 20192 net.cpp:100] Creating Layer relu7
I0922 01:37:12.149453 20192 net.cpp:434] relu7 <- fc7
I0922 01:37:12.149463 20192 net.cpp:395] relu7 -> fc7 (in-place)
I0922 01:37:12.149724 20192 net.cpp:150] Setting up relu7
I0922 01:37:12.149742 20192 net.cpp:157] Top shape: 6 4096 (24576)
I0922 01:37:12.149747 20192 net.cpp:165] Memory required for data: 691114080
I0922 01:37:12.149770 20192 layer_factory.hpp:77] Creating layer drop7
I0922 01:37:12.149790 20192 net.cpp:100] Creating Layer drop7
I0922 01:37:12.149796 20192 net.cpp:434] drop7 <- fc7
I0922 01:37:12.149806 20192 net.cpp:395] drop7 -> fc7 (in-place)
I0922 01:37:12.149889 20192 net.cpp:150] Setting up drop7
I0922 01:37:12.149906 20192 net.cpp:157] Top shape: 6 4096 (24576)
I0922 01:37:12.149910 20192 net.cpp:165] Memory required for data: 691212384
I0922 01:37:12.149915 20192 layer_factory.hpp:77] Creating layer fc8_2
I0922 01:37:12.149929 20192 net.cpp:100] Creating Layer fc8_2
I0922 01:37:12.149938 20192 net.cpp:434] fc8_2 <- fc7
I0922 01:37:12.149948 20192 net.cpp:408] fc8_2 -> fc8_2
I0922 01:37:12.150209 20192 net.cpp:150] Setting up fc8_2
I0922 01:37:12.150228 20192 net.cpp:157] Top shape: 6 2 (12)
I0922 01:37:12.150233 20192 net.cpp:165] Memory required for data: 691212432
I0922 01:37:12.150241 20192 layer_factory.hpp:77] Creating layer fc8_2_fc8_2_0_split
I0922 01:37:12.150259 20192 net.cpp:100] Creating Layer fc8_2_fc8_2_0_split
I0922 01:37:12.150264 20192 net.cpp:434] fc8_2_fc8_2_0_split <- fc8_2
I0922 01:37:12.150270 20192 net.cpp:408] fc8_2_fc8_2_0_split -> fc8_2_fc8_2_0_split_0
I0922 01:37:12.150285 20192 net.cpp:408] fc8_2_fc8_2_0_split -> fc8_2_fc8_2_0_split_1
I0922 01:37:12.150296 20192 net.cpp:408] fc8_2_fc8_2_0_split -> fc8_2_fc8_2_0_split_2
I0922 01:37:12.150382 20192 net.cpp:150] Setting up fc8_2_fc8_2_0_split
I0922 01:37:12.150396 20192 net.cpp:157] Top shape: 6 2 (12)
I0922 01:37:12.150403 20192 net.cpp:157] Top shape: 6 2 (12)
I0922 01:37:12.150408 20192 net.cpp:157] Top shape: 6 2 (12)
I0922 01:37:12.150411 20192 net.cpp:165] Memory required for data: 691212576
I0922 01:37:12.150416 20192 layer_factory.hpp:77] Creating layer accuracy
I0922 01:37:12.150425 20192 net.cpp:100] Creating Layer accuracy
I0922 01:37:12.150430 20192 net.cpp:434] accuracy <- fc8_2_fc8_2_0_split_0
I0922 01:37:12.150436 20192 net.cpp:434] accuracy <- label_data_1_split_0
I0922 01:37:12.150444 20192 net.cpp:408] accuracy -> accuracy
I0922 01:37:12.150456 20192 net.cpp:150] Setting up accuracy
I0922 01:37:12.150462 20192 net.cpp:157] Top shape: (1)
I0922 01:37:12.150466 20192 net.cpp:165] Memory required for data: 691212580
I0922 01:37:12.150470 20192 layer_factory.hpp:77] Creating layer accuracy/top1
I0922 01:37:12.150486 20192 net.cpp:100] Creating Layer accuracy/top1
I0922 01:37:12.150498 20192 net.cpp:434] accuracy/top1 <- fc8_2_fc8_2_0_split_1
I0922 01:37:12.150504 20192 net.cpp:434] accuracy/top1 <- label_data_1_split_1
I0922 01:37:12.150519 20192 net.cpp:408] accuracy/top1 -> accuracy@1
I0922 01:37:12.150535 20192 net.cpp:150] Setting up accuracy/top1
I0922 01:37:12.150542 20192 net.cpp:157] Top shape: (1)
I0922 01:37:12.150547 20192 net.cpp:165] Memory required for data: 691212584
I0922 01:37:12.150552 20192 layer_factory.hpp:77] Creating layer accuracy/top2
I0922 01:37:12.150559 20192 net.cpp:100] Creating Layer accuracy/top2
I0922 01:37:12.150564 20192 net.cpp:434] accuracy/top2 <- fc8_2_fc8_2_0_split_2
I0922 01:37:12.150571 20192 net.cpp:434] accuracy/top2 <- label_data_1_split_2
I0922 01:37:12.150584 20192 net.cpp:408] accuracy/top2 -> accuracy@5
I0922 01:37:12.150593 20192 net.cpp:150] Setting up accuracy/top2
I0922 01:37:12.150604 20192 net.cpp:157] Top shape: (1)
I0922 01:37:12.150609 20192 net.cpp:165] Memory required for data: 691212588
I0922 01:37:12.150614 20192 net.cpp:228] accuracy/top2 does not need backward computation.
I0922 01:37:12.150619 20192 net.cpp:228] accuracy/top1 does not need backward computation.
I0922 01:37:12.150625 20192 net.cpp:228] accuracy does not need backward computation.
I0922 01:37:12.150630 20192 net.cpp:228] fc8_2_fc8_2_0_split does not need backward computation.
I0922 01:37:12.150635 20192 net.cpp:228] fc8_2 does not need backward computation.
I0922 01:37:12.150641 20192 net.cpp:228] drop7 does not need backward computation.
I0922 01:37:12.150645 20192 net.cpp:228] relu7 does not need backward computation.
I0922 01:37:12.150650 20192 net.cpp:228] fc7 does not need backward computation.
I0922 01:37:12.150655 20192 net.cpp:228] drop6 does not need backward computation.
I0922 01:37:12.150660 20192 net.cpp:228] relu6 does not need backward computation.
I0922 01:37:12.150665 20192 net.cpp:228] fc6 does not need backward computation.
I0922 01:37:12.150686 20192 net.cpp:228] pool5 does not need backward computation.
I0922 01:37:12.150691 20192 net.cpp:228] relu5_3 does not need backward computation.
I0922 01:37:12.150696 20192 net.cpp:228] conv5_3 does not need backward computation.
I0922 01:37:12.150701 20192 net.cpp:228] relu5_2 does not need backward computation.
I0922 01:37:12.150704 20192 net.cpp:228] conv5_2 does not need backward computation.
I0922 01:37:12.150709 20192 net.cpp:228] relu5_1 does not need backward computation.
I0922 01:37:12.150713 20192 net.cpp:228] conv5_1 does not need backward computation.
I0922 01:37:12.150718 20192 net.cpp:228] pool4 does not need backward computation.
I0922 01:37:12.150725 20192 net.cpp:228] relu4_3 does not need backward computation.
I0922 01:37:12.150730 20192 net.cpp:228] conv4_3 does not need backward computation.
I0922 01:37:12.150734 20192 net.cpp:228] relu4_2 does not need backward computation.
I0922 01:37:12.150739 20192 net.cpp:228] conv4_2 does not need backward computation.
I0922 01:37:12.150743 20192 net.cpp:228] relu4_1 does not need backward computation.
I0922 01:37:12.150748 20192 net.cpp:228] conv4_1 does not need backward computation.
I0922 01:37:12.150753 20192 net.cpp:228] pool3 does not need backward computation.
I0922 01:37:12.150756 20192 net.cpp:228] relu3_3 does not need backward computation.
I0922 01:37:12.150761 20192 net.cpp:228] conv3_3 does not need backward computation.
I0922 01:37:12.150765 20192 net.cpp:228] relu3_2 does not need backward computation.
I0922 01:37:12.150774 20192 net.cpp:228] conv3_2 does not need backward computation.
I0922 01:37:12.150781 20192 net.cpp:228] relu3_1 does not need backward computation.
I0922 01:37:12.150790 20192 net.cpp:228] conv3_1 does not need backward computation.
I0922 01:37:12.150799 20192 net.cpp:228] pool2 does not need backward computation.
I0922 01:37:12.150813 20192 net.cpp:228] relu2_2 does not need backward computation.
I0922 01:37:12.150818 20192 net.cpp:228] conv2_2 does not need backward computation.
I0922 01:37:12.150823 20192 net.cpp:228] relu2_1 does not need backward computation.
I0922 01:37:12.150827 20192 net.cpp:228] conv2_1 does not need backward computation.
I0922 01:37:12.150832 20192 net.cpp:228] pool1 does not need backward computation.
I0922 01:37:12.150837 20192 net.cpp:228] relu1_2 does not need backward computation.
I0922 01:37:12.150841 20192 net.cpp:228] conv1_2 does not need backward computation.
I0922 01:37:12.150846 20192 net.cpp:228] relu1_1 does not need backward computation.
I0922 01:37:12.150851 20192 net.cpp:228] conv1_1 does not need backward computation.
I0922 01:37:12.150856 20192 net.cpp:228] label_data_1_split does not need backward computation.
I0922 01:37:12.150861 20192 net.cpp:228] data does not need backward computation.
I0922 01:37:12.150866 20192 net.cpp:270] This network produces output accuracy
I0922 01:37:12.150871 20192 net.cpp:270] This network produces output accuracy@1
I0922 01:37:12.150876 20192 net.cpp:270] This network produces output accuracy@5
I0922 01:37:12.150907 20192 net.cpp:283] Network initialization done.
I0922 01:37:12.151080 20192 solver.cpp:60] Solver scaffolding done.
I0922 01:37:12.152520 20192 caffe.cpp:155] Finetuning from models/vgg16_finetune/VGG16_SOD_finetune.caffemodel
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:537] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 538683157
I0922 01:37:12.437592 20192 upgrade_proto.cpp:66] Attempting to upgrade input file specified using deprecated input fields: models/vgg16_finetune/VGG16_SOD_finetune.caffemodel
I0922 01:37:12.437625 20192 upgrade_proto.cpp:69] Successfully upgraded file specified using deprecated input fields.
W0922 01:37:12.437631 20192 upgrade_proto.cpp:71] Note that future Caffe releases will only support input layers and not input fields.
I0922 01:37:12.546931 20192 net.cpp:761] Ignoring source layer fc8-SOD100
I0922 01:37:12.546983 20192 net.cpp:761] Ignoring source layer prob
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:537] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 538683157
I0922 01:37:12.838675 20192 upgrade_proto.cpp:66] Attempting to upgrade input file specified using deprecated input fields: models/vgg16_finetune/VGG16_SOD_finetune.caffemodel
I0922 01:37:12.838701 20192 upgrade_proto.cpp:69] Successfully upgraded file specified using deprecated input fields.
W0922 01:37:12.839686 20192 upgrade_proto.cpp:71] Note that future Caffe releases will only support input layers and not input fields.
I0922 01:37:12.948000 20192 net.cpp:761] Ignoring source layer fc8-SOD100
I0922 01:37:12.948070 20192 net.cpp:761] Ignoring source layer prob
I0922 01:37:12.950181 20192 caffe.cpp:251] Starting Optimization
I0922 01:37:12.950207 20192 solver.cpp:279] Solving VGG16_SOD_finetune
I0922 01:37:12.950217 20192 solver.cpp:280] Learning Rate Policy: step
I0922 01:37:12.953189 20192 solver.cpp:337] Iteration 0, Testing net (#0)
I0922 01:37:13.029263 20192 net.cpp:693] Ignoring source layer loss
I0922 01:37:26.690140 20192 solver.cpp:404] Test net output #0: accuracy = 0.556667
I0922 01:37:26.690194 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.556667
I0922 01:37:26.690208 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.57
I0922 01:37:27.342373 20192 solver.cpp:228] Iteration 0, loss = 8.82171
I0922 01:37:27.342439 20192 sgd_solver.cpp:106] Iteration 0, lr = 5e-07
I0922 01:37:49.030778 20192 solver.cpp:228] Iteration 10, loss = 1.84607
I0922 01:37:49.030894 20192 sgd_solver.cpp:106] Iteration 10, lr = 5e-07
I0922 01:38:10.705724 20192 solver.cpp:228] Iteration 20, loss = 8.65133
I0922 01:38:10.705793 20192 sgd_solver.cpp:106] Iteration 20, lr = 5e-07
I0922 01:38:32.393301 20192 solver.cpp:228] Iteration 30, loss = 3.87182
I0922 01:38:32.393446 20192 sgd_solver.cpp:106] Iteration 30, lr = 5e-07
I0922 01:38:54.070945 20192 solver.cpp:228] Iteration 40, loss = 2.89628
I0922 01:38:54.071002 20192 sgd_solver.cpp:106] Iteration 40, lr = 5e-07
I0922 01:39:15.751794 20192 solver.cpp:228] Iteration 50, loss = 2.04628
I0922 01:39:15.751929 20192 sgd_solver.cpp:106] Iteration 50, lr = 5e-07
I0922 01:39:37.449551 20192 solver.cpp:228] Iteration 60, loss = 7.27449
I0922 01:39:37.449605 20192 sgd_solver.cpp:106] Iteration 60, lr = 5e-07
I0922 01:39:59.133090 20192 solver.cpp:228] Iteration 70, loss = 1.29722
I0922 01:39:59.133214 20192 sgd_solver.cpp:106] Iteration 70, lr = 5e-07
I0922 01:40:20.821912 20192 solver.cpp:228] Iteration 80, loss = 8.42239
I0922 01:40:20.821976 20192 sgd_solver.cpp:106] Iteration 80, lr = 5e-07
I0922 01:40:42.503674 20192 solver.cpp:228] Iteration 90, loss = 7.34234
I0922 01:40:42.503815 20192 sgd_solver.cpp:106] Iteration 90, lr = 5e-07
I0922 01:41:02.019404 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_100.caffemodel
I0922 01:41:06.100354 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_100.solverstate
I0922 01:41:07.229460 20192 solver.cpp:337] Iteration 100, Testing net (#0)
I0922 01:41:07.229521 20192 net.cpp:693] Ignoring source layer loss
I0922 01:41:20.801637 20192 solver.cpp:404] Test net output #0: accuracy = 0.313333
I0922 01:41:20.801770 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.313333
I0922 01:41:20.801784 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.56
I0922 01:41:21.425405 20192 solver.cpp:228] Iteration 100, loss = 1.79094
I0922 01:41:21.425452 20192 sgd_solver.cpp:106] Iteration 100, lr = 5e-07
I0922 01:41:43.115429 20192 solver.cpp:228] Iteration 110, loss = 2.44279
I0922 01:41:43.115489 20192 sgd_solver.cpp:106] Iteration 110, lr = 5e-07
I0922 01:42:04.806414 20192 solver.cpp:228] Iteration 120, loss = 7.52758
I0922 01:42:04.806546 20192 sgd_solver.cpp:106] Iteration 120, lr = 5e-07
I0922 01:42:26.496688 20192 solver.cpp:228] Iteration 130, loss = 1.6676
I0922 01:42:26.496742 20192 sgd_solver.cpp:106] Iteration 130, lr = 5e-07
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I0922 01:44:56.151361 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_200.caffemodel
I0922 01:45:00.030613 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_200.solverstate
I0922 01:45:01.152221 20192 solver.cpp:337] Iteration 200, Testing net (#0)
I0922 01:45:01.152279 20192 net.cpp:693] Ignoring source layer loss
I0922 01:45:14.721511 20192 solver.cpp:404] Test net output #0: accuracy = 0.283333
I0922 01:45:14.721563 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.283333
I0922 01:45:14.721571 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.553333
I0922 01:45:15.343565 20192 solver.cpp:228] Iteration 200, loss = 1.87637
I0922 01:45:15.343611 20192 sgd_solver.cpp:106] Iteration 200, lr = 5e-07
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I0922 01:48:50.044467 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_300.caffemodel
I0922 01:48:53.902447 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_300.solverstate
I0922 01:48:55.024320 20192 solver.cpp:337] Iteration 300, Testing net (#0)
I0922 01:48:55.024377 20192 net.cpp:693] Ignoring source layer loss
I0922 01:49:08.591318 20192 solver.cpp:404] Test net output #0: accuracy = 0.363333
I0922 01:49:08.591444 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.363333
I0922 01:49:08.591455 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.59
I0922 01:49:09.217864 20192 solver.cpp:228] Iteration 300, loss = 7.57986
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I0922 01:52:43.914285 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_400.caffemodel
I0922 01:52:47.783277 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_400.solverstate
I0922 01:52:48.909245 20192 solver.cpp:337] Iteration 400, Testing net (#0)
I0922 01:52:48.909307 20192 net.cpp:693] Ignoring source layer loss
I0922 01:53:02.478171 20192 solver.cpp:404] Test net output #0: accuracy = 0.343333
I0922 01:53:02.478221 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.343333
I0922 01:53:02.478230 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.506667
I0922 01:53:03.099495 20192 solver.cpp:228] Iteration 400, loss = 7.18754
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I0922 01:56:37.749680 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_500.caffemodel
I0922 01:56:41.625918 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_500.solverstate
I0922 01:56:42.752894 20192 solver.cpp:337] Iteration 500, Testing net (#0)
I0922 01:56:42.752952 20192 net.cpp:693] Ignoring source layer loss
I0922 01:56:56.318064 20192 solver.cpp:404] Test net output #0: accuracy = 0.436667
I0922 01:56:56.318193 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.436667
I0922 01:56:56.318204 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.58
I0922 01:56:56.940533 20192 solver.cpp:228] Iteration 500, loss = 1.26916
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I0922 02:00:31.664950 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_600.caffemodel
I0922 02:00:35.547122 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_600.solverstate
I0922 02:00:36.669003 20192 solver.cpp:337] Iteration 600, Testing net (#0)
I0922 02:00:36.669062 20192 net.cpp:693] Ignoring source layer loss
I0922 02:00:50.241605 20192 solver.cpp:404] Test net output #0: accuracy = 0.51
I0922 02:00:50.241654 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.51
I0922 02:00:50.241662 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.56
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I0922 02:04:25.536478 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_700.caffemodel
I0922 02:04:29.418000 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_700.solverstate
I0922 02:04:30.537816 20192 solver.cpp:337] Iteration 700, Testing net (#0)
I0922 02:04:30.537871 20192 net.cpp:693] Ignoring source layer loss
I0922 02:04:44.112304 20192 solver.cpp:404] Test net output #0: accuracy = 0.343333
I0922 02:04:44.112454 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.343333
I0922 02:04:44.112465 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.55
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I0922 02:08:19.454905 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_800.caffemodel
I0922 02:08:23.311015 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_800.solverstate
I0922 02:08:24.428520 20192 solver.cpp:337] Iteration 800, Testing net (#0)
I0922 02:08:24.428577 20192 net.cpp:693] Ignoring source layer loss
I0922 02:08:37.998280 20192 solver.cpp:404] Test net output #0: accuracy = 0.463333
I0922 02:08:37.998335 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.463333
I0922 02:08:37.998343 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.586667
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I0922 02:12:13.323681 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_900.caffemodel
I0922 02:12:17.211520 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_900.solverstate
I0922 02:12:18.322170 20192 solver.cpp:337] Iteration 900, Testing net (#0)
I0922 02:12:18.322227 20192 net.cpp:693] Ignoring source layer loss
I0922 02:12:31.897240 20192 solver.cpp:404] Test net output #0: accuracy = 0.356667
I0922 02:12:31.897408 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.356667
I0922 02:12:31.897423 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.506667
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I0922 02:15:47.704697 20192 sgd_solver.cpp:106] Iteration 990, lr = 5e-07
I0922 02:16:07.222769 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1000.caffemodel
I0922 02:16:11.105559 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1000.solverstate
I0922 02:16:12.227474 20192 solver.cpp:337] Iteration 1000, Testing net (#0)
I0922 02:16:12.227527 20192 net.cpp:693] Ignoring source layer loss
I0922 02:16:25.800796 20192 solver.cpp:404] Test net output #0: accuracy = 0.546667
I0922 02:16:25.800846 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.546667
I0922 02:16:25.800858 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.59
I0922 02:16:26.425688 20192 solver.cpp:228] Iteration 1000, loss = 1.46272
I0922 02:16:26.425731 20192 sgd_solver.cpp:106] Iteration 1000, lr = 5e-07
I0922 02:16:48.110170 20192 solver.cpp:228] Iteration 1010, loss = 1.46132
I0922 02:16:48.110297 20192 sgd_solver.cpp:106] Iteration 1010, lr = 5e-07
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I0922 02:17:09.796093 20192 sgd_solver.cpp:106] Iteration 1020, lr = 5e-07
I0922 02:17:31.471506 20192 solver.cpp:228] Iteration 1030, loss = 2.02222
I0922 02:17:31.471644 20192 sgd_solver.cpp:106] Iteration 1030, lr = 5e-07
I0922 02:17:53.157194 20192 solver.cpp:228] Iteration 1040, loss = 1.33403
I0922 02:17:53.157253 20192 sgd_solver.cpp:106] Iteration 1040, lr = 5e-07
I0922 02:18:14.845609 20192 solver.cpp:228] Iteration 1050, loss = 6.74352
I0922 02:18:14.845736 20192 sgd_solver.cpp:106] Iteration 1050, lr = 5e-07
I0922 02:18:36.535383 20192 solver.cpp:228] Iteration 1060, loss = 7.62754
I0922 02:18:36.535450 20192 sgd_solver.cpp:106] Iteration 1060, lr = 5e-07
I0922 02:18:58.230749 20192 solver.cpp:228] Iteration 1070, loss = 0.8054
I0922 02:18:58.230885 20192 sgd_solver.cpp:106] Iteration 1070, lr = 5e-07
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I0922 02:19:41.627343 20192 solver.cpp:228] Iteration 1090, loss = 7.11778
I0922 02:19:41.627468 20192 sgd_solver.cpp:106] Iteration 1090, lr = 5e-07
I0922 02:20:01.141665 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1100.caffemodel
I0922 02:20:05.020057 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1100.solverstate
I0922 02:20:06.136786 20192 solver.cpp:337] Iteration 1100, Testing net (#0)
I0922 02:20:06.136843 20192 net.cpp:693] Ignoring source layer loss
I0922 02:20:19.708964 20192 solver.cpp:404] Test net output #0: accuracy = 0.41
I0922 02:20:19.709147 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.41
I0922 02:20:19.709159 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.54
I0922 02:20:20.331017 20192 solver.cpp:228] Iteration 1100, loss = 7.7275
I0922 02:20:20.331068 20192 sgd_solver.cpp:106] Iteration 1100, lr = 5e-07
I0922 02:20:42.011168 20192 solver.cpp:228] Iteration 1110, loss = 1.21157
I0922 02:20:42.011230 20192 sgd_solver.cpp:106] Iteration 1110, lr = 5e-07
I0922 02:21:03.698889 20192 solver.cpp:228] Iteration 1120, loss = 7.08423
I0922 02:21:03.699020 20192 sgd_solver.cpp:106] Iteration 1120, lr = 5e-07
I0922 02:21:25.378567 20192 solver.cpp:228] Iteration 1130, loss = 7.24719
I0922 02:21:25.378617 20192 sgd_solver.cpp:106] Iteration 1130, lr = 5e-07
I0922 02:21:47.063626 20192 solver.cpp:228] Iteration 1140, loss = 0.941929
I0922 02:21:47.063745 20192 sgd_solver.cpp:106] Iteration 1140, lr = 5e-07
I0922 02:22:08.740000 20192 solver.cpp:228] Iteration 1150, loss = 0.996181
I0922 02:22:08.740053 20192 sgd_solver.cpp:106] Iteration 1150, lr = 5e-07
I0922 02:22:30.435076 20192 solver.cpp:228] Iteration 1160, loss = 0.621067
I0922 02:22:30.435215 20192 sgd_solver.cpp:106] Iteration 1160, lr = 5e-07
I0922 02:22:52.121646 20192 solver.cpp:228] Iteration 1170, loss = 1.28843
I0922 02:22:52.121701 20192 sgd_solver.cpp:106] Iteration 1170, lr = 5e-07
I0922 02:23:13.793426 20192 solver.cpp:228] Iteration 1180, loss = 7.58851
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I0922 02:23:35.482444 20192 solver.cpp:228] Iteration 1190, loss = 1.78271
I0922 02:23:35.482501 20192 sgd_solver.cpp:106] Iteration 1190, lr = 5e-07
I0922 02:23:54.998775 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1200.caffemodel
I0922 02:23:58.877717 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1200.solverstate
I0922 02:23:59.997350 20192 solver.cpp:337] Iteration 1200, Testing net (#0)
I0922 02:23:59.997411 20192 net.cpp:693] Ignoring source layer loss
I0922 02:24:13.570315 20192 solver.cpp:404] Test net output #0: accuracy = 0.493333
I0922 02:24:13.570361 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.493333
I0922 02:24:13.570370 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.553333
I0922 02:24:14.194548 20192 solver.cpp:228] Iteration 1200, loss = 1.22608
I0922 02:24:14.194597 20192 sgd_solver.cpp:106] Iteration 1200, lr = 5e-07
I0922 02:24:35.867651 20192 solver.cpp:228] Iteration 1210, loss = 7.41951
I0922 02:24:35.867792 20192 sgd_solver.cpp:106] Iteration 1210, lr = 5e-07
I0922 02:24:57.555593 20192 solver.cpp:228] Iteration 1220, loss = 7.20371
I0922 02:24:57.555649 20192 sgd_solver.cpp:106] Iteration 1220, lr = 5e-07
I0922 02:25:19.243866 20192 solver.cpp:228] Iteration 1230, loss = 0.953153
I0922 02:25:19.243988 20192 sgd_solver.cpp:106] Iteration 1230, lr = 5e-07
I0922 02:25:40.932745 20192 solver.cpp:228] Iteration 1240, loss = 1.42086
I0922 02:25:40.932806 20192 sgd_solver.cpp:106] Iteration 1240, lr = 5e-07
I0922 02:26:02.622503 20192 solver.cpp:228] Iteration 1250, loss = 7.75839
I0922 02:26:02.622617 20192 sgd_solver.cpp:106] Iteration 1250, lr = 5e-07
I0922 02:26:24.313287 20192 solver.cpp:228] Iteration 1260, loss = 7.90705
I0922 02:26:24.313336 20192 sgd_solver.cpp:106] Iteration 1260, lr = 5e-07
I0922 02:26:46.005094 20192 solver.cpp:228] Iteration 1270, loss = 8.00056
I0922 02:26:46.005211 20192 sgd_solver.cpp:106] Iteration 1270, lr = 5e-07
I0922 02:27:07.692965 20192 solver.cpp:228] Iteration 1280, loss = 1.0436
I0922 02:27:07.693017 20192 sgd_solver.cpp:106] Iteration 1280, lr = 5e-07
I0922 02:27:29.388129 20192 solver.cpp:228] Iteration 1290, loss = 7.41834
I0922 02:27:29.388273 20192 sgd_solver.cpp:106] Iteration 1290, lr = 5e-07
I0922 02:27:48.895799 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1300.caffemodel
I0922 02:27:52.793560 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1300.solverstate
I0922 02:27:53.918193 20192 solver.cpp:337] Iteration 1300, Testing net (#0)
I0922 02:27:53.918246 20192 net.cpp:693] Ignoring source layer loss
I0922 02:28:07.487591 20192 solver.cpp:404] Test net output #0: accuracy = 0.526667
I0922 02:28:07.487711 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.526667
I0922 02:28:07.487722 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.596667
I0922 02:28:08.108968 20192 solver.cpp:228] Iteration 1300, loss = 7.07765
I0922 02:28:08.109026 20192 sgd_solver.cpp:106] Iteration 1300, lr = 5e-07
I0922 02:28:29.791476 20192 solver.cpp:228] Iteration 1310, loss = 0.989928
I0922 02:28:29.791533 20192 sgd_solver.cpp:106] Iteration 1310, lr = 5e-07
I0922 02:28:51.467869 20192 solver.cpp:228] Iteration 1320, loss = 7.45614
I0922 02:28:51.468006 20192 sgd_solver.cpp:106] Iteration 1320, lr = 5e-07
I0922 02:29:13.150943 20192 solver.cpp:228] Iteration 1330, loss = 7.44739
I0922 02:29:13.150997 20192 sgd_solver.cpp:106] Iteration 1330, lr = 5e-07
I0922 02:29:34.834890 20192 solver.cpp:228] Iteration 1340, loss = 7.59586
I0922 02:29:34.835014 20192 sgd_solver.cpp:106] Iteration 1340, lr = 5e-07
I0922 02:29:56.519991 20192 solver.cpp:228] Iteration 1350, loss = 8.60641
I0922 02:29:56.520043 20192 sgd_solver.cpp:106] Iteration 1350, lr = 5e-07
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I0922 02:30:18.207638 20192 sgd_solver.cpp:106] Iteration 1360, lr = 5e-07
I0922 02:30:39.891413 20192 solver.cpp:228] Iteration 1370, loss = 1.38225
I0922 02:30:39.891476 20192 sgd_solver.cpp:106] Iteration 1370, lr = 5e-07
I0922 02:31:01.569058 20192 solver.cpp:228] Iteration 1380, loss = 8.26437
I0922 02:31:01.569181 20192 sgd_solver.cpp:106] Iteration 1380, lr = 5e-07
I0922 02:31:23.257077 20192 solver.cpp:228] Iteration 1390, loss = 2.10678
I0922 02:31:23.257133 20192 sgd_solver.cpp:106] Iteration 1390, lr = 5e-07
I0922 02:31:42.775655 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1400.caffemodel
I0922 02:31:46.652645 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1400.solverstate
I0922 02:31:47.778007 20192 solver.cpp:337] Iteration 1400, Testing net (#0)
I0922 02:31:47.778064 20192 net.cpp:693] Ignoring source layer loss
I0922 02:32:01.352669 20192 solver.cpp:404] Test net output #0: accuracy = 0.483333
I0922 02:32:01.352715 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.483333
I0922 02:32:01.352725 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.51
I0922 02:32:01.976089 20192 solver.cpp:228] Iteration 1400, loss = 1.78196
I0922 02:32:01.976141 20192 sgd_solver.cpp:106] Iteration 1400, lr = 5e-07
I0922 02:32:23.659358 20192 solver.cpp:228] Iteration 1410, loss = 9.21893
I0922 02:32:23.659481 20192 sgd_solver.cpp:106] Iteration 1410, lr = 5e-07
I0922 02:32:45.341306 20192 solver.cpp:228] Iteration 1420, loss = 1.77955
I0922 02:32:45.341357 20192 sgd_solver.cpp:106] Iteration 1420, lr = 5e-07
I0922 02:33:07.020743 20192 solver.cpp:228] Iteration 1430, loss = 1.70664
I0922 02:33:07.020867 20192 sgd_solver.cpp:106] Iteration 1430, lr = 5e-07
I0922 02:33:28.704629 20192 solver.cpp:228] Iteration 1440, loss = 2.34412
I0922 02:33:28.704684 20192 sgd_solver.cpp:106] Iteration 1440, lr = 5e-07
I0922 02:33:50.384531 20192 solver.cpp:228] Iteration 1450, loss = 1.02627
I0922 02:33:50.384655 20192 sgd_solver.cpp:106] Iteration 1450, lr = 5e-07
I0922 02:34:12.067344 20192 solver.cpp:228] Iteration 1460, loss = 7.4725
I0922 02:34:12.067399 20192 sgd_solver.cpp:106] Iteration 1460, lr = 5e-07
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I0922 02:34:33.760321 20192 sgd_solver.cpp:106] Iteration 1470, lr = 5e-07
I0922 02:34:55.449208 20192 solver.cpp:228] Iteration 1480, loss = 10.2266
I0922 02:34:55.449262 20192 sgd_solver.cpp:106] Iteration 1480, lr = 5e-07
I0922 02:35:17.136941 20192 solver.cpp:228] Iteration 1490, loss = 4.7369
I0922 02:35:17.137055 20192 sgd_solver.cpp:106] Iteration 1490, lr = 5e-07
I0922 02:35:36.659608 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1500.caffemodel
I0922 02:35:40.543851 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1500.solverstate
I0922 02:35:41.672538 20192 solver.cpp:337] Iteration 1500, Testing net (#0)
I0922 02:35:41.672595 20192 net.cpp:693] Ignoring source layer loss
I0922 02:35:55.242097 20192 solver.cpp:404] Test net output #0: accuracy = 0.0566667
I0922 02:35:55.242218 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.0566667
I0922 02:35:55.242231 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.59
I0922 02:35:55.865361 20192 solver.cpp:228] Iteration 1500, loss = 9.47331
I0922 02:35:55.865420 20192 sgd_solver.cpp:106] Iteration 1500, lr = 5e-07
I0922 02:36:17.549490 20192 solver.cpp:228] Iteration 1510, loss = 5.41269
I0922 02:36:17.549551 20192 sgd_solver.cpp:106] Iteration 1510, lr = 5e-07
I0922 02:36:39.229354 20192 solver.cpp:228] Iteration 1520, loss = 11.8449
I0922 02:36:39.229477 20192 sgd_solver.cpp:106] Iteration 1520, lr = 5e-07
I0922 02:37:00.921643 20192 solver.cpp:228] Iteration 1530, loss = 8.34462
I0922 02:37:00.921705 20192 sgd_solver.cpp:106] Iteration 1530, lr = 5e-07
I0922 02:37:22.609479 20192 solver.cpp:228] Iteration 1540, loss = 16.1043
I0922 02:37:22.609602 20192 sgd_solver.cpp:106] Iteration 1540, lr = 5e-07
I0922 02:37:44.289552 20192 solver.cpp:228] Iteration 1550, loss = 3.80488
I0922 02:37:44.289613 20192 sgd_solver.cpp:106] Iteration 1550, lr = 5e-07
I0922 02:38:05.979727 20192 solver.cpp:228] Iteration 1560, loss = 10.3579
I0922 02:38:05.979862 20192 sgd_solver.cpp:106] Iteration 1560, lr = 5e-07
I0922 02:38:27.670666 20192 solver.cpp:228] Iteration 1570, loss = 25.2206
I0922 02:38:27.670719 20192 sgd_solver.cpp:106] Iteration 1570, lr = 5e-07
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I0922 02:38:49.365597 20192 sgd_solver.cpp:106] Iteration 1580, lr = 5e-07
I0922 02:39:11.050189 20192 solver.cpp:228] Iteration 1590, loss = 50.7224
I0922 02:39:11.050245 20192 sgd_solver.cpp:106] Iteration 1590, lr = 5e-07
I0922 02:39:30.576694 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1600.caffemodel
I0922 02:39:34.448443 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1600.solverstate
I0922 02:39:35.566329 20192 solver.cpp:337] Iteration 1600, Testing net (#0)
I0922 02:39:35.566392 20192 net.cpp:693] Ignoring source layer loss
I0922 02:39:49.137400 20192 solver.cpp:404] Test net output #0: accuracy = 0.536667
I0922 02:39:49.137450 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.536667
I0922 02:39:49.137457 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.536667
I0922 02:39:49.759608 20192 solver.cpp:228] Iteration 1600, loss = 57.3979
I0922 02:39:49.759656 20192 sgd_solver.cpp:106] Iteration 1600, lr = 5e-07
I0922 02:40:11.437719 20192 solver.cpp:228] Iteration 1610, loss = 49.9066
I0922 02:40:11.437858 20192 sgd_solver.cpp:106] Iteration 1610, lr = 5e-07
I0922 02:40:33.118825 20192 solver.cpp:228] Iteration 1620, loss = 11.3814
I0922 02:40:33.118880 20192 sgd_solver.cpp:106] Iteration 1620, lr = 5e-07
I0922 02:40:54.811624 20192 solver.cpp:228] Iteration 1630, loss = 43.6683
I0922 02:40:54.811744 20192 sgd_solver.cpp:106] Iteration 1630, lr = 5e-07
I0922 02:41:16.492822 20192 solver.cpp:228] Iteration 1640, loss = 16.8202
I0922 02:41:16.492877 20192 sgd_solver.cpp:106] Iteration 1640, lr = 5e-07
I0922 02:41:38.165634 20192 solver.cpp:228] Iteration 1650, loss = 11.6205
I0922 02:41:38.165802 20192 sgd_solver.cpp:106] Iteration 1650, lr = 5e-07
I0922 02:41:59.854276 20192 solver.cpp:228] Iteration 1660, loss = 7.24523
I0922 02:41:59.854326 20192 sgd_solver.cpp:106] Iteration 1660, lr = 5e-07
I0922 02:42:21.550976 20192 solver.cpp:228] Iteration 1670, loss = 6.66865
I0922 02:42:21.551115 20192 sgd_solver.cpp:106] Iteration 1670, lr = 5e-07
I0922 02:42:43.243430 20192 solver.cpp:228] Iteration 1680, loss = 1.70623
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I0922 02:43:04.919531 20192 solver.cpp:228] Iteration 1690, loss = 2.07439
I0922 02:43:04.919662 20192 sgd_solver.cpp:106] Iteration 1690, lr = 5e-07
I0922 02:43:24.441213 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1700.caffemodel
I0922 02:43:28.321859 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1700.solverstate
I0922 02:43:29.439419 20192 solver.cpp:337] Iteration 1700, Testing net (#0)
I0922 02:43:29.439476 20192 net.cpp:693] Ignoring source layer loss
I0922 02:43:43.022119 20192 solver.cpp:404] Test net output #0: accuracy = 0.523333
I0922 02:43:43.022254 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.523333
I0922 02:43:43.022265 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.57
I0922 02:43:43.645609 20192 solver.cpp:228] Iteration 1700, loss = 1.57065
I0922 02:43:43.645670 20192 sgd_solver.cpp:106] Iteration 1700, lr = 5e-07
I0922 02:44:05.332638 20192 solver.cpp:228] Iteration 1710, loss = 7.68604
I0922 02:44:05.332696 20192 sgd_solver.cpp:106] Iteration 1710, lr = 5e-07
I0922 02:44:27.016898 20192 solver.cpp:228] Iteration 1720, loss = 1.37333
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I0922 02:44:48.698350 20192 solver.cpp:228] Iteration 1730, loss = 0.997813
I0922 02:44:48.698415 20192 sgd_solver.cpp:106] Iteration 1730, lr = 5e-07
I0922 02:45:10.379380 20192 solver.cpp:228] Iteration 1740, loss = 7.71136
I0922 02:45:10.379508 20192 sgd_solver.cpp:106] Iteration 1740, lr = 5e-07
I0922 02:45:32.066867 20192 solver.cpp:228] Iteration 1750, loss = 8.03612
I0922 02:45:32.066923 20192 sgd_solver.cpp:106] Iteration 1750, lr = 5e-07
I0922 02:45:53.766355 20192 solver.cpp:228] Iteration 1760, loss = 1.2078
I0922 02:45:53.766489 20192 sgd_solver.cpp:106] Iteration 1760, lr = 5e-07
I0922 02:46:15.454743 20192 solver.cpp:228] Iteration 1770, loss = 7.39881
I0922 02:46:15.454797 20192 sgd_solver.cpp:106] Iteration 1770, lr = 5e-07
I0922 02:46:37.132457 20192 solver.cpp:228] Iteration 1780, loss = 1.89981
I0922 02:46:37.132575 20192 sgd_solver.cpp:106] Iteration 1780, lr = 5e-07
I0922 02:46:58.817821 20192 solver.cpp:228] Iteration 1790, loss = 7.46176
I0922 02:46:58.817875 20192 sgd_solver.cpp:106] Iteration 1790, lr = 5e-07
I0922 02:47:18.323820 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1800.caffemodel
I0922 02:47:22.198632 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1800.solverstate
I0922 02:47:23.318060 20192 solver.cpp:337] Iteration 1800, Testing net (#0)
I0922 02:47:23.318119 20192 net.cpp:693] Ignoring source layer loss
I0922 02:47:36.888865 20192 solver.cpp:404] Test net output #0: accuracy = 0.00666667
I0922 02:47:36.888916 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.00666667
I0922 02:47:36.888923 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.593333
I0922 02:47:37.511713 20192 solver.cpp:228] Iteration 1800, loss = 7.44644
I0922 02:47:37.511766 20192 sgd_solver.cpp:106] Iteration 1800, lr = 5e-07
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I0922 02:47:59.199417 20192 sgd_solver.cpp:106] Iteration 1810, lr = 5e-07
I0922 02:48:20.875993 20192 solver.cpp:228] Iteration 1820, loss = 1.27699
I0922 02:48:20.876047 20192 sgd_solver.cpp:106] Iteration 1820, lr = 5e-07
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I0922 02:49:04.235891 20192 solver.cpp:228] Iteration 1840, loss = 7.3143
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I0922 02:49:47.613268 20192 sgd_solver.cpp:106] Iteration 1860, lr = 5e-07
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I0922 02:51:12.175688 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1900.caffemodel
I0922 02:51:16.055665 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_1900.solverstate
I0922 02:51:17.174374 20192 solver.cpp:337] Iteration 1900, Testing net (#0)
I0922 02:51:17.174432 20192 net.cpp:693] Ignoring source layer loss
I0922 02:51:30.743861 20192 solver.cpp:404] Test net output #0: accuracy = 0.496667
I0922 02:51:30.743978 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.496667
I0922 02:51:30.743988 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.503333
I0922 02:51:31.366503 20192 solver.cpp:228] Iteration 1900, loss = 2.19369
I0922 02:51:31.366552 20192 sgd_solver.cpp:106] Iteration 1900, lr = 5e-07
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I0922 02:51:53.045184 20192 sgd_solver.cpp:106] Iteration 1910, lr = 5e-07
I0922 02:52:14.720286 20192 solver.cpp:228] Iteration 1920, loss = 1.92892
I0922 02:52:14.720422 20192 sgd_solver.cpp:106] Iteration 1920, lr = 5e-07
I0922 02:52:36.400501 20192 solver.cpp:228] Iteration 1930, loss = 8.71882
I0922 02:52:36.400557 20192 sgd_solver.cpp:106] Iteration 1930, lr = 5e-07
I0922 02:52:58.096454 20192 solver.cpp:228] Iteration 1940, loss = 7.41462
I0922 02:52:58.096575 20192 sgd_solver.cpp:106] Iteration 1940, lr = 5e-07
I0922 02:53:19.782616 20192 solver.cpp:228] Iteration 1950, loss = 2.70413
I0922 02:53:19.782673 20192 sgd_solver.cpp:106] Iteration 1950, lr = 5e-07
I0922 02:53:41.468271 20192 solver.cpp:228] Iteration 1960, loss = 3.95671
I0922 02:53:41.468394 20192 sgd_solver.cpp:106] Iteration 1960, lr = 5e-07
I0922 02:54:03.160725 20192 solver.cpp:228] Iteration 1970, loss = 7.87776
I0922 02:54:03.160776 20192 sgd_solver.cpp:106] Iteration 1970, lr = 5e-07
I0922 02:54:24.853914 20192 solver.cpp:228] Iteration 1980, loss = 1.6035
I0922 02:54:24.854044 20192 sgd_solver.cpp:106] Iteration 1980, lr = 5e-07
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I0922 02:54:46.553200 20192 sgd_solver.cpp:106] Iteration 1990, lr = 5e-07
I0922 02:55:06.070456 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2000.caffemodel
I0922 02:55:09.948032 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2000.solverstate
I0922 02:55:11.062129 20192 solver.cpp:337] Iteration 2000, Testing net (#0)
I0922 02:55:11.062186 20192 net.cpp:693] Ignoring source layer loss
I0922 02:55:24.637744 20192 solver.cpp:404] Test net output #0: accuracy = 0.0866667
I0922 02:55:24.637814 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.0866667
I0922 02:55:24.637823 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.583333
I0922 02:55:25.260288 20192 solver.cpp:228] Iteration 2000, loss = 8.50182
I0922 02:55:25.260335 20192 sgd_solver.cpp:106] Iteration 2000, lr = 5e-07
I0922 02:55:46.960001 20192 solver.cpp:228] Iteration 2010, loss = 4.77259
I0922 02:55:46.965315 20192 sgd_solver.cpp:106] Iteration 2010, lr = 5e-07
I0922 02:56:08.647270 20192 solver.cpp:228] Iteration 2020, loss = 8.11068
I0922 02:56:08.647323 20192 sgd_solver.cpp:106] Iteration 2020, lr = 5e-07
I0922 02:56:30.327682 20192 solver.cpp:228] Iteration 2030, loss = 5.24831
I0922 02:56:30.327826 20192 sgd_solver.cpp:106] Iteration 2030, lr = 5e-07
I0922 02:56:52.013770 20192 solver.cpp:228] Iteration 2040, loss = 17.1808
I0922 02:56:52.013828 20192 sgd_solver.cpp:106] Iteration 2040, lr = 5e-07
I0922 02:57:13.701064 20192 solver.cpp:228] Iteration 2050, loss = 13.3935
I0922 02:57:13.701187 20192 sgd_solver.cpp:106] Iteration 2050, lr = 5e-07
I0922 02:57:35.388416 20192 solver.cpp:228] Iteration 2060, loss = 5.85538
I0922 02:57:35.388471 20192 sgd_solver.cpp:106] Iteration 2060, lr = 5e-07
I0922 02:57:57.082919 20192 solver.cpp:228] Iteration 2070, loss = 16.0034
I0922 02:57:57.083039 20192 sgd_solver.cpp:106] Iteration 2070, lr = 5e-07
I0922 02:58:18.777732 20192 solver.cpp:228] Iteration 2080, loss = 11.0271
I0922 02:58:18.777797 20192 sgd_solver.cpp:106] Iteration 2080, lr = 5e-07
I0922 02:58:40.473978 20192 solver.cpp:228] Iteration 2090, loss = 18.6787
I0922 02:58:40.474119 20192 sgd_solver.cpp:106] Iteration 2090, lr = 5e-07
I0922 02:59:00.005137 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2100.caffemodel
I0922 02:59:03.874054 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2100.solverstate
I0922 02:59:04.990639 20192 solver.cpp:337] Iteration 2100, Testing net (#0)
I0922 02:59:04.990694 20192 net.cpp:693] Ignoring source layer loss
I0922 02:59:18.560803 20192 solver.cpp:404] Test net output #0: accuracy = 0
I0922 02:59:18.560922 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0
I0922 02:59:18.560933 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.526667
I0922 02:59:19.183478 20192 solver.cpp:228] Iteration 2100, loss = 23.2672
I0922 02:59:19.183526 20192 sgd_solver.cpp:106] Iteration 2100, lr = 5e-07
I0922 02:59:40.868093 20192 solver.cpp:228] Iteration 2110, loss = 15.0521
I0922 02:59:40.868157 20192 sgd_solver.cpp:106] Iteration 2110, lr = 5e-07
I0922 03:00:02.553920 20192 solver.cpp:228] Iteration 2120, loss = 29.3779
I0922 03:00:02.554052 20192 sgd_solver.cpp:106] Iteration 2120, lr = 5e-07
I0922 03:00:24.240768 20192 solver.cpp:228] Iteration 2130, loss = 12.1205
I0922 03:00:24.240824 20192 sgd_solver.cpp:106] Iteration 2130, lr = 5e-07
I0922 03:00:45.921686 20192 solver.cpp:228] Iteration 2140, loss = 11.9971
I0922 03:00:45.921829 20192 sgd_solver.cpp:106] Iteration 2140, lr = 5e-07
I0922 03:01:07.613570 20192 solver.cpp:228] Iteration 2150, loss = 20.9826
I0922 03:01:07.613626 20192 sgd_solver.cpp:106] Iteration 2150, lr = 5e-07
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I0922 03:01:29.307273 20192 sgd_solver.cpp:106] Iteration 2160, lr = 5e-07
I0922 03:01:50.996248 20192 solver.cpp:228] Iteration 2170, loss = 22.585
I0922 03:01:50.996304 20192 sgd_solver.cpp:106] Iteration 2170, lr = 5e-07
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I0922 03:02:12.683189 20192 sgd_solver.cpp:106] Iteration 2180, lr = 5e-07
I0922 03:02:34.371830 20192 solver.cpp:228] Iteration 2190, loss = 37.43
I0922 03:02:34.371886 20192 sgd_solver.cpp:106] Iteration 2190, lr = 5e-07
I0922 03:02:53.908305 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2200.caffemodel
I0922 03:02:57.805238 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2200.solverstate
I0922 03:02:58.935834 20192 solver.cpp:337] Iteration 2200, Testing net (#0)
I0922 03:02:58.935900 20192 net.cpp:693] Ignoring source layer loss
I0922 03:03:12.505753 20192 solver.cpp:404] Test net output #0: accuracy = 0.58
I0922 03:03:12.505800 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.58
I0922 03:03:12.505807 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.58
I0922 03:03:13.128203 20192 solver.cpp:228] Iteration 2200, loss = 36.0441
I0922 03:03:13.128254 20192 sgd_solver.cpp:106] Iteration 2200, lr = 5e-07
I0922 03:03:34.810039 20192 solver.cpp:228] Iteration 2210, loss = 28.6941
I0922 03:03:34.810204 20192 sgd_solver.cpp:106] Iteration 2210, lr = 5e-07
I0922 03:03:56.493953 20192 solver.cpp:228] Iteration 2220, loss = 22.9694
I0922 03:03:56.494004 20192 sgd_solver.cpp:106] Iteration 2220, lr = 5e-07
I0922 03:04:18.194773 20192 solver.cpp:228] Iteration 2230, loss = 5.59057
I0922 03:04:18.194910 20192 sgd_solver.cpp:106] Iteration 2230, lr = 5e-07
I0922 03:04:39.885287 20192 solver.cpp:228] Iteration 2240, loss = 8.26999
I0922 03:04:39.885337 20192 sgd_solver.cpp:106] Iteration 2240, lr = 5e-07
I0922 03:05:01.567905 20192 solver.cpp:228] Iteration 2250, loss = 3.03484
I0922 03:05:01.568030 20192 sgd_solver.cpp:106] Iteration 2250, lr = 5e-07
I0922 03:05:23.260545 20192 solver.cpp:228] Iteration 2260, loss = 2.19272
I0922 03:05:23.260597 20192 sgd_solver.cpp:106] Iteration 2260, lr = 5e-07
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I0922 03:05:44.952013 20192 sgd_solver.cpp:106] Iteration 2270, lr = 5e-07
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I0922 03:06:06.653693 20192 sgd_solver.cpp:106] Iteration 2280, lr = 5e-07
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I0922 03:06:28.341068 20192 sgd_solver.cpp:106] Iteration 2290, lr = 5e-07
I0922 03:06:47.862215 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2300.caffemodel
I0922 03:06:51.756708 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2300.solverstate
I0922 03:06:52.889695 20192 solver.cpp:337] Iteration 2300, Testing net (#0)
I0922 03:06:52.889770 20192 net.cpp:693] Ignoring source layer loss
I0922 03:07:06.460934 20192 solver.cpp:404] Test net output #0: accuracy = 0.463333
I0922 03:07:06.461061 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.463333
I0922 03:07:06.461072 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.59
I0922 03:07:07.082567 20192 solver.cpp:228] Iteration 2300, loss = 6.13545
I0922 03:07:07.082614 20192 sgd_solver.cpp:106] Iteration 2300, lr = 5e-07
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I0922 03:07:28.770479 20192 sgd_solver.cpp:106] Iteration 2310, lr = 5e-07
I0922 03:07:50.454978 20192 solver.cpp:228] Iteration 2320, loss = 2.39849
I0922 03:07:50.455102 20192 sgd_solver.cpp:106] Iteration 2320, lr = 5e-07
I0922 03:08:12.138594 20192 solver.cpp:228] Iteration 2330, loss = 2.98157
I0922 03:08:12.138648 20192 sgd_solver.cpp:106] Iteration 2330, lr = 5e-07
I0922 03:08:33.823130 20192 solver.cpp:228] Iteration 2340, loss = 8.25696
I0922 03:08:33.823251 20192 sgd_solver.cpp:106] Iteration 2340, lr = 5e-07
I0922 03:08:55.507939 20192 solver.cpp:228] Iteration 2350, loss = 3.99713
I0922 03:08:55.507994 20192 sgd_solver.cpp:106] Iteration 2350, lr = 5e-07
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I0922 03:09:17.188724 20192 sgd_solver.cpp:106] Iteration 2360, lr = 5e-07
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I0922 03:09:38.884879 20192 sgd_solver.cpp:106] Iteration 2370, lr = 5e-07
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I0922 03:10:22.273293 20192 solver.cpp:228] Iteration 2390, loss = 9.77534
I0922 03:10:22.273349 20192 sgd_solver.cpp:106] Iteration 2390, lr = 5e-07
I0922 03:10:41.796305 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2400.caffemodel
I0922 03:10:45.624991 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2400.solverstate
I0922 03:10:46.742518 20192 solver.cpp:337] Iteration 2400, Testing net (#0)
I0922 03:10:46.742571 20192 net.cpp:693] Ignoring source layer loss
I0922 03:11:00.313906 20192 solver.cpp:404] Test net output #0: accuracy = 0.516667
I0922 03:11:00.313957 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.516667
I0922 03:11:00.313966 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.516667
I0922 03:11:00.939339 20192 solver.cpp:228] Iteration 2400, loss = 26.0489
I0922 03:11:00.939402 20192 sgd_solver.cpp:106] Iteration 2400, lr = 5e-07
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I0922 03:11:22.634274 20192 sgd_solver.cpp:106] Iteration 2410, lr = 5e-07
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I0922 03:11:44.320976 20192 sgd_solver.cpp:106] Iteration 2420, lr = 5e-07
I0922 03:12:06.010506 20192 solver.cpp:228] Iteration 2430, loss = 43.6683
I0922 03:12:06.010658 20192 sgd_solver.cpp:106] Iteration 2430, lr = 5e-07
I0922 03:12:27.695050 20192 solver.cpp:228] Iteration 2440, loss = 37.43
I0922 03:12:27.695102 20192 sgd_solver.cpp:106] Iteration 2440, lr = 5e-07
I0922 03:12:49.394265 20192 solver.cpp:228] Iteration 2450, loss = 6.13226
I0922 03:12:49.394393 20192 sgd_solver.cpp:106] Iteration 2450, lr = 5e-07
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I0922 03:13:11.083923 20192 sgd_solver.cpp:106] Iteration 2460, lr = 5e-07
I0922 03:13:32.778319 20192 solver.cpp:228] Iteration 2470, loss = 13.4449
I0922 03:13:32.778450 20192 sgd_solver.cpp:106] Iteration 2470, lr = 5e-07
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I0922 03:14:16.149591 20192 solver.cpp:228] Iteration 2490, loss = 2.94929
I0922 03:14:16.149709 20192 sgd_solver.cpp:106] Iteration 2490, lr = 5e-07
I0922 03:14:35.669828 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2500.caffemodel
I0922 03:14:39.563359 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2500.solverstate
I0922 03:14:40.688874 20192 solver.cpp:337] Iteration 2500, Testing net (#0)
I0922 03:14:40.688930 20192 net.cpp:693] Ignoring source layer loss
I0922 03:14:54.262549 20192 solver.cpp:404] Test net output #0: accuracy = 0.333333
I0922 03:14:54.262657 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.333333
I0922 03:14:54.262667 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.57
I0922 03:14:54.885042 20192 solver.cpp:228] Iteration 2500, loss = 7.32333
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I0922 03:15:38.247217 20192 sgd_solver.cpp:106] Iteration 2520, lr = 5e-07
I0922 03:15:59.930904 20192 solver.cpp:228] Iteration 2530, loss = 1.27297
I0922 03:15:59.930958 20192 sgd_solver.cpp:106] Iteration 2530, lr = 5e-07
I0922 03:16:21.614037 20192 solver.cpp:228] Iteration 2540, loss = 1.84305
I0922 03:16:21.614148 20192 sgd_solver.cpp:106] Iteration 2540, lr = 5e-07
I0922 03:16:43.302624 20192 solver.cpp:228] Iteration 2550, loss = 7.65768
I0922 03:16:43.302675 20192 sgd_solver.cpp:106] Iteration 2550, lr = 5e-07
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I0922 03:17:04.986121 20192 sgd_solver.cpp:106] Iteration 2560, lr = 5e-07
I0922 03:17:26.680413 20192 solver.cpp:228] Iteration 2570, loss = 7.12898
I0922 03:17:26.680471 20192 sgd_solver.cpp:106] Iteration 2570, lr = 5e-07
I0922 03:17:48.369436 20192 solver.cpp:228] Iteration 2580, loss = 0.568999
I0922 03:17:48.369616 20192 sgd_solver.cpp:106] Iteration 2580, lr = 5e-07
I0922 03:18:10.051416 20192 solver.cpp:228] Iteration 2590, loss = 1.85612
I0922 03:18:10.051472 20192 sgd_solver.cpp:106] Iteration 2590, lr = 5e-07
I0922 03:18:29.568312 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2600.caffemodel
I0922 03:18:33.444262 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2600.solverstate
I0922 03:18:34.565132 20192 solver.cpp:337] Iteration 2600, Testing net (#0)
I0922 03:18:34.565198 20192 net.cpp:693] Ignoring source layer loss
I0922 03:18:48.135578 20192 solver.cpp:404] Test net output #0: accuracy = 0.473333
I0922 03:18:48.135628 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.473333
I0922 03:18:48.135638 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.546667
I0922 03:18:48.758399 20192 solver.cpp:228] Iteration 2600, loss = 7.35898
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I0922 03:19:10.447607 20192 sgd_solver.cpp:106] Iteration 2610, lr = 5e-07
I0922 03:19:32.141371 20192 solver.cpp:228] Iteration 2620, loss = 2.4605
I0922 03:19:32.141427 20192 sgd_solver.cpp:106] Iteration 2620, lr = 5e-07
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I0922 03:20:15.500303 20192 solver.cpp:228] Iteration 2640, loss = 1.12211
I0922 03:20:15.500358 20192 sgd_solver.cpp:106] Iteration 2640, lr = 5e-07
I0922 03:20:37.194025 20192 solver.cpp:228] Iteration 2650, loss = 2.98698
I0922 03:20:37.194145 20192 sgd_solver.cpp:106] Iteration 2650, lr = 5e-07
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I0922 03:22:23.504518 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2700.caffemodel
I0922 03:22:27.381981 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2700.solverstate
I0922 03:22:28.506079 20192 solver.cpp:337] Iteration 2700, Testing net (#0)
I0922 03:22:28.506139 20192 net.cpp:693] Ignoring source layer loss
I0922 03:22:42.083343 20192 solver.cpp:404] Test net output #0: accuracy = 0.563333
I0922 03:22:42.083461 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.563333
I0922 03:22:42.083473 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.563333
I0922 03:22:42.705435 20192 solver.cpp:228] Iteration 2700, loss = 1.79631
I0922 03:22:42.705485 20192 sgd_solver.cpp:106] Iteration 2700, lr = 5e-07
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I0922 03:23:04.387905 20192 sgd_solver.cpp:106] Iteration 2710, lr = 5e-07
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I0922 03:23:26.079589 20192 sgd_solver.cpp:106] Iteration 2720, lr = 5e-07
I0922 03:23:47.759603 20192 solver.cpp:228] Iteration 2730, loss = 3.65219
I0922 03:23:47.759656 20192 sgd_solver.cpp:106] Iteration 2730, lr = 5e-07
I0922 03:24:09.442440 20192 solver.cpp:228] Iteration 2740, loss = 2.54538
I0922 03:24:09.442559 20192 sgd_solver.cpp:106] Iteration 2740, lr = 5e-07
I0922 03:24:31.135393 20192 solver.cpp:228] Iteration 2750, loss = 8.65565
I0922 03:24:31.135444 20192 sgd_solver.cpp:106] Iteration 2750, lr = 5e-07
I0922 03:24:52.820122 20192 solver.cpp:228] Iteration 2760, loss = 2.1197
I0922 03:24:52.820261 20192 sgd_solver.cpp:106] Iteration 2760, lr = 5e-07
I0922 03:25:14.501343 20192 solver.cpp:228] Iteration 2770, loss = 2.83105
I0922 03:25:14.501395 20192 sgd_solver.cpp:106] Iteration 2770, lr = 5e-07
I0922 03:25:36.186866 20192 solver.cpp:228] Iteration 2780, loss = 11.6065
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I0922 03:25:57.871870 20192 sgd_solver.cpp:106] Iteration 2790, lr = 5e-07
I0922 03:26:17.386111 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2800.caffemodel
I0922 03:26:21.271657 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2800.solverstate
I0922 03:26:22.393067 20192 solver.cpp:337] Iteration 2800, Testing net (#0)
I0922 03:26:22.393123 20192 net.cpp:693] Ignoring source layer loss
I0922 03:26:35.968865 20192 solver.cpp:404] Test net output #0: accuracy = 0
I0922 03:26:35.968917 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0
I0922 03:26:35.968925 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.58
I0922 03:26:36.592061 20192 solver.cpp:228] Iteration 2800, loss = 13.0769
I0922 03:26:36.592111 20192 sgd_solver.cpp:106] Iteration 2800, lr = 5e-07
I0922 03:26:58.279829 20192 solver.cpp:228] Iteration 2810, loss = 9.43679
I0922 03:26:58.279944 20192 sgd_solver.cpp:106] Iteration 2810, lr = 5e-07
I0922 03:27:19.963783 20192 solver.cpp:228] Iteration 2820, loss = 11.2763
I0922 03:27:19.963845 20192 sgd_solver.cpp:106] Iteration 2820, lr = 5e-07
I0922 03:27:41.642007 20192 solver.cpp:228] Iteration 2830, loss = 10.606
I0922 03:27:41.642138 20192 sgd_solver.cpp:106] Iteration 2830, lr = 5e-07
I0922 03:28:03.329617 20192 solver.cpp:228] Iteration 2840, loss = 14.7382
I0922 03:28:03.329673 20192 sgd_solver.cpp:106] Iteration 2840, lr = 5e-07
I0922 03:28:25.021818 20192 solver.cpp:228] Iteration 2850, loss = 9.36636
I0922 03:28:25.021944 20192 sgd_solver.cpp:106] Iteration 2850, lr = 5e-07
I0922 03:28:46.722476 20192 solver.cpp:228] Iteration 2860, loss = 12.0089
I0922 03:28:46.722530 20192 sgd_solver.cpp:106] Iteration 2860, lr = 5e-07
I0922 03:29:08.397452 20192 solver.cpp:228] Iteration 2870, loss = 37.2219
I0922 03:29:08.397578 20192 sgd_solver.cpp:106] Iteration 2870, lr = 5e-07
I0922 03:29:30.078336 20192 solver.cpp:228] Iteration 2880, loss = 27.491
I0922 03:29:30.078389 20192 sgd_solver.cpp:106] Iteration 2880, lr = 5e-07
I0922 03:29:51.765385 20192 solver.cpp:228] Iteration 2890, loss = 35.2381
I0922 03:29:51.765511 20192 sgd_solver.cpp:106] Iteration 2890, lr = 5e-07
I0922 03:30:11.285862 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2900.caffemodel
I0922 03:30:15.150897 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_2900.solverstate
I0922 03:30:16.270041 20192 solver.cpp:337] Iteration 2900, Testing net (#0)
I0922 03:30:16.270098 20192 net.cpp:693] Ignoring source layer loss
I0922 03:30:29.834591 20192 solver.cpp:404] Test net output #0: accuracy = 0
I0922 03:30:29.834710 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0
I0922 03:30:29.834720 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.53
I0922 03:30:30.458173 20192 solver.cpp:228] Iteration 2900, loss = 43.6683
I0922 03:30:30.458230 20192 sgd_solver.cpp:106] Iteration 2900, lr = 5e-07
I0922 03:30:52.146962 20192 solver.cpp:228] Iteration 2910, loss = 56.1449
I0922 03:30:52.147022 20192 sgd_solver.cpp:106] Iteration 2910, lr = 5e-07
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I0922 03:31:13.832932 20192 sgd_solver.cpp:106] Iteration 2920, lr = 5e-07
I0922 03:31:35.519518 20192 solver.cpp:228] Iteration 2930, loss = 22.993
I0922 03:31:35.519572 20192 sgd_solver.cpp:106] Iteration 2930, lr = 5e-07
I0922 03:31:57.209218 20192 solver.cpp:228] Iteration 2940, loss = 11.0967
I0922 03:31:57.209367 20192 sgd_solver.cpp:106] Iteration 2940, lr = 5e-07
I0922 03:32:18.898639 20192 solver.cpp:228] Iteration 2950, loss = 15.4545
I0922 03:32:18.898692 20192 sgd_solver.cpp:106] Iteration 2950, lr = 5e-07
I0922 03:32:40.591775 20192 solver.cpp:228] Iteration 2960, loss = 20.654
I0922 03:32:40.591903 20192 sgd_solver.cpp:106] Iteration 2960, lr = 5e-07
I0922 03:33:02.277648 20192 solver.cpp:228] Iteration 2970, loss = 17.8024
I0922 03:33:02.277699 20192 sgd_solver.cpp:106] Iteration 2970, lr = 5e-07
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I0922 03:33:23.962353 20192 sgd_solver.cpp:106] Iteration 2980, lr = 5e-07
I0922 03:33:45.644040 20192 solver.cpp:228] Iteration 2990, loss = 18.4719
I0922 03:33:45.644098 20192 sgd_solver.cpp:106] Iteration 2990, lr = 5e-07
I0922 03:34:05.159657 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3000.caffemodel
I0922 03:34:09.046759 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3000.solverstate
I0922 03:34:10.173414 20192 solver.cpp:337] Iteration 3000, Testing net (#0)
I0922 03:34:10.173472 20192 net.cpp:693] Ignoring source layer loss
I0922 03:34:23.740034 20192 solver.cpp:404] Test net output #0: accuracy = 0
I0922 03:34:23.740082 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0
I0922 03:34:23.740092 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.573333
I0922 03:34:24.363775 20192 solver.cpp:228] Iteration 3000, loss = 22.3993
I0922 03:34:24.363824 20192 sgd_solver.cpp:106] Iteration 3000, lr = 5e-07
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I0922 03:34:46.035699 20192 sgd_solver.cpp:106] Iteration 3010, lr = 5e-07
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I0922 03:35:07.710820 20192 sgd_solver.cpp:106] Iteration 3020, lr = 5e-07
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I0922 03:36:12.745542 20192 sgd_solver.cpp:106] Iteration 3050, lr = 5e-07
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I0922 03:36:34.974400 20192 sgd_solver.cpp:106] Iteration 3060, lr = 5e-07
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I0922 03:36:56.104557 20192 sgd_solver.cpp:106] Iteration 3070, lr = 5e-07
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I0922 03:37:17.787175 20192 sgd_solver.cpp:106] Iteration 3080, lr = 5e-07
I0922 03:37:39.471736 20192 solver.cpp:228] Iteration 3090, loss = 3.86002
I0922 03:37:39.471863 20192 sgd_solver.cpp:106] Iteration 3090, lr = 5e-07
I0922 03:37:59.985667 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3100.caffemodel
I0922 03:38:02.965111 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3100.solverstate
I0922 03:38:04.085505 20192 solver.cpp:337] Iteration 3100, Testing net (#0)
I0922 03:38:04.085559 20192 net.cpp:693] Ignoring source layer loss
I0922 03:38:17.661042 20192 solver.cpp:404] Test net output #0: accuracy = 0.243333
I0922 03:38:17.661157 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.243333
I0922 03:38:17.661169 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.54
I0922 03:38:18.284111 20192 solver.cpp:228] Iteration 3100, loss = 8.07393
I0922 03:38:18.284154 20192 sgd_solver.cpp:106] Iteration 3100, lr = 5e-07
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I0922 03:39:01.657833 20192 sgd_solver.cpp:106] Iteration 3120, lr = 5e-07
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I0922 03:39:23.338457 20192 sgd_solver.cpp:106] Iteration 3130, lr = 5e-07
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I0922 03:41:33.460794 20192 sgd_solver.cpp:106] Iteration 3190, lr = 5e-07
I0922 03:41:52.984284 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3200.caffemodel
I0922 03:41:56.879667 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3200.solverstate
I0922 03:41:58.008244 20192 solver.cpp:337] Iteration 3200, Testing net (#0)
I0922 03:41:58.008298 20192 net.cpp:693] Ignoring source layer loss
I0922 03:42:11.578274 20192 solver.cpp:404] Test net output #0: accuracy = 0.386667
I0922 03:42:11.578316 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.386667
I0922 03:42:11.578325 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.56
I0922 03:42:12.201166 20192 solver.cpp:228] Iteration 3200, loss = 1.65423
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I0922 03:42:55.566675 20192 sgd_solver.cpp:106] Iteration 3220, lr = 5e-07
I0922 03:43:17.255542 20192 solver.cpp:228] Iteration 3230, loss = 7.70406
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I0922 03:45:46.880178 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3300.caffemodel
I0922 03:45:50.754336 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3300.solverstate
I0922 03:45:51.872977 20192 solver.cpp:337] Iteration 3300, Testing net (#0)
I0922 03:45:51.873034 20192 net.cpp:693] Ignoring source layer loss
I0922 03:46:05.448515 20192 solver.cpp:404] Test net output #0: accuracy = 0.2
I0922 03:46:05.448640 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.2
I0922 03:46:05.448653 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.583333
I0922 03:46:06.070842 20192 solver.cpp:228] Iteration 3300, loss = 0.867718
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I0922 03:46:49.446112 20192 solver.cpp:228] Iteration 3320, loss = 7.01867
I0922 03:46:49.446279 20192 sgd_solver.cpp:106] Iteration 3320, lr = 5e-07
I0922 03:47:11.120079 20192 solver.cpp:228] Iteration 3330, loss = 7.59636
I0922 03:47:11.120133 20192 sgd_solver.cpp:106] Iteration 3330, lr = 5e-07
I0922 03:47:32.811661 20192 solver.cpp:228] Iteration 3340, loss = 7.74335
I0922 03:47:32.811794 20192 sgd_solver.cpp:106] Iteration 3340, lr = 5e-07
I0922 03:47:54.504851 20192 solver.cpp:228] Iteration 3350, loss = 1.37973
I0922 03:47:54.504904 20192 sgd_solver.cpp:106] Iteration 3350, lr = 5e-07
I0922 03:48:16.186513 20192 solver.cpp:228] Iteration 3360, loss = 1.4807
I0922 03:48:16.186635 20192 sgd_solver.cpp:106] Iteration 3360, lr = 5e-07
I0922 03:48:37.875170 20192 solver.cpp:228] Iteration 3370, loss = 7.8229
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I0922 03:49:21.248070 20192 solver.cpp:228] Iteration 3390, loss = 7.21106
I0922 03:49:21.248121 20192 sgd_solver.cpp:106] Iteration 3390, lr = 5e-07
I0922 03:49:40.762511 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3400.caffemodel
I0922 03:49:44.650557 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3400.solverstate
I0922 03:49:45.771107 20192 solver.cpp:337] Iteration 3400, Testing net (#0)
I0922 03:49:45.771162 20192 net.cpp:693] Ignoring source layer loss
I0922 03:49:59.346385 20192 solver.cpp:404] Test net output #0: accuracy = 0.39
I0922 03:49:59.346433 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.39
I0922 03:49:59.346441 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.533333
I0922 03:49:59.967352 20192 solver.cpp:228] Iteration 3400, loss = 0.674076
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I0922 03:51:26.705569 20192 solver.cpp:228] Iteration 3440, loss = 2.71599
I0922 03:51:26.705624 20192 sgd_solver.cpp:106] Iteration 3440, lr = 5e-07
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I0922 03:51:48.379376 20192 sgd_solver.cpp:106] Iteration 3450, lr = 5e-07
I0922 03:52:10.069567 20192 solver.cpp:228] Iteration 3460, loss = 1.12192
I0922 03:52:10.069620 20192 sgd_solver.cpp:106] Iteration 3460, lr = 5e-07
I0922 03:52:31.755870 20192 solver.cpp:228] Iteration 3470, loss = 1.79426
I0922 03:52:31.755997 20192 sgd_solver.cpp:106] Iteration 3470, lr = 5e-07
I0922 03:52:53.446024 20192 solver.cpp:228] Iteration 3480, loss = 8.56247
I0922 03:52:53.446079 20192 sgd_solver.cpp:106] Iteration 3480, lr = 5e-07
I0922 03:53:15.122557 20192 solver.cpp:228] Iteration 3490, loss = 2.57787
I0922 03:53:15.122685 20192 sgd_solver.cpp:106] Iteration 3490, lr = 5e-07
I0922 03:53:34.650468 20192 solver.cpp:454] Snapshotting to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3500.caffemodel
I0922 03:53:38.541631 20192 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/imagenet/VGG16_SOD_finetune_nArt_iter_3500.solverstate
I0922 03:53:39.662082 20192 solver.cpp:337] Iteration 3500, Testing net (#0)
I0922 03:53:39.662138 20192 net.cpp:693] Ignoring source layer loss
I0922 03:53:53.229095 20192 solver.cpp:404] Test net output #0: accuracy = 0.336667
I0922 03:53:53.229264 20192 solver.cpp:404] Test net output #1: accuracy@1 = 0.336667
I0922 03:53:53.229275 20192 solver.cpp:404] Test net output #2: accuracy@5 = 0.58
I0922 03:53:53.849627 20192 solver.cpp:228] Iteration 3500, loss = 2.73832
I0922 03:53:53.849678 20192 sgd_solver.cpp:106] Iteration 3500, lr = 5e-07
I0922 03:54:15.535290 20192 solver.cpp:228] Iteration 3510, loss = 2.8076
I0922 03:54:15.535352 20192 sgd_solver.cpp:106] Iteration 3510, lr = 5e-07
I0922 03:54:37.218643 20192 solver.cpp:228] Iteration 3520, loss = 8.76214
I0922 03:54:37.218775 20192 sgd_solver.cpp:106] Iteration 3520, lr = 5e-07
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