我已经为两类分类训练了Cifar10
(caffe )模型。行人和非行人。训练看起来不错,我已经更新了caffemodel
文件中的权重。我使用了两个标签,1 用于行人,2 用于非行人,以及行人图像 (64 x 160) 和背景图像 (64 x 160)。训练后,我用正图像(行人图像)和负图像(背景图像)进行测试。我的测试prototxt
文件如下图
name: "CIFAR10_quick_test"
layers
{
name: "data"
type: MEMORY_DATA
top: "data"
top: "label"
memory_data_param
{
batch_size: 1
channels: 3
height: 160
width: 64
}
transform_param
{
crop_size: 64
mirror: false
mean_file: "../../examples/cifar10/mean.binaryproto"
}
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
}
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "relu1"
type: RELU
bottom: "pool1"
top: "pool1"
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "pool1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "pool2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "pool3"
type: POOLING
bottom: "conv3"
top: "pool3"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layers {
name: "ip1"
type: INNER_PRODUCT
bottom: "pool3"
top: "ip1"
blobs_lr: 1
blobs_lr: 2
inner_product_param {
num_output: 64
}
}
layers {
name: "ip2"
type: INNER_PRODUCT
bottom: "ip1"
top: "ip2"
blobs_lr: 1
blobs_lr: 2
inner_product_param {
num_output: 10
}
}
layers {
name: "prob"
type: SOFTMAX
bottom: "ip2"
top: "prob"
}
为了测试,我使用test_predict_imagenet.cpp
并做了一些修改,特别是路径和图像大小。
我无法弄清楚测试输出。当我用正面图像进行测试时,我得到的输出为
I0813 01:55:30.378114 7668 test_predict_cifarnet.cpp:72] 1
I0813 01:55:30.379082 7668 test_predict_cifarnet.cpp:72] 3.90971e-007
I0813 01:55:30.381088 7668 test_predict_cifarnet.cpp:72] 0.00406029
I0813 01:55:30.383090 7668 test_predict_cifarnet.cpp:72] 0.995887
I0813 01:55:30.384119 7668 test_predict_cifarnet.cpp:72] 1.96203e-006
I0813 01:55:30.385095 7668 test_predict_cifarnet.cpp:72] 3.50333e-005
I0813 01:55:30.386119 7668 test_predict_cifarnet.cpp:72] 1.2796e-008
I0813 01:55:30.387097 7668 test_predict_cifarnet.cpp:72] 1.48836e-005
I0813 01:55:30.389093 7668 test_predict_cifarnet.cpp:72] 1.12237e-007
I0813 01:55:30.390100 7668 test_predict_cifarnet.cpp:72] 4.71238e-008
I0813 01:55:30.391101 7668 test_predict_cifarnet.cpp:72] 9.04134e-008
当我使用负面图像进行测试时,我得到的输出为
I0813 01:53:40.896139 10856 test_predict_cifarnet.cpp:72] 1
I0813 01:53:40.897117 10856 test_predict_cifarnet.cpp:72] 6.20882e-006
I0813 01:53:40.898115 10856 test_predict_cifarnet.cpp:72] 7.10468e-005
I0813 01:53:40.900184 10856 test_predict_cifarnet.cpp:72] 0.999911
I0813 01:53:40.901185 10856 test_predict_cifarnet.cpp:72] 3.4275e-006
I0813 01:53:40.902189 10856 test_predict_cifarnet.cpp:72] 2.38526e-007
I0813 01:53:40.903192 10856 test_predict_cifarnet.cpp:72] 2.29073e-007
I0813 01:53:40.905187 10856 test_predict_cifarnet.cpp:72] 1.7243e-006
I0813 01:53:40.906188 10856 test_predict_cifarnet.cpp:72] 5.40765e-007
I0813 01:53:40.908195 10856 test_predict_cifarnet.cpp:72] 1.57534e-006
I0813 01:53:40.909195 10856 test_predict_cifarnet.cpp:72] 3.72312e-006
如何理解测试输出?
是否有任何更有效的测试算法来测试来自视频源的模型(从视频剪辑中逐帧)?