wrong results using trained caffe net from c++

[unitready]

Hello,

I tried to use my trained caffe net with my data from C++. I implemented standart caffe example classification.cpp for deploy. In train/test phase with python scripts the net achieved accuracy = 0.93, but now when I went to deploy I got some strange results. I have two classes:

• environment
• object

and I need to get the prob of object detection. I believed that the results will be presented in the form of two probs in Softmax output blob if the net have two outputs in FC-layer (prob1 + prob2 == 1.0f), but the result is puzzling. In output vector I get two identical values for every image. Here are input and output layers:

layer {
 name: "data"
 top: "data"
 type: "Input"
 input_param { shape: { dim: 1 dim: 3 dim: 227 dim: 227 }}
}
layer {
 name: "fc6"
 top: "fc6"
 type: "InnerProduct"
 bottom: "drop5"
 inner_product_param {
 num_output: 2
 weight_filler {
 type: "xavier"
 std: 0.1
 }
 }
}
layer {
 name: "prob"
 top: "prob"
 type: "Softmax"
 bottom: "fc6"
}

My C++ code sample for the regular use:

Blob<float>* input_layer = m_net->input_blobs()[0];
input_layer->Reshape(1, m_numChannels, m_inputGeometry.height, m_inputGeometry.width);
m_net->Reshape();
std::vector<cv::Mat> input_channels;
Blob<float>* input_layer = m_net->input_blobs()[0];
int width = input_layer->width();
int height = input_layer->height();
float* input_data = input_layer->mutable_cpu_data();

for(int i = 0; i < input_layer->channels(); ++i){
   cv::Mat channel(height, width, CV_32FC1, input_data);
   input_channels->push_back(channel);
   input_data += width * height;
}

cv::split(image_float, *input_channels);
m_net->Forward();
Blob<float>* output_layer = m_net->output_blobs()[0];
const float* begin = output_layer->cpu_data();
const float* end = begin + output_layer->channels();
QVector<float> output = QVector<float>(end - begin, *begin)

In addition, the results are similar to random (and duplicated for each 
class), the smallest probability value is magic 0.443142. This value is 
often found in the output vector. What am I doing wrong?

[mprl]

Are you sure you are doing the same preprocessing during training and classification ?
I had the same problem because i forgot to scale my images between 0 and 1 in my classification code.

[unitready]

Yes, I scale images with

cv::Mat image_float;
image.convertTo(image_float, CV_32FC3);

понедельник, 25 июля 2016 г., 18:05:51 UTC+3 пользователь mprl написал: