The result of opencv3.3 dnn module not match the caffe prediction - opencv

Question

I used opencv dnn classification, but the result do not match the caffe prediction. What confused me was that some images could get similar result to caffe,a small number of images not.When I changed BGR to RGB, Most of the results ware wrong.
similar result:
different result:
blobFromImage(norm_img, 1.0, cv::Size(64, 64));when used default parameters changed BGR to RGB ,but the result would wrong .so I used like this blobFromImage(norm_img, 1.0, cv::Size(64, 64), cv::Scalar(),false); .most of result would matched caffe prediction,why a small number of images not?
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utils/trace.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
using namespace std;
/* Find best class for the blob (i. e. class with maximal probability) */
static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
{
Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
Point classNumber;
minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
*classId = classNumber.x;
}
static std::vector<String> readClassNames(const char *filename = "./config/type.txt")
{
std::vector<String> classNames;
std::ifstream fp(filename);
if (!fp.is_open())
{
std::cerr << "File with classes labels not found: " << filename << std::endl;
exit(-1);
}
std::string name;
while (!fp.eof())
{
std::getline(fp, name);
if (name.length())
classNames.push_back(name.substr(name.find(' ') + 1));
}
fp.close();
return classNames;
}
int main(int argc, char **argv)
{
CV_TRACE_FUNCTION();
String modelTxt = "./config/HCCR3755_res20_deploy.prototxt";
String modelBin = "./config/HCCR3755-res20_iter_790000.caffemodel";
String imageFile = "./config/b9.jpg";
Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
if (net.empty())
{
std::cerr << "Can't load network by using the following files: " << std::endl;
std::cerr << "prototxt: " << modelTxt << std::endl;
std::cerr << "caffemodel: " << modelBin << std::endl;
exit(-1);
}
Mat img = imread(imageFile);
FileStorage fs("./config/mean.xml", FileStorage::READ);
Mat _mean;
fs["vocabulary"] >> _mean;
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
cv::Mat img_resize;
resize(img, img_resize, Size(64, 64));
cv::Mat img_float;
img_resize.convertTo(img_float, CV_32FC3);
cv::Mat norm_img;
cv::subtract(img_float, _mean, norm_img);
Mat inputBlob = blobFromImage(norm_img, 1.0, cv::Size(64, 64), cv::Scalar(),false); //Convert Mat to batch of images
Mat prob;
cv::TickMeter t;
for (int i = 0; i < 1; i++)
{
CV_TRACE_REGION("forward");
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
t.start();
//! [Make forward pass]
prob = net.forward("prob");
//std::cout << prob << std::endl;//compute output
//! [Make forward pass]
t.stop();
}
int classId;
double classProb;
getMaxClass(prob, &classId, &classProb);//find the best class
//! [Gather output]
//! [Print results]
std::vector<String> classNames = readClassNames();
std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
//! [Print results]
std::cout << "Time: " << (double)t.getTimeMilli() / t.getCounter() << " ms (average from " << t.getCounter() << " iterations)" << std::endl;
getchar();
return 0;
} //main