Caffe，全称Convolutional Architecture for Fast Feature Embedding，是一个兼具表达性、速度和思维模块化的深度学习框架。由伯克利人工智能研究小组和伯克利视觉和学习中心开发。虽然其内核是用C++编写的，但Caffe有Python和Matlab 相关接口。Caffe支持多种类型的深度学习架构，面向图像分类和图像分割，还支持CNN、RCNN、LSTM和全连接神经网络设计。Caffe支持基于GPU和CPU的加速计算内核库，如NVIDIA cuDNN和Intel MKL。
使用 /* 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 = "/sdcard/synset_words.txt"){ std::vector<String> classNames; std::ifstream fp(filename); if (!fp.is_open()) { LOGI("error"); return classNames; } 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;}void main(){ CV_TRACE_FUNCTION(); String modelTxt = "bvlc_googlenet.prototxt"; String modelBin = "bvlc_googlenet.caffemodel"; String imageFile = "/sdcard/air_plane.jpg"; Net net; try { net = dnn::readNetFromCaffe(modelTxt, modelBin); }catch (cv::Exception& e) { LOGI("jason error %s", e.what()); } Mat img = oneMat.clone(); //GoogLeNet accepts only 224x224 BGR-images Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224), Scalar(104, 117, 123), false); //Convert Mat to batch of images Mat prob; cv::TickMeter t; for (int i = 0; i < 10; i++) { CV_TRACE_REGION("forward"); net.setInput(inputBlob, "data"); //set the network input t.start(); prob = net.forward("prob"); //compute output t.stop(); } int classId; double classProb; getMaxClass(prob, &classId, &classProb);//find the best class std::vector<String> classNames = readClassNames(); LOGI("Best class: %s", classNames.at(classId).c_str()); LOGI("Probability: %f", classProb*100);}