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The opencv3.0 and 2.4 SVM interfaces are different and can be performed in the following format: ML::SVM::P arams Params; Params.svmtype = ml::svm::c_svc; Params.kerneltype = ML::SVM::P oly; Params.gamma = 3; ptr But note that if the error is best to see the opencv3.0 document, which has function prototypes and explanations, I in the actual operation of the process, also made a number of changes 1) Set parameters SVM has a lot of parameters, but the c_svc and RBF related to only gamma and C, so

Summary of IT training work No matter what work you do in this world, you should reap something. These two days I also think this question, two years of training work, what I have harvested. Have learned to encourage a person to do a job that he is not good at, so that he can take thousands of dollars out of his pocket and then put the money in the boss's pocket. Of course, far more than these, in addition

This blog is written in a very detailed https://blog.csdn.net/qq_30028077/article/details/50483205 Now, let's write about the course of my training. 1. Training Sample Image search: You can refer to my previous article, through the URL to download Web images quickly find positive samples, here I do is strawberry recognition https://blog.csdn.net/qq_35971623/article/details/79684219 Negative sample Collectio

Original URL: http://blog.csdn.net/u014114990/article/details/47781233 One Caffe supports the continuation of training on other people's models. Here are the examples given caffe-master0818\examples\imagenet\resume_training.sh [CPP] view plain copy #!/usr/bin/env Sh./build/tool S/caffe train \--solver=models/bvlc_reference_caffenet/solver.prototxt \--SNAPSHOT=MODELS/BVLC_REFERENCE_CAF Fenet/caffenet_train_10000.solverstate.h5Second, Caffe also suppor

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