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I made a mistake when testing the accuracy and actually I was comparing the 'svm_predict' and 'svm_predict_probability'. These two functions may return different results even all the parameters are the same. In my case 'svm_predict' has a hight accuracy. And if you enable probability it might need more time during training(to calculate the probabilites). OpenCV also has optimized some basic math functions with SIMD, like 'exp', so it might be faster when doing kernel calculations which need exp.

Hope this will be helpful to someone who met the same question as me

Hi there, I am evaluating OpenCV's SVM and libsvm 3.18 but surprisingly they have different results. It's said OpenCV's SVM is based on libsvm but OpenCV's SVM is much faster during training and has a higher accuracy. All tests were done with the same parameters and training dataset.

So what's the difference between the two svm libs and how to make them perform exactly the same(I mean the accuracy)?

I am using CUDA for computer vision and machine learning, like image processing, calculating HOG, training SVM, etc. I've also noticed the issues in computing from http://stackoverflow.com/questions/10765518/how-to-quantify-the-processing-tradeoffs-of-cuda-devices-for-c-kernels

So I am wondering:

1. Are there differences among the nvidia gpus when using CUDA for computer vision processing? I mean if some are good at and some are not.
2. If yes can you recommend some card models? I'm oriented on a geforce series and my budget is about $300-$500

Thanks