# Can I use svm.SVC for complex image classification?

I've been trying to implement the classical Cat vs dogs classifier using sklearn.svm.SVC. However, I wasn't able to have more than 51% correct ratio (which is basically random, since there's only two classes). I tried varying gamma and C with no luck.

I started wondering if SVC is any good at all, for complex images.

I've used 400 x 300 grayscale images as input.

SVC is fine with hand-written digits but is it any good with more complex images ?

UPDATE : I just ran GridSearchCV to lookup good parameters for fit but it yielded Best parameters set found on development set:

{'C': 1, 'kernel': 'linear', 'gamma':0.001}


Which still gives me 0.51 ratio of good guess ...