I am using a pre-trained CNN from model zoo to perform feature extraction, as here
Caffe DocsI am extracting features from training and test image sets, training a classifier (SVM) on the training data, and then using it to generate predictions on the test feature vectors. Using this method is about 80% accurate on the test data. I want to improve on this. Would it help to:
1. Produce more training images by augmenting (jitter, fliplr, flipud, etc)?
2. Produce multiple test datasets by augmenting and then averaging the predicted probabilities across the augmented datasets?
Also, should the extracted feature vectors be normalized before training/testing or is this already done by the pre-trained CNN? E.g:
from sklearn import preprocessing
scaler1 = preprocessing.StandardScaler()
Xtrain = scaler1.fit_transform(X_train)
Xtest = scaler1.transform(X_test)
Thanks!